OXIESEC PANEL

Name	Size	Modified	Perms
📁 ..	-	05/19/2025 10:07:15 AM	rwxr-xr-x
📄 00075c93132acf7a6e46e48d2291ce41.spc	5.69 KB	03/06/2025 09:42:25 AM	rw-r--r--
📄 0102169e52b6a27a410e7b237202fe84.spc	140.81 KB	03/06/2025 09:42:25 AM	rw-r--r--
📄 027d4dde1e82475da3d9afe4844afb1d.spc	2.63 KB	03/06/2025 09:42:25 AM	rw-r--r--
📄 03036edfece701eaa1537fea4014dd44.spc	56.35 KB	03/06/2025 09:42:25 AM	rw-r--r--
📄 0446f65691fba260d3eabbd1377240f8.spc	5.75 KB	03/06/2025 09:42:25 AM	rw-r--r--
📄 04d0c6cc2bf146b1318b78f84416b912.spc	124.45 KB	03/06/2025 09:42:25 AM	rw-r--r--
📄 0582678c8cfff117f770f9368b70c2b5.spc	19.33 KB	03/06/2025 09:42:25 AM	rw-r--r--
📄 0601d608f5e2ea8e198130b17fe6ef01.spc	157 bytes	03/06/2025 09:42:25 AM	rw-r--r--
📄 061ad7f2b0116c570fdc35c36824c7c6.spc	42.24 KB	03/06/2025 09:42:25 AM	rw-r--r--
📄 06e0c598a46c483b6b9d775e1ba1ecd4.spc	124.09 KB	03/06/2025 09:42:25 AM	rw-r--r--
📄 0802b12194f292de0e9d9617ac014785.spc	290.02 KB	03/06/2025 09:42:25 AM	rw-r--r--
📄 083aed319a0b5c8691e31d9150d8005e.spc	19.84 KB	03/06/2025 09:42:25 AM	rw-r--r--
📄 0a3bf48c84477cd58dbc2036a0331134.spc	70.63 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 0b5e5f226006af7e46d02ba8ce216a45.spc	54.71 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 0b73d04c6bba0acaf2f9a569f388313a.spc	33.59 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 0b8a46fca237497cfc90498f9eb909ab.spc	686.66 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 0ce2bdd7061489c6136e7614d421b874.spc	47.7 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 0de8a2204854bb5dd311607494c671e4.spc	828.58 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 0e15494dca4aeb24ea769582482c5162.spc	150.58 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 0eaec40cfb584fcb55fcdfb5d76684b9.spc	16.95 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 0ed254d4d9db6e3afe193b00bc6471bb.spc	89.85 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 0f079d9bb09fef940c38ee73b52b91d4.spc	34.42 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 0f5e21d9d8354d10ea23d99101259ba2.spc	42.06 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 0ffc1fa29a6bad7fb49e55940c374610.spc	75.61 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 1071b4a15b6c2fe6f7a96f194d0ba524.spc	196 bytes	03/06/2025 09:42:26 AM	rw-r--r--
📄 10ae571a6266a8e21b0fbb15f552a1cb.spc	13.15 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 118c129ff99a905e4e9325e388b841fe.spc	45.34 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 131a4ad07dda46888cbbc1cb4c710a91.spc	59.6 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 132dee0a955be7733cc009e546de18da.spc	100.76 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 142d8795402a4e8a520be8ebea6f54f3.spc	22.7 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 1469d584e9747d132077c9df3cda6c97.spc	121.15 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 1479626ef8ef423638ca62f43b3e0f8d.spc	95.45 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 16e016e3ca27d793aa9172c1913c3f23.spc	26.74 KB	03/06/2025 09:42:26 AM	rw-r--r--
📄 19f3a21c36072f501f634db8e658bc9f.spc	16.6 KB	03/06/2025 09:42:27 AM	rw-r--r--
📄 1b8954ae7aab6fd9784cbcc827133f80.spc	186 bytes	03/06/2025 09:42:27 AM	rw-r--r--
📄 1c0bbac8beea30e555f26fd02994e7a5.spc	19.96 KB	03/06/2025 09:42:27 AM	rw-r--r--
📄 1c1a63fc25720b7c22c9c28fa2aa9379.spc	236.54 KB	03/06/2025 09:42:27 AM	rw-r--r--
📄 1f1672e0ecc5e7a6d278c930015520ab.spc	166 bytes	03/06/2025 09:42:27 AM	rw-r--r--
📄 1f4cf3ae9ba91935f556711c1cfc34d4.spc	88.33 KB	03/06/2025 09:42:27 AM	rw-r--r--
📄 1f5e96e3f1a01f95ab611ec1458fe470.spc	169.16 KB	03/06/2025 09:42:27 AM	rw-r--r--
📄 20a75b688975a2d5d342eae9f4c33411.spc	1.22 MB	03/06/2025 09:42:27 AM	rw-r--r--
📄 225d97aca36305a8b407ea6d8d5b187e.spc	55.08 KB	03/06/2025 09:42:27 AM	rw-r--r--
📄 242d3dabf79d13154fcc384ff8b2d25e.spc	113.19 KB	03/06/2025 09:42:27 AM	rw-r--r--
📄 25512b0d18ae6e4d20d027abbc467365.spc	31.2 KB	03/06/2025 09:42:27 AM	rw-r--r--
📄 25948504a82cd8da1985fddd4500c1c7.spc	153.7 KB	03/06/2025 09:42:27 AM	rw-r--r--
📄 26e0c631724f3653c10c3123546ab5e2.spc	110.09 KB	03/06/2025 09:42:28 AM	rw-r--r--
📄 2704664dff0e40e19de087fe00892bc2.spc	24.51 KB	03/06/2025 09:42:28 AM	rw-r--r--
📄 274ae07ff50cfde2bda57a71703b62f4.spc	2.54 KB	03/06/2025 09:42:28 AM	rw-r--r--
📄 2799184659106c88b5072a3e3f763a4d.spc	2.54 KB	03/06/2025 09:42:28 AM	rw-r--r--
📄 2801f3bdd649962fa663f608c2383280.spc	154.53 KB	03/06/2025 09:42:28 AM	rw-r--r--
📄 28099e26c5c9a06acb85a41ccd789efc.spc	500.36 KB	03/06/2025 09:42:28 AM	rw-r--r--
📄 2aabe0323264e3f60916621039be0e76.spc	42.37 KB	03/06/2025 09:42:29 AM	rw-r--r--
📄 2abcd685295b4a261ad2e866188e5e11.spc	125.3 KB	03/06/2025 09:42:29 AM	rw-r--r--
📄 2aed529f6407470bef913050a1d118ef.spc	151 bytes	03/06/2025 09:42:29 AM	rw-r--r--
📄 2b2654a64e8b0f5d9cf497e0883b2042.spc	96.1 KB	03/06/2025 09:42:29 AM	rw-r--r--
📄 2dae1abba28ecd05f3e1e91f308cf8c4.spc	87.25 KB	03/06/2025 09:42:29 AM	rw-r--r--
📄 2db16a36af8daf383cb739dd57a44d90.spc	147.19 KB	03/06/2025 09:42:29 AM	rw-r--r--
📄 2de250597c053bd81359233c14c51db4.spc	286.38 KB	03/06/2025 09:42:29 AM	rw-r--r--
📄 2fb670ecdcda7db936aa7d2f018a79e4.spc	23.75 KB	03/06/2025 09:42:29 AM	rw-r--r--
📄 30d5af6cd4c10ea02520bcaba31f3d1c.spc	141.02 KB	03/06/2025 09:42:29 AM	rw-r--r--
📄 31591159e55bceb27be71ce43cd1517e.spc	443.64 KB	03/06/2025 09:42:29 AM	rw-r--r--
📄 31f817c15425941589a9819216265501.spc	68.33 KB	03/06/2025 09:42:30 AM	rw-r--r--
📄 34661b0e5b23f423b303c946172b39f8.spc	20.99 KB	03/06/2025 09:42:29 AM	rw-r--r--
📄 3567037b5acd1842946ba40397edead4.spc	84.5 KB	03/06/2025 09:42:30 AM	rw-r--r--
📄 37cf2adae9335c54f1dbc436922e6cfc.spc	181 bytes	03/06/2025 09:42:30 AM	rw-r--r--
📄 389ae768f4ecb350b56b92da3b04c1ac.spc	180.5 KB	03/06/2025 09:42:30 AM	rw-r--r--
📄 3bcfb7838de30c68c7acc437c16935cc.spc	142.35 KB	03/06/2025 09:42:30 AM	rw-r--r--
📄 3ca755a78dd04c91695e5fcee845991f.spc	42.02 KB	03/06/2025 09:42:31 AM	rw-r--r--
📄 3d135369c757ae57c3c873e6070d5ac6.spc	46.18 KB	03/06/2025 09:42:31 AM	rw-r--r--
📄 3e4e8d898fc42bca52bf888c3a33ef23.spc	614.85 KB	03/06/2025 09:42:33 AM	rw-r--r--
📄 3e804b49f84699d48348b3bee312090d.spc	25.24 KB	03/06/2025 09:42:33 AM	rw-r--r--
📄 3f92b590befbddc6f7237f2ff7a2ca21.spc	407.55 KB	03/06/2025 09:42:33 AM	rw-r--r--
📄 3f93802ae5a285cffaf04f22ceb596fb.spc	307.02 KB	03/06/2025 09:42:33 AM	rw-r--r--
📄 419e5468f73de12da7ac55b064ff6e04.spc	19.87 KB	03/06/2025 09:42:33 AM	rw-r--r--
📄 43cdef0c688f38c395285fd09bd1d8b6.spc	163 bytes	03/06/2025 09:42:33 AM	rw-r--r--
📄 445a8424173fb9de0f08493a09557c92.spc	39.14 KB	03/06/2025 09:42:33 AM	rw-r--r--
📄 447b88825763019604aca4e363415120.spc	3.18 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 44a6e222af7ac1e000190688f3824d27.spc	103.66 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 45ec354e05ea3a553e89c9f9d1ee7a6f.spc	67.86 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 48926180fcc9ab4ab897cfbc5279409e.spc	170 bytes	03/06/2025 09:42:34 AM	rw-r--r--
📄 4904c558085c30a9ca52969c7f875cf8.spc	155 bytes	03/06/2025 09:42:34 AM	rw-r--r--
📄 490fd4abfc32189cff5d5f38ddaaff5b.spc	22.31 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 491e4a0adc576f7c32fdb7ee38bb0997.spc	88.77 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 492e918dde587df3095914b1f67cd6ee.spc	31.56 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 4b6d0ec22ab2dc33cffefef17cf9a288.spc	87.42 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 4b6fa8105439c52ea4f2c1f18e0957e2.spc	181 bytes	03/06/2025 09:42:34 AM	rw-r--r--
📄 4c3facd12e8f40cab8114677f681ebaf.spc	134 bytes	03/06/2025 09:42:34 AM	rw-r--r--
📄 4ca537d8aa4727a23841361c475118e1.spc	42.22 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 4d1bb795413f82f68c666caa0c0c27bb.spc	148.14 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 4e8baeaef3679f9460ffdecddbb1f6a7.spc	35.08 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 5064bfc366d30fb3250bafeacadc83db.spc	22.11 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 50ba71d2f35fb5e96b224d907d33d263.spc	720.35 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 51447ae67b6d856982df0ea0496cf24b.spc	18.89 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 522fe4b133aa24cb42c79b24ecb5c838.spc	134.37 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 5432740bf8126b80fd18515165c5cf20.spc	22.07 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 55fad6ae8bfedfb81fafc8bd5112a79f.spc	31.16 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 577d0589862161e3aaa25ed8758b2765.spc	29.23 KB	03/06/2025 09:42:34 AM	rw-r--r--
📄 57e4b3f5cadf2d33fa630cb96499349a.spc	154 bytes	03/06/2025 09:42:35 AM	rw-r--r--
📄 588359e68ff59d4ef53aaf3edb6a44cf.spc	6.77 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 5a06bba505584742f9e590f2f81e0ff3.spc	128 bytes	03/06/2025 09:42:35 AM	rw-r--r--
📄 5c6d487a1fbb288f7f0f73b55f6b2df1.spc	41.86 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 5c744a4198beb7326dcf101f961486b4.spc	56.94 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 5ef8b2cef4776b3e3f9f79092ce932ca.spc	124.66 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 5fc9bcdda34ec7e72510b177a0164b7b.spc	602.71 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 60bc545cda9dafe75484b88be1dd4ae9.spc	67.49 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 60fe2edd86b212feb0f71552066ef55d.spc	186.19 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 6270f865db79068a5dedb78ba877e7a0.spc	100.02 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 6370a4a7eb9be29e32a96811bc0dd7b6.spc	19.59 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 63f55618eba7b0585577fb08ea8818dc.spc	41.42 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 651d4f6bf953adef7c5b8f05f01e1151.spc	32.47 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 66267a4d6dba7887fc7d4a1aa2da9b75.spc	123.73 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 663e01e065755a523f15f61782ef2fd0.spc	28.3 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 66506bf5272cefcb56085b4b77463bd8.spc	280.88 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 669fa07a0da20a5d0da35d972d5fcac5.spc	99.77 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 679d4423c6016aeb24557d767a509bbb.spc	46.29 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 69185497e5f13fd4744a2c39d76fb812.spc	32.55 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 69b3abe3aa88d144d3591d56873de72b.spc	150 bytes	03/06/2025 09:42:35 AM	rw-r--r--
📄 6a0ab3ec7216c8caa50e266f3ff16993.spc	22.35 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 6c7544e2246a222bbde6ad7d0406c8db.spc	200.49 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 6c7c31384f1626a700bc8fbd2f584add.spc	57.94 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 6cf9d92f08956fa74016014a99dc793e.spc	28.51 KB	03/06/2025 09:42:35 AM	rw-r--r--
📄 6ee565104f90a8212dd7e14133d6e4ae.spc	193 bytes	03/06/2025 09:42:35 AM	rw-r--r--
📄 6eede30400457c4277e91f133b7a27a6.spc	60.73 KB	03/06/2025 09:42:36 AM	rw-r--r--
📄 71f0008f42879a3e550851568b3494d0.spc	1.8 MB	03/06/2025 09:42:36 AM	rw-r--r--
📄 72ff111641b87f5d234c3ded91959234.spc	136.69 KB	03/06/2025 09:42:36 AM	rw-r--r--
📄 7400954e05f62bf45dcf5c1087223df4.spc	2.63 KB	03/06/2025 09:42:36 AM	rw-r--r--
📄 7463e454c4ab831179d37c4e86643dc2.spc	266.75 KB	03/06/2025 09:42:36 AM	rw-r--r--
📄 74a014a9f44690e4a045b1753c1ef5c9.spc	159 bytes	03/06/2025 09:42:36 AM	rw-r--r--
📄 76c5af11f8b8bd34c58097da8689d2a2.spc	185.34 KB	03/06/2025 09:42:36 AM	rw-r--r--
📄 76e6555adc909bfac627ceaba91df128.spc	167.17 KB	03/06/2025 09:42:36 AM	rw-r--r--
📄 7ab2d04babf16e9d06f787e38f186748.spc	89.36 KB	03/06/2025 09:42:36 AM	rw-r--r--
📄 7b9723cc6339397505e29dbe8497b10a.spc	150 bytes	03/06/2025 09:42:36 AM	rw-r--r--
📄 7beeb799568e0584e407e788f7ec973d.spc	56.61 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 7c6fcaa553a1353d8fcc5535064f4008.spc	50.63 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 7d1ab5c52f2c3ab33c409e73bedecc5e.spc	3.92 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 7dc75885832733a5ea185d98887b82df.spc	33.47 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 7e7185a8ee7828937f2a05f0aa956f10.spc	162 bytes	03/06/2025 09:42:37 AM	rw-r--r--
📄 80d5eace46cca553f0c7e7a631ed6703.spc	123.39 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 828f89042b67ae6fca8657110997c302.spc	3.94 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 8341235cab83dcb126f0ad2b5812b63d.spc	88.67 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 8353d08af859970965abdbc71f5bf9a3.spc	37.15 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 83efe9d0463ea50fa82517c4a92ed589.spc	158 bytes	03/06/2025 09:42:37 AM	rw-r--r--
📄 8409cc881139fa5d466af5d757f19609.spc	48.09 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 84ed1e5a5cc7fb7930a3132f1dd7ae0a.spc	112.98 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 850a6a7f962502618892db457e231fe7.spc	131.61 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 852c8f9b4effcea04904f4e1e55da3f4.spc	459.08 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 874091b93fb13db66a5fdbe72fbb8d6b.spc	1.59 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 8b768f4e406ced9a4cba62897a7bc651.spc	97.91 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 8bbfe139e67e5f738b9ffcb18a7c5b86.spc	47.7 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 8d0823d0a4bd13241dc275b14e801667.spc	183.86 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 8d3db038a087fbdc54054a9282afa2c6.spc	164 bytes	03/06/2025 09:42:37 AM	rw-r--r--
📄 8d64115de2ca5294e771c8614663c953.spc	162 bytes	03/06/2025 09:42:37 AM	rw-r--r--
📄 8e24588eb23460ceefe5195686c2a6db.spc	101.38 KB	03/06/2025 09:42:37 AM	rw-r--r--
📄 906b5d59de170e528244744b925a8af5.spc	97.16 KB	03/06/2025 09:42:37 AM	rw-r--r--
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engineering. Laser-based techniques are promising for autonomous compound
detection because the optical response of materials encodes enough electronic
and vibrational information for remote chemical identification. This has been
exploited using the fingerprint region of infrared absorption spectra, which
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molecules, thus facilitating chemical identification. However, optical
identification using visible light has not been realized. Using decades of
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compounds and polymers over a broad range of frequencies from the ultraviolet
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measurement in the visible spectral region, away from absorption resonances.
The optical classifier proposed here could be applied to autonomous material
identification protocols or applications.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:342:" <a href="http://arxiv.org/find/cs/1/au:+Bikku_T/0/1/0/all/0/1">Thulasi Bikku</a>, <a href="http://arxiv.org/find/cs/1/au:+Fritz_R/0/1/0/all/0/1">Rub&#xe9;n A. Fritz</a>, <a href="http://arxiv.org/find/cs/1/au:+Colon_Y/0/1/0/all/0/1">Yamil J. Col&#xf3;n</a>, <a href="http://arxiv.org/find/cs/1/au:+Herrera_F/0/1/0/all/0/1">Felipe Herrera</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:1;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11833";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:123:"Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics. (arXiv:2204.11833v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11833";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1681:"We study the problem of reinforcement learning for a task encoded by a reward
machine. The task is defined over a set of properties in the environment,
called atomic propositions, and represented by Boolean variables. One
unrealistic assumption commonly used in the literature is that the truth values
of these propositions are accurately known. In real situations, however, these
truth values are uncertain since they come from sensors that suffer from
imperfections. At the same time, reward machines can be difficult to model
explicitly, especially when they encode complicated tasks. We develop a
reinforcement-learning algorithm that infers a reward machine that encodes the
underlying task while learning how to execute it, despite the uncertainties of
the propositions' truth values. In order to address such uncertainties, the
algorithm maintains a probabilistic estimate about the truth value of the
atomic propositions; it updates this estimate according to new sensory
measurements that arrive from the exploration of the environment. Additionally,
the algorithm maintains a hypothesis reward machine, which acts as an estimate
of the reward machine that encodes the task to be learned. As the agent
explores the environment, the algorithm updates the hypothesis reward machine
according to the obtained rewards and the estimate of the atomic propositions'
truth value. Finally, the algorithm uses a Q-learning procedure for the states
of the hypothesis reward machine to determine the policy that accomplishes the
task. We prove that the algorithm successfully infers the reward machine and
asymptotically learns a policy that accomplishes the respective task.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:343:" <a href="http://arxiv.org/find/cs/1/au:+Verginis_C/0/1/0/all/0/1">Christos Verginis</a>, <a href="http://arxiv.org/find/cs/1/au:+Koprulu_C/0/1/0/all/0/1">Cevahir Koprulu</a>, <a href="http://arxiv.org/find/cs/1/au:+Chinchali_S/0/1/0/all/0/1">Sandeep Chinchali</a>, <a href="http://arxiv.org/find/cs/1/au:+Topcu_U/0/1/0/all/0/1">Ufuk Topcu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:2;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11834";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:85:"Accelerating Machine Learning via the Weber-Fechner Law. (arXiv:2204.11834v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11834";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:578:"The Weber-Fechner Law observes that human perception scales as the logarithm
of the stimulus. We argue that learning algorithms for human concepts could
benefit from the Weber-Fechner Law. Specifically, we impose Weber-Fechner on
simple neural networks, with or without convolution, via the logarithmic power
series of their sorted output. Our experiments show surprising performance and
accuracy on the MNIST data set within a few training iterations and limited
computational resources, suggesting that Weber-Fechner can accelerate machine
learning of human concepts.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:80:" <a href="http://arxiv.org/find/cs/1/au:+Kausik_B/0/1/0/all/0/1">B.N. Kausik</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:3;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11835";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:166:"A Novel Scalable Apache Spark Based Feature Extraction Approaches for Huge Protein Sequence and their Clustering Performance Analysis. (arXiv:2204.11835v1 [q-bio.QM])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11835";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1872:"Genome sequencing projects are rapidly increasing the number of
high-dimensional protein sequence datasets. Clustering a high-dimensional
protein sequence dataset using traditional machine learning approaches poses
many challenges. Many different feature extraction methods exist and are widely
used. However, extracting features from millions of protein sequences becomes
impractical because they are not scalable with current algorithms. Therefore,
there is a need for an efficient feature extraction approach that extracts
significant features. We have proposed two scalable feature extraction
approaches for extracting features from huge protein sequences using Apache
Spark, which are termed 60d-SPF (60-dimensional Scalable Protein Feature) and
6d-SCPSF (6-dimensional Scalable Co-occurrence-based Probability-Specific
Feature). The proposed 60d-SPF and 6d-SCPSF approaches capture the statistical
properties of amino acids to create a fixed-length numeric feature vector that
represents each protein sequence in terms of 60-dimensional and 6-dimensional
features, respectively. The preprocessed huge protein sequences are used as an
input in two clustering algorithms, i.e., Scalable Random Sampling with
Iterative Optimization Fuzzy c-Means (SRSIO-FCM) and Scalable Literal Fuzzy
C-Means (SLFCM) for clustering. We have conducted extensive experiments on
various soybean protein datasets to demonstrate the effectiveness of the
proposed feature extraction methods, 60d-SPF, 6d-SCPSF, and existing feature
extraction methods on SRSIO-FCM and SLFCM clustering algorithms. The reported
results in terms of the Silhouette index and the Davies-Bouldin index show that
the proposed 60d-SPF extraction method on SRSIO-FCM and SLFCM clustering
algorithms achieves significantly better results than the proposed 6d-SCPSF and
existing feature extraction approaches.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:706:" <a href="http://arxiv.org/find/q-bio/1/au:+Jha_P/0/1/0/all/0/1">Preeti Jha</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Tiwari_A/0/1/0/all/0/1">Aruna Tiwari</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Bharill_N/0/1/0/all/0/1">Neha Bharill</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Ratnaparkhe_M/0/1/0/all/0/1">Milind Ratnaparkhe</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Patel_O/0/1/0/all/0/1">Om Prakash Patel</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Harshith_N/0/1/0/all/0/1">Nilagiri Harshith</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Mounika_M/0/1/0/all/0/1">Mukkamalla Mounika</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Nagendra_N/0/1/0/all/0/1">Neha Nagendra</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:4;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11836";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:147:"Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way. (arXiv:2204.11836v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11836";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1116:"Cookie banners, the pop ups that appear to collect your consent for data
collection, are a tempting ground for dark patterns. Dark patterns are design
elements that are used to influence the user's choice towards an option that is
not in their interest. The use of dark patterns renders consent elicitation
meaningless and voids the attempts to improve a fair collection and use of
data. Can machine learning be used to automatically detect the presence of dark
patterns in cookie banners? In this work, a dataset of cookie banners of 300
news websites was used to train a prediction model that does exactly that. The
machine learning pipeline we used includes feature engineering, parameter
search, training a Gradient Boosted Tree classifier and evaluation. The
accuracy of the trained model is promising, but allows a lot of room for
improvement. We provide an in-depth analysis of the interdisciplinary
challenges that automated dark pattern detection poses to artificial
intelligence. The dataset and all the code created using machine learning is
available at the url to repository removed for review.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:263:" <a href="http://arxiv.org/find/cs/1/au:+Soe_T/0/1/0/all/0/1">Than Htut Soe</a>, <a href="http://arxiv.org/find/cs/1/au:+Santos_C/0/1/0/all/0/1">Cristiana Teixeira Santos</a>, <a href="http://arxiv.org/find/cs/1/au:+Slavkovik_M/0/1/0/all/0/1">Marija Slavkovik</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:5;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11837";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:69:"A Mask-Based Adversarial Defense Scheme. (arXiv:2204.11837v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11837";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1124:"Adversarial attacks hamper the functionality and accuracy of Deep Neural
Networks (DNNs) by meddling with subtle perturbations to their inputs.In this
work, we propose a new Mask-based Adversarial Defense scheme (MAD) for DNNs to
mitigate the negative effect from adversarial attacks. To be precise, our
method promotes the robustness of a DNN by randomly masking a portion of
potential adversarial images, and as a result, the %classification result
output of the DNN becomes more tolerant to minor input perturbations. Compared
with existing adversarial defense techniques, our method does not need any
additional denoising structure, nor any change to a DNN's design. We have
tested this approach on a collection of DNN models for a variety of data sets,
and the experimental results confirm that the proposed method can effectively
improve the defense abilities of the DNNs against all of the tested adversarial
attack methods. In certain scenarios, the DNN models trained with MAD have
improved classification accuracy by as much as 20% to 90% compared to the
original models that are given adversarial inputs.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:317:" <a href="http://arxiv.org/find/cs/1/au:+Xu_W/0/1/0/all/0/1">Weizhen Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_C/0/1/0/all/0/1">Chenyi Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_F/0/1/0/all/0/1">Fangzhen Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Fang_L/0/1/0/all/0/1">Liangda Fang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:6;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11838";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:82:"Automating Neural Architecture Design without Search. (arXiv:2204.11838v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11838";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1499:"Neural structure search (NAS), as the mainstream approach to automate deep
neural architecture design, has achieved much success in recent years. However,
the performance estimation component adhering to NAS is often prohibitively
costly, which leads to the enormous computational demand. Though a large number
of efforts have been dedicated to alleviating this pain point, no consensus has
been made yet on which is optimal. In this paper, we study the automated
architecture design from a new perspective that eliminates the need to
sequentially evaluate each neural architecture generated during algorithm
execution. Specifically, the proposed approach is built by learning the
knowledge of high-level experts in designing state-of-the-art architectures,
and then the new architecture is directly generated upon the knowledge learned.
We implemented the proposed approach by using a graph neural network for link
prediction and acquired the knowledge from NAS-Bench-101. Compared to existing
peer competitors, we found a competitive network with minimal cost. In
addition, we also utilized the learned knowledge from NAS-Bench-101 to automate
architecture design in the DARTS search space, and achieved 97.82% accuracy on
CIFAR10, and 76.51% top-1 accuracy on ImageNet consuming only $2\times10^{-4}$
GPU days. This also demonstrates the high transferability of the proposed
approach, and can potentially lead to a new, more computationally efficient
paradigm in this research direction.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:156:" <a href="http://arxiv.org/find/cs/1/au:+Liang_Z/0/1/0/all/0/1">Zixuan Liang</a>, <a href="http://arxiv.org/find/cs/1/au:+Sun_Y/0/1/0/all/0/1">Yanan Sun</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:7;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11839";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:62:"AU-NN: ANFIS Unit Neural Network. (arXiv:2204.11839v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11839";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:814:"In this paper is described the ANFIS Unit Neural Network, a deep neural
network where each neuron is an independent ANFIS. Two use cases of this
network are shown to test the capability of the network. (i) Classification of
five imagined words. (ii) Incremental learning in the task of detecting
Imagined Word Segments vs. Idle State Segments. In both cases, the proposed
network outperforms the conventional methods. Additionally, is described a
process of classification where instead of taking the whole instance as one
example, each instance is decomposed into a set of smaller instances, and the
classification is done by a majority vote over all the predictions of the set.
The codes to build the AU-NN used in this paper, are available on the github
repository https://github.com/tonahdztoro/AU_NN.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:324:" <a href="http://arxiv.org/find/cs/1/au:+Hernandez_del_Toro_T/0/1/0/all/0/1">Tonatiuh Hern&#xe1;ndez-del-Toro</a>, <a href="http://arxiv.org/find/cs/1/au:+Reyes_Garcia_C/0/1/0/all/0/1">Carlos A. Reyes-Garc&#xed;a</a>, <a href="http://arxiv.org/find/cs/1/au:+Villasenor_Pineda_L/0/1/0/all/0/1">Luis Villase&#xf1;or-Pineda</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:8;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11840";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:116:"Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-machine Interface. (arXiv:2204.11840v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11840";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1492:"Objective: Brain-machine interfaces (BMIs) aim to provide direct brain
control of devices such as prostheses and computer cursors, which have
demonstrated great potential for mobility restoration. One major limitation of
current BMIs lies in the unstable performance in online control due to the
variability of neural signals, which seriously hinders the clinical
availability of BMIs. Method: To deal with the neural variability in online BMI
control, we propose a dynamic ensemble Bayesian filter (DyEnsemble). DyEnsemble
extends Bayesian filters with a dynamic measurement model, which adjusts its
parameters in time adaptively with neural changes. This is achieved by learning
a pool of candidate functions and dynamically weighting and assembling them
according to neural signals. In this way, DyEnsemble copes with variability in
signals and improves the robustness of online control. Results: Online BMI
experiments with a human participant demonstrate that, compared with the
velocity Kalman filter, DyEnsemble significantly improves the control accuracy
(increases the success rate by 13.9% and reduces the reach time by 13.5% in the
random target pursuit task) and robustness (performs more stably over different
experiment days). Conclusion: Our results demonstrate the superiority of
DyEnsemble in online BMI control. Significance: DyEnsemble frames a novel and
flexible framework for robust neural decoding, which is beneficial to different
neural decoding applications.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:695:" <a href="http://arxiv.org/find/cs/1/au:+Qi_Y/0/1/0/all/0/1">Yu Qi</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhu_X/0/1/0/all/0/1">Xinyun Zhu</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_K/0/1/0/all/0/1">Kedi Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Ren_F/0/1/0/all/0/1">Feixiao Ren</a>, <a href="http://arxiv.org/find/cs/1/au:+Jiang_H/0/1/0/all/0/1">Hongjie Jiang</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhu_J/0/1/0/all/0/1">Junming Zhu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_J/0/1/0/all/0/1">Jianmin Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Pan_G/0/1/0/all/0/1">Gang Pan</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_Y/0/1/0/all/0/1">Yueming Wang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:9;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11841";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:96:"A Closer Look at Personalization in Federated Image Classification. (arXiv:2204.11841v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11841";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1679:"Federated Learning (FL) is developed to learn a single global model across
the decentralized data, while is susceptible when realizing client-specific
personalization in the presence of statistical heterogeneity. However, studies
focus on learning a robust global model or personalized classifiers, which
yield divergence due to inconsistent objectives. This paper shows that it is
possible to achieve flexible personalization after the convergence of the
global model by introducing representation learning. In this paper, we first
analyze and determine that non-IID data harms representation learning of the
global model. Existing FL methods adhere to the scheme of jointly learning
representations and classifiers, where the global model is an average of
classification-based local models that are consistently subject to
heterogeneity from non-IID data. As a solution, we separate representation
learning from classification learning in FL and propose RepPer, an independent
two-stage personalized FL framework.We first learn the client-side feature
representation models that are robust to non-IID data and aggregate them into a
global common representation model. After that, we achieve personalization by
learning a classifier head for each client, based on the common representation
obtained at the former stage. Notably, the proposed two-stage learning scheme
of RepPer can be potentially used for lightweight edge computing that involves
devices with constrained computation power.Experiments on various datasets
(CIFAR-10/100, CINIC-10) and heterogeneous data setup show that RepPer
outperforms alternatives in flexibility and personalization on non-IID data.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:557:" <a href="http://arxiv.org/find/cs/1/au:+Jing_C/0/1/0/all/0/1">Changxing Jing</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_Y/0/1/0/all/0/1">Yan Huang</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhuang_Y/0/1/0/all/0/1">Yihong Zhuang</a>, <a href="http://arxiv.org/find/cs/1/au:+Sun_L/0/1/0/all/0/1">Liyan Sun</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_Y/0/1/0/all/0/1">Yue Huang</a>, <a href="http://arxiv.org/find/cs/1/au:+Xiao_Z/0/1/0/all/0/1">Zhenlong Xiao</a>, <a href="http://arxiv.org/find/cs/1/au:+Ding_X/0/1/0/all/0/1">Xinghao Ding</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:10;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11842";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:88:"Adaptive Online Value Function Approximation with Wavelets. (arXiv:2204.11842v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11842";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1343:"Using function approximation to represent a value function is necessary for
continuous and high-dimensional state spaces. Linear function approximation has
desirable theoretical guarantees and often requires less compute and samples
than neural networks, but most approaches suffer from an exponential growth in
the number of functions as the dimensionality of the state space increases. In
this work, we introduce the wavelet basis for reinforcement learning. Wavelets
can effectively be used as a fixed basis and additionally provide the ability
to adaptively refine the basis set as learning progresses, making it feasible
to start with a minimal basis set. This adaptive method can either increase the
granularity of the approximation at a point in state space, or add in
interactions between different dimensions as necessary. We prove that wavelets
are both necessary and sufficient if we wish to construct a function
approximator that can be adaptively refined without loss of precision. We
further demonstrate that a fixed wavelet basis set performs comparably against
the high-performing Fourier basis on Mountain Car and Acrobot, and that the
adaptive methods provide a convenient approach to addressing an oversized
initial basis set, while demonstrating performance comparable to, or greater
than, the fixed wavelet basis.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:424:" <a href="http://arxiv.org/find/cs/1/au:+Beukman_M/0/1/0/all/0/1">Michael Beukman</a>, <a href="http://arxiv.org/find/cs/1/au:+Mitchley_M/0/1/0/all/0/1">Michael Mitchley</a>, <a href="http://arxiv.org/find/cs/1/au:+Wookey_D/0/1/0/all/0/1">Dean Wookey</a>, <a href="http://arxiv.org/find/cs/1/au:+James_S/0/1/0/all/0/1">Steven James</a>, <a href="http://arxiv.org/find/cs/1/au:+Konidaris_G/0/1/0/all/0/1">George Konidaris</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:11;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11843";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"A Computational Theory of Learning Flexible Reward-Seeking Behavior with Place Cells. (arXiv:2204.11843v1 [q-bio.NC])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11843";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1462:"An important open question in computational neuroscience is how various
spatially tuned neurons, such as place cells, are used to support the learning
of reward-seeking behavior of an animal. Existing computational models either
lack biological plausibility or fall short of behavioral flexibility when
environments change. In this paper, we propose a computational theory that
achieves behavioral flexibility with better biological plausibility. We first
train a mixture of Gaussian distributions to model the ensemble of firing
fields of place cells. Then we propose a Hebbian-like rule to learn the
synaptic strength matrix among place cells. This matrix is interpreted as the
transition rate matrix of a continuous time Markov chain to generate the
sequential replay of place cells. During replay, the synaptic strengths from
place cells to medium spiny neurons (MSN) are learned by a temporal-difference
like rule to store place-reward associations. After replay, the activation of
MSN will ramp up when an animal approaches the rewarding place, so the animal
can move along the direction where the MSN activation is increasing to find the
rewarding place. We implement our theory into a high-fidelity virtual rat in
the MuJoCo physics simulator. In a complex maze, the rat shows significantly
better learning efficiency and behavioral flexibility than a rat that
implements a neuroscience-inspired reinforcement learning algorithm, deep
Q-network.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:82:" <a href="http://arxiv.org/find/q-bio/1/au:+Gao_Y/0/1/0/all/0/1">Yuanxiang Gao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:12;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11844";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"From Monolith to Microservices: Static and Dynamic Analysis Comparison. (arXiv:2204.11844v1 [cs.SE])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11844";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1025:"One of the most challenging problems in the migration of a monolith to a
microservices architecture is the identification of the microservices
boundaries. Several approaches have been recently proposed for the automatic
identification of microservices, which, even though following the same basic
steps, diverge on how data of the monolith system is collected and analysed. In
this paper, we compare the decompositions generated for two monolith systems
into a set of candidate microservices, when static and dynamic analysis data
collection techniques are used. The decompositions are generated using a
combination of similarity measures and are evaluated according to a complexity
metric to answer the following research question: which collection of monolith
data, static or dynamic analysis, allows to generate better decompositions? As
result of the analysis we conclude that neither of the analysis techniques,
static nor dynamic, outperforms the other, but the dynamic collection of data
requires more effort.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:261:" <a href="http://arxiv.org/find/cs/1/au:+Andrade_B/0/1/0/all/0/1">Bernardo Andrade</a>, <a href="http://arxiv.org/find/cs/1/au:+Santos_S/0/1/0/all/0/1">Samuel Santos</a>, <a href="http://arxiv.org/find/cs/1/au:+Silva_A/0/1/0/all/0/1">Ant&#xf3;nio Rito Silva</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:13;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11845";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"Logistic-ELM: A Novel Fault Diagnosis Method for Rolling Bearings. (arXiv:2204.11845v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11845";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1602:"The fault diagnosis of rolling bearings is a critical technique to realize
predictive maintenance for mechanical condition monitoring. In real industrial
systems, the main challenges for the fault diagnosis of rolling bearings
pertain to the accuracy and real-time requirements. Most existing methods focus
on ensuring the accuracy, and the real-time requirement is often neglected. In
this paper, considering both requirements, we propose a novel fast fault
diagnosis method for rolling bearings, based on extreme learning machine (ELM)
and logistic mapping, named logistic-ELM. First, we identify 14 kinds of
time-domain features from the original vibration signals according to
mechanical vibration principles and adopt the sequential forward selection
(SFS) strategy to select optimal features from them to ensure the basic
predictive accuracy and efficiency. Next, we propose the logistic-ELM for fast
fault classification, where the biases in ELM are omitted and the random input
weights are replaced by the chaotic logistic mapping sequence which involves a
higher uncorrelation to obtain more accurate results with fewer hidden neurons.
We conduct extensive experiments on the rolling bearing vibration signal
dataset of the Case Western Reserve University (CWRU) Bearing Data Centre. The
experimental results show that the proposed approach outperforms existing SOTA
comparison methods in terms of the predictive accuracy, and the highest
accuracy is 100% in seven separate sub data environments. The relevant code is
publicly available at https://github.com/TAN-OpenLab/logistic-ELM.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:384:" <a href="http://arxiv.org/find/cs/1/au:+Tan_Z/0/1/0/all/0/1">Zhenhua Tan</a>, <a href="http://arxiv.org/find/cs/1/au:+Ning_J/0/1/0/all/0/1">Jingyu Ning</a>, <a href="http://arxiv.org/find/cs/1/au:+Peng_K/0/1/0/all/0/1">Kai Peng</a>, <a href="http://arxiv.org/find/cs/1/au:+Xia_Z/0/1/0/all/0/1">Zhenche Xia</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_D/0/1/0/all/0/1">Danke Wu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:14;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11846";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:54:"Graphical Residual Flows. (arXiv:2204.11846v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11846";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:981:"Graphical flows add further structure to normalizing flows by encoding
non-trivial variable dependencies. Previous graphical flow models have focused
primarily on a single flow direction: the normalizing direction for density
estimation, or the generative direction for inference. However, to use a single
flow to perform tasks in both directions, the model must exhibit stable and
efficient flow inversion. This work introduces graphical residual flows, a
graphical flow based on invertible residual networks. Our approach to
incorporating dependency information in the flow, means that we are able to
calculate the Jacobian determinant of these flows exactly. Our experiments
confirm that graphical residual flows provide stable and accurate inversion
that is also more time-efficient than alternative flows with similar task
performance. Furthermore, our model provides performance competitive with other
graphical flows for both density estimation and inference tasks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:163:" <a href="http://arxiv.org/find/cs/1/au:+Mouton_J/0/1/0/all/0/1">Jacobie Mouton</a>, <a href="http://arxiv.org/find/cs/1/au:+Kroon_S/0/1/0/all/0/1">Steve Kroon</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:15;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11847";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"SIReN-VAE: Leveraging Flows and Amortized Inference for Bayesian Networks. (arXiv:2204.11847v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11847";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:930:"Initial work on variational autoencoders assumed independent latent variables
with simple distributions. Subsequent work has explored incorporating more
complex distributions and dependency structures: including normalizing flows in
the encoder network allows latent variables to entangle non-linearly, creating
a richer class of distributions for the approximate posterior, and stacking
layers of latent variables allows more complex priors to be specified for the
generative model. This work explores incorporating arbitrary dependency
structures, as specified by Bayesian networks, into VAEs. This is achieved by
extending both the prior and inference network with graphical residual flows -
residual flows that encode conditional independence by masking the weight
matrices of the flow's residual blocks. We compare our model's performance on
several synthetic datasets and show its potential in data-sparse settings.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:163:" <a href="http://arxiv.org/find/cs/1/au:+Mouton_J/0/1/0/all/0/1">Jacobie Mouton</a>, <a href="http://arxiv.org/find/cs/1/au:+Kroon_S/0/1/0/all/0/1">Steve Kroon</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:16;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11848";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:120:"On Leveraging Variational Graph Embeddings for Open World Compositional Zero-Shot Learning. (arXiv:2204.11848v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11848";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1401:"Humans are able to identify and categorize novel compositions of known
concepts. The task in Compositional Zero-Shot learning (CZSL) is to learn
composition of primitive concepts, i.e. objects and states, in such a way that
even their novel compositions can be zero-shot classified. In this work, we do
not assume any prior knowledge on the feasibility of novel compositions
i.e.open-world setting, where infeasible compositions dominate the search
space. We propose a Compositional Variational Graph Autoencoder (CVGAE)
approach for learning the variational embeddings of the primitive concepts
(nodes) as well as feasibility of their compositions (via edges). Such
modelling makes CVGAE scalable to real-world application scenarios. This is in
contrast to SOTA method, CGE, which is computationally very expensive. e.g.for
benchmark C-GQA dataset, CGE requires 3.94 x 10^5 nodes, whereas CVGAE requires
only 1323 nodes. We learn a mapping of the graph and image embeddings onto a
common embedding space. CVGAE adopts a deep metric learning approach and learns
a similarity metric in this space via bi-directional contrastive loss between
projected graph and image embeddings. We validate the effectiveness of our
approach on three benchmark datasets.We also demonstrate via an image retrieval
task that the representations learnt by CVGAE are better suited for
compositional generalization.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:261:" <a href="http://arxiv.org/find/cs/1/au:+Anwaar_M/0/1/0/all/0/1">Muhammad Umer Anwaar</a>, <a href="http://arxiv.org/find/cs/1/au:+Pan_Z/0/1/0/all/0/1">Zhihui Pan</a>, <a href="http://arxiv.org/find/cs/1/au:+Kleinsteuber_M/0/1/0/all/0/1">Martin Kleinsteuber</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:17;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11849";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:133:"Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users. (arXiv:2204.11849v1 [q-fin.RM])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11849";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1657:"Risk assessment is a substantial problem for financial institutions that has
been extensively studied both for its methodological richness and its various
practical applications. With the expansion of inclusive finance, recent
attentions are paid to micro and small-sized enterprises (MSEs). Compared with
large companies, MSEs present a higher exposure rate to default owing to their
insecure financial stability. Conventional efforts learn classifiers from
historical data with elaborate feature engineering. However, the main obstacle
for MSEs involves severe deficiency in credit-related information, which may
degrade the performance of prediction. Besides, financial activities have
diverse explicit and implicit relations, which have not been fully exploited
for risk judgement in commercial banks. In particular, the observations on real
data show that various relationships between company users have additional
power in financial risk analysis. In this paper, we consider a graph of banking
data, and propose a novel HIDAM model for the purpose. Specifically, we attempt
to incorporate heterogeneous information network with rich attributes on
multi-typed nodes and links for modeling the scenario of business banking
service. To enhance feature representation of MSEs, we extract interactive
information through meta-paths and fully exploit path information. Furthermore,
we devise a hierarchical attention mechanism respectively to learn the
importance of contents inside each meta-path and the importance of different
metapahs. Experimental results verify that HIDAM outperforms state-of-the-art
competitors on real-world banking data.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:410:" <a href="http://arxiv.org/find/q-fin/1/au:+Zhang_Z/0/1/0/all/0/1">Zheng Zhang</a>, <a href="http://arxiv.org/find/q-fin/1/au:+Ji_Y/0/1/0/all/0/1">Yingsheng Ji</a>, <a href="http://arxiv.org/find/q-fin/1/au:+Shen_J/0/1/0/all/0/1">Jiachen Shen</a>, <a href="http://arxiv.org/find/q-fin/1/au:+Zhang_X/0/1/0/all/0/1">Xi Zhang</a>, <a href="http://arxiv.org/find/q-fin/1/au:+Yang_G/0/1/0/all/0/1">Guangwen Yang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:18;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11850";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:104:"Memory Efficient Invertible Neural Networks for 3D Photoacoustic Imaging. (arXiv:2204.11850v1 [eess.IV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11850";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:882:"Photoacoustic imaging (PAI) can image high-resolution structures of clinical
interest such as vascularity in cancerous tumor monitoring. When imaging human
subjects, geometric restrictions force limited-view data retrieval causing
imaging artifacts. Iterative physical model based approaches reduce artifacts
but require prohibitively time consuming PDE solves. Machine learning (ML) has
accelerated PAI by combining physical models and learned networks. However, the
depth and overall power of ML methods is limited by memory intensive training.
We propose using invertible neural networks (INNs) to alleviate memory
pressure. We demonstrate INNs can image 3D photoacoustic volumes in the setting
of limited-view, noisy, and subsampled data. The frugal constant memory usage
of INNs enables us to train an arbitrary depth of learned layers on a consumer
GPU with 16GB RAM.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:267:" <a href="http://arxiv.org/find/eess/1/au:+Orozco_R/0/1/0/all/0/1">Rafael Orozco</a>, <a href="http://arxiv.org/find/eess/1/au:+Louboutin_M/0/1/0/all/0/1">Mathias Louboutin</a>, <a href="http://arxiv.org/find/eess/1/au:+Herrmann_F/0/1/0/all/0/1">Felix J. Herrmann</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:19;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11852";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:72:"Graph Auto-Encoders for Network Completion. (arXiv:2204.11852v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11852";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:910:"Completing a graph means inferring the missing nodes and edges from a
partially observed network. Different methods have been proposed to solve this
problem, but none of them employed the pattern similarity of parts of the
graph. In this paper, we propose a model to use the learned pattern of
connections from the observed part of the network based on the Graph
Auto-Encoder technique and generalize these patterns to complete the whole
graph. Our proposed model achieved competitive performance with less
information needed. Empirical analysis of synthetic datasets and real-world
datasets from different domains show that our model can complete the network
with higher accuracy compared with baseline prediction models in most cases.
Furthermore, we also studied the character of the model and found it is
particularly suitable to complete a network that has more complex local
connection patterns.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:312:" <a href="http://arxiv.org/find/cs/1/au:+Zhang_Z/0/1/0/all/0/1">Zhang Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Tao_R/0/1/0/all/0/1">Ruyi Tao</a>, <a href="http://arxiv.org/find/cs/1/au:+Tao_Y/0/1/0/all/0/1">Yongzai Tao</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_J/0/1/0/all/0/1">Jiang Zhang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:20;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11853";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:108:"Real or Virtual: A Video Conferencing Background Manipulation-Detection System. (arXiv:2204.11853v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11853";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1862:"Recently, the popularity and wide use of the last-generation video
conferencing technologies created an exponential growth in its market size.
Such technology allows participants in different geographic regions to have a
virtual face-to-face meeting. Additionally, it enables users to employ a
virtual background to conceal their own environment due to privacy concerns or
to reduce distractions, particularly in professional settings. Nevertheless, in
scenarios where the users should not hide their actual locations, they may
mislead other participants by claiming their virtual background as a real one.
Therefore, it is crucial to develop tools and strategies to detect the
authenticity of the considered virtual background. In this paper, we present a
detection strategy to distinguish between real and virtual video conferencing
user backgrounds. We demonstrate that our detector is robust against two attack
scenarios. The first scenario considers the case where the detector is unaware
about the attacks and inn the second scenario, we make the detector aware of
the adversarial attacks, which we refer to Adversarial Multimedia Forensics
(i.e, the forensically-edited frames are included in the training set). Given
the lack of publicly available dataset of virtual and real backgrounds for
video conferencing, we created our own dataset and made them publicly available
[1]. Then, we demonstrate the robustness of our detector against different
adversarial attacks that the adversary considers. Ultimately, our detector's
performance is significant against the CRSPAM1372 [2] features, and
post-processing operations such as geometric transformations with different
quality factors that the attacker may choose. Moreover, our performance results
shows that we can perfectly identify a real from a virtual background with an
accuracy of 99.80%.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:506:" <a href="http://arxiv.org/find/cs/1/au:+Nowroozi_E/0/1/0/all/0/1">Ehsan Nowroozi</a>, <a href="http://arxiv.org/find/cs/1/au:+Mekdad_Y/0/1/0/all/0/1">Yassine Mekdad</a>, <a href="http://arxiv.org/find/cs/1/au:+Conti_M/0/1/0/all/0/1">Mauro Conti</a>, <a href="http://arxiv.org/find/cs/1/au:+Milani_S/0/1/0/all/0/1">Simone Milani</a>, <a href="http://arxiv.org/find/cs/1/au:+Uluagac_S/0/1/0/all/0/1">Selcuk Uluagac</a>, <a href="http://arxiv.org/find/cs/1/au:+Yanikoglu_B/0/1/0/all/0/1">Berrin Yanikoglu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:21;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11855";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:123:"Discovering Gateway Ports in Maritime Using Temporal Graph Neural Network Port Classification. (arXiv:2204.11855v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11855";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1228:"Vessel navigation is influenced by various factors, such as dynamic
environmental factors that change over time or static features such as vessel
type or depth of the ocean. These dynamic and static navigational factors
impose limitations on vessels, such as long waiting times in regions outside
the actual ports, and we call these waiting regions gateway ports. Identifying
gateway ports and their associated features such as congestion and available
utilities can enhance vessel navigation by planning on fuel optimization or
saving time in cargo operation. In this paper, we propose a novel temporal
graph neural network (TGNN) based port classification method to enable vessels
to discover gateway ports efficiently, thus optimizing their operations. The
proposed method processes vessel trajectory data to build dynamic graphs
capturing spatio-temporal dependencies between a set of static and dynamic
navigational features in the data, and it is evaluated in terms of port
classification accuracy on a real-world data set collected from ten vessels
operating in Halifax, NS, Canada. The experimental results indicate that our
TGNN-based port classification method provides an f-score of 95% in classifying
ports.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:337:" <a href="http://arxiv.org/find/cs/1/au:+Altan_D/0/1/0/all/0/1">Dogan Altan</a>, <a href="http://arxiv.org/find/cs/1/au:+Etemad_M/0/1/0/all/0/1">Mohammad Etemad</a>, <a href="http://arxiv.org/find/cs/1/au:+Marijan_D/0/1/0/all/0/1">Dusica Marijan</a>, <a href="http://arxiv.org/find/cs/1/au:+Kholodna_T/0/1/0/all/0/1">Tetyana Kholodna</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:22;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11857";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:111:"GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRU. (arXiv:2204.11857v1 [q-bio.QM])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11857";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1013:"The work for predicting drug and target affinity(DTA) is crucial for drug
development and repurposing. In this work, we propose a novel method called
GDGRU-DTA to predict the binding affinity between drugs and targets, which is
based on GraphDTA, but we consider that protein sequences are long sequences,
so simple CNN cannot capture the context dependencies in protein sequences
well. Therefore, we improve it by interpreting the protein sequences as time
series and extracting their features using Gate Recurrent Unit(GRU) and
Bidirectional Gate Recurrent Unit(BiGRU). For the drug, our processing method
is similar to that of GraphDTA, but uses two different graph convolution
methods. Subsequently, the representation of drugs and proteins are
concatenated for final prediction. We evaluate the proposed model on two
benchmark datasets. Our model outperforms some state-of-the-art deep learning
methods, and the results demonstrate the feasibility and excellent feature
capture ability of our model.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:331:" <a href="http://arxiv.org/find/q-bio/1/au:+Zhijian_L/0/1/0/all/0/1">Lyu Zhijian</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Shaohua_J/0/1/0/all/0/1">Jiang Shaohua</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Yigao_L/0/1/0/all/0/1">Liang Yigao</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Min_G/0/1/0/all/0/1">Gao Min</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:23;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11858";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:72:"Data Uncertainty without Prediction Models. (arXiv:2204.11858v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11858";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:761:"Data acquisition processes for machine learning are often costly. To
construct a high-performance prediction model with fewer data, a degree of
difficulty in prediction is often deployed as the acquisition function in
adding a new data point. The degree of difficulty is referred to as uncertainty
in prediction models. We propose an uncertainty estimation method named a
Distance-weighted Class Impurity without explicit use of prediction models. We
estimated uncertainty using distances and class impurities around the location,
and compared it with several methods based on prediction models for uncertainty
estimation by active learning tasks. We verified that the Distance-weighted
Class Impurity works effectively regardless of prediction models.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:159:" <a href="http://arxiv.org/find/cs/1/au:+Park_B/0/1/0/all/0/1">Bongjoon Park</a>, <a href="http://arxiv.org/find/cs/1/au:+Koh_E/0/1/0/all/0/1">Eunkyung Koh</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:24;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11859";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:59:"Mapping Research Trajectories. (arXiv:2204.11859v1 [cs.DL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11859";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1933:"Steadily growing amounts of information, such as annually published
scientific papers, have become so large that they elude an extensive manual
analysis. Hence, to maintain an overview, automated methods for the mapping and
visualization of knowledge domains are necessary and important, e.g., for
scientific decision makers. Of particular interest in this field is the
development of research topics of different entities (e.g., scientific authors
and venues) over time. However, existing approaches for their analysis are only
suitable for single entity types, such as venues, and they often do not capture
the research topics or the time dimension in an easily interpretable manner.

Hence, we propose a principled approach for \emph{mapping research
trajectories}, which is applicable to all kinds of scientific entities that can
be represented by sets of published papers. For this, we transfer ideas and
principles from the geographic visualization domain, specifically trajectory
maps and interactive geographic maps. Our visualizations depict the research
topics of entities over time in a straightforward interpr. manner. They can be
navigated by the user intuitively and restricted to specific elements of
interest. The maps are derived from a corpus of research publications (i.e.,
titles and abstracts) through a combination of unsupervised machine learning
methods.

In a practical demonstrator application, we exemplify the proposed approach
on a publication corpus from machine learning. We observe that our trajectory
visualizations of 30 top machine learning venues and 1000 major authors in this
field are well interpretable and are consistent with background knowledge drawn
from the entities' publications. Next to producing interactive, interpr.
visualizations supporting different kinds of analyses, our computed
trajectories are suitable for trajectory mining applications in the future.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:261:" <a href="http://arxiv.org/find/cs/1/au:+Schafermeier_B/0/1/0/all/0/1">Bastian Sch&#xe4;fermeier</a>, <a href="http://arxiv.org/find/cs/1/au:+Stumme_G/0/1/0/all/0/1">Gerd Stumme</a>, <a href="http://arxiv.org/find/cs/1/au:+Hanika_T/0/1/0/all/0/1">Tom Hanika</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:25;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11860";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:92:"Multi-objective Pointer Network for Combinatorial Optimization. (arXiv:2204.11860v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11860";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1575:"Multi-objective combinatorial optimization problems (MOCOPs), one type of
complex optimization problems, widely exist in various real applications.
Although meta-heuristics have been successfully applied to address MOCOPs, the
calculation time is often much longer. Recently, a number of deep reinforcement
learning (DRL) methods have been proposed to generate approximate optimal
solutions to the combinatorial optimization problems. However, the existing
studies on DRL have seldom focused on MOCOPs. This study proposes a
single-model deep reinforcement learning framework, called multi-objective
Pointer Network (MOPN), where the input structure of PN is effectively improved
so that the single PN is capable of solving MOCOPs. In addition, two training
strategies, based on representative model and transfer learning, respectively,
are proposed to further enhance the performance of MOPN in different
application scenarios. Moreover, compared to classical meta-heuristics, MOPN
only consumes much less time on forward propagation to obtain the Pareto front.
Meanwhile, MOPN is insensitive to problem scale, meaning that a trained MOPN is
able to address MOCOPs with different scales. To verify the performance of
MOPN, extensive experiments are conducted on three multi-objective traveling
salesman problems, in comparison with one state-of-the-art model DRL-MOA and
three classical multi-objective meta-heuristics. Experimental results
demonstrate that the proposed model outperforms all the comparative methods
with only 20\% to 40\% training time of DRL-MOA.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:310:" <a href="http://arxiv.org/find/cs/1/au:+Gao_L/0/1/0/all/0/1">Le-yang Gao</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_R/0/1/0/all/0/1">Rui Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_C/0/1/0/all/0/1">Chuang Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Jia_Z/0/1/0/all/0/1">Zhao-hong Jia</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:26;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11878";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:110:"Comeback Kid: Resilience for Mixed-Critical Wireless Network Resource Management. (arXiv:2204.11878v1 [cs.IT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11878";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1756:"The future sixth generation (6G) of communication systems is envisioned to
provide numerous applications in safety-critical contexts, e.g., driverless
traffic, modular industry, and smart cities, which require outstanding
performance, high reliability and fault tolerance, as well as autonomy.
Ensuring criticality awareness for diverse functional safety applications and
providing fault tolerance in an autonomous manner are essential for future 6G
systems. Therefore, this paper proposes jointly employing the concepts of
resilience and mixed criticality. In this contribution, we conduct physical
layer resource management in cloud-based networks under the rate-splitting
paradigm, which is a promising factor towards achieving high resilience. We
recapitulate the concepts individually, outline a joint metric to measure the
criticality-aware resilience, and verify its merits in a case study. We,
thereby, formulate a non-convex optimization problem, derive an efficient
iterative algorithm, propose four resilience mechanisms differing in quality
and time of adaption, and conduct numerical simulations. Towards this end, a
highly autonomous rate-splitting-enabled physical layer resource management
algorithm for future 6G networks respecting mixed-critical QoS levels and
providing high levels of resilience is proposed. Results emphasize the
appreciable improvements of implementing mixed criticality-aware resilience
under channel outages and strict quality of service (QoS) demands. The
rate-splitting paradigm is particularly shown to overcome state-of-the-art
interference management techniques, and the resilience and throughput adaption
over consecutive outage events reveals the proposed schemes suitability for
future 6G networks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:338:" <a href="http://arxiv.org/find/cs/1/au:+Reifert_R/0/1/0/all/0/1">Robert-Jeron Reifert</a>, <a href="http://arxiv.org/find/cs/1/au:+Roth_S/0/1/0/all/0/1">Stefan Roth</a>, <a href="http://arxiv.org/find/cs/1/au:+Ahmad_A/0/1/0/all/0/1">Alaa Alameer Ahmad</a>, <a href="http://arxiv.org/find/cs/1/au:+Sezgin_A/0/1/0/all/0/1">Aydin Sezgin</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:27;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11887";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"Evolutionary latent space search for driving human portrait generation. (arXiv:2204.11887v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11887";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:716:"This article presents an evolutionary approach for synthetic human portraits
generation based on the latent space exploration of a generative adversarial
network. The idea is to produce different human face images very similar to a
given target portrait. The approach applies StyleGAN2 for portrait generation
and FaceNet for face similarity evaluation. The evolutionary search is based on
exploring the real-coded latent space of StyleGAN2. The main results over both
synthetic and real images indicate that the proposed approach generates
accurate and diverse solutions, which represent realistic human portraits. The
proposed research can contribute to improving the security of face recognition
systems.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:269:" <a href="http://arxiv.org/find/cs/1/au:+Machin_B/0/1/0/all/0/1">Benjam&#xed;n Mach&#xed;n</a>, <a href="http://arxiv.org/find/cs/1/au:+Nesmachnow_S/0/1/0/all/0/1">Sergio Nesmachnow</a>, <a href="http://arxiv.org/find/cs/1/au:+Toutouh_J/0/1/0/all/0/1">Jamal Toutouh</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:28;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11891";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:108:"ProCST: Boosting Semantic Segmentation using Progressive Cyclic Style-Transfer. (arXiv:2204.11891v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11891";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1881:"Using synthetic data for training neural networks that achieve good
performance on real-world data is an important task as it has the potential to
reduce the need for costly data annotation. Yet, a network that is trained on
synthetic data alone does not perform well on real data due to the domain gap
between the two. Reducing this gap, also known as domain adaptation, has been
widely studied in recent years. In the unsupervised domain adaptation (UDA)
framework, unlabeled real data is used during training with labeled synthetic
data to obtain a neural network that performs well on real data. In this work,
we focus on image data. For the semantic segmentation task, it has been shown
that performing image-to-image translation from source to target, and then
training a network for segmentation on source annotations - leads to poor
results. Therefore a joint training of both is essential, which has been a
common practice in many techniques. Yet, closing the large domain gap between
the source and the target by directly performing the adaptation between the two
is challenging. In this work, we propose a novel two-stage framework for
improving domain adaptation techniques. In the first step, we progressively
train a multi-scale neural network to perform an initial transfer between the
source data to the target data. We denote the new transformed data as "Source
in Target" (SiT). Then, we use the generated SiT data as the input to any
standard UDA approach. This new data has a reduced domain gap from the desired
target domain, and the applied UDA approach further closes the gap. We
demonstrate the improvement achieved by our framework with two state-of-the-art
methods for semantic segmentation, DAFormer and ProDA, on two UDA tasks, GTA5
to Cityscapes and Synthia to Cityscapes. Code and state-of-the-art checkpoints
of ProCST+DAFormer are provided.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:259:" <a href="http://arxiv.org/find/cs/1/au:+Ettedgui_S/0/1/0/all/0/1">Shahaf Ettedgui</a>, <a href="http://arxiv.org/find/cs/1/au:+Abu_Hussein_S/0/1/0/all/0/1">Shady Abu-Hussein</a>, <a href="http://arxiv.org/find/cs/1/au:+Giryes_R/0/1/0/all/0/1">Raja Giryes</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:29;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11894";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:91:"Properly learning monotone functions via local reconstruction. (arXiv:2204.11894v1 [cs.DS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11894";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1299:"We give a $2^{\tilde{O}(\sqrt{n}/\varepsilon)}$-time algorithm for properly
learning monotone Boolean functions under the uniform distribution over
$\{0,1\}^n$. Our algorithm is robust to adversarial label noise and has a
running time nearly matching that of the state-of-the-art improper learning
algorithm of Bshouty and Tamon [BT96] and an information-theoretic lower bound
of [BCO+15]. Prior to this work, no proper learning algorithm with running time
smaller than $2^{\Omega(n)}$ was known to exist.

The core of our proper learner is a local computation algorithm for sorting
binary labels on a poset. Our algorithm is built on a body of work on
distributed greedy graph algorithms; specifically we rely on a recent work of
Ghaffari and Uitto [GU19], which gives an efficient algorithm for computing
maximal matchings in a graph in the LCA model of [RTVX11, ARVX11]. The
applications of our local sorting algorithm extend beyond learning on the
Boolean cube: we also give a tolerant tester for Boolean functions over general
posets that distinguishes functions that are $\varepsilon/3$-close to monotone
from those that are $\varepsilon$-far. Previous tolerant testers for the
Boolean cube only distinguished between $\varepsilon/\Omega(\sqrt{n})$-close
and $\varepsilon$-far.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:253:" <a href="http://arxiv.org/find/cs/1/au:+Lange_J/0/1/0/all/0/1">Jane Lange</a>, <a href="http://arxiv.org/find/cs/1/au:+Rubinfeld_R/0/1/0/all/0/1">Ronitt Rubinfeld</a>, <a href="http://arxiv.org/find/cs/1/au:+Vasilyan_A/0/1/0/all/0/1">Arsen Vasilyan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:30;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11897";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:52:"Reinforcement Teaching. (arXiv:2204.11897v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11897";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1188:"We propose Reinforcement Teaching: a framework for meta-learning in which a
teaching policy is learned, through reinforcement, to control a student's
learning process. The student's learning process is modelled as a Markov reward
process and the teacher, with its action-space, interacts with the induced
Markov decision process. We show that, for many learning processes, the
student's learnable parameters form a Markov state. To avoid having the teacher
learn directly from parameters, we propose the Parameter Embedder that learns a
representation of a student's state from its input/output behaviour. Next, we
use learning progress to shape the teacher's reward towards maximizing the
student's performance. To demonstrate the generality of Reinforcement Teaching,
we conducted experiments in which a teacher learns to significantly improve
supervised and reinforcement learners by using a combination of learning
progress reward and a Parameter Embedded state. These results show that
Reinforcement Teaching is not only an expressive framework capable of unifying
different approaches, but also provides meta-learning with the plethora of
tools from reinforcement learning.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:431:" <a href="http://arxiv.org/find/cs/1/au:+Lewandowski_A/0/1/0/all/0/1">Alex Lewandowski</a>, <a href="http://arxiv.org/find/cs/1/au:+Muslimani_C/0/1/0/all/0/1">Calarina Muslimani</a>, <a href="http://arxiv.org/find/cs/1/au:+Taylor_M/0/1/0/all/0/1">Matthew E. Taylor</a>, <a href="http://arxiv.org/find/cs/1/au:+Luo_J/0/1/0/all/0/1">Jun Luo</a>, <a href="http://arxiv.org/find/cs/1/au:+Schuurmans_D/0/1/0/all/0/1">Dale Schuurmans</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:31;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11902";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:102:"Learning First-Order Symbolic Planning Representations That Are Grounded. (arXiv:2204.11902v1 [cs.AI])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11902";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1209:"Two main approaches have been developed for learning first-order planning
(action) models from unstructured data: combinatorial approaches that yield
crisp action schemas from the structure of the state space, and deep learning
approaches that produce action schemas from states represented by images. A
benefit of the former approach is that the learned action schemas are similar
to those that can be written by hand; a benefit of the latter is that the
learned representations (predicates) are grounded on the images, and as a
result, new instances can be given in terms of images. In this work, we develop
a new formulation for learning crisp first-order planning models that are
grounded on parsed images, a step to combine the benefits of the two
approaches. Parsed images are assumed to be given in a simple O2D language
(objects in 2D) that involves a small number of unary and binary predicates
like "left", "above", "shape", etc. After learning, new planning instances can
be given in terms of pairs of parsed images, one for the initial situation and
the other for the goal. Learning and planning experiments are reported for
several domains including Blocks, Sokoban, IPC Grid, and Hanoi.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:266:" <a href="http://arxiv.org/find/cs/1/au:+Liberman_A/0/1/0/all/0/1">Andr&#xe9;s Occhipinti Liberman</a>, <a href="http://arxiv.org/find/cs/1/au:+Geffner_H/0/1/0/all/0/1">Hector Geffner</a>, <a href="http://arxiv.org/find/cs/1/au:+Bonet_B/0/1/0/all/0/1">Blai Bonet</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:32;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11908";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:111:"Comparison study of the combination of the SPSA algorithm and the PSO algorithm. (arXiv:2204.11908v1 [eess.SY])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11908";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1581:"Particle swarm optimization (PSO) is attracting an ever-growing attention and
more than ever it has found many application areas for many challenging
optimization problems. It is, however, a known fact that PSO has a severe
drawback in the update of its global best (gbest) particle, which has a crucial
role of guiding the rest of the swarm. In this paper, we propose three
efficient solutions to remedy this problem using the SPSA Algorithm. In the
first approach, gbest is updated with respect to a global estimation of the
gradient and can avoid getting trapped into a local optimum. The second
approach is based on the formation of an alternative or artificial global best
particle, the so-called aGB, which can replace the native gbest particle for a
better guidance, the decision of which is held by a fair competition between
the two. The third approach is based on the update of the swarm particle. For
this purpose we use simultaneous perturbation stochastic approximation (SPSA)
for its low cost. Since SPSA is applied only to the gbest (not to the entire
swarm) or to the entire swarm, both approaches result thus in a negligible
overhead cost for the entire PSO process. Both approaches are shown to
significantly improve the performance of PSO over a wide range of non-linear
functions, especially if SPSA and PSO parameters are well selected to fit the
problem at hand. As in the basic PSO application, experimental results show
that the proposed approaches significantly improved the quality of the
Optimization process as measured by a statistic analysis.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:255:" <a href="http://arxiv.org/find/eess/1/au:+Ngansop_B/0/1/0/all/0/1">Bertrand Ngansop</a>, <a href="http://arxiv.org/find/eess/1/au:+Gotz_S/0/1/0/all/0/1">Stefan G&#xf6;tz</a>, <a href="http://arxiv.org/find/eess/1/au:+Eckl_M/0/1/0/all/0/1">Martin Eckl</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:33;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11909";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:130:"How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language. (arXiv:2204.11909v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11909";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1394:"More than 43% of the languages spoken in the world are endangered, and
language loss currently occurs at an accelerated rate because of globalization
and neocolonialism. Saving and revitalizing endangered languages has become
very important for maintaining the cultural diversity on our planet. In this
work, we focus on discussing how NLP can help revitalize endangered languages.
We first suggest three principles that may help NLP practitioners to foster
mutual understanding and collaboration with language communities, and we
discuss three ways in which NLP can potentially assist in language education.
We then take Cherokee, a severely-endangered Native American language, as a
case study. After reviewing the language's history, linguistic features, and
existing resources, we (in collaboration with Cherokee community members)
arrive at a few meaningful ways NLP practitioners can collaborate with
community partners. We suggest two approaches to enrich the Cherokee language's
resources with machine-in-the-loop processing, and discuss several NLP tools
that people from the Cherokee community have shown interest in. We hope that
our work serves not only to inform the NLP community about Cherokee, but also
to provide inspiration for future work on endangered languages in general. Our
code and data will be open-sourced at
https://github.com/ZhangShiyue/RevitalizeCherokee

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:238:" <a href="http://arxiv.org/find/cs/1/au:+Zhang_S/0/1/0/all/0/1">Shiyue Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Frey_B/0/1/0/all/0/1">Ben Frey</a>, <a href="http://arxiv.org/find/cs/1/au:+Bansal_M/0/1/0/all/0/1">Mohit Bansal</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:34;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11910";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:160:"Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection. (arXiv:2204.11910v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11910";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1322:"We introduce a new setting, optimize-and-estimate structured bandits. Here, a
policy must select a batch of arms, each characterized by its own context, that
would allow it to both maximize reward and maintain an accurate (ideally
unbiased) population estimate of the reward. This setting is inherent to many
public and private sector applications and often requires handling delayed
feedback, small data, and distribution shifts. We demonstrate its importance on
real data from the United States Internal Revenue Service (IRS). The IRS
performs yearly audits of the tax base. Two of its most important objectives
are to identify suspected misreporting and to estimate the "tax gap" - the
global difference between the amount paid and true amount owed. We cast these
two processes as a unified optimize-and-estimate structured bandit. We provide
a novel mechanism for unbiased population estimation that achieves rewards
comparable to baseline approaches. This approach has the potential to improve
audit efficacy, while maintaining policy-relevant estimates of the tax gap.
This has important social consequences given that the current tax gap is
estimated at nearly half a trillion dollars. We suggest that this problem
setting is fertile ground for further research and we highlight its interesting
challenges.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:665:" <a href="http://arxiv.org/find/cs/1/au:+Henderson_P/0/1/0/all/0/1">Peter Henderson</a>, <a href="http://arxiv.org/find/cs/1/au:+Chugg_B/0/1/0/all/0/1">Ben Chugg</a>, <a href="http://arxiv.org/find/cs/1/au:+Anderson_B/0/1/0/all/0/1">Brandon Anderson</a>, <a href="http://arxiv.org/find/cs/1/au:+Altenburger_K/0/1/0/all/0/1">Kristen Altenburger</a>, <a href="http://arxiv.org/find/cs/1/au:+Turk_A/0/1/0/all/0/1">Alex Turk</a>, <a href="http://arxiv.org/find/cs/1/au:+Guyton_J/0/1/0/all/0/1">John Guyton</a>, <a href="http://arxiv.org/find/cs/1/au:+Goldin_J/0/1/0/all/0/1">Jacob Goldin</a>, <a href="http://arxiv.org/find/cs/1/au:+Ho_D/0/1/0/all/0/1">Daniel E. Ho</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:35;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11911";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:94:"DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation. (arXiv:2204.11911v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11911";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1592:"Automatic tooth instance segmentation on 3D dental models is a fundamental
task for computer-aided orthodontic treatments. Existing learning-based methods
rely heavily on expensive point-wise annotations. To alleviate this problem, we
are the first to explore a low-cost annotation way for 3D tooth instance
segmentation, i.e., labeling all tooth centroids and only a few teeth for each
dental model. Regarding the challenge when only weak annotation is provided, we
present a dental arch prior-assisted 3D tooth segmentation method, namely
DArch. Our DArch consists of two stages, including tooth centroid detection and
tooth instance segmentation. Accurately detecting the tooth centroids can help
locate the individual tooth, thus benefiting the segmentation. Thus, our DArch
proposes to leverage the dental arch prior to assist the detection.
Specifically, we firstly propose a coarse-to-fine method to estimate the dental
arch, in which the dental arch is initially generated by Bezier curve
regression, and then a graph-based convolutional network (GCN) is trained to
refine it. With the estimated dental arch, we then propose a novel Arch-aware
Point Sampling (APS) method to assist the tooth centroid proposal generation.
Meantime, a segmentor is independently trained using a patch-based training
strategy, aiming to segment a tooth instance from a 3D patch centered at the
tooth centroid. Experimental results on $4,773$ dental models have shown our
DArch can accurately segment each tooth of a dental model, and its performance
is superior to the state-of-the-art methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:467:" <a href="http://arxiv.org/find/cs/1/au:+Qiu_L/0/1/0/all/0/1">Liangdong Qiu</a>, <a href="http://arxiv.org/find/cs/1/au:+Ye_C/0/1/0/all/0/1">Chongjie Ye</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_P/0/1/0/all/0/1">Pei Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_Y/0/1/0/all/0/1">Yunbi Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Han_X/0/1/0/all/0/1">Xiaoguang Han</a>, <a href="http://arxiv.org/find/cs/1/au:+Cui_S/0/1/0/all/0/1">Shuguang Cui</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:36;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11914";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:80:"Generating and Visualizing Trace Link Explanations. (arXiv:2204.11914v1 [cs.SE])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11914";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1637:"Recent breakthroughs in deep-learning (DL) approaches have resulted in the
dynamic generation of trace links that are far more accurate than was
previously possible. However, DL-generated links lack clear explanations, and
therefore non-experts in the domain can find it difficult to understand the
underlying semantics of the link, making it hard for them to evaluate the
link's correctness or suitability for a specific software engineering task. In
this paper we present a novel NLP pipeline for generating and visualizing trace
link explanations. Our approach identifies domain-specific concepts, retrieves
a corpus of concept-related sentences, mines concept definitions and usage
examples, and identifies relations between cross-artifact concepts in order to
explain the links. It applies a post-processing step to prioritize the most
likely acronyms and definitions and to eliminate non-relevant ones. We evaluate
our approach using project artifacts from three different domains of
interstellar telescopes, positive train control, and electronic health-care
systems, and then report coverage, correctness, and potential utility of the
generated definitions. We design and utilize an explanation interface which
leverages concept definitions and relations to visualize and explain trace link
rationales, and we report results from a user study that was conducted to
evaluate the effectiveness of the explanation interface. Results show that the
explanations presented in the interface helped non-experts to understand the
underlying semantics of a trace link and improved their ability to vet the
correctness of the link.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:420:" <a href="http://arxiv.org/find/cs/1/au:+Liu_Y/0/1/0/all/0/1">Yalin Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Lin_J/0/1/0/all/0/1">Jinfeng Lin</a>, <a href="http://arxiv.org/find/cs/1/au:+Anuyah_O/0/1/0/all/0/1">Oghenemaro Anuyah</a>, <a href="http://arxiv.org/find/cs/1/au:+Metoyer_R/0/1/0/all/0/1">Ronald Metoyer</a>, <a href="http://arxiv.org/find/cs/1/au:+Cleland_Huang_J/0/1/0/all/0/1">Jane Cleland-Huang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:37;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11918";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:106:"Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items. (arXiv:2204.11918v1 [cs.RO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11918";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:845:"Interactive 3D simulations have enabled breakthroughs in robotics and
computer vision, but simulating the broad diversity of environments needed for
deep learning requires large corpora of photo-realistic 3D object models. To
address this need, we present Google Scanned Objects, an open-source collection
of over one thousand 3D-scanned household items released under a Creative
Commons license; these models are preprocessed for use in Ignition Gazebo and
the Bullet simulation platforms, but are easily adaptable to other simulators.
We describe our object scanning and curation pipeline, then provide statistics
about the contents of the dataset and its usage. We hope that the diversity,
quality, and flexibility of Google Scanned Objects will lead to advances in
interactive simulation, synthetic perception, and robotic learning.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:674:" <a href="http://arxiv.org/find/cs/1/au:+Downs_L/0/1/0/all/0/1">Laura Downs</a>, <a href="http://arxiv.org/find/cs/1/au:+Francis_A/0/1/0/all/0/1">Anthony Francis</a>, <a href="http://arxiv.org/find/cs/1/au:+Koenig_N/0/1/0/all/0/1">Nate Koenig</a>, <a href="http://arxiv.org/find/cs/1/au:+Kinman_B/0/1/0/all/0/1">Brandon Kinman</a>, <a href="http://arxiv.org/find/cs/1/au:+Hickman_R/0/1/0/all/0/1">Ryan Hickman</a>, <a href="http://arxiv.org/find/cs/1/au:+Reymann_K/0/1/0/all/0/1">Krista Reymann</a>, <a href="http://arxiv.org/find/cs/1/au:+McHugh_T/0/1/0/all/0/1">Thomas B. McHugh</a>, <a href="http://arxiv.org/find/cs/1/au:+Vanhoucke_V/0/1/0/all/0/1">Vincent Vanhoucke</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:38;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11920";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Quo Vadis, Optical Network Architecture? Towards an Optical-processing-enabled Paradigm. (arXiv:2204.11920v1 [cs.NI])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11920";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1929:"Among various aspects in optical network architectures, handling transit
traffic at intermediate nodes represents a defining characteristic for
classification. In this context, the transition from the first generation of
optical-electrical-optical (O-E-O) mode to the second generation of
optical-bypass marked a paradigm shift in redesigning optical transport
networks towards greater network efficiency. Optical-bypass operation has then
become the \textit{de facto} approach adopted by the majority of carriers in
both metro and backbone networks in the last two decades and has remained
basically unchanged. However, in optical-bypass network, the fact that
in-transit lightpaths crossing a common intermediate node must be separated in
either time, frequency or spatial domain to avoid adversarial interference
appears to be a critical shortcoming as the interaction of such lightpaths in
optical domain may result in efficient computing and/or signal processing
operations for saving spectral resources. Inspired by the accelerated
progresses in optical signal processing technologies and the integration of
computing and communications, we introduce in this paper a new architectural
paradigm for future optical networks and highlight how this new architecture
has the potential to shatter the \textit{status quo}. Indeed, our proposal is
centered on exploiting the superposition of in-transit lightpaths at
intermediate nodes to generate more spectrally efficient lightpaths and how to
harness this opportunity from network design perspectives. We present two case
studies featuring optical aggregation and optical XOR encoding to demonstrate
the merit of optical-processing-enabled operation compared to its counterpart,
optical-bypass. Numerical results on realistic network typologies are provided,
revealing that a spectral saving up to $30\%$ could be achieved thanks to
adopting optical-processing network.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:79:" <a href="http://arxiv.org/find/cs/1/au:+Hai_D/0/1/0/all/0/1">Dao Thanh Hai</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:39;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11922";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:153:"Super-Prompting: Utilizing Model-Independent Contextual Data to Reduce Data Annotation Required in Visual Commonsense Tasks. (arXiv:2204.11922v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11922";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1284:"Pre-trained language models have shown excellent results in few-shot learning
scenarios using in-context learning. Although it is impressive, the size of
language models can be prohibitive to make them usable in on-device
applications, such as sensors or smartphones. With smaller language models,
task-specific data annotation is needed to fine-tune the language model for a
specific purpose. However, data annotation can have a substantial financial and
time burden for small research groups, startups, and even companies. In this
paper, we analyze different prompt-based fine-tuning techniques to improve
results on both language and multimodal causal transformer models. To evaluate
our results, we use a dataset focusing on visual commonsense reasoning in time.
Our results show that by simple model-agnostic prompt-based fine-tuning,
comparable results can be reached by only using 35%-40% of the fine-tuning
training dataset. The proposed approaches result in significant time and
financial savings. As the proposed methods make minimal architectural
assumptions, other researchers can use the results in their transformer models
with minimal adaptations. We plan to release the source code freely to make it
easier for the community to use and contribute to our work.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:170:" <a href="http://arxiv.org/find/cs/1/au:+Rezaei_N/0/1/0/all/0/1">Navid Rezaei</a>, <a href="http://arxiv.org/find/cs/1/au:+Reformat_M/0/1/0/all/0/1">Marek Z. Reformat</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:40;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11923";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Sparse-Dense Motion Modelling and Tracking for Manipulation without Prior Object Models. (arXiv:2204.11923v1 [cs.RO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11923";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:795:"This work presents an approach for modelling and tracking previously unseen
objects for robotic grasping tasks. Using the motion of objects in a scene, our
approach segments rigid entities from the scene and continuously tracks them to
create a dense and sparse model of the object and the environment. While the
dense tracking enables interaction with these models, the sparse tracking makes
this robust against fast movements and allows to redetect already modelled
objects.

The evaluation on a dual-arm grasping task demonstrates that our approach 1)
enables a robot to detect new objects online without a prior model and to grasp
these objects using only a simple parameterisable geometric representation, and
2) is much more robust compared to the state of the art methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:332:" <a href="http://arxiv.org/find/cs/1/au:+Rauch_C/0/1/0/all/0/1">Christian Rauch</a>, <a href="http://arxiv.org/find/cs/1/au:+Long_R/0/1/0/all/0/1">Ran Long</a>, <a href="http://arxiv.org/find/cs/1/au:+Ivan_V/0/1/0/all/0/1">Vladimir Ivan</a>, <a href="http://arxiv.org/find/cs/1/au:+Vijayakumar_S/0/1/0/all/0/1">Sethu Vijayakumar</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:41;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11924";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:159:"Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence. (arXiv:2204.11924v1 [math.OC])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11924";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1669:"One of the core problems in mean-field control and mean-field games is to
solve the corresponding McKean-Vlasov forward-backward stochastic differential
equations (MV-FBSDEs). Most existing methods are tailored to special cases in
which the mean-field interaction only depends on expectation or other moments
and thus inadequate to solve problems when the mean-field interaction has full
distribution dependence. In this paper, we propose a novel deep learning method
for computing MV-FBSDEs with a general form of mean-field interactions.
Specifically, built on fictitious play, we recast the problem into repeatedly
solving standard FBSDEs with explicit coefficient functions. These coefficient
functions are used to approximate the MV-FBSDEs' model coefficients with full
distribution dependence, and are updated by solving another supervising
learning problem using training data simulated from the last iteration's FBSDE
solutions. We use deep neural networks to solve standard BSDEs and approximate
coefficient functions in order to solve high-dimensional MV-FBSDEs. Under
proper assumptions on the learned functions, we prove that the convergence of
the proposed method is free of the curse of dimensionality (CoD) by using the
generalized maximum mean discrepancy metric previously developed in [Han, Hu
and Long, <a href="/abs/2104.12036">arXiv:2104.12036</a>]. The proved theorem shows the advantage of the
method in high dimensions. We present the numerical performance in
high-dimensional MV-FBSDE problems, including a mean-field game example of the
well-known Cucker-Smale model whose cost depends on the full distribution of
the forward process.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:236:" <a href="http://arxiv.org/find/math/1/au:+Han_J/0/1/0/all/0/1">Jiequn Han</a>, <a href="http://arxiv.org/find/math/1/au:+Hu_R/0/1/0/all/0/1">Ruimeng Hu</a>, <a href="http://arxiv.org/find/math/1/au:+Long_J/0/1/0/all/0/1">Jihao Long</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:42;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11926";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"Pursuit-Evasion in Graphs: Zombies, Lazy Zombies and a Survivor. (arXiv:2204.11926v1 [math.CO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11926";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1433:"We study zombies and survivor, a variant of the game of cops and robber on
graphs. In this variant, the single survivor plays the role of the robber and
attempts to escape from the zombies that play the role of the cops. The zombies
are restricted, on their turn, to always follow an edge of a shortest path
towards the survivor. Let $z(G)$ be the smallest number of zombies required to
catch the survivor on a graph $G$ with $n$ vertices. We show that there exist
outerplanar graphs and visibility graphs of simple polygons such that $z(G) =
\Theta(n)$. We also show that there exist maximum-degree-$3$ outerplanar graphs
such that $z(G) = \Omega\left(n/\log(n)\right)$.

Let $z_L(G)$ be the smallest number of lazy zombies (zombies that can stay
still on their turn) required to catch the survivor on a graph $G$. We
establish that lazy zombies are more powerful than normal zombies but less
powerful than cops. We prove that $z_L(G) = 2$ for connected outerplanar
graphs. We show that $z_L(G)\leq k$ for connected graphs with treedepth $k$.
This result implies that $z_L(G)$ is at most $(k+1)\log n$ for connected graphs
with treewidth $k$, $O(\sqrt{n})$ for connected planar graphs, $O(\sqrt{gn})$
for connected graphs with genus $g$ and $O(h\sqrt{hn})$ for connected graphs
with any excluded $h$-vertex minor. Our results on lazy zombies still hold when
an adversary chooses the initial positions of the zombies.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:262:" <a href="http://arxiv.org/find/math/1/au:+Bose_P/0/1/0/all/0/1">Prosenjit Bose</a>, <a href="http://arxiv.org/find/math/1/au:+Carufel_J/0/1/0/all/0/1">Jean-Lou De Carufel</a>, <a href="http://arxiv.org/find/math/1/au:+Shermer_T/0/1/0/all/0/1">Thomas Shermer</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:43;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11927";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:104:"Fractional Graph Coloring for Functional Compression with Side Information. (arXiv:2204.11927v1 [cs.IT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11927";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1240:"We describe a rational approach to reduce the computational and communication
complexities of lossless point-to-point compression for computation with side
information. The traditional method relies on building a characteristic graph
with vertices representing the source symbols and with edges that assign a
source symbol to a collection of independent sets to be distinguished for the
exact recovery of the function. Our approach uses fractional coloring for a
b-fold coloring of characteristic graphs to provide a linear programming
relaxation to the traditional coloring method and achieves coding at a
fine-grained granularity. We derive the fundamental lower bound for
compression, given by the fractional characteristic graph entropy, through
generalizing the notion of K\"orner's graph entropy. We demonstrate the coding
gains of fractional coloring over traditional coloring via a computation
example. We conjecture that the integrality gap between fractional coloring and
traditional coloring approaches the smallest b that attains the fractional
chromatic number to losslessly represent the independent sets for a given
characteristic graph, up to a linear scaling which is a function of the
fractional chromatic number.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:79:" <a href="http://arxiv.org/find/cs/1/au:+Malak_D/0/1/0/all/0/1">Derya Malak</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:44;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11929";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:81:"Temporal Relevance Analysis for Video Action Models. (arXiv:2204.11929v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11929";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:890:"In this paper, we provide a deep analysis of temporal modeling for action
recognition, an important but underexplored problem in the literature. We first
propose a new approach to quantify the temporal relationships between frames
captured by CNN-based action models based on layer-wise relevance propagation.
We then conduct comprehensive experiments and in-depth analysis to provide a
better understanding of how temporal modeling is affected by various factors
such as dataset, network architecture, and input frames. With this, we further
study some important questions for action recognition that lead to interesting
findings. Our analysis shows that there is no strong correlation between
temporal relevance and model performance; and action models tend to capture
local temporal information, but less long-range dependencies. Our codes and
models will be publicly available.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:563:" <a href="http://arxiv.org/find/cs/1/au:+Fan_Q/0/1/0/all/0/1">Quanfu Fan</a>, <a href="http://arxiv.org/find/cs/1/au:+Kim_D/0/1/0/all/0/1">Donghyun Kim</a>, <a href="http://arxiv.org/find/cs/1/au:+Chun-Fu/0/1/0/all/0/1">Chun-Fu</a> (Richard) <a href="http://arxiv.org/find/cs/1/au:+Chen/0/1/0/all/0/1">Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Sclaroff_S/0/1/0/all/0/1">Stan Sclaroff</a>, <a href="http://arxiv.org/find/cs/1/au:+Saenko_K/0/1/0/all/0/1">Kate Saenko</a>, <a href="http://arxiv.org/find/cs/1/au:+Bargal_S/0/1/0/all/0/1">Sarah Adel Bargal</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:45;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11933";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:148:"Cleanformer: A microphone array configuration-invariant, streaming, multichannel neural enhancement frontend for ASR. (arXiv:2204.11933v1 [eess.AS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11933";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1361:"This work introduces the Cleanformer, a streaming multichannel neural based
enhancement frontend for automatic speech recognition (ASR). This model has a
conformer-based architecture which takes as inputs a single channel each of raw
and enhanced signals, and uses self-attention to derive a time-frequency mask.
The enhanced input is generated by a multichannel adaptive noise cancellation
algorithm known as Speech Cleaner, which makes use of noise context to derive
its filter taps. The time-frequency mask is applied to the noisy input to
produce enhanced output features for ASR. Detailed evaluations are presented
with simulated and re-recorded datasets in speech-based and non-speech-based
noise that show significant reduction in word error rate (WER) when using a
large-scale state-of-the-art ASR model. It also will be shown to significantly
outperform enhancement using a beamformer with ideal steering. The enhancement
model is agnostic of the number of microphones and array configuration and,
therefore, can be used with different microphone arrays without the need for
retraining. It is demonstrated that performance improves with more microphones,
up to 4, with each additional microphone providing a smaller marginal benefit.
Specifically, for an SNR of -6dB, relative WER improvements of about 80\% are
shown in both noise conditions.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:269:" <a href="http://arxiv.org/find/eess/1/au:+Caroselli_J/0/1/0/all/0/1">Joseph Caroselli</a>, <a href="http://arxiv.org/find/eess/1/au:+Naranayan_A/0/1/0/all/0/1">Arun Naranayan</a>, <a href="http://arxiv.org/find/eess/1/au:+OMalley_T/0/1/0/all/0/1">Tom O&#x27;Malley</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:46;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11934";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:85:"On-demand compute reduction with stochastic wav2vec 2.0. (arXiv:2204.11934v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11934";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1072:"Squeeze and Efficient Wav2vec (SEW) is a recently proposed architecture that
squeezes the input to the transformer encoder for compute efficient
pre-training and inference with wav2vec 2.0 (W2V2) models. In this work, we
propose stochastic compression for on-demand compute reduction for W2V2 models.
As opposed to using a fixed squeeze factor, we sample it uniformly during
training. We further introduce query and key-value pooling mechanisms that can
be applied to each transformer layer for further compression. Our results for
models pre-trained on 960h Librispeech dataset and fine-tuned on 10h of
transcribed data show that using the same stochastic model, we get a smooth
trade-off between word error rate (WER) and inference time with only marginal
WER degradation compared to the W2V2 and SEW models trained for a specific
setting. We further show that we can fine-tune the same stochastically
pre-trained model to a specific configuration to recover the WER difference
resulting in significant computational savings on pre-training models from
scratch.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:322:" <a href="http://arxiv.org/find/cs/1/au:+Vyas_A/0/1/0/all/0/1">Apoorv Vyas</a>, <a href="http://arxiv.org/find/cs/1/au:+Hsu_W/0/1/0/all/0/1">Wei-Ning Hsu</a>, <a href="http://arxiv.org/find/cs/1/au:+Auli_M/0/1/0/all/0/1">Michael Auli</a>, <a href="http://arxiv.org/find/cs/1/au:+Baevski_A/0/1/0/all/0/1">Alexei Baevski</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:47;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11936";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:71:"Discrete-Continuous Smoothing and Mapping. (arXiv:2204.11936v1 [cs.RO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11936";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1692:"We describe a general approach to smoothing and mapping with a class of
discrete-continuous factor graphs commonly encountered in robotics
applications. While there are openly available tools providing flexible and
easy-to-use interfaces for specifying and solving optimization problems
formulated in terms of either discrete or continuous graphical models, at
present, no similarly general tools exist enabling the same functionality for
hybrid discrete-continuous problems. We aim to address this problem. In
particular, we provide a library, DC-SAM, extending existing tools for
optimization problems defined in terms of factor graphs to the setting of
discrete-continuous models. A key contribution of our work is a novel solver
for efficiently recovering approximate solutions to discrete-continuous
optimization problems. The key insight to our approach is that while joint
inference over continuous and discrete state spaces is often hard, many
commonly encountered discrete-continuous problems can naturally be split into a
"discrete part" and a "continuous part" that can individually be solved easily.
Leveraging this structure, we optimize discrete and continuous variables in an
alternating fashion. In consequence, our proposed work enables straightforward
representation of and approximate inference in discrete-continuous graphical
models. We also provide a method to recover the uncertainty in estimates of
both discrete and continuous variables. We demonstrate the versatility of our
approach through its application to three distinct robot perception
applications: point-cloud registration, robust pose graph optimization, and
object-based mapping and localization.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:326:" <a href="http://arxiv.org/find/cs/1/au:+Doherty_K/0/1/0/all/0/1">Kevin J. Doherty</a>, <a href="http://arxiv.org/find/cs/1/au:+Lu_Z/0/1/0/all/0/1">Ziqi Lu</a>, <a href="http://arxiv.org/find/cs/1/au:+Singh_K/0/1/0/all/0/1">Kurran Singh</a>, <a href="http://arxiv.org/find/cs/1/au:+Leonard_J/0/1/0/all/0/1">John J. Leonard</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:48;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11939";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:83:"Robust Dual-Graph Regularized Moving Object Detection. (arXiv:2204.11939v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11939";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1291:"Moving object detection and its associated background-foreground separation
have been widely used in a lot of applications, including computer vision,
transportation and surveillance. Due to the presence of the static background,
a video can be naturally decomposed into a low-rank background and a sparse
foreground. Many regularization techniques, such as matrix nuclear norm, have
been imposed on the background. In the meanwhile, sparsity or smoothness based
regularizations, such as total variation and $\ell_1$, can be imposed on the
foreground. Moreover, graph Laplacians are further imposed to capture the
complicated geometry of background images. Recently, weighted regularization
techniques including the weighted nuclear norm regularization have been
proposed in the image processing community to promote adaptive sparsity while
achieving efficient performance. In this paper, we propose a robust dual-graph
regularized moving object detection model based on the weighted nuclear norm
regularization, which is solved by the alternating direction method of
multipliers (ADMM). Numerical experiments on body movement data sets have
demonstrated the effectiveness of this method in separating moving objects from
background, and the great potential in robotic applications.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:307:" <a href="http://arxiv.org/find/cs/1/au:+Qin_J/0/1/0/all/0/1">Jing Qin</a>, <a href="http://arxiv.org/find/cs/1/au:+Shen_R/0/1/0/all/0/1">Ruilong Shen</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhu_R/0/1/0/all/0/1">Ruihan Zhu</a>, <a href="http://arxiv.org/find/cs/1/au:+Xie_B/0/1/0/all/0/1">Biyun Xie</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:49;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11942";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:73:"Meta-AF: Meta-Learning for Adaptive Filters. (arXiv:2204.11942v1 [cs.SD])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11942";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1681:"Adaptive filtering algorithms are pervasive throughout modern society and
have had a significant impact on a wide variety of domains including audio
processing, telecommunications, biomedical sensing, astropyhysics and
cosmology, seismology, and many more. Adaptive filters typically operate via
specialized online, iterative optimization methods such as least-mean squares
or recursive least squares and aim to process signals in unknown or
nonstationary environments. Such algorithms, however, can be slow and laborious
to develop, require domain expertise to create, and necessitate mathematical
insight for improvement. In this work, we seek to go beyond the limits of
human-derived adaptive filter algorithms and present a comprehensive framework
for learning online, adaptive signal processing algorithms or update rules
directly from data. To do so, we frame the development of adaptive filters as a
meta-learning problem in the context of deep learning and use a form of
self-supervision to learn online iterative update rules for adaptive filters.
To demonstrate our approach, we focus on audio applications and systematically
develop meta-learned adaptive filters for five canonical audio problems
including system identification, acoustic echo cancellation, blind
equalization, multi-channel dereverberation, and beamforming. For each
application, we compare against common baselines and/or current
state-of-the-art methods and show we can learn high-performing adaptive filters
that operate in real-time and, in most cases, significantly out perform all
past specially developed methods for each task using a single general-purpose
configuration of our method.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:259:" <a href="http://arxiv.org/find/cs/1/au:+Casebeer_J/0/1/0/all/0/1">Jonah Casebeer</a>, <a href="http://arxiv.org/find/cs/1/au:+Bryan_N/0/1/0/all/0/1">Nicholas J. Bryan</a>, <a href="http://arxiv.org/find/cs/1/au:+Smaragdis_P/0/1/0/all/0/1">Paris Smaragdis</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:50;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11950";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:84:"Strategic Signaling for Utility Control in Audit Games. (arXiv:2204.11950v1 [cs.GT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11950";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1472:"As an effective method to protect the daily access to sensitive data against
malicious attacks, the audit mechanism has been widely deployed in various
practical fields. In order to examine security vulnerabilities and prevent the
leakage of sensitive data in a timely manner, the database logging system
usually employs an online signaling scheme to issue an alert when suspicious
access is detected. Defenders can audit alerts to reduce potential damage. This
interaction process between a defender and an attacker can be modeled as an
audit game. In previous studies, it was found that sending real-time signals in
the audit game to warn visitors can improve the benefits of the defender.
However, the previous approaches usually assume perfect information of the
attacker, or simply concentrate on the utility of the defender. In this paper,
we introduce a brand-new zero-determinant (ZD) strategy to study the sequential
audit game with online signaling, which empowers the defender to unilaterally
control the utility of visitors when accessing sensitive data. In addition, an
optimization scheme based on the ZD strategy is designed to effectively
maximize the utility difference between the defender and the attacker.
Extensive simulation results show that our proposed scheme enhances the
security management and control capabilities of the defender to better handle
different access requests and safeguard the system security in a cost-efficient
manner.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:231:" <a href="http://arxiv.org/find/cs/1/au:+Chen_J/0/1/0/all/0/1">Jianan Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Hu_Q/0/1/0/all/0/1">Qin Hu</a>, <a href="http://arxiv.org/find/cs/1/au:+Jiang_H/0/1/0/all/0/1">Honglu Jiang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:51;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11951";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:70:"Byzantine-Resilient Counting in Networks. (arXiv:2204.11951v1 [cs.DC])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11951";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1923:"We present two distributed algorithms for the {\em Byzantine counting
problem}, which is concerned with estimating the size of a network in the
presence of a large number of Byzantine nodes.

In an $n$-node network ($n$ is unknown), our first algorithm, which is {\em
deterministic}, finishes in $O(\log{n})$ rounds and is time-optimal. This
algorithm can tolerate up to $O(n^{1 - \gamma})$ arbitrarily (adversarially)
placed Byzantine nodes for any arbitrarily small (but fixed) positive constant
$\gamma$. It outputs a (fixed) constant factor estimate of $\log{n}$ that would
be known to all but $o(1)$ fraction of the good nodes. This algorithm works for
\emph{any} bounded degree expander network. However, this algorithms assumes
that good nodes can send arbitrarily large-sized messages in a round.

Our second algorithm is {\em randomized} and most good nodes send only
small-sized messages (Throughout this paper, a small-sized message is defined
to be one that contains $O(\log{n})$ bits in addition to at most a constant
number of node IDs.). This algorithm works in \emph{almost all} $d$-regular
graphs. It tolerates up to $B(n) = n^{\frac{1}{2} - \xi}$ (note that $n$ and
$B(n)$ are unknown to the algorithm) arbitrarily (adversarially) placed
Byzantine nodes, where $\xi$ is any arbitrarily small (but fixed) positive
constant. This algorithm takes $O(B(n)\log^2{n})$ rounds and outputs a (fixed)
constant factor estimate of $\log{n}$ with probability at least $1 - o(1)$. The
said estimate is known to most nodes, i.e., $\geq (1 - \beta)n$ nodes for any
arbitrarily small (but fixed) positive constant $\beta$.

To complement our algorithms, we also present an impossibility result that
shows that it is impossible to estimate the network size with any reasonable
approximation with any non-trivial probability of success if the network does
not have sufficient vertex expansion.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:272:" <a href="http://arxiv.org/find/cs/1/au:+Chatterjee_S/0/1/0/all/0/1">Soumyottam Chatterjee</a>, <a href="http://arxiv.org/find/cs/1/au:+Pandurangan_G/0/1/0/all/0/1">Gopal Pandurangan</a>, <a href="http://arxiv.org/find/cs/1/au:+Robinson_P/0/1/0/all/0/1">Peter Robinson</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:52;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11953";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:147:"Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials. (arXiv:2204.11953v1 [cond-mat.mtrl-sci])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11953";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1778:"Self-supervised neural language models have recently achieved unprecedented
success, from natural language processing to learning the languages of
biological sequences and organic molecules. These models have demonstrated
superior performance in the generation, structure classification, and
functional predictions for proteins and molecules with learned representations.
However, most of the masking-based pre-trained language models are not designed
for generative design, and their black-box nature makes it difficult to
interpret their design logic. Here we propose BLMM Crystal Transformer, a
neural network based probabilistic generative model for generative and
tinkering design of inorganic materials. Our model is built on the blank
filling language model for text generation and has demonstrated unique
advantages in learning the "materials grammars" together with high-quality
generation, interpretability, and data efficiency. It can generate chemically
valid materials compositions with as high as 89.7\% charge neutrality and
84.8\% balanced electronegativity, which are more than 4 and 8 times higher
compared to a pseudo random sampling baseline. The probabilistic generation
process of BLMM allows it to recommend tinkering operations based on learned
materials chemistry and makes it useful for materials doping. Combined with the
TCSP crysal structure prediction algorithm, We have applied our model to
discover a set of new materials as validated using DFT calculations. Our work
thus brings the unsupervised transformer language models based generative
artificial intelligence to inorganic materials. A user-friendly web app has
been developed for computational materials doping and can be accessed freely at
\url{www.materialsatlas.org/blmtinker}.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:618:" <a href="http://arxiv.org/find/cond-mat/1/au:+Wei_L/0/1/0/all/0/1">Lai Wei</a>, <a href="http://arxiv.org/find/cond-mat/1/au:+Li_Q/0/1/0/all/0/1">Qinyang Li</a>, <a href="http://arxiv.org/find/cond-mat/1/au:+Song_Y/0/1/0/all/0/1">Yuqi Song</a>, <a href="http://arxiv.org/find/cond-mat/1/au:+Stefanov_S/0/1/0/all/0/1">Stanislav Stefanov</a>, <a href="http://arxiv.org/find/cond-mat/1/au:+Siriwardane_E/0/1/0/all/0/1">Edirisuriya M. D. Siriwardane</a>, <a href="http://arxiv.org/find/cond-mat/1/au:+Chen_F/0/1/0/all/0/1">Fanglin Chen</a>, <a href="http://arxiv.org/find/cond-mat/1/au:+Hu_J/0/1/0/all/0/1">Jianjun Hu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:53;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11955";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:120:"The non-GRS properties for the twisted generalized Reed-Solomon code and its extended code. (arXiv:2204.11955v1 [cs.IT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11955";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:472:"In 2017, Beelen et al. firstly introduced twisted generalized Reed-Solomon
(in short, TGRS) codes, and constructed a large subclass of MDS TGRS codes.
Later, they proved that TGRS code is non-GRS when the code rate is less than
one half. In this letter, basing on the dual code of the TGRS code or the
extended TGRS code, by using the Schur product, we prove that almost all of
TGRS codes and extended TGRS codes are non-GRS when the code rate more than one
half.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:155:" <a href="http://arxiv.org/find/cs/1/au:+Zhu_C/0/1/0/all/0/1">Canze Zhu</a>, <a href="http://arxiv.org/find/cs/1/au:+Liao_Q/0/1/0/all/0/1">Qunying Liao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:54;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11956";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"Spontaneous Emergence of Computation in Network Cascades. (arXiv:2204.11956v1 [physics.soc-ph])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11956";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:835:"Neuronal network computation and computation by avalanche supporting networks
are of interest to the fields of physics, computer science (computation theory
as well as statistical or machine learning) and neuroscience. Here we show that
computation of complex Boolean functions arises spontaneously in threshold
networks as a function of connectivity and antagonism (inhibition), computed by
logic automata (motifs) in the form of computational cascades. We explain the
emergent inverse relationship between the computational complexity of the
motifs and their rank-ordering by function probabilities due to motifs, and its
relationship to symmetry in function space. We also show that the optimal
fraction of inhibition observed here supports results in computational
neuroscience, relating to optimal information processing.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:292:" <a href="http://arxiv.org/find/physics/1/au:+Wilkerson_G/0/1/0/all/0/1">Galen Wilkerson</a>, <a href="http://arxiv.org/find/physics/1/au:+Moschoyiannis_S/0/1/0/all/0/1">Sotiris Moschoyiannis</a>, <a href="http://arxiv.org/find/physics/1/au:+Jensen_H/0/1/0/all/0/1">Henrik Jeldtoft Jensen</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:55;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11960";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:73:"The equivalence of GRS codes and EGRS codes. (arXiv:2204.11960v1 [cs.IT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11960";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:460:"Generalized Reed-Solomon and extended generalized Reed-Solomon (abbreviation
to GRS and EGRS) codes are the most well-known family of MDS codes with wide
applications in coding theory and practice. Let $\mathbb{F}_q$ be the $q$
elements finite field, where $q$ is the power of a prime. For a linear code
$\mathcal{C}$ over $\mathbb{F}_q$ with length $2\le n\le q$, we prove that
$\mathcal{C}$ is a GRS code if and only if $\mathcal{C}$ is a EGRS code.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:75:" <a href="http://arxiv.org/find/cs/1/au:+Zhu_C/0/1/0/all/0/1">Canze Zhu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:56;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11964";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:102:"SceneTrilogy: On Scene Sketches and its Relationship with Text and Photo. (arXiv:2204.11964v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11964";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1303:"We for the first time extend multi-modal scene understanding to include that
of free-hand scene sketches. This uniquely results in a trilogy of scene data
modalities (sketch, text, and photo), where each offers unique perspectives for
scene understanding, and together enable a series of novel scene-specific
applications across discriminative (retrieval) and generative (captioning)
tasks. Our key objective is to learn a common three-way embedding space that
enables many-to-many modality interactions (e.g, sketch+text $\rightarrow$
photo retrieval). We importantly leverage the information bottleneck theory to
achieve this goal, where we (i) decouple intra-modality information by
minimising the mutual information between modality-specific and
modality-agnostic components via a conditional invertible neural network, and
(ii) align \textit{cross-modalities information} by maximising the mutual
information between their modality-agnostic components using InfoNCE, with a
specific multihead attention mechanism to allow many-to-many modality
interactions. We spell out a few insights on the complementarity of each
modality for scene understanding, and study for the first time a series of
scene-specific applications like joint sketch- and text-based image retrieval,
sketch captioning.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:337:" <a href="http://arxiv.org/find/cs/1/au:+Chowdhury_P/0/1/0/all/0/1">Pinaki Nath Chowdhury</a>, <a href="http://arxiv.org/find/cs/1/au:+Bhunia_A/0/1/0/all/0/1">Ayan Kumar Bhunia</a>, <a href="http://arxiv.org/find/cs/1/au:+Xiang_T/0/1/0/all/0/1">Tao Xiang</a>, <a href="http://arxiv.org/find/cs/1/au:+Song_Y/0/1/0/all/0/1">Yi-Zhe Song</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:57;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11965";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:79:"Bug Characteristics in Quantum Software Ecosystem. (arXiv:2204.11965v1 [cs.SE])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11965";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1928:"With the advance in quantum computing in recent years, quantum software
becomes vital for exploring the full potential of quantum computing systems.
Quantum programming is different from classical programming, for example, the
state of a quantum program is probabilistic in nature, and a quantum computer
is error-prone due to the instability of quantum mechanisms. Therefore, the
characteristics of bugs in quantum software projects may be very different from
that of classical software projects. This work aims to understand the
characteristics of bugs in quantum software projects, in order to provide
insights to help devise effective testing and debugging mechanisms. To achieve
this goal, we conduct an empirical study on the bug reports of 125 quantum
software projects. We observe that quantum software projects are more buggy
than classical software projects and that quantum project bugs are more costly
to fix than classical project bugs. We also identify the types of the bugs and
the quantum programming components where they occurred. Our study shows that
the bugs are spread across different components, but quantum-specific bugs
particularly appear in the compiler, gate operation, and state preparation
components. The three most occurring types of bugs are Program anomaly bugs,
Configuration bugs, and Data type and structure bugs. Our study highlights some
particularly challenging areas in quantum software development, such as the
lack of scientific quantum computation libraries that implement comprehensive
mathematical functions for quantum computing. Quantum developers also seek
specialized data manipulation libraries for quantum software engineering like
Numpy for quantum computing. Our findings also provide insights for future work
to advance the quantum program development, testing, and debugging of quantum
software, such as providing tooling support for debugging low-level circuits.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:324:" <a href="http://arxiv.org/find/cs/1/au:+aoun_M/0/1/0/all/0/1">Mohamed Raed El aoun</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_H/0/1/0/all/0/1">Heng Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Khomh_F/0/1/0/all/0/1">Foutse Khomh</a>, <a href="http://arxiv.org/find/cs/1/au:+Tidjon_L/0/1/0/all/0/1">Lionel Tidjon</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:58;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11966";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:104:"Estimating and Penalizing Induced Preference Shifts in Recommender Systems. (arXiv:2204.11966v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11966";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1498:"The content that a recommender system (RS) shows to users influences them.
Therefore, when choosing which recommender to deploy, one is implicitly also
choosing to induce specific internal states in users. Even more, systems
trained via long-horizon optimization will have direct incentives to manipulate
users, e.g. shift their preferences so they are easier to satisfy. In this work
we focus on induced preference shifts in users. We argue that - before
deployment - system designers should: estimate the shifts a recommender would
induce; evaluate whether such shifts would be undesirable; and even actively
optimize to avoid problematic shifts. These steps involve two challenging
ingredients: estimation requires anticipating how hypothetical policies would
influence user preferences if deployed - we do this by using historical user
interaction data to train predictive user model which implicitly contains their
preference dynamics; evaluation and optimization additionally require metrics
to assess whether such influences are manipulative or otherwise unwanted - we
use the notion of "safe shifts", that define a trust region within which
behavior is safe. In simulated experiments, we show that our learned preference
dynamics model is effective in estimating user preferences and how they would
respond to new recommenders. Additionally, we show that recommenders that
optimize for staying in the trust region can avoid manipulative behaviors while
still generating engagement.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:349:" <a href="http://arxiv.org/find/cs/1/au:+Carroll_M/0/1/0/all/0/1">Micah Carroll</a>, <a href="http://arxiv.org/find/cs/1/au:+Hadfield_Menell_D/0/1/0/all/0/1">Dylan Hadfield-Menell</a>, <a href="http://arxiv.org/find/cs/1/au:+Russell_S/0/1/0/all/0/1">Stuart Russell</a>, <a href="http://arxiv.org/find/cs/1/au:+Dragan_A/0/1/0/all/0/1">Anca Dragan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:59;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11970";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:131:"Visual Acuity Prediction on Real-Life Patient Data Using a Machine Learning Based Multistage System. (arXiv:2204.11970v1 [eess.IV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11970";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1713:"In ophthalmology, intravitreal operative medication therapy (IVOM) is
widespread treatment for diseases such as the age-related macular degeneration
(AMD), the diabetic macular edema (DME), as well as the retinal vein occlusion
(RVO). However, in real-world settings, patients often suffer from loss of
vision on time scales of years despite therapy, whereas the prediction of the
visual acuity (VA) and the earliest possible detection of deterioration under
real-life conditions is challenging due to heterogeneous and incomplete data.
In this contribution, we present a workflow for the development of a
research-compatible data corpus fusing different IT systems of the department
of ophthalmology of a German maximum care hospital. The extensive data corpus
allows predictive statements of the expected progression of a patient and his
or her VA in each of the three diseases. Within our proposed multistage system,
we classify the VA progression into the three groups of therapy "winners",
"stabilizers", and "losers" (WSL scheme). Our OCT biomarker classification
using an ensemble of deep neural networks results in a classification accuracy
(F1-score) of over 98 %, enabling us to complete incomplete OCT documentations
while allowing us to exploit them for a more precise VA modelling process. Our
VA prediction requires at least four VA examinations and optionally OCT
biomarkers from the same time period to predict the VA progression within a
forecasted time frame. While achieving a prediction accuracy of up to 69 %
(macro average F1-score) when considering all three WSL-based progression
groups, this corresponds to an improvement by 11 % in comparison to our
ophthalmic expertise (58 %).

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:705:" <a href="http://arxiv.org/find/eess/1/au:+Schlosser_T/0/1/0/all/0/1">Tobias Schlosser</a>, <a href="http://arxiv.org/find/eess/1/au:+Beuth_F/0/1/0/all/0/1">Frederik Beuth</a>, <a href="http://arxiv.org/find/eess/1/au:+Meyer_T/0/1/0/all/0/1">Trixy Meyer</a>, <a href="http://arxiv.org/find/eess/1/au:+Kumar_A/0/1/0/all/0/1">Arunodhayan Sampath Kumar</a>, <a href="http://arxiv.org/find/eess/1/au:+Stolze_G/0/1/0/all/0/1">Gabriel Stolze</a>, <a href="http://arxiv.org/find/eess/1/au:+Furashova_O/0/1/0/all/0/1">Olga Furashova</a>, <a href="http://arxiv.org/find/eess/1/au:+Engelmann_K/0/1/0/all/0/1">Katrin Engelmann</a>, <a href="http://arxiv.org/find/eess/1/au:+Kowerko_D/0/1/0/all/0/1">Danny Kowerko</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:60;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11972";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:111:"Gate-Level Side-Channel Leakage Assessment with Architecture Correlation Analysis. (arXiv:2204.11972v1 [cs.CR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11972";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:949:"While side-channel leakage is traditionally evaluated from a fabricated chip,
it is more time-efficient and cost-effective to do so during the design phase
of the chip. We present a methodology to rank the gates of a design according
to their contribution to the side-channel leakage of the chip. The methodology
relies on logic synthesis, logic simulation, gate-level power estimation, and
gate leakage assessment to compute a ranking. The ranking metric can be defined
as a specific test by correlating gate-level activity with a leakage model, or
else as a non-specific test by evaluating gate-level activity in response to
distinct test vector groups. Our results show that only a minority of the gates
in a design contribute most of the side-channel leakage. We demonstrate this
property for several designs, including a hardware AES coprocessor and a
cryptographic hardware/software interface in a five-stage pipelined RISC
processor.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:836:" <a href="http://arxiv.org/find/cs/1/au:+Kiaei_P/0/1/0/all/0/1">Pantea Kiaei</a>, <a href="http://arxiv.org/find/cs/1/au:+Yao_Y/0/1/0/all/0/1">Yuan Yao</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_Z/0/1/0/all/0/1">Zhenyuan Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Fern_N/0/1/0/all/0/1">Nicole Fern</a>, <a href="http://arxiv.org/find/cs/1/au:+Breunesse_C/0/1/0/all/0/1">Cees-Bart Breunesse</a>, <a href="http://arxiv.org/find/cs/1/au:+Woudenberg_J/0/1/0/all/0/1">Jasper Van Woudenberg</a>, <a href="http://arxiv.org/find/cs/1/au:+Gillis_K/0/1/0/all/0/1">Kate Gillis</a>, <a href="http://arxiv.org/find/cs/1/au:+Dich_A/0/1/0/all/0/1">Alex Dich</a>, <a href="http://arxiv.org/find/cs/1/au:+Grossmann_P/0/1/0/all/0/1">Peter Grossmann</a>, <a href="http://arxiv.org/find/cs/1/au:+Schaumont_P/0/1/0/all/0/1">Patrick Schaumont</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:61;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11980";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:87:"Decentralisation Conscious Players And System Reliability. (arXiv:2204.11980v1 [cs.GT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11980";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1087:"We propose a game-theoretic model of the reliability of decentralised systems
based on Varian's model of system reliability, to which we add a new normalised
total effort case that models \textit{decentralisation conscious players} who
prioritise decentralisation. We derive the Nash equilibria in the normalised
total effort game. In these equilibria, either one or two values are played by
players that do not free ride. The speed at which players can adjust their
contributions can determine how an equilibrium is reached and equilibrium
values. The behaviour of decentralisation conscious players is robust to
deviations by other players. Our results highlight the role that
decentralisation conscious players can play in maintaining decentralisation.
They also highlight, however, that by supporting an equilibrium that requires
an important contribution they cannot be expected to increase decentralisation
as contributing the equilibrium value may still imply a loss for many players.
We also discuss practical constraints on decentralisation in the context of our
model.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:165:" <a href="http://arxiv.org/find/cs/1/au:+Azouvi_S/0/1/0/all/0/1">Sarah Azouvi</a>, <a href="http://arxiv.org/find/cs/1/au:+Hicks_A/0/1/0/all/0/1">Alexander Hicks</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:62;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11981";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:109:"End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning. (arXiv:2204.11981v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11981";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:929:"To enable heterogeneous computing systems with autonomous programming and
optimization capabilities, we propose a unified, end-to-end, programmable graph
representation learning (PGL) framework that is capable of mining the
complexity of high-level programs down to the universal intermediate
representation, extracting the specific computational patterns and predicting
which code segments would run best on a specific core in heterogeneous hardware
platforms. The proposed framework extracts multi-fractal topological features
from code graphs, utilizes graph autoencoders to learn how to partition the
graph into computational kernels, and exploits graph neural networks (GNN) to
predict the correct assignment to a processor type. In the evaluation, we
validate the PGL framework and demonstrate a maximum speedup of 6.42x compared
to the thread-based execution, and 2.02x compared to the state-of-the-art
technique.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:572:" <a href="http://arxiv.org/find/cs/1/au:+Xiao_Y/0/1/0/all/0/1">Yao Xiao</a>, <a href="http://arxiv.org/find/cs/1/au:+Ma_G/0/1/0/all/0/1">Guixiang Ma</a>, <a href="http://arxiv.org/find/cs/1/au:+Ahmed_N/0/1/0/all/0/1">Nesreen K. Ahmed</a>, <a href="http://arxiv.org/find/cs/1/au:+Capota_M/0/1/0/all/0/1">Mihai Capota</a>, <a href="http://arxiv.org/find/cs/1/au:+Willke_T/0/1/0/all/0/1">Theodore Willke</a>, <a href="http://arxiv.org/find/cs/1/au:+Nazarian_S/0/1/0/all/0/1">Shahin Nazarian</a>, <a href="http://arxiv.org/find/cs/1/au:+Bogdan_P/0/1/0/all/0/1">Paul Bogdan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:63;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11982";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:132:"BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation. (arXiv:2204.11982v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11982";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1255:"Vision-based bronchoscopy (VB) models require the registration of the virtual
lung model with the frames from the video bronchoscopy to provide effective
guidance during the biopsy. The registration can be achieved by either tracking
the position and orientation of the bronchoscopy camera or by calibrating its
deviation from the pose (position and orientation) simulated in the virtual
lung model. Recent advances in neural networks and temporal image processing
have provided new opportunities for guided bronchoscopy. However, such progress
has been hindered by the lack of comparative experimental conditions.

In the present paper, we share a novel synthetic dataset allowing for a fair
comparison of methods. Moreover, this paper investigates several neural network
architectures for the learning of temporal information at different levels of
subject personalization. In order to improve orientation measurement, we also
present a standardized comparison framework and a novel metric for camera
orientation learning. Results on the dataset show that the proposed metric and
architectures, as well as the standardized conditions, provide notable
improvements to current state-of-the-art camera pose estimation in video
bronchoscopy.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:454:" <a href="http://arxiv.org/find/cs/1/au:+Borrego_Carazo_J/0/1/0/all/0/1">Juan Borrego-Carazo</a>, <a href="http://arxiv.org/find/cs/1/au:+Sanchez_C/0/1/0/all/0/1">Carles S&#xe1;nchez</a>, <a href="http://arxiv.org/find/cs/1/au:+Castells_Rufas_D/0/1/0/all/0/1">David Castells-Rufas</a>, <a href="http://arxiv.org/find/cs/1/au:+Carrabina_J/0/1/0/all/0/1">Jordi Carrabina</a>, <a href="http://arxiv.org/find/cs/1/au:+Gil_D/0/1/0/all/0/1">D&#xe9;bora Gil</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:64;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11985";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:69:"When adversarial examples are excusable. (arXiv:2204.11985v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11985";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1134:"Neural networks work remarkably well in practice and theoretically they can
be universal approximators. However, they still make mistakes and a specific
type of them called adversarial errors seem inexcusable to humans. In this
work, we analyze both test errors and adversarial errors on a well controlled
but highly non-linear visual classification problem. We find that, when
approximating training on infinite data, test errors tend to be close to the
ground truth decision boundary. Qualitatively speaking these are also more
difficult for a human. By contrast, adversarial examples can be found almost
everywhere and are often obvious mistakes. However, when we constrain
adversarial examples to the manifold, we observe a 90\% reduction in
adversarial errors. If we inflate the manifold by training with Gaussian noise
we observe a similar effect. In both cases, the remaining adversarial errors
tend to be close to the ground truth decision boundary. Qualitatively, the
remaining adversarial errors are similar to test errors on difficult examples.
They do not have the customary quality of being inexcusable mistakes.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:178:" <a href="http://arxiv.org/find/cs/1/au:+Kindermans_P/0/1/0/all/0/1">Pieter-Jan Kindermans</a>, <a href="http://arxiv.org/find/cs/1/au:+Staats_C/0/1/0/all/0/1">Charles Staats</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:65;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11989";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:125:"C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval. (arXiv:2204.11989v1 [cs.IR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11989";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:671:"Pretrained language models have improved effectiveness on numerous tasks,
including ad-hoc retrieval. Recent work has shown that continuing to pretrain a
language model with auxiliary objectives before fine-tuning on the retrieval
task can further improve retrieval effectiveness. Unlike monolingual retrieval,
designing an appropriate auxiliary task for cross-language mappings is
challenging. To address this challenge, we use comparable Wikipedia articles in
different languages to further pretrain off-the-shelf multilingual pretrained
models before fine-tuning on the retrieval task. We show that our approach
yields improvements in retrieval effectiveness.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:436:" <a href="http://arxiv.org/find/cs/1/au:+Yang_E/0/1/0/all/0/1">Eugene Yang</a>, <a href="http://arxiv.org/find/cs/1/au:+Nair_S/0/1/0/all/0/1">Suraj Nair</a>, <a href="http://arxiv.org/find/cs/1/au:+Chandradevan_R/0/1/0/all/0/1">Ramraj Chandradevan</a>, <a href="http://arxiv.org/find/cs/1/au:+Iglesias_Flores_R/0/1/0/all/0/1">Rebecca Iglesias-Flores</a>, <a href="http://arxiv.org/find/cs/1/au:+Oard_D/0/1/0/all/0/1">Douglas W. Oard</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:66;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11992";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:144:"Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit. (arXiv:2204.11992v1 [cs.AI])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11992";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1433:"Vehicle routing problems (VRPs) can be divided into two major categories:
offline VRPs, which consider a given set of trip requests to be served, and
online VRPs, which consider requests as they arrive in real-time. Based on
discussions with public transit agencies, we identify a real-world problem that
is not addressed by existing formulations: booking trips with flexible pickup
windows (e.g., 3 hours) in advance (e.g., the day before) and confirming tight
pickup windows (e.g., 30 minutes) at the time of booking. Such a service model
is often required in paratransit service settings, where passengers typically
book trips for the next day over the phone. To address this gap between offline
and online problems, we introduce a novel formulation, the offline vehicle
routing problem with online bookings. This problem is very challenging
computationally since it faces the complexity of considering large sets of
requests -- similar to offline VRPs -- but must abide by strict constraints on
running time -- similar to online VRPs. To solve this problem, we propose a
novel computational approach, which combines an anytime algorithm with a
learning-based policy for real-time decisions. Based on a paratransit dataset
obtained from our partner transit agency, we demonstrate that our novel
formulation and computational approach lead to significantly better outcomes in
this service setting than existing algorithms.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:620:" <a href="http://arxiv.org/find/cs/1/au:+Sivagnanam_A/0/1/0/all/0/1">Amutheezan Sivagnanam</a>, <a href="http://arxiv.org/find/cs/1/au:+Kadir_S/0/1/0/all/0/1">Salah Uddin Kadir</a>, <a href="http://arxiv.org/find/cs/1/au:+Mukhopadhyay_A/0/1/0/all/0/1">Ayan Mukhopadhyay</a>, <a href="http://arxiv.org/find/cs/1/au:+Pugliese_P/0/1/0/all/0/1">Philip Pugliese</a>, <a href="http://arxiv.org/find/cs/1/au:+Dubey_A/0/1/0/all/0/1">Abhishek Dubey</a>, <a href="http://arxiv.org/find/cs/1/au:+Samaranayake_S/0/1/0/all/0/1">Samitha Samaranayake</a>, <a href="http://arxiv.org/find/cs/1/au:+Laszka_A/0/1/0/all/0/1">Aron Laszka</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:67;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11994";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:140:"Contrastive learning-based computational histopathology predict differential expression of cancer driver genes. (arXiv:2204.11994v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11994";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1389:"Digital pathological analysis is run as the main examination used for cancer
diagnosis. Recently, deep learning-driven feature extraction from pathology
images is able to detect genetic variations and tumor environment, but few
studies focus on differential gene expression in tumor cells. In this paper, we
propose a self-supervised contrastive learning framework, HistCode, to infer
differential gene expressions from whole slide images (WSIs). We leveraged
contrastive learning on large-scale unannotated WSIs to derive slide-level
histopathological feature in latent space, and then transfer it to tumor
diagnosis and prediction of differentially expressed cancer driver genes. Our
extensive experiments showed that our method outperformed other
state-of-the-art models in tumor diagnosis tasks, and also effectively
predicted differential gene expressions. Interestingly, we found the higher
fold-changed genes can be more precisely predicted. To intuitively illustrate
the ability to extract informative features from pathological images, we
spatially visualized the WSIs colored by the attentive scores of image tiles.
We found that the tumor and necrosis areas were highly consistent with the
annotations of experienced pathologists. Moreover, the spatial heatmap
generated by lymphocyte-specific gene expression patterns was also consistent
with the manually labeled WSI.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:543:" <a href="http://arxiv.org/find/cs/1/au:+Huang_H/0/1/0/all/0/1">Haojue Huang</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhou_G/0/1/0/all/0/1">Gongming Zhou</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_X/0/1/0/all/0/1">Xuejun Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Deng_L/0/1/0/all/0/1">Lei Deng</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_C/0/1/0/all/0/1">Chen Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_D/0/1/0/all/0/1">Dachuan Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_H/0/1/0/all/0/1">Hui Liu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:68;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12000";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"AI Personification: Estimating the Personality of Language Models. (arXiv:2204.12000v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1480:"Technology for open-ended language generation, a key application of
artificial intelligence, has advanced to a great extent in recent years.
Large-scale language models, which are trained on large corpora of text, are
being used in a wide range of applications everywhere, from virtual assistants
to conversational bots. While these language models output fluent text,
existing research shows that these models can and do capture human biases. Many
of these biases, especially those that could potentially cause harm, are being
well investigated. On the other hand, studies that infer and change personality
traits inherited by these models have been scarce or non-existent. In this
work, we explore the personality traits of several large-scale language models
designed for open-ended text generation and the datasets used for training
them. Our work builds on the popular Big Five factors and develops robust
methods that quantify the personality traits of these models and their
underlying datasets. In particular, we trigger the models with a questionnaire
designed for personality assessment and subsequently classify the text
responses into quantifiable traits using a Zero-shot classifier. Our
classification sheds light on an important anthropomorphic element found in
such AI models and can help stakeholders decide how they should be applied and
how society could perceive them. We augment our analysis by studying approaches
that can alter these personalities.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:260:" <a href="http://arxiv.org/find/cs/1/au:+Karra_S/0/1/0/all/0/1">Saketh Reddy Karra</a>, <a href="http://arxiv.org/find/cs/1/au:+Nguyen_S/0/1/0/all/0/1">Son Nguyen</a>, <a href="http://arxiv.org/find/cs/1/au:+Tulabandhula_T/0/1/0/all/0/1">Theja Tulabandhula</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:69;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12005";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:111:"gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identification. (arXiv:2204.12005v1 [eess.SY])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12005";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1637:"A parametric adaptive physics-informed greedy Latent Space Dynamics
Identification (gLaSDI) method is proposed for accurate, efficient, and robust
data-driven reduced-order modeling of high-dimensional nonlinear dynamical
systems. In the proposed gLaSDI framework, an autoencoder discovers intrinsic
nonlinear latent representations of high-dimensional data, while dynamics
identification (DI) models capture local latent-space dynamics. An interactive
training algorithm is adopted for the autoencoder and local DI models, which
enables identification of simple latent-space dynamics and enhances accuracy
and efficiency of data-driven reduced-order modeling. To maximize and
accelerate the exploration of the parameter space for the optimal model
performance, an adaptive greedy sampling algorithm integrated with a
physics-informed residual-based error indicator and random-subset evaluation is
introduced to search for the optimal training samples on-the-fly. Further, to
exploit local latent-space dynamics captured by the local DI models for an
improved modeling accuracy with a minimum number of local DI models in the
parameter space, an efficient k-nearest neighbor convex interpolation scheme is
employed. The effectiveness of the proposed framework is demonstrated by
modeling various nonlinear dynamical problems, including Burgers equations,
nonlinear heat conduction, and radial advection. The proposed adaptive greedy
sampling outperforms the conventional predefined uniform sampling in terms of
accuracy. Compared with the high-fidelity models, gLaSDI achieves 66 to 4,417x
speed-up with 1 to 5% relative errors.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:413:" <a href="http://arxiv.org/find/eess/1/au:+He_X/0/1/0/all/0/1">Xiaolong He</a>, <a href="http://arxiv.org/find/eess/1/au:+Choi_Y/0/1/0/all/0/1">Youngsoo Choi</a>, <a href="http://arxiv.org/find/eess/1/au:+Fries_W/0/1/0/all/0/1">William D. Fries</a>, <a href="http://arxiv.org/find/eess/1/au:+Belof_J/0/1/0/all/0/1">Jon Belof</a>, <a href="http://arxiv.org/find/eess/1/au:+Chen_J/0/1/0/all/0/1">Jiun-Shyan Chen</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:70;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12006";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:96:"Parametric Dynamic Mode Decomposition for Reduced Order Modeling. (arXiv:2204.12006v1 [math.NA])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12006";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1077:"Dynamic Mode Decomposition (DMD) is a model-order reduction approach, whereby
spatial modes of fixed temporal frequencies are extracted from numerical or
experimental data sets. The DMD low-rank or reduced operator is typically
obtained by singular value decomposition of the temporal data sets. For
parameter-dependent models, as found in many multi-query applications such as
uncertainty quantification or design optimization, the only parametric DMD
technique developed was a stacked approach, with data sets at multiples
parameter values were aggregated together, increasing the computational work
needed to devise low-rank dynamical reduced-order models. In this paper, we
present two novel approach to carry out parametric DMD: one based on the
interpolation of the reduced-order DMD eigenpair and the other based on the
interpolation of the reduced DMD (Koopman) operator. Numerical results are
presented for diffusion-dominated nonlinear dynamical problems, including a
multiphysics radiative transfer example. All three parametric DMD approaches
are compared.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:338:" <a href="http://arxiv.org/find/math/1/au:+Huhn_Q/0/1/0/all/0/1">Quincy A. Huhn</a>, <a href="http://arxiv.org/find/math/1/au:+Tano_M/0/1/0/all/0/1">Mauricio E. Tano</a>, <a href="http://arxiv.org/find/math/1/au:+Ragusa_J/0/1/0/all/0/1">Jean C. Ragusa</a>, <a href="http://arxiv.org/find/math/1/au:+Choi_Y/0/1/0/all/0/1">Youngsoo Choi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:71;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12007";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:132:"Assessing the ability of generative adversarial networks to learn canonical medical image statistics. (arXiv:2204.12007v1 [eess.IV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12007";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1090:"In recent years, generative adversarial networks (GANs) have gained
tremendous popularity for potential applications in medical imaging, such as
medical image synthesis, restoration, reconstruction, translation, as well as
objective image quality assessment. Despite the impressive progress in
generating high-resolution, perceptually realistic images, it is not clear if
modern GANs reliably learn the statistics that are meaningful to a downstream
medical imaging application. In this work, the ability of a state-of-the-art
GAN to learn the statistics of canonical stochastic image models (SIMs) that
are relevant to objective assessment of image quality is investigated. It is
shown that although the employed GAN successfully learned several basic first-
and second-order statistics of the specific medical SIMs under consideration
and generated images with high perceptual quality, it failed to correctly learn
several per-image statistics pertinent to the these SIMs, highlighting the
urgent need to assess medical image GANs in terms of objective measures of
image quality.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:601:" <a href="http://arxiv.org/find/eess/1/au:+Kelkar_V/0/1/0/all/0/1">Varun A. Kelkar</a>, <a href="http://arxiv.org/find/eess/1/au:+Gotsis_D/0/1/0/all/0/1">Dimitrios S. Gotsis</a>, <a href="http://arxiv.org/find/eess/1/au:+Brooks_F/0/1/0/all/0/1">Frank J. Brooks</a>, <a href="http://arxiv.org/find/eess/1/au:+KC_P/0/1/0/all/0/1">Prabhat KC</a>, <a href="http://arxiv.org/find/eess/1/au:+Myers_K/0/1/0/all/0/1">Kyle J. Myers</a>, <a href="http://arxiv.org/find/eess/1/au:+Zeng_R/0/1/0/all/0/1">Rongping Zeng</a>, <a href="http://arxiv.org/find/eess/1/au:+Anastasio_M/0/1/0/all/0/1">Mark A. Anastasio</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:72;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12008";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:113:"Virtual Reality Applications in Software Engineering Education: A Systematic Review. (arXiv:2204.12008v1 [cs.SE])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12008";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1271:"Requirement Engineering (RE) is a Software Engineering (SE) process of
defining, documenting, and maintaining the requirements from a problem. It is
one of the most complex processes of SE because it addresses the relation
between customer and developer. RE learning may be abstract and complex for
most students because many of them cannot visualize the subject directly
applied. Through the advancement of technology, Virtual Reality (VR) hardware
is becoming increasingly more accessible, and it is not rare to use it in
education. Little research and systematic studies explain the integration
between SE and VR, and even less between RE and VR. Hence, this systematic
review proposes to select and present studies that relate the use of VR
applications to teach SE and RE concepts. We selected nine studies to include
in this review. Despite the lack of articles addressing the topic, the results
from this study showed that the use of VR technologies for learning SE is still
very seminal. The projects based essentially on visualization. There are lack
of tasks to build modeling artifacts, and also interaction with stakeholders
and other software engineers. Learning tasks and the monitoring of students'
progress by teachers also need to be considered.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:564:" <a href="http://arxiv.org/find/cs/1/au:+Andrade_G/0/1/0/all/0/1">Gustavo Vargas de Andrade</a>, <a href="http://arxiv.org/find/cs/1/au:+Gomes_A/0/1/0/all/0/1">Andr&#xe9; Luiz Cordeiro Gomes</a>, <a href="http://arxiv.org/find/cs/1/au:+Hoinoski_F/0/1/0/all/0/1">Felipe Rohr Hoinoski</a>, <a href="http://arxiv.org/find/cs/1/au:+Ferreira_M/0/1/0/all/0/1">Mar&#xed;lia Guterres Ferreira</a>, <a href="http://arxiv.org/find/cs/1/au:+Schoeffel_P/0/1/0/all/0/1">Pablo Schoeffel</a>, <a href="http://arxiv.org/find/cs/1/au:+Vahldick_A/0/1/0/all/0/1">Adilson Vahldick</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:73;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12010";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:113:"Theoretical Understanding of the Information Flow on Continual Learning Performance. (arXiv:2204.12010v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12010";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1619:"Continual learning (CL) is a setting in which an agent has to learn from an
incoming stream of data sequentially. CL performance evaluates the model's
ability to continually learn and solve new problems with incremental available
information over time while retaining previous knowledge. Despite the numerous
previous solutions to bypass the catastrophic forgetting (CF) of previously
seen tasks during the learning process, most of them still suffer significant
forgetting, expensive memory cost, or lack of theoretical understanding of
neural networks' conduct while learning new tasks. While the issue that CL
performance degrades under different training regimes has been extensively
studied empirically, insufficient attention has been paid from a theoretical
angle. In this paper, we establish a probabilistic framework to analyze
information flow through layers in networks for task sequences and its impact
on learning performance. Our objective is to optimize the information
preservation between layers while learning new tasks to manage task-specific
knowledge passing throughout the layers while maintaining model performance on
previous tasks. In particular, we study CL performance's relationship with
information flow in the network to answer the question "How can knowledge of
information flow between layers be used to alleviate CF?". Our analysis
provides novel insights of information adaptation within the layers during the
incremental task learning process. Through our experiments, we provide
empirical evidence and practically highlight the performance improvement across
multiple tasks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:167:" <a href="http://arxiv.org/find/cs/1/au:+Andle_J/0/1/0/all/0/1">Josh Andle</a>, <a href="http://arxiv.org/find/cs/1/au:+Sekeh_S/0/1/0/all/0/1">Salimeh Yasaei Sekeh</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:74;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12013";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:114:"Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs. (arXiv:2204.12013v1 [cs.DC])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12013";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1418:"DNN models across many domains continue to grow in size, resulting in high
resource requirements for effective training, and unpalatable (and often
unaffordable) costs for organizations and research labs across scales. This
paper aims to significantly reduce training costs with effective use of
preemptible instances, i.e., those that can be obtained at a much cheaper price
while idle, but may be preempted whenever requested by priority users. Doing
so, however, requires new forms of resiliency and efficiency to cope with the
possibility of frequent preemptions - a failure model that is drastically
different from the occasional failures in normal cluster settings that existing
checkpointing techniques target.

We present Bamboo, a distributed system that tackles these challenges by
introducing redundant computations into the training pipeline, i.e., whereby
one node performs computations over not only its own layers but also over some
layers in its neighbor. Our key insight is that training large models often
requires pipeline parallelism where "pipeline bubbles" naturally exist. Bamboo
carefully fills redundant computations into these bubbles, providing resilience
at a low cost. Across a variety of widely used DNN models, Bamboo outperforms
traditional checkpointing by 3.7x in training throughput, and reduces costs by
2.4x compared to a setting where on-demand instances are used.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:655:" <a href="http://arxiv.org/find/cs/1/au:+Thorpe_J/0/1/0/all/0/1">John Thorpe</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_P/0/1/0/all/0/1">Pengzhan Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Eyolfson_J/0/1/0/all/0/1">Jonathan Eyolfson</a>, <a href="http://arxiv.org/find/cs/1/au:+Qiao_Y/0/1/0/all/0/1">Yifan Qiao</a>, <a href="http://arxiv.org/find/cs/1/au:+Jia_Z/0/1/0/all/0/1">Zhihao Jia</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_M/0/1/0/all/0/1">Minjia Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Netravali_R/0/1/0/all/0/1">Ravi Netravali</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_G/0/1/0/all/0/1">Guoqing Harry Xu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:75;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12020";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:88:"Balancing Age-Energy Tradeoff in Sleep-Wake Server Systems. (arXiv:2204.12020v1 [cs.IT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12020";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1595:"The surging demand for fresh information from various Internet of Things
(IoT) applications requires oceans of data to be transmitted and processed
timely. How to guarantee information freshness while reducing energy
consumption thus becomes imperative. We consider a multi-source single-server
queueing system, where we aim to design the optimal sleep-wake strategy for the
server to reduce its energy consumption while guaranteeing users' information
freshness. We propose a sleep-wake strategy that relies on an idling scheme
called Conditional Sleep (CS) scheme. We show that the proposed CS scheme can
achieve a smaller Age of Information (AoI) than the widely-used Hysteresis Time
(HT) scheme and Bernoulli Sleep (BS) scheme, while retaining the same power
consumption and Peak Age of Information (PAoI). Moreover, we find that
increasing the sleep period length can always reduce energy consumption and
enlarge the PAoI, but it does not always increase AoI. We also find that using
PAoI as the information freshness metric in designing the optimal sleep-wake
strategies would make the server sleep infinitely long. Our analysis reveals
that this result is due to the PAoI being a first-order statistic. We further
extend our discussion to the scenario where data sources choose sampling rates
strategically based on the sleep-wake strategy of the server. We show that
increasing the sleeping period length for the server while guaranteeing users'
PAoI could lead to a minor reduction of the server's energy consumption but
significantly increase the data sources' sampling costs.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:304:" <a href="http://arxiv.org/find/cs/1/au:+Xu_J/0/1/0/all/0/1">Jin Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_X/0/1/0/all/0/1">Xinyuan Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_Q/0/1/0/all/0/1">Qisheng Huang</a>, <a href="http://arxiv.org/find/cs/1/au:+Sun_P/0/1/0/all/0/1">Peng Sun</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:76;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12022";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"Estimating the Resize Parameter in End-to-end Learned Image Compression. (arXiv:2204.12022v1 [eess.IV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12022";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1239:"We describe a search-free resizing framework that can further improve the
rate-distortion tradeoff of recent learned image compression models. Our
approach is simple: compose a pair of differentiable downsampling/upsampling
layers that sandwich a neural compression model. To determine resize factors
for different inputs, we utilize another neural network jointly trained with
the compression model, with the end goal of minimizing the rate-distortion
objective. Our results suggest that "compression friendly" downsampled
representations can be quickly determined during encoding by using an auxiliary
network and differentiable image warping. By conducting extensive experimental
tests on existing deep image compression models, we show results that our new
resizing parameter estimation framework can provide Bj{\o}ntegaard-Delta rate
(BD-rate) improvement of about 10% against leading perceptual quality engines.
We also carried out a subjective quality study, the results of which show that
our new approach yields favorable compressed images. To facilitate reproducible
research in this direction, the implementation used in this paper is being made
freely available online at: https://github.com/treammm/ResizeCompression.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:426:" <a href="http://arxiv.org/find/eess/1/au:+Chen_L/0/1/0/all/0/1">Li-Heng Chen</a>, <a href="http://arxiv.org/find/eess/1/au:+Bampis_C/0/1/0/all/0/1">Christos G. Bampis</a>, <a href="http://arxiv.org/find/eess/1/au:+Li_Z/0/1/0/all/0/1">Zhi Li</a>, <a href="http://arxiv.org/find/eess/1/au:+Krasula_L/0/1/0/all/0/1">Luk&#xe1;&#x161; Krasula</a>, <a href="http://arxiv.org/find/eess/1/au:+Bovik_A/0/1/0/all/0/1">Alan C. Bovik</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:77;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12024";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Reprint: a randomized extrapolation based on principal components for data augmentation. (arXiv:2204.12024v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12024";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1501:"Data scarcity and data imbalance have attracted a lot of attention in many
fields. Data augmentation, explored as an effective approach to tackle them,
can improve the robustness and efficiency of classification models by
generating new samples. This paper presents REPRINT, a simple and effective
hidden-space data augmentation method for imbalanced data classification. Given
hidden-space representations of samples in each class, REPRINT extrapolates, in
a randomized fashion, augmented examples for target class by using subspaces
spanned by principal components to summarize distribution structure of both
source and target class. Consequently, the examples generated would diversify
the target while maintaining the original geometry of target distribution.
Besides, this method involves a label refinement component which allows to
synthesize new soft labels for augmented examples. Compared with different NLP
data augmentation approaches under a range of data imbalanced scenarios on four
text classification benchmark, REPRINT shows prominent improvements. Moreover,
through comprehensive ablation studies, we show that label refinement is better
than label-preserving for augmented examples, and that our method suggests
stable and consistent improvements in terms of suitable choices of principal
components. Moreover, REPRINT is appealing for its easy-to-use since it
contains only one hyperparameter determining the dimension of subspace and
requires low computational resource.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:384:" <a href="http://arxiv.org/find/cs/1/au:+Wei_J/0/1/0/all/0/1">Jiale Wei</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_Q/0/1/0/all/0/1">Qiyuan Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Peng_P/0/1/0/all/0/1">Pai Peng</a>, <a href="http://arxiv.org/find/cs/1/au:+Guedj_B/0/1/0/all/0/1">Benjamin Guedj</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_L/0/1/0/all/0/1">Le Li</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:78;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12026";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:68:"BATS: Best Action Trajectory Stitching. (arXiv:2204.12026v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12026";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1397:"The problem of offline reinforcement learning focuses on learning a good
policy from a log of environment interactions. Past efforts for developing
algorithms in this area have revolved around introducing constraints to online
reinforcement learning algorithms to ensure the actions of the learned policy
are constrained to the logged data. In this work, we explore an alternative
approach by planning on the fixed dataset directly. Specifically, we introduce
an algorithm which forms a tabular Markov Decision Process (MDP) over the
logged data by adding new transitions to the dataset. We do this by using
learned dynamics models to plan short trajectories between states. Since exact
value iteration can be performed on this constructed MDP, it becomes easy to
identify which trajectories are advantageous to add to the MDP. Crucially,
since most transitions in this MDP come from the logged data, trajectories from
the MDP can be rolled out for long periods with confidence. We prove that this
property allows one to make upper and lower bounds on the value function up to
appropriate distance metrics. Finally, we demonstrate empirically how
algorithms that uniformly constrain the learned policy to the entire dataset
can result in unwanted behavior, and we show an example in which simply
behavior cloning the optimal policy of the MDP created by our algorithm avoids
this problem.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:411:" <a href="http://arxiv.org/find/cs/1/au:+Char_I/0/1/0/all/0/1">Ian Char</a>, <a href="http://arxiv.org/find/cs/1/au:+Mehta_V/0/1/0/all/0/1">Viraj Mehta</a>, <a href="http://arxiv.org/find/cs/1/au:+Villaflor_A/0/1/0/all/0/1">Adam Villaflor</a>, <a href="http://arxiv.org/find/cs/1/au:+Dolan_J/0/1/0/all/0/1">John M. Dolan</a>, <a href="http://arxiv.org/find/cs/1/au:+Schneider_J/0/1/0/all/0/1">Jeff Schneider</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:79;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12027";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:155:"On Routing, Wavelength, Network Coding Assignment and Protection Configuration Problem in Optical-processing-enabled Networks. (arXiv:2204.12027v1 [cs.NI])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12027";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1491:"In optical-processing-enabled network, transitional lightpaths crossing the
same node could be optically encoded to each other to achieve greater spectral
efficiency. In this context, we present a new research problem, entitled,
routing, wavelength, network coding assignment and protection configuration
(RWNCA-PC) arisen in exploiting photonic network coding (NC) for dedicated path
protection in wavelength division multiplexing (WDM) networks with an extra
degree of freedom in the selection of protection triggering mechanism, that is,
network-side and client-side, tailoring to each connection. In order to
maximize the NC benefits, we thus provide a weighted multi-objective
optimization model for solving RWNCA-PC problem so as to minimize the
wavelength count as the strictly prioritized goal and the redundant resources
measured by the number of client-side connections as the secondary objective.
Numerical results on the realistic COST239 network reveal that a saving of up
to $25\%$ wavelength resources could be achieved thanks to the optimal use of
NC compared to the non-coding designs and among coding-aware designs, the use
of mixed protection configurations would be spectrally more efficient than the
design with only network-side protection scheme. Our proposal yields the
highest spectrum efficiency compared to all reference designs and moreover,
features an average saving of more than $40\%$ transponder count compared with
its single objective counterpart.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:79:" <a href="http://arxiv.org/find/cs/1/au:+Hai_D/0/1/0/all/0/1">Dao Thanh Hai</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:80;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12031";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:77:"Boundary Smoothing for Named Entity Recognition. (arXiv:2204.12031v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12031";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:786:"Neural named entity recognition (NER) models may easily encounter the
over-confidence issue, which degrades the performance and calibration. Inspired
by label smoothing and driven by the ambiguity of boundary annotation in NER
engineering, we propose boundary smoothing as a regularization technique for
span-based neural NER models. It re-assigns entity probabilities from annotated
spans to the surrounding ones. Built on a simple but strong baseline, our model
achieves results better than or competitive with previous state-of-the-art
systems on eight well-known NER benchmarks. Further empirical analysis suggests
that boundary smoothing effectively mitigates over-confidence, improves model
calibration, and brings flatter neural minima and more smoothed loss
landscapes.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:151:" <a href="http://arxiv.org/find/cs/1/au:+Zhu_E/0/1/0/all/0/1">Enwei Zhu</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_J/0/1/0/all/0/1">Jinpeng Li</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:81;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12034";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:81:"A construction of optimal locally recoverable codes. (arXiv:2204.12034v1 [cs.IT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12034";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:457:"Locally recoverable codes are widely used in distributed and cloud storage
systems. The objective of this paper is to present a construction of near MDS
codes with oval polynomials and then determine the locality of the codes. It
turns out that the near MDS codes and their duals are both distance-optimal and
dimension-optimal locally recoverable codes. The lengths of the locally
recoverable codes are different from known ones in the literature.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:153:" <a href="http://arxiv.org/find/cs/1/au:+Li_X/0/1/0/all/0/1">Xiaoru Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Heng_Z/0/1/0/all/0/1">Ziling Heng</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:82;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12035";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:84:"Information Fusion: Scaling Subspace-Driven Approaches. (arXiv:2204.12035v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12035";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:784:"In this work, we seek to exploit the deep structure of multi-modal data to
robustly exploit the group subspace distribution of the information using the
Convolutional Neural Network (CNN) formalism. Upon unfolding the set of
subspaces constituting each data modality, and learning their corresponding
encoders, an optimized integration of the generated inherent information is
carried out to yield a characterization of various classes. Referred to as deep
Multimodal Robust Group Subspace Clustering (DRoGSuRe), this approach is
compared against the independently developed state-of-the-art approach named
Deep Multimodal Subspace Clustering (DMSC). Experiments on different multimodal
datasets show that our approach is competitive and more robust in the presence
of noise.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:159:" <a href="http://arxiv.org/find/cs/1/au:+Ghanem_S/0/1/0/all/0/1">Sally Ghanem</a>, <a href="http://arxiv.org/find/cs/1/au:+Krim_H/0/1/0/all/0/1">Hamid Krim</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:83;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12036";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:102:"Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction. (arXiv:2204.12036v1 [cs.AI])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12036";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1606:"Temporal knowledge prediction is a crucial task for the event early warning
that has gained increasing attention in recent years, which aims to predict the
future facts by using relevant historical facts on the temporal knowledge
graphs. There are two main difficulties in this prediction task. First, from
the historical facts point of view, how to model the evolutionary patterns of
the facts to predict the query accurately. Second, from the query perspective,
how to handle the two cases where the query contains seen and unseen entities
in a unified framework. Driven by the two problems, we propose a novel adaptive
pseudo-siamese policy network for temporal knowledge prediction based on
reinforcement learning. Specifically, we design the policy network in our model
as a pseudo-siamese policy network that consists of two sub-policy networks. In
sub-policy network I, the agent searches for the answer for the query along the
entity-relation paths to capture the static evolutionary patterns. And in
sub-policy network II, the agent searches for the answer for the query along
the relation-time paths to deal with unseen entities. Moreover, we develop a
temporal relation encoder to capture the temporal evolutionary patterns.
Finally, we design a gating mechanism to adaptively integrate the results of
the two sub-policy networks to help the agent focus on the destination answer.
To assess our model performance, we conduct link prediction on four benchmark
datasets, the experimental results demonstrate that our method obtains
considerable performance compared with existing methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:389:" <a href="http://arxiv.org/find/cs/1/au:+Shao_P/0/1/0/all/0/1">Pengpeng Shao</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_T/0/1/0/all/0/1">Tong Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Che_F/0/1/0/all/0/1">Feihu Che</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_D/0/1/0/all/0/1">Dawei Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Tao_J/0/1/0/all/0/1">Jianhua Tao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:84;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12037";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:113:"Causal Reasoning with Spatial-temporal Representation Learning: A Prospective Study. (arXiv:2204.12037v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12037";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1699:"Spatial-temporal representation learning is ubiquitous in various real-world
applications, including visual comprehension, video understanding, multi-modal
analysis, human-computer interaction, and urban computing. Due to the emergence
of huge amounts of multi-modal heterogeneous spatial/temporal/spatial-temporal
data in big data era, the existing visual methods rely heavily on large-scale
data annotations and supervised learning to learn a powerful big model.
However, the lack of interpretability, robustness, and out-of-distribution
generalization are becoming the bottleneck problems of these models, which
hinders the progress of interpretable and reliable artificial intelligence. The
majority of the existing methods are based on correlation learning with the
assumption that the data are independent and identically distributed, which
lack an unified guidance and analysis about why modern spatial-temporal
representation learning methods have limited interpretability and easily
collapse into dataset bias. Inspired by the strong inference ability of
human-level agents, recent years have therefore witnessed great effort in
developing causal reasoning paradigms to realize robust representation and
model learning with good interpretability. In this paper, we conduct a
comprehensive review of existing causal reasoning methods for spatial-temporal
representation learning, covering fundamental theories, models, and datasets.
The limitations of current methods and datasets are also discussed. Moreover,
we propose some primary challenges, opportunities, and future research
directions for benchmarking causal reasoning algorithms in spatial-temporal
representation learning.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:378:" <a href="http://arxiv.org/find/cs/1/au:+Liu_Y/0/1/0/all/0/1">Yang Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Wei_Y/0/1/0/all/0/1">Yushen Wei</a>, <a href="http://arxiv.org/find/cs/1/au:+Yan_H/0/1/0/all/0/1">Hong Yan</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_G/0/1/0/all/0/1">Guanbin Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Lin_L/0/1/0/all/0/1">Liang Lin</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:85;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12039";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"Learning Weighting Map for Bit-Depth Expansion within a Rational Range. (arXiv:2204.12039v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12039";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1327:"Bit-depth expansion (BDE) is one of the emerging technologies to display high
bit-depth (HBD) image from low bit-depth (LBD) source. Existing BDE methods
have no unified solution for various BDE situations, and directly learn a
mapping for each pixel from LBD image to the desired value in HBD image, which
may change the given high-order bits and lead to a huge deviation from the
ground truth. In this paper, we design a bit restoration network (BRNet) to
learn a weight for each pixel, which indicates the ratio of the replenished
value within a rational range, invoking an accurate solution without modifying
the given high-order bit information. To make the network adaptive for any
bit-depth degradation, we investigate the issue in an optimization perspective
and train the network under progressive training strategy for better
performance. Moreover, we employ Wasserstein distance as a visual quality
indicator to evaluate the difference of color distribution between restored
image and the ground truth. Experimental results show our method can restore
colorful images with fewer artifacts and false contours, and outperforms
state-of-the-art methods with higher PSNR/SSIM results and lower Wasserstein
distance. The source code will be made available at
https://github.com/yuqing-liu-dut/bit-depth-expansion

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:530:" <a href="http://arxiv.org/find/cs/1/au:+Liu_Y/0/1/0/all/0/1">Yuqing Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Jia_Q/0/1/0/all/0/1">Qi Jia</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_J/0/1/0/all/0/1">Jian Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Fan_X/0/1/0/all/0/1">Xin Fan</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_S/0/1/0/all/0/1">Shanshe Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Ma_S/0/1/0/all/0/1">Siwei Ma</a>, <a href="http://arxiv.org/find/cs/1/au:+Gao_W/0/1/0/all/0/1">Wen Gao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:86;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12043";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:94:"An Efficient Dynamic Sampling Policy For Monte Carlo Tree Search. (arXiv:2204.12043v1 [cs.AI])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12043";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:527:"We consider the popular tree-based search strategy within the framework of
reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of
finite-horizon Markov decision process. We propose a dynamic sampling tree
policy that efficiently allocates limited computational budget to maximize the
probability of correct selection of the best action at the root node of the
tree. Experimental results on Tic-Tac-Toe and Gomoku show that the proposed
tree policy is more efficient than other competing methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:233:" <a href="http://arxiv.org/find/cs/1/au:+Zhang_G/0/1/0/all/0/1">Gongbo Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Peng_Y/0/1/0/all/0/1">Yijie Peng</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_Y/0/1/0/all/0/1">Yilong Xu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:87;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12044";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"ISTRBoost: Importance Sampling Transfer Regression using Boosting. (arXiv:2204.12044v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12044";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1317:"Current Instance Transfer Learning (ITL) methodologies use domain adaptation
and sub-space transformation to achieve successful transfer learning. However,
these methodologies, in their processes, sometimes overfit on the target
dataset or suffer from negative transfer if the test dataset has a high
variance. Boosting methodologies have been shown to reduce the risk of
overfitting by iteratively re-weighing instances with high-residual. However,
this balance is usually achieved with parameter optimization, as well as
reducing the skewness in weights produced due to the size of the source
dataset. While the former can be achieved, the latter is more challenging and
can lead to negative transfer. We introduce a simpler and more robust fix to
this problem by building upon the popular boosting ITL regression methodology,
two-stage TrAdaBoost.R2. Our methodology,~\us{}, is a boosting and
random-forest based ensemble methodology that utilizes importance sampling to
reduce the skewness due to the source dataset. We show that~\us{}~performs
better than competitive transfer learning methodologies $63\%$ of the time. It
also displays consistency in its performance over diverse datasets with varying
complexities, as opposed to the sporadic results observed for other transfer
learning methodologies.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:315:" <a href="http://arxiv.org/find/cs/1/au:+Gupta_S/0/1/0/all/0/1">Shrey Gupta</a>, <a href="http://arxiv.org/find/cs/1/au:+Bi_J/0/1/0/all/0/1">Jianzhao Bi</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_Y/0/1/0/all/0/1">Yang Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Wildani_A/0/1/0/all/0/1">Avani Wildani</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:88;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12046";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"Experience Report: Standards-Based Grading at Scale in Algorithms. (arXiv:2204.12046v1 [cs.CY])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12046";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:587:"We report our experiences implementing standards-based grading at scale in an
Algorithms course, which serves as the terminal required CS Theory course in
our department's undergraduate curriculum. The course had 200-400 students,
taught by two instructors, eight graduate teaching assistants, and supported by
two additional graders and several undergraduate course assistants. We
highlight the role of standards-based grading in supporting our students during
the COVID-19 pandemic. We conclude by detailing the successes and adjustments
we would make to the course structure.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:326:" <a href="http://arxiv.org/find/cs/1/au:+Chen_L/0/1/0/all/0/1">Lijun Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Grochow_J/0/1/0/all/0/1">Joshua A. Grochow</a>, <a href="http://arxiv.org/find/cs/1/au:+Layer_R/0/1/0/all/0/1">Ryan Layer</a>, <a href="http://arxiv.org/find/cs/1/au:+Levet_M/0/1/0/all/0/1">Michael Levet</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:89;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12048";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:87:"Thompson Sampling for Bandit Learning in Matching Markets. (arXiv:2204.12048v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12048";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1153:"The problem of two-sided matching markets has a wide range of real-world
applications and has been extensively studied in the literature. A line of
recent works have focused on the problem setting where the preferences of
one-side market participants are unknown \emph{a priori} and are learned by
iteratively interacting with the other side of participants. All these works
are based on explore-then-commit (ETC) and upper confidence bound (UCB)
algorithms, two common strategies in multi-armed bandits (MAB). Thompson
sampling (TS) is another popular approach, which attracts lots of attention due
to its easier implementation and better empirical performances. In many
problems, even when UCB and ETC-type algorithms have already been analyzed,
researchers are still trying to study TS for its benefits. However, the
convergence analysis of TS is much more challenging and remains open in many
problem settings. In this paper, we provide the first regret analysis for TS in
the new setting of iterative matching markets. Extensive experiments
demonstrate the practical advantages of the TS-type algorithm over the ETC and
UCB-type baselines.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:228:" <a href="http://arxiv.org/find/cs/1/au:+Kong_F/0/1/0/all/0/1">Fang Kong</a>, <a href="http://arxiv.org/find/cs/1/au:+Yin_J/0/1/0/all/0/1">Junming Yin</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_S/0/1/0/all/0/1">Shuai Li</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:90;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12050";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:124:"Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks. (arXiv:2204.12050v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12050";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1256:"Malicious intelligent algorithms greatly threaten the security of social
users' privacy by detecting and analyzing the uploaded photos to social network
platforms. The destruction to DNNs brought by the adversarial attack sparks the
potential that adversarial examples serve as a new protection mechanism for
privacy security in social networks. However, the existing adversarial example
does not have recoverability for serving as an effective protection mechanism.
To address this issue, we propose a recoverable generative adversarial network
to generate self-recoverable adversarial examples. By modeling the adversarial
attack and recovery as a united task, our method can minimize the error of the
recovered examples while maximizing the attack ability, resulting in better
recoverability of adversarial examples. To further boost the recoverability of
these examples, we exploit a dimension reducer to optimize the distribution of
adversarial perturbation. The experimental results prove that the adversarial
examples generated by the proposed method present superior recoverability,
attack ability, and robustness on different datasets and network architectures,
which ensure its effectiveness as a protection mechanism in social networks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:315:" <a href="http://arxiv.org/find/cs/1/au:+Zhang_J/0/1/0/all/0/1">Jiawei Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_J/0/1/0/all/0/1">Jinwei Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_H/0/1/0/all/0/1">Hao Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Luo_X/0/1/0/all/0/1">Xiangyang Luo</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:91;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12052";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"Pretraining Chinese BERT for Detecting Word Insertion and Deletion Errors. (arXiv:2204.12052v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12052";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1256:"Chinese BERT models achieve remarkable progress in dealing with grammatical
errors of word substitution. However, they fail to handle word insertion and
deletion because BERT assumes the existence of a word at each position. To
address this, we present a simple and effective Chinese pretrained model. The
basic idea is to enable the model to determine whether a word exists at a
particular position. We achieve this by introducing a special token
\texttt{[null]}, the prediction of which stands for the non-existence of a
word. In the training stage, we design pretraining tasks such that the model
learns to predict \texttt{[null]} and real words jointly given the surrounding
context. In the inference stage, the model readily detects whether a word
should be inserted or deleted with the standard masked language modeling
function. We further create an evaluation dataset to foster research on word
insertion and deletion. It includes human-annotated corrections for 7,726
erroneous sentences. Results show that existing Chinese BERT performs poorly on
detecting insertion and deletion errors. Our approach significantly improves
the F1 scores from 24.1\% to 78.1\% for word insertion and from 26.5\% to
68.5\% for word deletion, respectively.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:541:" <a href="http://arxiv.org/find/cs/1/au:+Zhou_C/0/1/0/all/0/1">Cong Zhou</a>, <a href="http://arxiv.org/find/cs/1/au:+Dai_Y/0/1/0/all/0/1">Yong Dai</a>, <a href="http://arxiv.org/find/cs/1/au:+Tang_D/0/1/0/all/0/1">Duyu Tang</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_E/0/1/0/all/0/1">Enbo Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Feng_Z/0/1/0/all/0/1">Zhangyin Feng</a>, <a href="http://arxiv.org/find/cs/1/au:+Kuang_L/0/1/0/all/0/1">Li Kuang</a>, <a href="http://arxiv.org/find/cs/1/au:+Shi_S/0/1/0/all/0/1">Shuming Shi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:92;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12055";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:89:"Faster Fundamental Graph Algorithms via Learned Predictions. (arXiv:2204.12055v1 [cs.DS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12055";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1254:"We consider the question of speeding up classic graph algorithms with
machine-learned predictions. In this model, algorithms are furnished with extra
advice learned from past or similar instances. Given the additional
information, we aim to improve upon the traditional worst-case run-time
guarantees. Our contributions are the following:

(i) We give a faster algorithm for minimum-weight bipartite matching via
learned duals, improving the recent result by Dinitz, Im, Lavastida, Moseley
and Vassilvitskii (NeurIPS, 2021);

(ii) We extend the learned dual approach to the single-source shortest path
problem (with negative edge lengths), achieving an almost linear runtime given
sufficiently accurate predictions which improves upon the classic fastest
algorithm due to Goldberg (SIAM J. Comput., 1995);

(iii) We provide a general reduction-based framework for learning-based graph
algorithms, leading to new algorithms for degree-constrained subgraph and
minimum-cost $0$-$1$ flow, based on reductions to bipartite matching and the
shortest path problem.

Finally, we give a set of general learnability theorems, showing that the
predictions required by our algorithms can be efficiently learned in a PAC
fashion.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:328:" <a href="http://arxiv.org/find/cs/1/au:+Chen_J/0/1/0/all/0/1">Justin Y. Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Silwal_S/0/1/0/all/0/1">Sandeep Silwal</a>, <a href="http://arxiv.org/find/cs/1/au:+Vakilian_A/0/1/0/all/0/1">Ali Vakilian</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_F/0/1/0/all/0/1">Fred Zhang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:93;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12057";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:55:"Privacy-Utility Trade-Off. (arXiv:2204.12057v1 [cs.IT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12057";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:878:"In this paper, we investigate the privacy-utility trade-off (PUT) problem,
which considers the minimal privacy loss at a fixed expense of utility. Several
different kinds of privacy in the PUT problem are studied, including
differential privacy, approximate differential privacy, maximal information,
maximal leakage, Renyi differential privacy, Sibson mutual information and
mutual information. The average Hamming distance is used to measure the
distortion caused by the privacy mechanism. We consider two scenarios: global
privacy and local privacy. In the framework of global privacy framework, the
privacy-distortion function is upper-bounded by the privacy loss of a special
mechanism, and lower-bounded by the optimal privacy loss with any possible
prior input distribution. In the framework of local privacy, we generalize a
coloring method for the PUT problem.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:153:" <a href="http://arxiv.org/find/cs/1/au:+Zhong_H/0/1/0/all/0/1">Hao Zhong</a>, <a href="http://arxiv.org/find/cs/1/au:+Bu_K/0/1/0/all/0/1">Kaifeng Bu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:94;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12061";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:94:"PLOD: An Abbreviation Detection Dataset for Scientific Documents. (arXiv:2204.12061v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12061";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1007:"The detection and extraction of abbreviations from unstructured texts can
help to improve the performance of Natural Language Processing tasks, such as
machine translation and information retrieval. However, in terms of publicly
available datasets, there is not enough data for training
deep-neural-networks-based models to the point of generalising well over data.
This paper presents PLOD, a large-scale dataset for abbreviation detection and
extraction that contains 160k+ segments automatically annotated with
abbreviations and their long forms. We performed manual validation over a set
of instances and a complete automatic validation for this dataset. We then used
it to generate several baseline models for detecting abbreviations and long
forms. The best models achieved an F1-score of 0.92 for abbreviations and 0.89
for detecting their corresponding long forms. We release this dataset along
with our code and all the models publicly in
https://github.com/surrey-nlp/AbbreviationDetRepo.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:425:" <a href="http://arxiv.org/find/cs/1/au:+Zilio_L/0/1/0/all/0/1">Leonardo Zilio</a>, <a href="http://arxiv.org/find/cs/1/au:+Saadany_H/0/1/0/all/0/1">Hadeel Saadany</a>, <a href="http://arxiv.org/find/cs/1/au:+Sharma_P/0/1/0/all/0/1">Prashant Sharma</a>, <a href="http://arxiv.org/find/cs/1/au:+Kanojia_D/0/1/0/all/0/1">Diptesh Kanojia</a>, <a href="http://arxiv.org/find/cs/1/au:+Orasan_C/0/1/0/all/0/1">Constantin Orasan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:95;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12062";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:125:"Scheduling Virtual Conferences Fairly: Achieving Equitable Participant and Speaker Satisfaction. (arXiv:2204.12062v1 [cs.HC])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12062";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1402:"Recently, almost all conferences have moved to virtual mode due to the
pandemic-induced restrictions on travel and social gathering. Contrary to
in-person conferences, virtual conferences face the challenge of efficiently
scheduling talks, accounting for the availability of participants from
different timezones and their interests in attending different talks. A natural
objective for conference organizers is to maximize efficiency, e.g., total
expected audience participation across all talks. However, we show that
optimizing for efficiency alone can result in an unfair virtual conference
schedule, where individual utilities for participants and speakers can be
highly unequal. To address this, we formally define fairness notions for
participants and speakers, and derive suitable objectives to account for them.
As the efficiency and fairness objectives can be in conflict with each other,
we propose a joint optimization framework that allows conference organizers to
design schedules that balance (i.e., allow trade-offs) among efficiency,
participant fairness and speaker fairness objectives. While the optimization
problem can be solved using integer programming to schedule smaller
conferences, we provide two scalable techniques to cater to bigger conferences.
Extensive evaluations over multiple real-world datasets show the efficacy and
flexibility of our proposed approaches.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:433:" <a href="http://arxiv.org/find/cs/1/au:+Patro_G/0/1/0/all/0/1">Gourab K. Patro</a>, <a href="http://arxiv.org/find/cs/1/au:+Jana_P/0/1/0/all/0/1">Prithwish Jana</a>, <a href="http://arxiv.org/find/cs/1/au:+Chakraborty_A/0/1/0/all/0/1">Abhijnan Chakraborty</a>, <a href="http://arxiv.org/find/cs/1/au:+Gummadi_K/0/1/0/all/0/1">Krishna P. Gummadi</a>, <a href="http://arxiv.org/find/cs/1/au:+Ganguly_N/0/1/0/all/0/1">Niloy Ganguly</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:96;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12063";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"A Review-aware Graph Contrastive Learning Framework for Recommendation. (arXiv:2204.12063v1 [cs.IR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12063";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1850:"Most modern recommender systems predict users preferences with two
components: user and item embedding learning, followed by the user-item
interaction modeling. By utilizing the auxiliary review information accompanied
with user ratings, many of the existing review-based recommendation models
enriched user/item embedding learning ability with historical reviews or better
modeled user-item interactions with the help of available user-item target
reviews. Though significant progress has been made, we argue that current
solutions for review-based recommendation suffer from two drawbacks. First, as
review-based recommendation can be naturally formed as a user-item bipartite
graph with edge features from corresponding user-item reviews, how to better
exploit this unique graph structure for recommendation? Second, while most
current models suffer from limited user behaviors, can we exploit the unique
self-supervised signals in the review-aware graph to guide two recommendation
components better? To this end, in this paper, we propose a novel Review-aware
Graph Contrastive Learning (RGCL) framework for review-based recommendation.
Specifically, we first construct a review-aware user-item graph with
feature-enhanced edges from reviews, where each edge feature is composed of
both the user-item rating and the corresponding review semantics. This graph
with feature-enhanced edges can help attentively learn each neighbor node
weight for user and item representation learning. After that, we design two
additional contrastive learning tasks (i.e., Node Discrimination and Edge
Discrimination) to provide self-supervised signals for the two components in
recommendation process. Finally, extensive experiments over five benchmark
datasets demonstrate the superiority of our proposed RGCL compared to the
state-of-the-art baselines.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:533:" <a href="http://arxiv.org/find/cs/1/au:+Shuai_J/0/1/0/all/0/1">Jie Shuai</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_K/0/1/0/all/0/1">Kun Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_L/0/1/0/all/0/1">Le Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Sun_P/0/1/0/all/0/1">Peijie Sun</a>, <a href="http://arxiv.org/find/cs/1/au:+Hong_R/0/1/0/all/0/1">Richang Hong</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_M/0/1/0/all/0/1">Meng Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_Y/0/1/0/all/0/1">Yong Li</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:97;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12064";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:118:"PP-MARL: Efficient Privacy-Preserving MARL for Cooperative Intelligence in Communication. (arXiv:2204.12064v1 [cs.MA])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12064";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1223:"Artificial intelligence (AI) has been introduced in communication networks
and services to improve efficiency via self-optimization. Cooperative
intelligence (CI), also known as collective intelligence and collaborative
intelligence, is expected to become an integral element in next-generation
networks because it can aggregate the capabilities and intelligence of multiple
devices. However, privacy issues may intimidate, obstruct, and hinder the
deployment of CI in practice because collaboration heavily relies on data and
information sharing. Additional practical constraints in communication (e.g.,
limited bandwidth) further limit the performance of CI. To overcome these
challenges, we propose PP-MARL, an efficient privacy-preserving learning scheme
based on multi-agent reinforcement learning (MARL). We apply and evaluate our
scheme in two communication-related use cases: mobility management in
drone-assisted communication and network control with edge intelligence.
Simulation results reveal that the proposed scheme can achieve efficient and
reliable collaboration with 1.1-6 times better privacy protection and lower
overheads (e.g., 84-91% reduction in bandwidth) than state-of-the-art
approaches.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:241:" <a href="http://arxiv.org/find/cs/1/au:+Yuan_T/0/1/0/all/0/1">Tingting Yuan</a>, <a href="http://arxiv.org/find/cs/1/au:+Chung_H/0/1/0/all/0/1">Hwei-Ming Chung</a>, <a href="http://arxiv.org/find/cs/1/au:+Fu_X/0/1/0/all/0/1">Xiaoming Fu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:98;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12067";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:96:"An Overview of Recent Work in Media Forensics: Methods and Threats. (arXiv:2204.12067v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12067";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:430:"In this paper, we review recent work in media forensics for digital images,
video, audio (specifically speech), and documents. For each data modality, we
discuss synthesis and manipulation techniques that can be used to create and
modify digital media. We then review technological advancements for detecting
and quantifying such manipulations. Finally, we consider open issues and
suggest directions for future research.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:598:" <a href="http://arxiv.org/find/cs/1/au:+Bhagtani_K/0/1/0/all/0/1">Kratika Bhagtani</a>, <a href="http://arxiv.org/find/cs/1/au:+Yadav_A/0/1/0/all/0/1">Amit Kumar Singh Yadav</a>, <a href="http://arxiv.org/find/cs/1/au:+Bartusiak_E/0/1/0/all/0/1">Emily R. Bartusiak</a>, <a href="http://arxiv.org/find/cs/1/au:+Xiang_Z/0/1/0/all/0/1">Ziyue Xiang</a>, <a href="http://arxiv.org/find/cs/1/au:+Shao_R/0/1/0/all/0/1">Ruiting Shao</a>, <a href="http://arxiv.org/find/cs/1/au:+Baireddy_S/0/1/0/all/0/1">Sriram Baireddy</a>, <a href="http://arxiv.org/find/cs/1/au:+Delp_E/0/1/0/all/0/1">Edward J. Delp</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:99;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12069";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:127:"Suggesting Relevant Questions for a Query Using Statistical Natural Language Processing Technique. (arXiv:2204.12069v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12069";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:991:"Suggesting similar questions for a user query has many applications ranging
from reducing search time of users on e-commerce websites, training of
employees in companies to holistic learning for students. The use of Natural
Language Processing techniques for suggesting similar questions is prevalent
over the existing architecture. Mainly two approaches are studied for finding
text similarity namely syntactic and semantic, however each has its draw-backs
and fail to provide the desired outcome. In this article, a self-learning
combined approach is proposed for determining textual similarity that
introduces a robust weighted syntactic and semantic similarity index for
determining similar questions from a predetermined database, this approach
learns the optimal combination of the mentioned approaches for a database under
consideration. Comprehensive analysis has been carried out to justify the
efficiency and efficacy of the proposed approach over the existing literature.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:513:" <a href="http://arxiv.org/find/cs/1/au:+Nayak_S/0/1/0/all/0/1">Shriniwas Nayak</a>, <a href="http://arxiv.org/find/cs/1/au:+Kanetkar_A/0/1/0/all/0/1">Anuj Kanetkar</a>, <a href="http://arxiv.org/find/cs/1/au:+Hirudkar_H/0/1/0/all/0/1">Hrushabh Hirudkar</a>, <a href="http://arxiv.org/find/cs/1/au:+Ghotkar_A/0/1/0/all/0/1">Archana Ghotkar</a>, <a href="http://arxiv.org/find/cs/1/au:+Sonawane_S/0/1/0/all/0/1">Sheetal Sonawane</a>, <a href="http://arxiv.org/find/cs/1/au:+Litake_O/0/1/0/all/0/1">Onkar Litake</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:100;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12070";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:94:"Symlink: A New Dataset for Scientific Symbol-Description Linking. (arXiv:2204.12070v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12070";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:618:"Mathematical symbols and descriptions appear in various forms across document
section boundaries without explicit markup. In this paper, we present a new
large-scale dataset that emphasizes extracting symbols and descriptions in
scientific documents. Symlink annotates scientific papers of 5 different
domains (i.e., computer science, biology, physics, mathematics, and economics).
Our experiments on Symlink demonstrate the challenges of the symbol-description
linking task for existing models and call for further research effort in this
area. We will publicly release Symlink to facilitate future research.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:349:" <a href="http://arxiv.org/find/cs/1/au:+Lai_V/0/1/0/all/0/1">Viet Dac Lai</a>, <a href="http://arxiv.org/find/cs/1/au:+Veyseh_A/0/1/0/all/0/1">Amir Pouran Ben Veyseh</a>, <a href="http://arxiv.org/find/cs/1/au:+Dernoncourt_F/0/1/0/all/0/1">Franck Dernoncourt</a>, <a href="http://arxiv.org/find/cs/1/au:+Nguyen_T/0/1/0/all/0/1">Thien Huu Nguyen</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:101;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12071";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:135:"Modeling the Noticeability of User-Avatar Movement Inconsistency for Sense of Body Ownership Intervention. (arXiv:2204.12071v1 [cs.HC])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12071";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1570:"An avatar mirroring the user's movement is commonly adopted in Virtual
Reality(VR). Maintaining the user-avatar movement consistency provides the user
a sense of body ownership and thus an immersive experience. However, breaking
this consistency can enable new interaction functionalities, such as pseudo
haptic feedback or input augmentation, at the expense of immersion. We propose
to quantify the probability of users noticing the movement inconsistency while
the inconsistency amplitude is being enlarged, which aims to guide the
intervention of the users' sense of body ownership in VR. We applied angular
offsets to the avatar's shoulder and elbow joints and recorded whether the user
identified the inconsistency through a series of three user studies and built a
statistical model based on the results. Results show that the noticeability of
movement inconsistency increases roughly quadratically with the enlargement of
offsets and the offsets at two joints negatively affect the probability
distributions of each other. Leveraging the model, we implemented a technique
that amplifies the user's arm movements with unnoticeable offsets and then
evaluated implementations with different parameters(offset strength, offset
distribution). Results show that the technique with medium-level and
balanced-distributed offsets achieves the best overall performance. Finally, we
demonstrated our model's extendability in interventions in the sense of body
ownership with three VR applications including stroke rehabilitation, action
game and widget arrangement.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:621:" <a href="http://arxiv.org/find/cs/1/au:+Li_Z/0/1/0/all/0/1">Zhipeng Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Jiang_Y/0/1/0/all/0/1">Yu Jiang</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhu_Y/0/1/0/all/0/1">Yihao Zhu</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_R/0/1/0/all/0/1">Ruijia Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_R/0/1/0/all/0/1">Ruolin Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_Y/0/1/0/all/0/1">Yuntao Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Yan_Y/0/1/0/all/0/1">Yukang Yan</a>, <a href="http://arxiv.org/find/cs/1/au:+Shi_Y/0/1/0/all/0/1">Yuanchun Shi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:102;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12072";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:92:"Know Thy Student: Interactive Learning with Gaussian Processes. (arXiv:2204.12072v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12072";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1549:"Learning often involves interaction between multiple agents. Human
teacher-student settings best illustrate how interactions result in efficient
knowledge passing where the teacher constructs a curriculum based on their
students' abilities. Prior work in machine teaching studies how the teacher
should construct optimal teaching datasets assuming the teacher knows
everything about the student. However, in the real world, the teacher doesn't
have complete information about the student. The teacher must interact and
diagnose the student, before teaching. Our work proposes a simple diagnosis
algorithm which uses Gaussian processes for inferring student-related
information, before constructing a teaching dataset. We apply this to two
settings. One is where the student learns from scratch and the teacher must
figure out the student's learning algorithm parameters, eg. the regularization
parameters in ridge regression or support vector machines. Two is where the
student has partially explored the environment and the teacher must figure out
the important areas the student has not explored; we study this in the offline
reinforcement learning setting where the teacher must provide demonstrations to
the student and avoid sending redundant trajectories. Our experiments highlight
the importance of diagosing before teaching and demonstrate how students can
learn more efficiently with the help of an interactive teacher. We conclude by
outlining where diagnosing combined with teaching would be more desirable than
passive learning.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:235:" <a href="http://arxiv.org/find/cs/1/au:+Wang_R/0/1/0/all/0/1">Rose E. Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_M/0/1/0/all/0/1">Mike Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Goodman_N/0/1/0/all/0/1">Noah Goodman</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:103;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12073";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:108:"One-pass additive-error subset selection for $\ell_{p}$ subspace approximation. (arXiv:2204.12073v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12073";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1302:"We consider the problem of subset selection for $\ell_{p}$ subspace
approximation, that is, to efficiently find a \emph{small} subset of data
points such that solving the problem optimally for this subset gives a good
approximation to solving the problem optimally for the original input.
Previously known subset selection algorithms based on volume sampling and
adaptive sampling \cite{DeshpandeV07}, for the general case of $p \in [1,
\infty)$, require multiple passes over the data. In this paper, we give a
one-pass subset selection with an additive approximation guarantee for
$\ell_{p}$ subspace approximation, for any $p \in [1, \infty)$. Earlier subset
selection algorithms that give a one-pass multiplicative $(1+\epsilon)$
approximation work under the special cases. Cohen \textit{et al.}
\cite{CohenMM17} gives a one-pass subset section that offers multiplicative
$(1+\epsilon)$ approximation guarantee for the special case of $\ell_{2}$
subspace approximation. Mahabadi \textit{et al.} \cite{MahabadiRWZ20} gives a
one-pass \emph{noisy} subset selection with $(1+\epsilon)$ approximation
guarantee for $\ell_{p}$ subspace approximation when $p \in \{1, 2\}$. Our
subset selection algorithm gives a weaker, additive approximation guarantee,
but it works for any $p \in [1, \infty)$.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:172:" <a href="http://arxiv.org/find/cs/1/au:+Deshpande_A/0/1/0/all/0/1">Amit Deshpande</a>, <a href="http://arxiv.org/find/cs/1/au:+Pratap_R/0/1/0/all/0/1">Rameshwar Pratap</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:104;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12076";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"ATST: Audio Representation Learning with Teacher-Student Transformer. (arXiv:2204.12076v1 [eess.AS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12076";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:805:"Self-supervised learning (SSL) learns knowledge from a large amount of
unlabeled data, and then transfers the knowledge to a specific problem with a
limited number of labeled data. SSL has achieved promising results in various
domains. This work addresses the problem of segment-level general audio SSL,
and proposes a new transformer-based teacher-student SSL model, named ATST. A
transformer encoder is developed on a recently emerged teacher-student baseline
scheme, which largely improves the modeling capability of pre-training. In
addition, a new strategy for positive pair creation is designed to fully
leverage the capability of transformer. Extensive experiments have been
conducted, and the proposed model achieves the new state-of-the-art results on
almost all of the downstream tasks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:152:" <a href="http://arxiv.org/find/eess/1/au:+Li_X/0/1/0/all/0/1">Xian Li</a>, <a href="http://arxiv.org/find/eess/1/au:+Li_X/0/1/0/all/0/1">Xiaofei Li</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:105;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12077";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:121:"AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images. (arXiv:2204.12077v1 [eess.IV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12077";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1576:"Various deep learning methods have been proposed to segment breast lesion
from ultrasound images. However, similar intensity distributions, variable
tumor morphology and blurred boundaries present challenges for breast lesions
segmentation, especially for malignant tumors with irregular shapes.
Considering the complexity of ultrasound images, we develop an adaptive
attention U-net (AAU-net) to segment breast lesions automatically and stably
from ultrasound images. Specifically, we introduce a hybrid adaptive attention
module, which mainly consists of a channel self-attention block and a spatial
self-attention block, to replace the traditional convolution operation.
Compared with the conventional convolution operation, the design of the hybrid
adaptive attention module can help us capture more features under different
receptive fields. Different from existing attention mechanisms, the hybrid
adaptive attention module can guide the network to adaptively select more
robust representation in channel and space dimensions to cope with more complex
breast lesions segmentation. Extensive experiments with several
state-of-the-art deep learning segmentation methods on three public breast
ultrasound datasets show that our method has better performance on breast
lesion segmentation. Furthermore, robustness analysis and external experiments
demonstrate that our proposed AAU-net has better generalization performance on
the segmentation of breast lesions. Moreover, the hybrid adaptive attention
module can be flexibly applied to existing network frameworks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:322:" <a href="http://arxiv.org/find/eess/1/au:+Chen_G/0/1/0/all/0/1">Gongping Chen</a>, <a href="http://arxiv.org/find/eess/1/au:+Dai_Y/0/1/0/all/0/1">Yu Dai</a>, <a href="http://arxiv.org/find/eess/1/au:+Zhang_J/0/1/0/all/0/1">Jianxun Zhang</a>, <a href="http://arxiv.org/find/eess/1/au:+Yap_M/0/1/0/all/0/1">Moi Hoon Yap</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:106;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12079";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:106:"Exact Wirelength of Embedding 3-Ary n-Cubes into certain Cylinders and Trees. (arXiv:2204.12079v1 [cs.DM])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12079";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:678:"Graph embeddings play a significant role in the design and analysis of
parallel algorithms. It is a mapping of the topological structure of a guest
graph G into a host graph H, which is represented as a one-to-one mapping from
the vertex set of the guest graph to the vertex set of the host graph. In
multiprocessing systems the interconnection networks enhance the efficient
communication between the components in the system. Obtaining minimum
wirelength in embedding problems is significant in the designing of network and
simulating one architecture by another. In this paper, we determine the
wirelength of embedding 3-ary n-cubes into cylinders and certain trees.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:154:" <a href="http://arxiv.org/find/cs/1/au:+S_R/0/1/0/all/0/1">Rajeshwari S</a>, <a href="http://arxiv.org/find/cs/1/au:+Rajesh_M/0/1/0/all/0/1">M Rajesh</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:107;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12081";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:127:"Cyber-Physical Vulnerability Assessment of P2P Energy Exchanges in Active Distribution Networks. (arXiv:2204.12081v1 [eess.SY])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12081";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1166:"Owing to the decreasing costs of distributed energy resources (DERs) as well
as decarbonization policies, power systems are undergoing a modernization
process. The large deployment of DERs together with internet of things (IoT)
devices provide a platform for peer-to-peer (P2P) energy trading in active
distribution networks. However, P2P energy trading with IoT devices have driven
the grid more vulnerable to cyber-physical threats. To this end, in this paper,
a resilience-oriented P2P energy exchange model is developed considering three
phase unbalanced distribution systems. In addition, various scenarios for
vulnerability assessment of P2P energy exchanges considering adverse prosumers
and consumers, who provide false information regarding the price and quantity
with the goal of maximum financial benefit and system operation disruption, are
considered. Techno-economic survivability analysis against these attacks are
investigated on a IEEE 13-node unbalanced distribution test system. Simulation
results demonstrate that adverse peers can affect the physical operation of
grid, maximize their benefits, and cause financial loss of other agents.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:157:" <a href="http://arxiv.org/find/eess/1/au:+Haggi_H/0/1/0/all/0/1">Hamed Haggi</a>, <a href="http://arxiv.org/find/eess/1/au:+Sun_W/0/1/0/all/0/1">Wei Sun</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:108;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12084";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:88:"U-Net with ResNet Backbone for Garment Landmarking Purpose. (arXiv:2204.12084v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12084";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:482:"We build a heatmap-based landmark detection model to locate important
landmarks on 2D RGB garment images. The main goal is to detect edges, corners
and suitable interior region of the garments. This let us re-create 3D garments
in modern 3D editing software by incorporate landmark detection model and
texture unwrapping. We use a U-net architecture with ResNet backbone to build
the model. With an appropriate loss function, we are able to train a moderately
robust model.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:81:" <a href="http://arxiv.org/find/cs/1/au:+Hong_K/0/1/0/all/0/1">Khay Boon Hong</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:109;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12085";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Time Series Prediction by Multi-task GPR with Spatiotemporal Information Transformation. (arXiv:2204.12085v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12085";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1082:"Making an accurate prediction of an unknown system only from a short-term
time series is difficult due to the lack of sufficient information, especially
in a multi-step-ahead manner. However, a high-dimensional short-term time
series contains rich dynamical information, and also becomes increasingly
available in many fields. In this work, by exploiting spatiotemporal
information (STI) transformation scheme that transforms such
high-dimensional/spatial information to temporal information, we developed a
new method called MT-GPRMachine to achieve accurate prediction from a
short-term time series. Specifically, we first construct a specific multi-task
GPR which is multiple linked STI mappings to transform high dimensional/spatial
information into temporal/dynamical information of any given target variable,
and then makes multi step-ahead prediction of the target variable by solving
those STI mappings. The multi-step-ahead prediction results on various
synthetic and real-world datasets clearly validated that MT-GPRMachine
outperformed other existing approaches.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:308:" <a href="http://arxiv.org/find/cs/1/au:+Tao_P/0/1/0/all/0/1">Peng Tao</a>, <a href="http://arxiv.org/find/cs/1/au:+Hao_X/0/1/0/all/0/1">Xiaohu Hao</a>, <a href="http://arxiv.org/find/cs/1/au:+Cheng_J/0/1/0/all/0/1">Jie Cheng</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_L/0/1/0/all/0/1">Luonan Chen</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:110;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12088";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:122:"A physics-informed deep neural network for surrogate modeling in classical elasto-plasticity. (arXiv:2204.12088v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12088";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1485:"In this work, we present a deep neural network architecture that can
efficiently approximate classical elasto-plastic constitutive relations. The
network is enriched with crucial physics aspects of classical
elasto-plasticity, including additive decomposition of strains into elastic and
plastic parts, and nonlinear incremental elasticity. This leads to a
Physics-Informed Neural Network (PINN) surrogate model named here as
Elasto-Plastic Neural Network (EPNN). Detailed analyses show that embedding
these physics into the architecture of the neural network facilitates a more
efficient training of the network with less training data, while also enhancing
the extrapolation capability for loading regimes outside the training data. The
architecture of EPNN is model and material-independent, i.e. it can be adapted
to a wide range of elasto-plastic material types, including geomaterials and
metals; and experimental data can potentially be directly used in training the
network. To demonstrate the robustness of the proposed architecture, we adapt
its general framework to the elasto-plastic behavior of sands. We use synthetic
data generated from material point simulations based on a relatively advanced
dilatancy-based constitutive model for granular materials to train the neural
network. The superiority of EPNN over regular neural network architectures is
explored through predicting unseen strain-controlled loading paths for sands
with different initial densities.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:252:" <a href="http://arxiv.org/find/cs/1/au:+Eghbalian_M/0/1/0/all/0/1">Mahdad Eghbalian</a>, <a href="http://arxiv.org/find/cs/1/au:+Pouragha_M/0/1/0/all/0/1">Mehdi Pouragha</a>, <a href="http://arxiv.org/find/cs/1/au:+Wan_R/0/1/0/all/0/1">Richard Wan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:111;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12089";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:97:"Acquiring a Dynamic Light Field through a Single-Shot Coded Image. (arXiv:2204.12089v1 [eess.IV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12089";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1111:"We propose a method for compressively acquiring a dynamic light field (a 5-D
volume) through a single-shot coded image (a 2-D measurement). We designed an
imaging model that synchronously applies aperture coding and pixel-wise
exposure coding within a single exposure time. This coding scheme enables us to
effectively embed the original information into a single observed image. The
observed image is then fed to a convolutional neural network (CNN) for
light-field reconstruction, which is jointly trained with the camera-side
coding patterns. We also developed a hardware prototype to capture a real 3-D
scene moving over time. We succeeded in acquiring a dynamic light field with
5x5 viewpoints over 4 temporal sub-frames (100 views in total) from a single
observed image. Repeating capture and reconstruction processes over time, we
can acquire a dynamic light field at 4x the frame rate of the camera. To our
knowledge, our method is the first to achieve a finer temporal resolution than
the camera itself in compressive light-field acquisition. Our software is
available from our project webpage

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:525:" <a href="http://arxiv.org/find/eess/1/au:+Mizuno_R/0/1/0/all/0/1">Ryoya Mizuno</a>, <a href="http://arxiv.org/find/eess/1/au:+Takahashi_K/0/1/0/all/0/1">Keita Takahashi</a>, <a href="http://arxiv.org/find/eess/1/au:+Yoshida_M/0/1/0/all/0/1">Michitaka Yoshida</a>, <a href="http://arxiv.org/find/eess/1/au:+Tsutake_C/0/1/0/all/0/1">Chihiro Tsutake</a>, <a href="http://arxiv.org/find/eess/1/au:+Fujii_T/0/1/0/all/0/1">Toshiaki Fujii</a>, <a href="http://arxiv.org/find/eess/1/au:+Nagahara_H/0/1/0/all/0/1">Hajime Nagahara</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:112;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12092";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:104:"Mask scalar prediction for improving robust automatic speech recognition. (arXiv:2204.12092v1 [eess.AS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12092";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1102:"Using neural network based acoustic frontends for improving robustness of
streaming automatic speech recognition (ASR) systems is challenging because of
the causality constraints and the resulting distortion that the frontend
processing introduces in speech. Time-frequency masking based approaches have
been shown to work well, but they need additional hyper-parameters to scale the
mask to limit speech distortion. Such mask scalars are typically hand-tuned and
chosen conservatively. In this work, we present a technique to predict mask
scalars using an ASR-based loss in an end-to-end fashion, with minimal increase
in the overall model size and complexity. We evaluate the approach on two
robust ASR tasks: multichannel enhancement in the presence of speech and
non-speech noise, and acoustic echo cancellation (AEC). Results show that the
presented algorithm consistently improves word error rate (WER) without the
need for any additional tuning over strong baselines that use hand-tuned
hyper-parameters: up to 16% for multichannel enhancement in noisy conditions,
and up to 7% for AEC.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:443:" <a href="http://arxiv.org/find/eess/1/au:+Narayanan_A/0/1/0/all/0/1">Arun Narayanan</a>, <a href="http://arxiv.org/find/eess/1/au:+Walker_J/0/1/0/all/0/1">James Walker</a>, <a href="http://arxiv.org/find/eess/1/au:+Panchapagesan_S/0/1/0/all/0/1">Sankaran Panchapagesan</a>, <a href="http://arxiv.org/find/eess/1/au:+Howard_N/0/1/0/all/0/1">Nathan Howard</a>, <a href="http://arxiv.org/find/eess/1/au:+Koizumi_Y/0/1/0/all/0/1">Yuma Koizumi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:113;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12093";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:99:"Approach to Predicting News -- A Precise Multi-LSTM Network With BERT. (arXiv:2204.12093v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12093";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1907:"Varieties of Democracy (V-Dem) is a new approach to conceptualizing and
measuring democracy and politics. It has information for 200 countries and is
one of the biggest databases for political science. According to the V-Dem
annual democracy report 2019, Taiwan is one of the two countries that got
disseminated false information from foreign governments the most. It also shows
that the "made-up news" has caused a great deal of confusion in Taiwanese
society and has serious impacts on global stability. Although there are several
applications helping distinguish the false information, we found out that the
pre-processing of categorizing the news is still done by human labor. However,
human labor may cause mistakes and cannot work for a long time. The growing
demands for automatic machines in the near decades show that while the machine
can do as good as humans or even better, using machines can reduce humans'
burden and cut down costs. Therefore, in this work, we build a predictive model
to classify the category of news. The corpora we used contains 28358 news and
200 news scraped from the online newspaper Liberty Times Net (LTN) website and
includes 8 categories: Technology, Entertainment, Fashion, Politics, Sports,
International, Finance, and Health. At first, we use Bidirectional Encoder
Representations from Transformers (BERT) for word embeddings which transform
each Chinese character into a (1,768) vector. Then, we use a Long Short-Term
Memory (LSTM) layer to transform word embeddings into sentence embeddings and
add another LSTM layer to transform them into document embeddings. Each
document embedding is an input for the final predicting model, which contains
two Dense layers and one Activation layer. And each document embedding is
transformed into 1 vector with 8 real numbers, then the highest one will
correspond to the 8 news categories with up to 99% accuracy.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:568:" <a href="http://arxiv.org/find/cs/1/au:+Chen_C/0/1/0/all/0/1">Chia-Lin Chen</a> (1), <a href="http://arxiv.org/find/cs/1/au:+Huang_P/0/1/0/all/0/1">Pei-Yu Huang</a> (2), <a href="http://arxiv.org/find/cs/1/au:+Huang_Y/0/1/0/all/0/1">Yi-Ting Huang</a> (3), <a href="http://arxiv.org/find/cs/1/au:+Lin_C/0/1/0/all/0/1">Chun Lin</a> (3) ((1) Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, (2) Management and Digital Innovation, University of London, Singapore, (3) Institute of Information Science, Academia Sinica, Taipei, Taiwan)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:114;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12095";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:81:"PyGOD: A Python Library for Graph Outlier Detection. (arXiv:2204.12095v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12095";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:796:"PyGOD is an open-source Python library for detecting outliers on graph data.
As the first comprehensive library of its kind, PyGOD supports a wide array of
leading graph-based methods for node-, edge-, subgraph-, and graph-level
outlier detection, under a unified, well-documented API designed for use by
both researchers and practitioners. To overcome the scalability issue in large
graphs, we provide advanced functionalities for selected models, including
mini-batch and sampling. PyGOD is equipped with best practices to foster code
reliability and maintainability, including unit testing, continuous
integration, and code coverage. To foster accessibility, PyGOD is released
under a permissive BSD-license at https://github.com/pygod-team/pygod/ and the
Python Package Index (PyPI).

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:1008:" <a href="http://arxiv.org/find/cs/1/au:+Liu_K/0/1/0/all/0/1">Kay Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Dou_Y/0/1/0/all/0/1">Yingtong Dou</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_Y/0/1/0/all/0/1">Yue Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Ding_X/0/1/0/all/0/1">Xueying Ding</a>, <a href="http://arxiv.org/find/cs/1/au:+Hu_X/0/1/0/all/0/1">Xiyang Hu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_R/0/1/0/all/0/1">Ruitong Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Ding_K/0/1/0/all/0/1">Kaize Ding</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_C/0/1/0/all/0/1">Canyu Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Peng_H/0/1/0/all/0/1">Hao Peng</a>, <a href="http://arxiv.org/find/cs/1/au:+Shu_K/0/1/0/all/0/1">Kai Shu</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_G/0/1/0/all/0/1">George H. Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Jia_Z/0/1/0/all/0/1">Zhihao Jia</a>, <a href="http://arxiv.org/find/cs/1/au:+Yu_P/0/1/0/all/0/1">Philip S. Yu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:115;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12100";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"Convergence of neural networks to Gaussian mixture distribution. (arXiv:2204.12100v1 [stat.ML])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12100";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:550:"We give a proof that, under relatively mild conditions, fully-connected
feed-forward deep random neural networks converge to a Gaussian mixture
distribution as only the width of the last hidden layer goes to infinity. We
conducted experiments for a simple model which supports our result. Moreover,
it gives a detailed description of the convergence, namely, the growth of the
last hidden layer gets the distribution closer to the Gaussian mixture, and the
other layer successively get the Gaussian mixture closer to the normal
distribution.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:256:" <a href="http://arxiv.org/find/stat/1/au:+Asao_Y/0/1/0/all/0/1">Yasuhiko Asao</a>, <a href="http://arxiv.org/find/stat/1/au:+Sakamoto_R/0/1/0/all/0/1">Ryotaro Sakamoto</a>, <a href="http://arxiv.org/find/stat/1/au:+Takagi_S/0/1/0/all/0/1">Shiro Takagi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:116;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12103";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:113:"Centimeter-level Positioning by Instantaneous Lidar-aided GNSS Ambiguity Resolution. (arXiv:2204.12103v1 [cs.RO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12103";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1277:"High-precision vehicle positioning is key to the implementation of modern
driving systems in urban environments. Global Navigation Satellite System
(GNSS) carrier phase measurements can provide millimeter- to centimeter-level
positioning, provided that the integer ambiguities are correctly resolved.
Abundant code measurements are often used to facilitate integer ambiguity
resolution (IAR), however, they suffer from signal blockage and multipath in
urban canyons. In this contribution, a lidar-aided instantaneous ambiguity
resolution method is proposed. Lidar measurements, in the form of 3D keypoints,
are generated by a learning-based point cloud registration method using a
pre-built HD map and integrated with GNSS observations in a mixed measurement
model to produce precise float solutions, which in turn increase the ambiguity
success rate. Closed-form expressions of the ambiguity variance matrix and the
associated Ambiguity Dilution of Precision (ADOP) are developed to provide a
priori evaluation of such lidar-aided ambiguity resolution performance. Both
analytical and experimental results show that the proposed method enables
successful instantaneous IAR with limited GNSS satellites and frequencies,
leading to centimeter-level vehicle positioning.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:263:" <a href="http://arxiv.org/find/cs/1/au:+Zhang_J/0/1/0/all/0/1">Junjie Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Khodabandeh_A/0/1/0/all/0/1">Amir Khodabandeh</a>, <a href="http://arxiv.org/find/cs/1/au:+Khoshelham_K/0/1/0/all/0/1">Kourosh Khoshelham</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:117;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12105";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:82:"Learning Dual-Pixel Alignment for Defocus Deblurring. (arXiv:2204.12105v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12105";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1744:"It is a challenging task to recover all-in-focus image from a single defocus
blurry image in real-world applications. On many modern cameras, dual-pixel
(DP) sensors create two-image views, based on which stereo information can be
exploited to benefit defocus deblurring. Despite existing DP defocus deblurring
methods achieving impressive results, they directly take naive concatenation of
DP views as input, while neglecting the disparity between left and right views
in the regions out of camera's depth of field (DoF). In this work, we propose a
Dual-Pixel Alignment Network (DPANet) for defocus deblurring. Generally, DPANet
is an encoder-decoder with skip-connections, where two branches with shared
parameters in the encoder are employed to extract and align deep features from
left and right views, and one decoder is adopted to fuse aligned features for
predicting the all-in-focus image. Due to that DP views suffer from different
blur amounts, it is not trivial to align left and right views. To this end, we
propose novel encoder alignment module (EAM) and decoder alignment module
(DAM). In particular, a correlation layer is suggested in EAM to measure the
disparity between DP views, whose deep features can then be accordingly aligned
using deformable convolutions. And DAM can further enhance the alignment of
skip-connected features from encoder and deep features in decoder. By
introducing several EAMs and DAMs, DP views in DPANet can be well aligned for
better predicting latent all-in-focus image. Experimental results on real-world
datasets show that our DPANet is notably superior to state-of-the-art
deblurring methods in reducing defocus blur while recovering visually plausible
sharp structures and textures.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:376:" <a href="http://arxiv.org/find/cs/1/au:+Li_Y/0/1/0/all/0/1">Yu Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Yi_Y/0/1/0/all/0/1">Yaling Yi</a>, <a href="http://arxiv.org/find/cs/1/au:+Ren_D/0/1/0/all/0/1">Dongwei Ren</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_Q/0/1/0/all/0/1">Qince Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Zuo_W/0/1/0/all/0/1">Wangmeng Zuo</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:118;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12106";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:91:"Razumikhin and Krasovskii Approaches for Safe Stabilization. (arXiv:2204.12106v1 [eess.SY])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12106";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1240:"This paper studies the stabilization and safety problems of nonlinear
time-delay systems. Following both Razumikhin and Krasovskii approaches, we
propose novel control Lyapunov functions/functionals for the stabilization
problem and novel control barrier functions/functionals for the safety problem.
The proposed control Lyapunov and barrier functions/functionals extend the
existing ones from the delay-free case to the time-delay case, and allow for
designing the stabilizing and safety controllers in closed-form. Since
analytical solutions to time-delay optimal control problems are hard to be
achieved, a sliding mode control based approach is developed to merge the
proposed control Lyapunov and barrier functions/functionals. Based on the
sliding surface functional, a feedback control law is established to
investigate the stabilization and safety objectives simultaneously. In
particular, the properties of the sliding surface functional are analyzed, and
further how to construct the sliding surface functional is discussed. Finally,
the proposed approaches are illustrated via two numerical examples from the
connected cruise control problem of automotive systems and the synchronization
problem of multi-agent systems.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:263:" <a href="http://arxiv.org/find/eess/1/au:+Ren_W/0/1/0/all/0/1">Wei Ren</a>, <a href="http://arxiv.org/find/eess/1/au:+Jungers_R/0/1/0/all/0/1">Raphael M. Jungers</a>, <a href="http://arxiv.org/find/eess/1/au:+Dimarogonas_D/0/1/0/all/0/1">Dimos V. Dimarogonas</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:119;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12108";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:136:"Toward Consistent and Efficient Map-based Visual-inertial Localization: Theory Framework and Filter Design. (arXiv:2204.12108v1 [cs.RO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12108";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1166:"This paper focuses on designing a consistent and efficient filter for
map-based visual-inertial localization. First, we propose a new Lie group with
its algebra, based on which a novel invariant extended Kalman filter (invariant
EKF) is designed. We theoretically prove that, when we do not consider the
uncertainty of the map information, the proposed invariant EKF can naturally
maintain the correct observability properties of the system. To consider the
uncertainty of the map information, we introduce a Schmidt filter. With the
Schmidt filter, the uncertainty of the map information can be taken into
consideration to avoid over-confident estimation while the computation cost
only increases linearly with the size of the map keyframes. In addition, we
introduce an easily implemented observability-constrained technique because
directly combining the invariant EKF with the Schmidt filter cannot maintain
the correct observability properties of the system that considers the
uncertainty of the map information. Finally, we validate our proposed system's
high consistency, accuracy, and efficiency via extensive simulations and
real-world experiments.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:396:" <a href="http://arxiv.org/find/cs/1/au:+Zhang_Z/0/1/0/all/0/1">Zhuqing Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Song_Y/0/1/0/all/0/1">Yang Song</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_S/0/1/0/all/0/1">Shoudong Huang</a>, <a href="http://arxiv.org/find/cs/1/au:+Xiong_R/0/1/0/all/0/1">Rong Xiong</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_Y/0/1/0/all/0/1">Yue Wang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:120;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12109";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:94:"Instance-Specific Feature Propagation for Referring Segmentation. (arXiv:2204.12109v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12109";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1289:"Referring segmentation aims to generate a segmentation mask for the target
instance indicated by a natural language expression. There are typically two
kinds of existing methods: one-stage methods that directly perform segmentation
on the fused vision and language features; and two-stage methods that first
utilize an instance segmentation model for instance proposal and then select
one of these instances via matching them with language features. In this work,
we propose a novel framework that simultaneously detects the target-of-interest
via feature propagation and generates a fine-grained segmentation mask. In our
framework, each instance is represented by an Instance-Specific Feature (ISF),
and the target-of-referring is identified by exchanging information among all
ISFs using our proposed Feature Propagation Module (FPM). Our instance-aware
approach learns the relationship among all objects, which helps to better
locate the target-of-interest than one-stage methods. Comparing to two-stage
methods, our approach collaboratively and interactively utilizes both vision
and language information for synchronous identification and segmentation. In
the experimental tests, our method outperforms previous state-of-the-art
methods on all three RefCOCO series datasets.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:236:" <a href="http://arxiv.org/find/cs/1/au:+Liu_C/0/1/0/all/0/1">Chang Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Jiang_X/0/1/0/all/0/1">Xudong Jiang</a>, <a href="http://arxiv.org/find/cs/1/au:+Ding_H/0/1/0/all/0/1">Henghui Ding</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:121;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12111";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification. (arXiv:2204.12111v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12111";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1031:"The relation classification is to identify semantic relations between two
entities in a given text. While existing models perform well for classifying
inverse relations with large datasets, their performance is significantly
reduced for few-shot learning. In this paper, we propose a function words
adaptively enhanced attention framework (FAEA) for few-shot inverse relation
classification, in which a hybrid attention model is designed to attend
class-related function words based on meta-learning. As the involvement of
function words brings in significant intra-class redundancy, an adaptive
message passing mechanism is introduced to capture and transfer inter-class
differences.We mathematically analyze the negative impact of function words
from dot-product measurement, which explains why message passing mechanism
effectively reduces the impact. Our experimental results show that FAEA
outperforms strong baselines, especially the inverse relation accuracy is
improved by 14.33% under 1-shot setting in FewRel1.0.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:395:" <a href="http://arxiv.org/find/cs/1/au:+Dou_C/0/1/0/all/0/1">Chunliu Dou</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_S/0/1/0/all/0/1">Shaojuan Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_X/0/1/0/all/0/1">Xiaowang Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Feng_Z/0/1/0/all/0/1">Zhiyong Feng</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_K/0/1/0/all/0/1">Kewen Wang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:122;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12112";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:125:"Reformulating Speaker Diarization as Community Detection With Emphasis On Topological Structure. (arXiv:2204.12112v1 [cs.SD])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12112";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1225:"Clustering-based speaker diarization has stood firm as one of the major
approaches in reality, despite recent development in end-to-end diarization.
However, clustering methods have not been explored extensively for speaker
diarization. Commonly-used methods such as k-means, spectral clustering, and
agglomerative hierarchical clustering only take into account properties such as
proximity and relative densities. In this paper we propose to view
clustering-based diarization as a community detection problem. By doing so the
topological structure is considered. This work has four major contributions.
First it is shown that Leiden community detection algorithm significantly
outperforms the previous methods on the clustering of speaker-segments. Second,
we propose to use uniform manifold approximation to reduce dimension while
retaining global and local topological structure. Third, a masked filtering
approach is introduced to extract "clean" speaker embeddings. Finally, the
community structure is applied to an end-to-end post-processing network to
obtain diarization results. The final system presents a relative DER reduction
of up to 70 percent. The breakdown contribution of each component is analyzed.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:156:" <a href="http://arxiv.org/find/cs/1/au:+Zheng_S/0/1/0/all/0/1">Siqi Zheng</a>, <a href="http://arxiv.org/find/cs/1/au:+Suo_H/0/1/0/all/0/1">Hongbin Suo</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:123;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12115";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:102:"Fast Successive-Cancellation Decoding of Polar Codes with Sequence Nodes. (arXiv:2204.12115v1 [cs.IT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12115";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1240:"Due to the sequential nature of the successive-cancellation (SC) algorithm,
the decoding of polar codes suffers from significant decoding latencies. Fast
SC decoding is able to speed up the SC decoding process, by implementing
parallel decoders at the intermediate levels of the SC decoding tree for some
special nodes with specific information and frozen bit patterns. To further
improve the parallelism of SC decoding, this paper present a new class of
special node composed of a sequence of rate one or single-parity-check
(SR1/SPC) nodes, which envelops a wide variety of existing special node types.
Then, we analyse the parity constraints caused by the frozen bits in each
descendant node, such that the decoding performance of the SR1/SPC node can be
preserved once the parity constraints are satisfied. Finally, a generalized
fast SC decoding algorithm is proposed for the SR1/SPC node, where the
corresponding parity constraints are taken into consideration. Simulation
results show that compared with the state-of-the-art fast SC decoding
algorithms, the proposed decoding algorithm achieves a higher degree of
parallelism, especially for high-rate polar codes, without tangibly altering
the error-correction performance.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:309:" <a href="http://arxiv.org/find/cs/1/au:+Lu_Y/0/1/0/all/0/1">Yang Lu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_M/0/1/0/all/0/1">Ming-Min Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Lei_M/0/1/0/all/0/1">Ming Lei</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_M/0/1/0/all/0/1">Min-Jian Zhao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:124;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12117";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:91:"On an Invariance Problem for Parameterized Concurrent Systems. (arXiv:2204.12117v1 [cs.LO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12117";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1093:"We consider concurrent systems consisting of replicated finite-state
processes that synchronize via joint interactions in a network with
user-defined topology. The system is specified using a resource logic with a
multiplicative connective and inductively defined predicates, reminiscent of
Separation Logic. The problem we consider is if a given formula in this logic
defines an invariant, namely whether any model of the formula, following an
arbitrary firing sequence of interactions, is transformed into another model of
the same formula. This property, called \emph{havoc invariance}, is
quintessential in proving the correctness of reconfiguration programs that
change the structure of the network at runtime. We show that the havoc
invariance problem is many-one reducible to the entailment problem $\phi
\models \psi$, asking if any model of $\phi$ is also a model of $\psi$.
Although, in general, havoc invariance is found to be undecidable, this
reduction allows to prove that havoc invariance is in 2EXP, for a general
fragment of the logic, with a 2EXP entailment problem.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:239:" <a href="http://arxiv.org/find/cs/1/au:+Bozga_M/0/1/0/all/0/1">Marius Bozga</a>, <a href="http://arxiv.org/find/cs/1/au:+Bueri_L/0/1/0/all/0/1">Lucas Bueri</a>, <a href="http://arxiv.org/find/cs/1/au:+Iosif_R/0/1/0/all/0/1">Radu Iosif</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:125;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12120";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:105:"Automated Generation of High-Performance Computational Fluid Dynamics Codes. (arXiv:2204.12120v1 [cs.MS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12120";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:873:"Domain-Specific Languages (DSLs) improve programmers productivity by
decoupling problem descriptions from algorithmic implementations. However, DSLs
for High-Performance Computing (HPC) have two critical non-functional
requirements: performance and scalability. This paper presents the automated
process of generating, from abstract mathematical specifications of
Computational Fluid Dynamics (CFD) problems, optimised parallel codes that
perform and scale as manually optimised ones. We consciously combine within
Saiph, a DSL for solving CFD problem, low-level optimisations and
parallelization strategies, enabling high-performance single-core executions
which effectively scale to multi-core and distributed environments. Our results
demonstrate how high-level DSLs can offer competitive performance by
transparently leveraging state-of-the-art HPC techniques.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:381:" <a href="http://arxiv.org/find/cs/1/au:+Macia_S/0/1/0/all/0/1">Sandra Maci&#xe0;</a>, <a href="http://arxiv.org/find/cs/1/au:+Martiinez_Ferrer_P/0/1/0/all/0/1">Pedro J. Mart&#x131;&#xed;nez-Ferrer</a>, <a href="http://arxiv.org/find/cs/1/au:+Ayguade_E/0/1/0/all/0/1">Eduard Ayguad&#xe9;</a>, <a href="http://arxiv.org/find/cs/1/au:+Beltran_V/0/1/0/all/0/1">Vicen&#xe7; Beltran</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:126;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12121";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:106:"Cross-media Scientific Research Achievements Query based on Ranking Learning. (arXiv:2204.12121v1 [cs.IR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12121";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1114:"With the advent of the information age, the scale of data on the Internet is
getting larger and larger, and it is full of text, images, videos, and other
information. Different from social media data and news data, scientific
research achievements information has the characteristics of many proper nouns
and strong ambiguity. The traditional single-mode query method based on
keywords can no longer meet the needs of scientific researchers and managers of
the Ministry of Science and Technology. Scientific research project information
and scientific research scholar information contain a large amount of valuable
scientific research achievement information. Evaluating the output capability
of scientific research projects and scientific research teams can effectively
assist managers in decision-making. In view of the above background, this paper
expounds on the research status from four aspects: characteristic learning of
scientific research results, cross-media research results query, ranking
learning of scientific research results, and cross-media scientific research
achievement query system.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:230:" <a href="http://arxiv.org/find/cs/1/au:+Wang_B/0/1/0/all/0/1">Benzhi Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Liang_M/0/1/0/all/0/1">Meiyu Liang</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_A/0/1/0/all/0/1">Ang Li</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:127;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12125";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:104:"A Robust Contrastive Alignment Method For Multi-Domain Text Classification. (arXiv:2204.12125v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12125";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1064:"Multi-domain text classification can automatically classify texts in various
scenarios. Due to the diversity of human languages, texts with the same label
in different domains may differ greatly, which brings challenges to the
multi-domain text classification. Current advanced methods use the
private-shared paradigm, capturing domain-shared features by a shared encoder,
and training a private encoder for each domain to extract domain-specific
features. However, in realistic scenarios, these methods suffer from
inefficiency as new domains are constantly emerging. In this paper, we propose
a robust contrastive alignment method to align text classification features of
various domains in the same feature space by supervised contrastive learning.
By this means, we only need two universal feature extractors to achieve
multi-domain text classification. Extensive experimental results show that our
method performs on par with or sometimes better than the state-of-the-art
method, which uses the complex multi-classifier in a private-shared framework.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:623:" <a href="http://arxiv.org/find/cs/1/au:+Li_X/0/1/0/all/0/1">Xuefeng Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Lei_H/0/1/0/all/0/1">Hao Lei</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_L/0/1/0/all/0/1">Liwen Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Dong_G/0/1/0/all/0/1">Guanting Dong</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_J/0/1/0/all/0/1">Jinzheng Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_J/0/1/0/all/0/1">Jiachi Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_W/0/1/0/all/0/1">Weiran Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_C/0/1/0/all/0/1">Chunyun Zhang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:128;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12129";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:70:"Mirror Games Against an Open Book Player. (arXiv:2204.12129v1 [cs.CC])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12129";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1069:"Mirror games were invented by Garg and Schnieder (ITCS 2019). Alice and Bob
take turns (with Alice playing first) in declaring numbers from the set {1,2,
...2n}. If a player picks a number that was previously played, that player
loses and the other player wins. If all numbers are declared without
repetition, the result is a draw. Bob has a simple mirror strategy that assures
he won't lose and requires no memory. On the other hand, Garg and Schenier
showed that every deterministic Alice needs memory of size linear in $n$ in
order to secure a draw.

Regarding probabilistic strategies, previous work showed that a model where
Alice has access to a secret random perfect matching over {1,2, ...2n} allows
her to achieve a draw in the game w.p. a least 1-1/n and using only polylog
bits of memory.

We show that the requirement for secret bits is crucial: for an `open book'
Alice with no secrets (Bob knows her memory but not future coin flips) and
memory of at most n/4c bits for any c&gt;2, there is a Bob that wins w.p. close to
1-2^{-c/2}.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:155:" <a href="http://arxiv.org/find/cs/1/au:+Magen_R/0/1/0/all/0/1">Roey Magen</a>, <a href="http://arxiv.org/find/cs/1/au:+Naor_M/0/1/0/all/0/1">Moni Naor</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:129;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12130";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:131:"LM-Debugger: An Interactive Tool for Inspection and Intervention in Transformer-Based Language Models. (arXiv:2204.12130v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12130";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1242:"The opaque nature and unexplained behavior of transformer-based language
models (LMs) have spurred a wide interest in interpreting their predictions.
However, current interpretation methods mostly focus on probing models from
outside, executing behavioral tests, and analyzing salience input features,
while the internal prediction construction process is largely not understood.
In this work, we introduce LM-Debugger, an interactive debugger tool for
transformer-based LMs, which provides a fine-grained interpretation of the
model's internal prediction process, as well as a powerful framework for
intervening in LM behavior. For its backbone, LM-Debugger relies on a recent
method that interprets the inner token representations and their updates by the
feed-forward layers in the vocabulary space. We demonstrate the utility of
LM-Debugger for single-prediction debugging, by inspecting the internal
disambiguation process done by GPT2. Moreover, we show how easily LM-Debugger
allows to shift model behavior in a direction of the user's choice, by
identifying a few vectors in the network and inducing effective interventions
to the prediction process. We release LM-Debugger as an open-source tool and a
demo over GPT2 models.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:638:" <a href="http://arxiv.org/find/cs/1/au:+Geva_M/0/1/0/all/0/1">Mor Geva</a>, <a href="http://arxiv.org/find/cs/1/au:+Caciularu_A/0/1/0/all/0/1">Avi Caciularu</a>, <a href="http://arxiv.org/find/cs/1/au:+Dar_G/0/1/0/all/0/1">Guy Dar</a>, <a href="http://arxiv.org/find/cs/1/au:+Roit_P/0/1/0/all/0/1">Paul Roit</a>, <a href="http://arxiv.org/find/cs/1/au:+Sadde_S/0/1/0/all/0/1">Shoval Sadde</a>, <a href="http://arxiv.org/find/cs/1/au:+Shlain_M/0/1/0/all/0/1">Micah Shlain</a>, <a href="http://arxiv.org/find/cs/1/au:+Tamir_B/0/1/0/all/0/1">Bar Tamir</a>, <a href="http://arxiv.org/find/cs/1/au:+Goldberg_Y/0/1/0/all/0/1">Yoav Goldberg</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:130;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12133";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:83:"Non-determinsitic algebraic rewriting as adjunction. (arXiv:2204.12133v1 [math.LO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12133";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:721:"We develop a general model theoretic semantics to rewriting beyond the usual
confluence and termination assumptions. This is based on preordered algebra
which is a model theory that extends many sorted algebra. In this framework we
characterise rewriting in arbitrary algebras rather than term algebras (called
algebraic rewriting) as a persistent adjunction and use this result, on the one
hand for proving the soundness and the completeness of an abstract
computational model of rewriting that underlies the non-deterministic
programming with Maude and CafeOBJ, and on the other hand for developing a
compositionality result for algebraic rewriting in the context of the
pushout-based modularisation technique.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:98:" <a href="http://arxiv.org/find/math/1/au:+Diaconescu_R/0/1/0/all/0/1">R&#x103;zvan Diaconescu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:131;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12139";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"Neural Maximum A Posteriori Estimation on Unpaired Data for Motion Deblurring. (arXiv:2204.12139v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12139";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1335:"Real-world dynamic scene deblurring has long been a challenging task since
paired blurry-sharp training data is unavailable. Conventional Maximum A
Posteriori estimation and deep learning-based deblurring methods are restricted
by handcrafted priors and synthetic blurry-sharp training pairs respectively,
thereby failing to generalize to real dynamic blurriness. To this end, we
propose a Neural Maximum A Posteriori (NeurMAP) estimation framework for
training neural networks to recover blind motion information and sharp content
from unpaired data. The proposed NeruMAP consists of a motion estimation
network and a deblurring network which are trained jointly to model the
(re)blurring process (i.e. likelihood function). Meanwhile, the motion
estimation network is trained to explore the motion information in images by
applying implicit dynamic motion prior, and in return enforces the deblurring
network training (i.e. providing sharp image prior). The proposed NeurMAP is an
orthogonal approach to existing deblurring neural networks, and is the first
framework that enables training image deblurring networks on unpaired datasets.
Experiments demonstrate our superiority on both quantitative metrics and visual
quality over state-of-the-art methods. Codes are available on
https://github.com/yjzhang96/NeurMAP-deblur.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:239:" <a href="http://arxiv.org/find/cs/1/au:+Zhang_Y/0/1/0/all/0/1">Youjian Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_C/0/1/0/all/0/1">Chaoyue Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Tao_D/0/1/0/all/0/1">Dacheng Tao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:132;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12143";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:93:"Deeper Insights into ViTs Robustness towards Common Corruptions. (arXiv:2204.12143v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12143";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1137:"Recent literature have shown design strategies from Convolutions Neural
Networks (CNNs) benefit Vision Transformers (ViTs) in various vision tasks.
However, it remains unclear how these design choices impact on robustness when
transferred to ViTs. In this paper, we make the first attempt to investigate
how CNN-like architectural designs and CNN-based data augmentation strategies
impact on ViTs' robustness towards common corruptions through an extensive and
rigorous benchmarking. We demonstrate that overlapping patch embedding and
convolutional Feed-Forward Network (FFN) boost performance on robustness.
Furthermore, adversarial noise training is powerful on ViTs while
fourier-domain augmentation fails. Moreover, we introduce a novel conditional
method enabling input-varied augmentations from two angles: (1) Generating
dynamic augmentation parameters conditioned on input images. It conduces to
state-of-the-art performance on robustness through conditional convolutions;
(2) Selecting most suitable augmentation strategy by an extra predictor helps
to achieve the best trade-off between clean accuracy and robustness.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:376:" <a href="http://arxiv.org/find/cs/1/au:+Tian_R/0/1/0/all/0/1">Rui Tian</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_Z/0/1/0/all/0/1">Zuxuan Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Dai_Q/0/1/0/all/0/1">Qi Dai</a>, <a href="http://arxiv.org/find/cs/1/au:+Hu_H/0/1/0/all/0/1">Han Hu</a>, <a href="http://arxiv.org/find/cs/1/au:+Jiang_Y/0/1/0/all/0/1">Yugang Jiang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:133;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12144";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:112:"Motion Planning and Robust Tracking for the Heat Equation using Boundary Control. (arXiv:2204.12144v1 [math.OC])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12144";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1467:"Robust output tracking is addressed in this paper for a heat equation with
Neumann boundary conditions and anti-collocated boundary input and output. The
desired reference tracking is solved using the well-known flatness and Lyapunov
approaches. The reference profile is obtained by solving the motion planning
problem for the nominal plant. To robustify the closed-loop system in the
presence of the disturbances and uncertainties, it is then augmented with PI
feedback plus a discontinuous component responsible for rejecting matched
disturbances with \textit{a priori} known magnitude bounds. Such control law
only requires the information of the system at the same boundary as the control
input is located. The resulting dynamic controller globally exponentially
stabilizes the error dynamics while also attenuating the influence of
Lipschitz-in-time external disturbances and parameter uncertainties. For the
case when the motion planning is performed over the uncertain plant, an
exponential Input-to-State Stability is obtained, preserving the boundedness of
the tracking error norm. The proposed controller relies on a discontinuous term
that however passes through an integrator, thereby minimizing the chattering
effect in the plant dynamics. The performance of the closed-loop system, thus
designed, is illustrated in simulations under different kinds of reference
trajectories in the presence of external disturbances and parameter
uncertainties.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:366:" <a href="http://arxiv.org/find/math/1/au:+Gutierrez_Oribio_D/0/1/0/all/0/1">Diego Guti&#xe9;rrez-Oribio</a>, <a href="http://arxiv.org/find/math/1/au:+Orlov_Y/0/1/0/all/0/1">Yury Orlov</a>, <a href="http://arxiv.org/find/math/1/au:+Stefanou_I/0/1/0/all/0/1">Ioannis Stefanou</a>, <a href="http://arxiv.org/find/math/1/au:+Plestan_F/0/1/0/all/0/1">Franck Plestan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:134;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12148";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:93:"Morest: Model-based RESTful API Testing with Execution Feedback. (arXiv:2204.12148v1 [cs.SE])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12148";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1380:"RESTful APIs are arguably the most popular endpoints for accessing Web
services. Blackbox testing is one of the emerging techniques for ensuring the
reliability of RESTful APIs. The major challenge in testing RESTful APIs is the
need for correct sequences of API operation calls for in-depth testing. To
build meaningful operation call sequences, researchers have proposed techniques
to learn and utilize the API dependencies based on OpenAPI specifications.
However, these techniques either lack the overall awareness of how all the APIs
are connected or the flexibility of adaptively fixing the learned knowledge. In
this paper, we propose Morest, a model-based RESTful API testing technique that
builds and maintains a dynamically updating RESTful-service Property Graph
(RPG) to model the behaviors of RESTful-services and guide the call sequence
generation. We empirically evaluated Morest and the results demonstrate that
Morest can successfully request an average of 152.66%-232.45% more API
operations, cover 26.16%-103.24% more lines of code, and detect 40.64%-215.94%
more bugs than state-of-the-art techniques. In total, we applied Morest to 6
real-world projects and found 44 bugs (13 of them cannot be detected by
existing approaches). Specifically, 2 of the confirmed bugs are from Bitbucket,
a famous code management service with more than 6 million users.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:682:" <a href="http://arxiv.org/find/cs/1/au:+Liu_Y/0/1/0/all/0/1">Yi Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_Y/0/1/0/all/0/1">Yuekang Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Deng_G/0/1/0/all/0/1">Gelei Deng</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_Y/0/1/0/all/0/1">Yang Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Wan_R/0/1/0/all/0/1">Ruiyuan Wan</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_R/0/1/0/all/0/1">Runchao Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Ji_D/0/1/0/all/0/1">Dandan Ji</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_S/0/1/0/all/0/1">Shiheng Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Bao_M/0/1/0/all/0/1">Minli Bao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:135;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12150";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:85:"Where and What: Driver Attention-based Object Detection. (arXiv:2204.12150v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12150";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1038:"Human drivers use their attentional mechanisms to focus on critical objects
and make decisions while driving. As human attention can be revealed from gaze
data, capturing and analyzing gaze information has emerged in recent years to
benefit autonomous driving technology. Previous works in this context have
primarily aimed at predicting "where" human drivers look at and lack knowledge
of "what" objects drivers focus on. Our work bridges the gap between
pixel-level and object-level attention prediction. Specifically, we propose to
integrate an attention prediction module into a pretrained object detection
framework and predict the attention in a grid-based style. Furthermore,
critical objects are recognized based on predicted attended-to areas. We
evaluate our proposed method on two driver attention datasets, BDD-A and
DR(eye)VE. Our framework achieves competitive state-of-the-art performance in
the attention prediction on both pixel-level and object-level but is far more
efficient (75.3 GFLOPs less) in computation.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:342:" <a href="http://arxiv.org/find/cs/1/au:+Rong_Y/0/1/0/all/0/1">Yao Rong</a>, <a href="http://arxiv.org/find/cs/1/au:+Kassautzki_N/0/1/0/all/0/1">Naemi-Rebecca Kassautzki</a>, <a href="http://arxiv.org/find/cs/1/au:+Fuhl_W/0/1/0/all/0/1">Wolfgang Fuhl</a>, <a href="http://arxiv.org/find/cs/1/au:+Kasneci_E/0/1/0/all/0/1">Enkelejda Kasneci</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:136;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12151";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:83:"ClothFormer:Taming Video Virtual Try-on in All Module. (arXiv:2204.12151v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12151";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1549:"The task of video virtual try-on aims to fit the target clothes to a person
in the video with spatio-temporal consistency. Despite tremendous progress of
image virtual try-on, they lead to inconsistency between frames when applied to
videos. Limited work also explored the task of video-based virtual try-on but
failed to produce visually pleasing and temporally coherent results. Moreover,
there are two other key challenges: 1) how to generate accurate warping when
occlusions appear in the clothing region; 2) how to generate clothes and
non-target body parts (e.g. arms, neck) in harmony with the complicated
background; To address them, we propose a novel video virtual try-on framework,
ClothFormer, which successfully synthesizes realistic, harmonious, and
spatio-temporal consistent results in complicated environment. In particular,
ClothFormer involves three major modules. First, a two-stage anti-occlusion
warping module that predicts an accurate dense flow mapping between the body
regions and the clothing regions. Second, an appearance-flow tracking module
utilizes ridge regression and optical flow correction to smooth the dense flow
sequence and generate a temporally smooth warped clothing sequence. Third, a
dual-stream transformer extracts and fuses clothing textures, person features,
and environment information to generate realistic try-on videos. Through
rigorous experiments, we demonstrate that our method highly surpasses the
baselines in terms of synthesized video quality both qualitatively and
quantitatively.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:307:" <a href="http://arxiv.org/find/cs/1/au:+Jiang_J/0/1/0/all/0/1">Jianbin Jiang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_T/0/1/0/all/0/1">Tan Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Yan_H/0/1/0/all/0/1">He Yan</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_J/0/1/0/all/0/1">Junhui Liu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:137;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12153";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"Cybertwin-enabled 6G Space-air-ground Integrated Networks: Architecture, Open Issue, and Challenges. (arXiv:2204.12153v1 [cs.NI])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12153";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1156:"Space-air-ground integrated network (SAGIN) is considered as a core
requirement in emerging 6G networks, which integrates the terrestrial and
non-terrestrial networks to reach the full network coverage and ubiquitous
services. To envision the ubiquitous intelligence and the deep integration in
6G SAGIN, a paradigm of cybertwin-enabled 6G SAGIN is presented in this paper.
Specifically, a cybertwin-enabled SAGIN architecture is first presented, where
a novel five-dimension digital twin (DT) model is presented. Particularly,
three categories of critical technologies are presented based on the cybertwin
of SAGIN, i.e., cybertwin-based multi-source heterogeneous network integration,
cybertwin-based integrated cloud-edge-end, and cybertwin-based integrated
sensing-communication-computing. Besides, two open issues in the
cybertwin-enabled SAGIN are studied, i.e., the networking decision and
optimization and the cybertwin-enabled cross-layer privacy and security, where
the challenges are discussed and the potential solutions are directed. In
addition, a case study with federal learning is developed and open research
issues are discussed.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:388:" <a href="http://arxiv.org/find/cs/1/au:+Yin_Z/0/1/0/all/0/1">Zhisheng Yin</a>, <a href="http://arxiv.org/find/cs/1/au:+Luan_T/0/1/0/all/0/1">Tom H. Luan</a>, <a href="http://arxiv.org/find/cs/1/au:+Cheng_N/0/1/0/all/0/1">Nan Cheng</a>, <a href="http://arxiv.org/find/cs/1/au:+Hui_Y/0/1/0/all/0/1">Yilong Hui</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_W/0/1/0/all/0/1">Wei Wang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:138;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12155";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:78:"Bias-Variance Decompositions for Margin Losses. (arXiv:2204.12155v1 [stat.ML])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12155";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:802:"We introduce a novel bias-variance decomposition for a range of strictly
convex margin losses, including the logistic loss (minimized by the classic
LogitBoost algorithm), as well as the squared margin loss and canonical
boosting loss. Furthermore, we show that, for all strictly convex margin
losses, the expected risk decomposes into the risk of a "central" model and a
term quantifying variation in the functional margin with respect to variations
in the training data. These decompositions provide a diagnostic tool for
practitioners to understand model overfitting/underfitting, and have
implications for additive ensemble models -- for example, when our
bias-variance decomposition holds, there is a corresponding "ambiguity"
decomposition, which can be used to quantify model diversity.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:240:" <a href="http://arxiv.org/find/stat/1/au:+Wood_D/0/1/0/all/0/1">Danny Wood</a>, <a href="http://arxiv.org/find/stat/1/au:+Mu_T/0/1/0/all/0/1">Tingting Mu</a>, <a href="http://arxiv.org/find/stat/1/au:+Brown_G/0/1/0/all/0/1">Gavin Brown</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:139;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12156";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Source-independent quantum random number generator against detector blinding attacks. (arXiv:2204.12156v1 [quant-ph])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12156";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1020:"Randomness, mainly in the form of random numbers, is the fundamental
prerequisite for the security of many cryptographic tasks. Quantum randomness
can be extracted even if adversaries are fully aware of the protocol and even
control the randomness source. However, an adversary can further manipulate the
randomness via detector blinding attacks, which are a hacking attack suffered
by protocols with trusted detectors. Here, by treating no-click events as valid
error events, we propose a quantum random number generation protocol that can
simultaneously address source vulnerability and ferocious detector blinding
attacks. The method can be extended to high-dimensional random number
generation. We experimentally demonstrate the ability of our protocol to
generate random numbers for two-dimensional measurement with a generation speed
of 0.515 Mbps, which is two orders of magnitude higher than that of
device-independent protocols that can address both issues of imperfect sources
and imperfect detectors.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:670:" <a href="http://arxiv.org/find/quant-ph/1/au:+Liu_W/0/1/0/all/0/1">Wen-Bo Liu</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Lu_Y/0/1/0/all/0/1">Yu-Shuo Lu</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Fu_Y/0/1/0/all/0/1">Yao Fu</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Huang_S/0/1/0/all/0/1">Si-Cheng Huang</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Yin_Z/0/1/0/all/0/1">Ze-Jie Yin</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Jiang_K/0/1/0/all/0/1">Kun Jiang</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Yin_H/0/1/0/all/0/1">Hua-Lei Yin</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Chen_Z/0/1/0/all/0/1">Zeng-Bing Chen</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:140;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12158";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:101:"Mixed Strategies for Security Games with General Defending Requirements. (arXiv:2204.12158v1 [cs.GT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12158";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1378:"The Stackelberg security game is played between a defender and an attacker,
where the defender needs to allocate a limited amount of resources to multiple
targets in order to minimize the loss due to adversarial attack by the
attacker. While allowing targets to have different values, classic settings
often assume uniform requirements to defend the targets. This enables existing
results that study mixed strategies (randomized allocation algorithms) to adopt
a compact representation of the mixed strategies.

In this work, we initiate the study of mixed strategies for the security
games in which the targets can have different defending requirements. In
contrast to the case of uniform defending requirement, for which an optimal
mixed strategy can be computed efficiently, we show that computing the optimal
mixed strategy is NP-hard for the general defending requirements setting.
However, we show that strong upper and lower bounds for the optimal mixed
strategy defending result can be derived. We propose an efficient
close-to-optimal Patching algorithm that computes mixed strategies that use
only few pure strategies. We also study the setting when the game is played on
a network and resource sharing is enabled between neighboring targets. Our
experimental results demonstrate the effectiveness of our algorithm in several
large real-world datasets.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:460:" <a href="http://arxiv.org/find/cs/1/au:+Bai_R/0/1/0/all/0/1">Rufan Bai</a>, <a href="http://arxiv.org/find/cs/1/au:+Lin_H/0/1/0/all/0/1">Haoxing Lin</a>, <a href="http://arxiv.org/find/cs/1/au:+Yang_X/0/1/0/all/0/1">Xinyu Yang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_X/0/1/0/all/0/1">Xiaowei Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_M/0/1/0/all/0/1">Minming Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Jia_W/0/1/0/all/0/1">Weijia Jia</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:141;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12159";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression. (arXiv:2204.12159v1 [cs.NE])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12159";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1376:"Currently, the genetic programming version of the gene-pool optimal mixing
evolutionary algorithm (GP-GOMEA) is among the top-performing algorithms for
symbolic regression (SR). A key strength of GP-GOMEA is its way of performing
variation, which dynamically adapts to the emergence of patterns in the
population. However, GP-GOMEA lacks a mechanism to optimize coefficients. In
this paper, we study how fairly simple approaches for optimizing coefficients
can be integrated into GP-GOMEA. In particular, we considered two variants of
Gaussian coefficient mutation. We performed experiments using different
settings on 23 benchmark problems, and used machine learning to estimate what
aspects of coefficient mutation matter most. We find that the most important
aspect is that the number of coefficient mutation attempts needs to be
commensurate with the number of mixing operations that GP-GOMEA performs. We
applied GP-GOMEA with the best-performing coefficient mutation approach to the
data sets of SRBench, a large SR benchmark, for which a ground-truth underlying
equation is known. We find that coefficient mutation can help re-discovering
the underlying equation by a substantial amount, but only when no noise is
added to the target variable. In the presence of noise, GP-GOMEA with
coefficient mutation discovers alternative but similarly-accurate equations.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:173:" <a href="http://arxiv.org/find/cs/1/au:+Virgolin_M/0/1/0/all/0/1">Marco Virgolin</a>, <a href="http://arxiv.org/find/cs/1/au:+Bosman_P/0/1/0/all/0/1">Peter A. N. Bosman</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:142;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12160";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"A Reduced Order Model for Joint Assemblies by Hyper-Reduction and Model-Driven Sampling. (arXiv:2204.12160v1 [cs.CE])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12160";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1633:"The dynamic behavior of jointed assemblies exhibiting friction nonlinearities
features amplitude-dependent dissipation and stiffness. To develop numerical
simulations for predictive and design purposes, macro-scale High Fidelity
Models (HFMs) of the contact interfaces are required. However, the high
computational cost of such HFMs impedes the feasibility of the simulations. To
this end, we propose a model-driven method for constructing hyper-reduced order
models of such assemblies. Focusing on steady-state analysis, we use the
Multi-Harmonic Balance Method (MHBM) to formulate the equations of motion in
frequency domain. The reduction basis is constructed through solving a set of
vibration problems corresponding to fictitious interface conditions.
Subsequently, a Galerkin projection reduces the order of the model.
Nonetheless, the necessary fine discretization of the interfaces represents a
bottleneck for achieving high speedups. For this reason, we implement an
adapted Energy Conserving Weighing and Sampling (ECSW) technique for Hyper
Reduction (HR), thereby allowing significant speedups for meshes of arbitrary
fineness. This feature is particularly advantageous since analysts typically
encounter a trade-off between accuracy and computational cost when deciding on
the mesh size, whose estimation is particularly challenging for problems of
this type. To assess the accuracy of our method without resorting to the HF
solution, we propose an error indicator with thresholds that have proven
reliable in our analyses. Finally, the accuracy and efficiency of the method
are demonstrated by two case studies.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:242:" <a href="http://arxiv.org/find/cs/1/au:+Morsy_A/0/1/0/all/0/1">Ahmed Amr Morsy</a>, <a href="http://arxiv.org/find/cs/1/au:+Kast_M/0/1/0/all/0/1">Mariella Kast</a>, <a href="http://arxiv.org/find/cs/1/au:+Tiso_P/0/1/0/all/0/1">Paolo Tiso</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:143;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12162";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:83:"Budgeted Out-tree Maximization with Submodular Prizes. (arXiv:2204.12162v1 [cs.DS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12162";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1728:"We consider a variant of the prize collecting Steiner tree problem in which
we are given a \emph{directed graph} $D=(V,A)$, a monotone submodular prize
function $p:2^V \rightarrow \mathbb{R}^+ \cup \{0\}$, a cost function $c:V
\rightarrow \mathbb{Z}^{+}$, a root vertex $r \in V$, and a budget $B$. The aim
is to find an out-subtree $T$ of $D$ rooted at $r$ that costs at most $B$ and
maximizes the prize function. We call this problem \emph{Directed Rooted
Submodular Out-tree} (\textbf{DRSO}).

Very recently, Ghuge and Nagarajan [SODA\ 2020] gave a quasi-polynomial-time
$O\left(\frac{\log n'}{\log \log n'}\right)$-approximation algorithm for the
case in which the costs are associated to the edges, where $n'$ is the number
of vertices in an optimal solution.

In this paper we give a polynomial-time algorithm for \textbf{DRSO} that
guarantees an approximation factor of $O(\sqrt{B}/\epsilon^3)$ at the cost of a
budget violation of a factor $1+\epsilon$, for any $\epsilon \in (0,1]$. The
same result holds for the edge-cost case, to our knowledge this is the first
polynomial-time approximation algorithm for this case.

We further show that the unrooted version of \textbf{DRSO} can be
approximated to a factor of $O(\sqrt{B})$ without budget violation, which is an
improvement over the factor $O(\Delta \sqrt{B})$ given in~[Kuo et al.\
IEEE/ACM\ Trans.\ Netw.\ 2015] for the undirected and unrooted case, where
$\Delta$ is the maximum degree of the graph. Finally, we provide some
new/improved approximation bounds for several related problems, including the
additive-prize version of \textbf{DRSO}, the maximum budgeted connected set
cover problem, and the budgeted sensor cover problem.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:266:" <a href="http://arxiv.org/find/cs/1/au:+DAngelo_G/0/1/0/all/0/1">Gianlorenzo D&#x27;Angelo</a>, <a href="http://arxiv.org/find/cs/1/au:+Delfaraz_E/0/1/0/all/0/1">Esmaeil Delfaraz</a>, <a href="http://arxiv.org/find/cs/1/au:+Gilbert_H/0/1/0/all/0/1">Hugo Gilbert</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:144;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12164";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:89:"CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets. (arXiv:2204.12164v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12164";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1429:"Over the course of the COVID-19 pandemic, large volumes of biomedical
information concerning this new disease have been published on social media.
Some of this information can pose a real danger to people's health,
particularly when false information is shared, for instance recommendations on
how to treat diseases without professional medical advice. Therefore, automatic
fact-checking resources and systems developed specifically for the medical
domain are crucial. While existing fact-checking resources cover
COVID-19-related information in news or quantify the amount of misinformation
in tweets, there is no dataset providing fact-checked COVID-19-related Twitter
posts with detailed annotations for biomedical entities, relations and relevant
evidence. We contribute CoVERT, a fact-checked corpus of tweets with a focus on
the domain of biomedicine and COVID-19-related (mis)information. The corpus
consists of 300 tweets, each annotated with medical named entities and
relations. We employ a novel crowdsourcing methodology to annotate all tweets
with fact-checking labels and supporting evidence, which crowdworkers search
for online. This methodology results in moderate inter-annotator agreement.
Furthermore, we use the retrieved evidence extracts as part of a fact-checking
pipeline, finding that the real-world evidence is more useful than the
knowledge indirectly available in pretrained language models.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:250:" <a href="http://arxiv.org/find/cs/1/au:+Mohr_I/0/1/0/all/0/1">Isabelle Mohr</a>, <a href="http://arxiv.org/find/cs/1/au:+Wuhrl_A/0/1/0/all/0/1">Amelie W&#xfc;hrl</a>, <a href="http://arxiv.org/find/cs/1/au:+Klinger_R/0/1/0/all/0/1">Roman Klinger</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:145;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12165";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:114:"When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?. (arXiv:2204.12165v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12165";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:888:"Word alignment has proven to benefit many-to-many neural machine translation
(NMT). However, high-quality ground-truth bilingual dictionaries were used for
pre-editing in previous methods, which are unavailable for most language pairs.
Meanwhile, the contrastive objective can implicitly utilize automatically
learned word alignment, which has not been explored in many-to-many NMT. This
work proposes a word-level contrastive objective to leverage word alignments
for many-to-many NMT. Empirical results show that this leads to 0.8 BLEU gains
for several language pairs. Analyses reveal that in many-to-many NMT, the
encoder's sentence retrieval performance highly correlates with the translation
quality, which explains when the proposed method impacts translation. This
motivates future exploration for many-to-many NMT to improve the encoder's
sentence retrieval performance.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:476:" <a href="http://arxiv.org/find/cs/1/au:+Mao_Z/0/1/0/all/0/1">Zhuoyuan Mao</a>, <a href="http://arxiv.org/find/cs/1/au:+Chu_C/0/1/0/all/0/1">Chenhui Chu</a>, <a href="http://arxiv.org/find/cs/1/au:+Dabre_R/0/1/0/all/0/1">Raj Dabre</a>, <a href="http://arxiv.org/find/cs/1/au:+Song_H/0/1/0/all/0/1">Haiyue Song</a>, <a href="http://arxiv.org/find/cs/1/au:+Wan_Z/0/1/0/all/0/1">Zhen Wan</a>, <a href="http://arxiv.org/find/cs/1/au:+Kurohashi_S/0/1/0/all/0/1">Sadao Kurohashi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:146;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12169";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:152:"Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemia. (arXiv:2204.12169v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12169";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1121:"Lower-and-middle income countries are faced with challenges arising from a
lack of data on cause of death (COD), which can limit decisions on population
health and disease management. A verbal autopsy(VA) can provide information
about a COD in areas without robust death registration systems. A VA consists
of structured data, combining numeric and binary features, and unstructured
data as part of an open-ended narrative text. This study assesses the
performance of various machine learning approaches when analyzing both the
structured and unstructured components of the VA report. The algorithms were
trained and tested via cross-validation in the three settings of binary
features, text features and a combination of binary and text features derived
from VA reports from rural South Africa. The results obtained indicate
narrative text features contain valuable information for determining COD and
that a combination of binary and text features improves the automated COD
classification task.

Keywords: Diabetes Mellitus, Verbal Autopsy, Cause of Death, Machine
Learning, Natural Language Processing

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:325:" <a href="http://arxiv.org/find/cs/1/au:+Manaka_T/0/1/0/all/0/1">Thokozile Manaka</a>, <a href="http://arxiv.org/find/cs/1/au:+Zyl_T/0/1/0/all/0/1">Terence Van Zyl</a>, <a href="http://arxiv.org/find/cs/1/au:+Wade_A/0/1/0/all/0/1">Alisha N Wade</a>, <a href="http://arxiv.org/find/cs/1/au:+Kar_D/0/1/0/all/0/1">Deepak Kar</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:147;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12173";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:96:"Map-based Visual-Inertial Localization: Consistency and Complexity. (arXiv:2204.12173v1 [cs.RO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12173";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1761:"Drift-free localization is essential for autonomous vehicles. In this paper,
we address the problem by proposing a filter-based framework, which integrates
the visual-inertial odometry and the measurements of the features in the
pre-built map. In this framework, the transformation between the odometry frame
and the map frame is augmented into the state and estimated on the fly.
Besides, we maintain only the keyframe poses in the map and employ Schmidt
extended Kalman filter to update the state partially, so that the uncertainty
of the map information can be consistently considered with low computational
cost. Moreover, we theoretically demonstrate that the ever-changing
linearization points of the estimated state can introduce spurious information
to the augmented system and make the original four-dimensional unobservable
subspace vanish, leading to inconsistent estimation in practice. To relieve
this problem, we employ first-estimate Jacobian (FEJ) to maintain the correct
observability properties of the augmented system. Furthermore, we introduce an
observability-constrained updating method to compensate for the significant
accumulated error after the long-term absence (can be 3 minutes and 1 km) of
map-based measurements. Through simulations, the consistent estimation of our
proposed algorithm is validated. Through real-world experiments, we demonstrate
that our proposed algorithm runs successfully on four kinds of datasets with
the lower computational cost (20% time-saving) and the better estimation
accuracy (45% trajectory error reduction) compared with the baseline algorithm
VINS-Fusion, whereas VINS-Fusion fails to give bounded localization performance
on three of four datasets because of its inconsistent estimation.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:398:" <a href="http://arxiv.org/find/cs/1/au:+Zhang_Z/0/1/0/all/0/1">Zhuqing Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Jiao_Y/0/1/0/all/0/1">Yanmei Jiao</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_S/0/1/0/all/0/1">Shoudong Huang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_Y/0/1/0/all/0/1">Yue Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Xiong_R/0/1/0/all/0/1">Rong Xiong</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:148;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12176";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:85:"Cross Pairwise Ranking for Unbiased Item Recommendation. (arXiv:2204.12176v1 [cs.IR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12176";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1537:"Most recommender systems optimize the model on observed interaction data,
which is affected by the previous exposure mechanism and exhibits many biases
like popularity bias. The loss functions, such as the mostly used pointwise
Binary Cross-Entropy and pairwise Bayesian Personalized Ranking, are not
designed to consider the biases in observed data. As a result, the model
optimized on the loss would inherit the data biases, or even worse, amplify the
biases. For example, a few popular items take up more and more exposure
opportunities, severely hurting the recommendation quality on niche items --
known as the notorious Mathew effect. In this work, we develop a new learning
paradigm named Cross Pairwise Ranking (CPR) that achieves unbiased
recommendation without knowing the exposure mechanism. Distinct from inverse
propensity scoring (IPS), we change the loss term of a sample -- we
innovatively sample multiple observed interactions once and form the loss as
the combination of their predictions. We prove in theory that this way offsets
the influence of user/item propensity on the learning, removing the influence
of data biases caused by the exposure mechanism. Advantageous to IPS, our
proposed CPR ensures unbiased learning for each training instance without the
need of setting the propensity scores. Experimental results demonstrate the
superiority of CPR over state-of-the-art debiasing solutions in both model
generalization and training efficiency. The codes are available at
https://github.com/Qcactus/CPR.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:457:" <a href="http://arxiv.org/find/cs/1/au:+Wan_Q/0/1/0/all/0/1">Qi Wan</a>, <a href="http://arxiv.org/find/cs/1/au:+He_X/0/1/0/all/0/1">Xiangnan He</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_X/0/1/0/all/0/1">Xiang Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_J/0/1/0/all/0/1">Jiancan Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Guo_W/0/1/0/all/0/1">Wei Guo</a>, <a href="http://arxiv.org/find/cs/1/au:+Tang_R/0/1/0/all/0/1">Ruiming Tang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:149;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12177";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"A Comparative Study on Approaches to Acoustic Scene Classification using CNNs. (arXiv:2204.12177v1 [cs.SD])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12177";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1019:"Acoustic scene classification is a process of characterizing and classifying
the environments from sound recordings. The first step is to generate features
(representations) from the recorded sound and then classify the background
environments. However, different kinds of representations have dramatic effects
on the accuracy of the classification. In this paper, we explored the three
such representations on classification accuracy using neural networks. We
investigated the spectrograms, MFCCs, and embeddings representations using
different CNN networks and autoencoders. Our dataset consists of sounds from
three settings of indoors and outdoors environments - thus the dataset contains
sound from six different kinds of environments. We found that the spectrogram
representation has the highest classification accuracy while MFCC has the
lowest classification accuracy. We reported our findings, insights as well as
some guidelines to achieve better accuracy for environment classification using
sounds.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:356:" <a href="http://arxiv.org/find/cs/1/au:+Ananya_I/0/1/0/all/0/1">Ishrat Jahan Ananya</a>, <a href="http://arxiv.org/find/cs/1/au:+Suad_S/0/1/0/all/0/1">Sarah Suad</a>, <a href="http://arxiv.org/find/cs/1/au:+Choudhury_S/0/1/0/all/0/1">Shadab Hafiz Choudhury</a>, <a href="http://arxiv.org/find/cs/1/au:+Khan_M/0/1/0/all/0/1">Mohammad Ashrafuzzaman Khan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:150;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12181";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:160:"Collaborative Target Search with a Visual Drone Swarm: An Adaptive Curriculum Embedded Multi-stage Reinforcement Learning Approach. (arXiv:2204.12181v1 [cs.RO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12181";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1147:"Equipping drones with target search capabilities is desirable for
applications in disaster management scenarios and smart warehouse delivery
systems. Instead of deploying a single drone, an intelligent drone swarm that
can collaborate with one another in maneuvering among obstacles will be more
effective in accomplishing the target search in a shorter amount of time. In
this work, we propose a data-efficient reinforcement learning-based approach,
Adaptive Curriculum Embedded Multi-Stage Learning (ACEMSL), to address the
challenges of carrying out a collaborative target search with a visual drone
swarm, namely the 3D sparse reward space exploration and the collaborative
behavior requirement. Specifically, we develop an adaptive embedded curriculum,
where the task difficulty level can be adaptively adjusted according to the
success rate achieved in training. Meanwhile, with multi-stage learning, ACEMSL
allows data-efficient training and individual-team reward allocation for the
collaborative drone swarm. The effectiveness and generalization capability of
our approach are validated using simulations and actual flight tests.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:255:" <a href="http://arxiv.org/find/cs/1/au:+Xiao_J/0/1/0/all/0/1">Jiaping Xiao</a>, <a href="http://arxiv.org/find/cs/1/au:+Pisutsin_P/0/1/0/all/0/1">Phumrapee Pisutsin</a>, <a href="http://arxiv.org/find/cs/1/au:+Feroskhan_M/0/1/0/all/0/1">Mir Feroskhan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:151;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12184";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:122:"SkillNet-NLG: General-Purpose Natural Language Generation with a Sparsely Activated Approach. (arXiv:2204.12184v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12184";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:944:"We present SkillNet-NLG, a sparsely activated approach that handles many
natural language generation tasks with one model. Different from traditional
dense models that always activate all the parameters, SkillNet-NLG selectively
activates relevant parts of the parameters to accomplish a task, where the
relevance is controlled by a set of predefined skills. The strength of such
model design is that it provides an opportunity to precisely adapt relevant
skills to learn new tasks effectively. We evaluate on Chinese natural language
generation tasks. Results show that, with only one model file, SkillNet-NLG
outperforms previous best performance methods on four of five tasks.
SkillNet-NLG performs better than two multi-task learning baselines (a dense
model and a Mixture-of-Expert model) and achieves comparable performance to
task-specific models. Lastly, SkillNet-NLG surpasses baseline systems when
being adapted to new tasks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:311:" <a href="http://arxiv.org/find/cs/1/au:+Liao_J/0/1/0/all/0/1">Junwei Liao</a>, <a href="http://arxiv.org/find/cs/1/au:+Tang_D/0/1/0/all/0/1">Duyu Tang</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_F/0/1/0/all/0/1">Fan Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Shi_S/0/1/0/all/0/1">Shuming Shi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:152;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12185";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:122:"TranSiam: Fusing Multimodal Visual Features Using Transformer for Medical Image Segmentation. (arXiv:2204.12185v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12185";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1422:"Automatic segmentation of medical images based on multi-modality is an
important topic for disease diagnosis. Although the convolutional neural
network (CNN) has been proven to have excellent performance in image
segmentation tasks, it is difficult to obtain global information. The lack of
global information will seriously affect the accuracy of the segmentation
results of the lesion area. In addition, there are visual representation
differences between multimodal data of the same patient. These differences will
affect the results of the automatic segmentation methods. To solve these
problems, we propose a segmentation method suitable for multimodal medical
images that can capture global information, named TranSiam. TranSiam is a 2D
dual path network that extracts features of different modalities. In each path,
we utilize convolution to extract detailed information in low level stage, and
design a ICMT block to extract global information in high level stage. ICMT
block embeds convolution in the transformer, which can extract global
information while retaining spatial and detailed information. Furthermore, we
design a novel fusion mechanism based on cross attention and selfattention,
called TMM block, which can effectively fuse features between different
modalities. On the BraTS 2019 and BraTS 2020 multimodal datasets, we have a
significant improvement in accuracy over other popular methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:304:" <a href="http://arxiv.org/find/cs/1/au:+Li_X/0/1/0/all/0/1">Xuejian Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Ma_S/0/1/0/all/0/1">Shiqiang Ma</a>, <a href="http://arxiv.org/find/cs/1/au:+Tang_J/0/1/0/all/0/1">Jijun Tang</a>, <a href="http://arxiv.org/find/cs/1/au:+Guo_F/0/1/0/all/0/1">Fei Guo</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:153;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12186";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:135:"Faster and Better Grammar-based Text-to-SQL Parsing via Clause-level Parallel Decoding and Alignment Loss. (arXiv:2204.12186v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12186";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:648:"Grammar-based parsers have achieved high performance in the cross-domain
text-to-SQL parsing task, but suffer from low decoding efficiency due to the
much larger number of actions for grammar selection than that of tokens in SQL
queries. Meanwhile, how to better align SQL clauses and question segments has
been a key challenge for parsing performance. Therefore, this paper proposes
clause-level parallel decoding and alignment loss to enhance two
high-performance grammar-based parsers, i.e., RATSQL and LGESQL. Experimental
results of two parsers show that our method obtains consistent improvements
both in accuracy and decoding speed.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:305:" <a href="http://arxiv.org/find/cs/1/au:+Wu_K/0/1/0/all/0/1">Kun Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_L/0/1/0/all/0/1">Lijie Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_Z/0/1/0/all/0/1">Zhenghua Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Xiao_X/0/1/0/all/0/1">Xinyan Xiao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:154;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12190";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:130:"Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method. (arXiv:2204.12190v1 [cs.AI])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12190";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:917:"How to coordinate the communication among intersections effectively in real
complex traffic scenarios with multi-intersection is challenging. Existing
approaches only enable the communication in a heuristic manner without
considering the content/importance of information to be shared. In this paper,
we propose a universal communication form UniComm between intersections.
UniComm embeds massive observations collected at one agent into crucial
predictions of their impact on its neighbors, which improves the communication
efficiency and is universal across existing methods. We also propose a concise
network UniLight to make full use of communications enabled by UniComm.
Experimental results on real datasets demonstrate that UniComm universally
improves the performance of existing state-of-the-art methods, and UniLight
significantly outperforms existing methods on a wide range of traffic
situations.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:392:" <a href="http://arxiv.org/find/cs/1/au:+Jiang_Q/0/1/0/all/0/1">Qize Jiang</a>, <a href="http://arxiv.org/find/cs/1/au:+Qin_M/0/1/0/all/0/1">Minhao Qin</a>, <a href="http://arxiv.org/find/cs/1/au:+Shi_S/0/1/0/all/0/1">Shengmin Shi</a>, <a href="http://arxiv.org/find/cs/1/au:+Sun_W/0/1/0/all/0/1">Weiwei Sun</a>, <a href="http://arxiv.org/find/cs/1/au:+Zheng_B/0/1/0/all/0/1">Baihua Zheng</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:155;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12191";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:92:"EmpHi: Generating Empathetic Responses with Human-like Intents. (arXiv:2204.12191v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12191";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:939:"In empathetic conversations, humans express their empathy to others with
empathetic intents. However, most existing empathetic conversational methods
suffer from a lack of empathetic intents, which leads to monotonous empathy. To
address the bias of the empathetic intents distribution between empathetic
dialogue models and humans, we propose a novel model to generate empathetic
responses with human-consistent empathetic intents, EmpHi for short. Precisely,
EmpHi learns the distribution of potential empathetic intents with a discrete
latent variable, then combines both implicit and explicit intent representation
to generate responses with various empathetic intents. Experiments show that
EmpHi outperforms state-of-the-art models in terms of empathy, relevance, and
diversity on both automatic and human evaluation. Moreover, the case studies
demonstrate the high interpretability and outstanding performance of our model.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:232:" <a href="http://arxiv.org/find/cs/1/au:+Chen_M/0/1/0/all/0/1">Mao Yan Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_S/0/1/0/all/0/1">Siheng Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Yang_Y/0/1/0/all/0/1">Yujiu Yang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:156;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12193";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams. (arXiv:2204.12193v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12193";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1299:"Devising intelligent agents able to live in an environment and learn by
observing the surroundings is a longstanding goal of Artificial Intelligence.
From a bare Machine Learning perspective, challenges arise when the agent is
prevented from leveraging large fully-annotated dataset, but rather the
interactions with supervisory signals are sparsely distributed over space and
time. This paper proposes a novel neural-network-based approach to
progressively and autonomously develop pixel-wise representations in a video
stream. The proposed method is based on a human-like attention mechanism that
allows the agent to learn by observing what is moving in the attended
locations. Spatio-temporal stochastic coherence along the attention trajectory,
paired with a contrastive term, leads to an unsupervised learning criterion
that naturally copes with the considered setting. Differently from most
existing works, the learned representations are used in open-set
class-incremental classification of each frame pixel, relying on few
supervisions. Our experiments leverage 3D virtual environments and they show
that the proposed agents can learn to distinguish objects just by observing the
video stream. Inheriting features from state-of-the art models is not as
powerful as one might expect.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:500:" <a href="http://arxiv.org/find/cs/1/au:+Tiezzi_M/0/1/0/all/0/1">Matteo Tiezzi</a>, <a href="http://arxiv.org/find/cs/1/au:+Marullo_S/0/1/0/all/0/1">Simone Marullo</a>, <a href="http://arxiv.org/find/cs/1/au:+Faggi_L/0/1/0/all/0/1">Lapo Faggi</a>, <a href="http://arxiv.org/find/cs/1/au:+Meloni_E/0/1/0/all/0/1">Enrico Meloni</a>, <a href="http://arxiv.org/find/cs/1/au:+Betti_A/0/1/0/all/0/1">Alessandro Betti</a>, <a href="http://arxiv.org/find/cs/1/au:+Melacci_S/0/1/0/all/0/1">Stefano Melacci</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:157;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12195";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:97:"Understanding User Satisfaction with Task-oriented Dialogue Systems. (arXiv:2204.12195v1 [cs.IR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12195";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1490:"$ $Dialogue systems are evaluated depending on their type and purpose. Two
categories are often distinguished: (1) task-oriented dialogue systems (TDS),
which are typically evaluated on utility, i.e., their ability to complete a
specified task, and (2) open domain chatbots, which are evaluated on the user
experience, i.e., based on their ability to engage a person. What is the
influence of user experience on the user satisfaction rating of TDS as opposed
to, or in addition to, utility? We collect data by providing an additional
annotation layer for dialogues sampled from the ReDial dataset, a widely used
conversational recommendation dataset. Unlike prior work, we annotate the
sampled dialogues at both the turn and dialogue level on six dialogue aspects:
relevance, interestingness, understanding, task completion, efficiency, and
interest arousal. The annotations allow us to study how different dialogue
aspects influence user satisfaction. We introduce a comprehensive set of user
experience aspects derived from the annotators' open comments that can
influence users' overall impression. We find that the concept of satisfaction
varies across annotators and dialogues, and show that a relevant turn is
significant for some annotators, while for others, an interesting turn is all
they need. Our analysis indicates that the proposed user experience aspects
provide a fine-grained analysis of user satisfaction that is not captured by a
monolithic overall human rating.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:261:" <a href="http://arxiv.org/find/cs/1/au:+Siro_C/0/1/0/all/0/1">Clemencia Siro</a>, <a href="http://arxiv.org/find/cs/1/au:+Aliannejadi_M/0/1/0/all/0/1">Mohammad Aliannejadi</a>, <a href="http://arxiv.org/find/cs/1/au:+Rijke_M/0/1/0/all/0/1">Maarten de Rijke</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:158;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12196";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:63:"Adaptive Split-Fusion Transformer. (arXiv:2204.12196v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12196";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1527:"Neural networks for visual content understanding have recently evolved from
convolutional ones (CNNs) to transformers. The prior (CNN) relies on
small-windowed kernels to capture the regional clues, demonstrating solid local
expressiveness. On the contrary, the latter (transformer) establishes
long-range global connections between localities for holistic learning.
Inspired by this complementary nature, there is a growing interest in designing
hybrid models to best utilize each technique. Current hybrids merely replace
convolutions as simple approximations of linear projection or juxtapose a
convolution branch with attention, without concerning the importance of
local/global modeling. To tackle this, we propose a new hybrid named Adaptive
Split-Fusion Transformer (ASF-former) to treat convolutional and attention
branches differently with adaptive weights. Specifically, an ASF-former encoder
equally splits feature channels into half to fit dual-path inputs. Then, the
outputs of dual-path are fused with weighting scalars calculated from visual
cues. We also design the convolutional path compactly for efficiency concerns.
Extensive experiments on standard benchmarks, such as ImageNet-1K, CIFAR-10,
and CIFAR-100, show that our ASF-former outperforms its CNN, transformer
counterparts, and hybrid pilots in terms of accuracy (83.9% on ImageNet-1K),
under similar conditions (12.9G MACs/56.7M Params, without large-scale
pre-training). The code is available at:
https://github.com/szx503045266/ASF-former.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:471:" <a href="http://arxiv.org/find/cs/1/au:+Su_Z/0/1/0/all/0/1">Zixuan Su</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_H/0/1/0/all/0/1">Hao Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_J/0/1/0/all/0/1">Jingjing Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Pang_L/0/1/0/all/0/1">Lei Pang</a>, <a href="http://arxiv.org/find/cs/1/au:+Ngo_C/0/1/0/all/0/1">Chong-Wah Ngo</a>, <a href="http://arxiv.org/find/cs/1/au:+Jiang_Y/0/1/0/all/0/1">Yu-Gang Jiang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:159;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12197";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:105:"Evaluating Automatic Difficulty Estimation of Logic Formalization Exercises. (arXiv:2204.12197v1 [cs.LO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12197";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1183:"Teaching logic effectively requires an understanding of the factors which
cause logic students to struggle. Formalization exercises, which require the
student to produce a formula corresponding to the natural language sentence,
are a good candidate for scrutiny since they tap into the students'
understanding of various aspects of logic. We correlate the difficulty of
formalization exercises predicted by a previously proposed difficulty
estimation algorithm with two empirical difficulty measures on the Grade
Grinder corpus, which contains student solutions to FOL exercises. We obtain a
moderate correlation with both measures, suggesting that the said algorithm
indeed taps into important sources of difficulty but leaves a fair amount of
variance uncaptured. We conduct an error analysis, closely examining exercises
which were misclassified, with the aim of identifying additional sources of
difficulty. We identify three additional factors which emerge from the
difficulty analysis, namely predicate complexity, pragmatic factors and
typicality of the exercises, and discuss the implications of automated
difficulty estimation for logic teaching and explainable AI.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:168:" <a href="http://arxiv.org/find/cs/1/au:+Mayn_A/0/1/0/all/0/1">Alexandra Mayn</a>, <a href="http://arxiv.org/find/cs/1/au:+Deemter_K/0/1/0/all/0/1">Kees van Deemter</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:160;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12200";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:76:"Hypergraph Contrastive Collaborative Filtering. (arXiv:2204.12200v1 [cs.IR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12200";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1736:"Collaborative Filtering (CF) has emerged as fundamental paradigms for
parameterizing users and items into latent representation space, with their
correlative patterns from interaction data. Among various CF techniques, the
development of GNN-based recommender systems, e.g., PinSage and LightGCN, has
offered the state-of-the-art performance. However, two key challenges have not
been well explored in existing solutions: i) The over-smoothing effect with
deeper graph-based CF architecture, may cause the indistinguishable user
representations and degradation of recommendation results. ii) The supervision
signals (i.e., user-item interactions) are usually scarce and skewed
distributed in reality, which limits the representation power of CF paradigms.
To tackle these challenges, we propose a new self-supervised recommendation
framework Hypergraph Contrastive Collaborative Filtering (HCCF) to jointly
capture local and global collaborative relations with a hypergraph-enhanced
cross-view contrastive learning architecture. In particular, the designed
hypergraph structure learning enhances the discrimination ability of GNN-based
CF paradigm, in comprehensively capturing the complex high-order dependencies
among users. Additionally, our HCCF model effectively integrates the hypergraph
structure encoding with self-supervised learning to reinforce the
representation quality of recommender systems, based on the hypergraph
self-discrimination. Extensive experiments on three benchmark datasets
demonstrate the superiority of our model over various state-of-the-art
recommendation methods, and the robustness against sparse user interaction
data. The implementation codes are available at https://github.com/akaxlh/HCCF.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:473:" <a href="http://arxiv.org/find/cs/1/au:+Xia_L/0/1/0/all/0/1">Lianghao Xia</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_C/0/1/0/all/0/1">Chao Huang</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_Y/0/1/0/all/0/1">Yong Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_J/0/1/0/all/0/1">Jiashu Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Yin_D/0/1/0/all/0/1">Dawei Yin</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_J/0/1/0/all/0/1">Jimmy Xiangji Huang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:161;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12201";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:116:"Accelerating Fully Homomorphic Encryption by Bridging Modular and Bit-Level Arithmetic. (arXiv:2204.12201v1 [cs.CR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12201";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1877:"The dramatic increase of data breaches in modern computing platforms has
emphasized that access control is not sufficient to protect sensitive user
data. Recent advances in cryptography allow end-to-end processing of encrypted
data without the need for decryption using Fully Homomorphic Encryption (FHE).
Such computation however, is still orders of magnitude slower than direct
(unencrypted) computation. Depending on the underlying cryptographic scheme,
FHE schemes can work natively either at bit-level using Boolean circuits, or
over integers using modular arithmetic. Operations on integers are limited to
addition/subtraction and multiplication. On the other hand, bit-level
arithmetic is much more comprehensive allowing more operations, such as
comparison and division. While modular arithmetic can emulate bit-level
computation, there is a significant cost in performance. In this work, we
propose a novel method, dubbed \emph{bridging}, that blends faster and
restricted modular computation with slower and comprehensive bit-level
computation, making them both usable within the same application and with the
same cryptographic scheme instantiation. We introduce and open source C++ types
representing the two distinct arithmetic modes, offering the possibility to
convert from one to the other. Experimental results show that bridging modular
and bit-level arithmetic computation can lead to 1-2 orders of magnitude
performance improvement for tested synthetic benchmarks, as well as two
real-world FHE applications: A URL denylisting case study, and a genotype
imputation application. Bridging performance enhancement comes from two
factors: 1) Reduced number of operations (especially ciphertext
multiplications), and 2) Arithmetic circuits with smaller multiplicative depth,
allowing more efficient encryption parameters with smaller polynomial degrees.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:340:" <a href="http://arxiv.org/find/cs/1/au:+Chielle_E/0/1/0/all/0/1">Eduardo Chielle</a>, <a href="http://arxiv.org/find/cs/1/au:+Mazonka_O/0/1/0/all/0/1">Oleg Mazonka</a>, <a href="http://arxiv.org/find/cs/1/au:+Gamil_H/0/1/0/all/0/1">Homer Gamil</a>, <a href="http://arxiv.org/find/cs/1/au:+Maniatakos_M/0/1/0/all/0/1">Michail Maniatakos</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:162;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12202";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:115:"Urban Change Detection Using a Dual-Task Siamese Network and Semi-Supervised Learning. (arXiv:2204.12202v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12202";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:813:"In this study, a Semi-Supervised Learning (SSL) method for improving urban
change detection from bi-temporal image pairs was presented. The proposed
method adapted a Dual-Task Siamese Difference network that not only predicts
changes with the difference decoder, but also segments buildings for both
images with a semantics decoder. First, the architecture was modified to
produce a second change prediction derived from the semantics predictions.
Second, SSL was adopted to improve supervised change detection. For unlabeled
data, we introduced a loss that encourages the network to predict consistent
changes across the two change outputs. The proposed method was tested on urban
change detection using the SpaceNet7 dataset. SSL achieved improved results
compared to three fully supervised benchmarks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:249:" <a href="http://arxiv.org/find/cs/1/au:+Hafner_S/0/1/0/all/0/1">Sebastian Hafner</a>, <a href="http://arxiv.org/find/cs/1/au:+Ban_Y/0/1/0/all/0/1">Yifang Ban</a>, <a href="http://arxiv.org/find/cs/1/au:+Nascetti_A/0/1/0/all/0/1">Andrea Nascetti</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:163;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12204";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:79:"Boosting Adversarial Transferability of MLP-Mixer. (arXiv:2204.12204v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12204";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1247:"The security of models based on new architectures such as MLP-Mixer and ViTs
needs to be studied urgently. However, most of the current researches are
mainly aimed at the adversarial attack against ViTs, and there is still
relatively little adversarial work on MLP-mixer. We propose an adversarial
attack method against MLP-Mixer called Maxwell's demon Attack (MA). MA breaks
the channel-mixing and token-mixing mechanism of MLP-Mixer by controlling the
part input of MLP-Mixer's each Mixer layer, and disturbs MLP-Mixer to obtain
the main information of images. Our method can mask the part input of the Mixer
layer, avoid overfitting of the adversarial examples to the source model, and
improve the transferability of cross-architecture. Extensive experimental
evaluation demonstrates the effectiveness and superior performance of the
proposed MA. Our method can be easily combined with existing methods and can
improve the transferability by up to 38.0% on MLP-based ResMLP. Adversarial
examples produced by our method on MLP-Mixer are able to exceed the
transferability of adversarial examples produced using DenseNet against CNNs.
To the best of our knowledge, we are the first work to study adversarial
transferability of MLP-Mixer.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:471:" <a href="http://arxiv.org/find/cs/1/au:+Lyu_H/0/1/0/all/0/1">Haoran Lyu</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_Y/0/1/0/all/0/1">Yajie Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Tan_Y/0/1/0/all/0/1">Yu-an Tan</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhou_H/0/1/0/all/0/1">Huipeng Zhou</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_Y/0/1/0/all/0/1">Yuhang Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_Q/0/1/0/all/0/1">Quanxin Zhang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:164;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12219";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:73:"A note on load balancing in DC microgrids. (arXiv:2204.12219v1 [eess.SY])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12219";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:916:"A problem of load balancing in isolated DC microgrids is considered in this
paper. Here, a DC load is fed by multiple heterogenous DC sources, each of
which is connected to the load via a boost converter. The gains of the DCC's
provide for a means to control the division of load current amongst the DC
sources. The primary objective of the control scheme is to minimise the total
losses in the network, while maintaining the output voltage within a desired
range, serving the load current demand and adhering to VI-characteristics of
the power sources. Under assumptions of
concavity/monotonocity/piece-wise-linearity of the VI-characteristics, the
problem is solved using a convex relaxation. It is shown that the solution to
the relaxed problem is tight. Thus, the resulting algorithm is guaranteed to
reach global optimality in a numerically efficient manner. Simulations are
provided for corroboration.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:173:" <a href="http://arxiv.org/find/eess/1/au:+Mohan_S/0/1/0/all/0/1">Shravan Mohan</a>, <a href="http://arxiv.org/find/eess/1/au:+Bhikkaji_B/0/1/0/all/0/1">Bharath Bhikkaji</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:165;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12223";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:93:"Context-Aware Sequence Alignment using 4D Skeletal Augmentation. (arXiv:2204.12223v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12223";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1343:"Temporal alignment of fine-grained human actions in videos is important for
numerous applications in computer vision, robotics, and mixed reality.
State-of-the-art methods directly learn image-based embedding space by
leveraging powerful deep convolutional neural networks. While being
straightforward, their results are far from satisfactory, the aligned videos
exhibit severe temporal discontinuity without additional post-processing steps.
The recent advancements in human body and hand pose estimation in the wild
promise new ways of addressing the task of human action alignment in videos. In
this work, based on off-the-shelf human pose estimators, we propose a novel
context-aware self-supervised learning architecture to align sequences of
actions. We name it CASA. Specifically, CASA employs self-attention and
cross-attention mechanisms to incorporate the spatial and temporal context of
human actions, which can solve the temporal discontinuity problem. Moreover, we
introduce a self-supervised learning scheme that is empowered by novel 4D
augmentation techniques for 3D skeleton representations. We systematically
evaluate the key components of our method. Our experiments on three public
datasets demonstrate CASA significantly improves phase progress and Kendall's
Tau scores over the previous state-of-the-art methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:321:" <a href="http://arxiv.org/find/cs/1/au:+Kwon_T/0/1/0/all/0/1">Taein Kwon</a>, <a href="http://arxiv.org/find/cs/1/au:+Tekin_B/0/1/0/all/0/1">Bugra Tekin</a>, <a href="http://arxiv.org/find/cs/1/au:+Tang_S/0/1/0/all/0/1">Siyu Tang</a>, <a href="http://arxiv.org/find/cs/1/au:+Pollefeys_M/0/1/0/all/0/1">Marc Pollefeys</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:166;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12225";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:92:"Flow-Adapter Architecture for Unsupervised Machine Translation. (arXiv:2204.12225v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12225";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:898:"In this work, we propose a flow-adapter architecture for unsupervised NMT. It
leverages normalizing flows to explicitly model the distributions of
sentence-level latent representations, which are subsequently used in
conjunction with the attention mechanism for the translation task. The primary
novelties of our model are: (a) capturing language-specific sentence
representations separately for each language using normalizing flows and (b)
using a simple transformation of these latent representations for translating
from one language to another. This architecture allows for unsupervised
training of each language independently. While there is prior work on latent
variables for supervised MT, to the best of our knowledge, this is the first
work that uses latent variables and normalizing flows for unsupervised MT. We
obtain competitive results on several unsupervised MT benchmarks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:249:" <a href="http://arxiv.org/find/cs/1/au:+Liu_Y/0/1/0/all/0/1">Yihong Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Jabbar_H/0/1/0/all/0/1">Haris Jabbar</a>, <a href="http://arxiv.org/find/cs/1/au:+Schutze_H/0/1/0/all/0/1">Hinrich Sch&#xfc;tze</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:167;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12227";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"Open or not open: Are conventional radio access networks more secure and trustworthy than Open-RAN?. (arXiv:2204.12227v1 [cs.CR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12227";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:881:"The Open-RAN architecture is a highly promising and future-oriented
architecture. It is intended to open up the radio access network and enable
more innovation and competition in the market. This will lead to RANs for
current 5G networks, but especially for future 6G networks, to move away from
the current centralised, provider-specific 3G RAN architecture and therefore
even better meet the requirements for future RANs. However, the change in
design has also created a drastic shift in the attack surface compared to
conventional RANs. In the past, this has often led to negative headlines, which
in summary have often associated O-RAN with faulty or inadequate security. In
this paper, we analyze what components are involved in an Open-RAN deployment,
how the current state of security is to be assessed and what measures need to
be taken to ensure secure operation.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:705:" <a href="http://arxiv.org/find/cs/1/au:+Klement_F/0/1/0/all/0/1">Felix Klement</a>, <a href="http://arxiv.org/find/cs/1/au:+Katzenbeisser_S/0/1/0/all/0/1">Stefan Katzenbeisser</a>, <a href="http://arxiv.org/find/cs/1/au:+Ulitzsch_V/0/1/0/all/0/1">Vincent Ulitzsch</a>, <a href="http://arxiv.org/find/cs/1/au:+Kramer_J/0/1/0/all/0/1">Juliane Kr&#xe4;mer</a>, <a href="http://arxiv.org/find/cs/1/au:+Stanczak_S/0/1/0/all/0/1">Slawomir Stanczak</a>, <a href="http://arxiv.org/find/cs/1/au:+Utkovski_Z/0/1/0/all/0/1">Zoran Utkovski</a>, <a href="http://arxiv.org/find/cs/1/au:+Bjelakovic_I/0/1/0/all/0/1">Igor Bjelakovic</a>, <a href="http://arxiv.org/find/cs/1/au:+Wunder_G/0/1/0/all/0/1">Gerhard Wunder</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:168;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12230";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"User Trust on an Explainable AI-based Medical Diagnosis Support System. (arXiv:2204.12230v1 [cs.HC])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12230";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1148:"Recent research has supported that system explainability improves user trust
and willingness to use medical AI for diagnostic support. In this paper, we use
chest disease diagnosis based on X-Ray images as a case study to investigate
user trust and reliance. Building off explainability, we propose a support
system where users (radiologists) can view causal explanations for final
decisions. After observing these causal explanations, users provided their
opinions of the model predictions and could correct explanations if they did
not agree. We measured user trust as the agreement between the model's and the
radiologist's diagnosis as well as the radiologists' feedback on the model
explanations. Additionally, they reported their trust in the system. We tested
our model on the CXR-Eye dataset and it achieved an overall accuracy of 74.1%.
However, the experts in our user study agreed with the model for only 46.4% of
the cases, indicating the necessity of improving the trust. The self-reported
trust score was 3.2 on a scale of 1.0 to 5.0, showing that the users tended to
trust the model but the trust still needs to be enhanced.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:326:" <a href="http://arxiv.org/find/cs/1/au:+Rong_Y/0/1/0/all/0/1">Yao Rong</a>, <a href="http://arxiv.org/find/cs/1/au:+Castner_N/0/1/0/all/0/1">Nora Castner</a>, <a href="http://arxiv.org/find/cs/1/au:+Bozkir_E/0/1/0/all/0/1">Efe Bozkir</a>, <a href="http://arxiv.org/find/cs/1/au:+Kasneci_E/0/1/0/all/0/1">Enkelejda Kasneci</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:169;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12231";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"IRC-safe Graph Autoencoder for an unsupervised anomaly detection. (arXiv:2204.12231v1 [hep-ph])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12231";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:652:"Anomaly detection through employing machine learning techniques has emerged
as a novel powerful tool in the search for new physics beyond the Standard
Model. Historically similar to the development of jet observables, theoretical
consistency has not always assumed a central role in the fast development of
algorithms and neural network architectures. In this work, we construct an
infrared and collinear safe autoencoder based on graph neural networks by
employing energy-weighted message passing. We demonstrate that whilst this
approach has theoretically favourable properties, it also exhibits formidable
sensitivity to non-QCD structures.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:556:" <a href="http://arxiv.org/find/hep-ph/1/au:+Atkinson_O/0/1/0/all/0/1">Oliver Atkinson</a>, <a href="http://arxiv.org/find/hep-ph/1/au:+Bhardwaj_A/0/1/0/all/0/1">Akanksha Bhardwaj</a>, <a href="http://arxiv.org/find/hep-ph/1/au:+Englert_C/0/1/0/all/0/1">Christoph Englert</a>, <a href="http://arxiv.org/find/hep-ph/1/au:+Konar_P/0/1/0/all/0/1">Partha Konar</a>, <a href="http://arxiv.org/find/hep-ph/1/au:+Ngairangbam_V/0/1/0/all/0/1">Vishal S. Ngairangbam</a>, <a href="http://arxiv.org/find/hep-ph/1/au:+Spannowsky_M/0/1/0/all/0/1">Michael Spannowsky</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:170;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12237";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:145:"Intercategorical Label Interpolation for Emotional Face Generation with Conditional Generative Adversarial Networks. (arXiv:2204.12237v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12237";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1127:"Generative adversarial networks offer the possibility to generate deceptively
real images that are almost indistinguishable from actual photographs. Such
systems however rely on the presence of large datasets to realistically
replicate the corresponding domain. This is especially a problem if not only
random new images are to be generated, but specific (continuous) features are
to be co-modeled. A particularly important use case in \emph{Human-Computer
Interaction} (HCI) research is the generation of emotional images of human
faces, which can be used for various use cases, such as the automatic
generation of avatars. The problem hereby lies in the availability of training
data. Most suitable datasets for this task rely on categorical emotion models
and therefore feature only discrete annotation labels. This greatly hinders the
learning and modeling of smooth transitions between displayed affective states.
To overcome this challenge, we explore the potential of label interpolation to
enhance networks trained on categorical datasets with the ability to generate
images conditioned on continuous features.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:602:" <a href="http://arxiv.org/find/cs/1/au:+Mertes_S/0/1/0/all/0/1">Silvan Mertes</a>, <a href="http://arxiv.org/find/cs/1/au:+Schiller_D/0/1/0/all/0/1">Dominik Schiller</a>, <a href="http://arxiv.org/find/cs/1/au:+Lingenfelser_F/0/1/0/all/0/1">Florian Lingenfelser</a>, <a href="http://arxiv.org/find/cs/1/au:+Kiderle_T/0/1/0/all/0/1">Thomas Kiderle</a>, <a href="http://arxiv.org/find/cs/1/au:+Kroner_V/0/1/0/all/0/1">Valentin Kroner</a>, <a href="http://arxiv.org/find/cs/1/au:+Diab_L/0/1/0/all/0/1">Lama Diab</a>, <a href="http://arxiv.org/find/cs/1/au:+Andre_E/0/1/0/all/0/1">Elisabeth Andr&#xe9;</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:171;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12243";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:141:"Modeling and Analysis of 2-Tier Heterogeneous Vehicular Networks Leveraging Roadside Units and Vehicle Relays. (arXiv:2204.12243v1 [eess.SP])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12243";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1289:"While roadside units (RSUs) play an essential role in vehicle-to-everything
(V2X) by communicating with users, some users in congestion areas may not be
well-served due to data traffic, signal attenuation, and interference. In these
cases, vehicle relays can be employed to enhance the network topology to better
serve those users. This paper leverages stochastic geometry to propose a novel
framework for the performance analysis of heterogeneous vehicular networks with
RSUs, vehicle relays, and vehicle users. We present a two-dimensional
analytical model where the spatial dependence between RSUs, vehicle relays,
vehicle users, and roads is accurately taken into account through a Cox point
process structure. Assuming relays are backhauled to RSUs over a reserved
wireless resource and users are associated with the closest RSU or relay, we
derive the probability that the typical user is associated with either an RSU
or a relay. Then, we derive the signal-to-interference ratio (SIR) coverage
probability of the typical user. Finally, using the derived formulas, we
evaluate the average effective rate of the typical user in the network. This
allows us to determine the gain of the average effective rate of users that
results from the deployment of relays in the network.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:179:" <a href="http://arxiv.org/find/eess/1/au:+Choi_C/0/1/0/all/0/1">Chang-Sik Choi</a>, <a href="http://arxiv.org/find/eess/1/au:+Baccelli_F/0/1/0/all/0/1">Fran&#xe7;ois Baccelli</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:172;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12244";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:98:"Hybridised Loss Functions for Improved Neural Network Generalisation. (arXiv:2204.12244v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12244";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1352:"Loss functions play an important role in the training of artificial neural
networks (ANNs), and can affect the generalisation ability of the ANN model,
among other properties. Specifically, it has been shown that the cross entropy
and sum squared error loss functions result in different training dynamics, and
exhibit different properties that are complementary to one another. It has
previously been suggested that a hybrid of the entropy and sum squared error
loss functions could combine the advantages of the two functions, while
limiting their disadvantages. The effectiveness of such hybrid loss functions
is investigated in this study. It is shown that hybridisation of the two loss
functions improves the generalisation ability of the ANNs on all problems
considered. The hybrid loss function that starts training with the sum squared
error loss function and later switches to the cross entropy error loss function
is shown to either perform the best on average, or to not be significantly
different than the best loss function tested for all problems considered. This
study shows that the minima discovered by the sum squared error loss function
can be further exploited by switching to cross entropy error loss function. It
can thus be concluded that hybridisation of the two loss functions could lead
to better performance in ANNs.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:259:" <a href="http://arxiv.org/find/cs/1/au:+Dickson_M/0/1/0/all/0/1">Matthew C. Dickson</a>, <a href="http://arxiv.org/find/cs/1/au:+Bosman_A/0/1/0/all/0/1">Anna S. Bosman</a>, <a href="http://arxiv.org/find/cs/1/au:+Malan_K/0/1/0/all/0/1">Katherine M. Malan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:173;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12253";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:105:"The Pseudo-Reachability Problem for Diagonalisable Linear Dynamical Systems. (arXiv:2204.12253v1 [cs.LO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12253";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:238:"We show that the discrete-time pseudo-reachability problem is decidable for
diagonalisable linear dynamical systems. To do this we develop a new approach
based on $o$-minimality of real numbers augmented with real exponentiation.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:523:" <a href="http://arxiv.org/find/cs/1/au:+DCosta_J/0/1/0/all/0/1">Julian D&#x27;Costa</a>, <a href="http://arxiv.org/find/cs/1/au:+Karimov_T/0/1/0/all/0/1">Toghrul Karimov</a>, <a href="http://arxiv.org/find/cs/1/au:+Ouaknine_J/0/1/0/all/0/1">Jo&#xeb;l Ouaknine</a>, <a href="http://arxiv.org/find/cs/1/au:+Salamati_M/0/1/0/all/0/1">Mahmoud Salamati</a>, <a href="http://arxiv.org/find/cs/1/au:+Soudjani_S/0/1/0/all/0/1">Sadegh Soudjani</a>, <a href="http://arxiv.org/find/cs/1/au:+Worrell_J/0/1/0/all/0/1">James Worrell</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:174;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12254";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:77:"Stopped Brownian-increment tamed Euler method. (arXiv:2204.12254v1 [math.PR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12254";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:474:"In this article we propose a new explicit Euler-type approximation method for
stochastic differential equations (SDEs). In this method, Brownian increments
in the recursion of the Euler method are replaced by suitable bounded functions
of the Brownian increments. We prove strong convergence rate one-half for a
large class of SDEs with polynomial coefficient functions whose local
monotonicity constant grows at most like the logarithm of a Lyapunov-type
function.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:178:" <a href="http://arxiv.org/find/math/1/au:+Hutzenthaler_M/0/1/0/all/0/1">Martin Hutzenthaler</a>, <a href="http://arxiv.org/find/math/1/au:+Kisker_K/0/1/0/all/0/1">Kai Kisker</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:175;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12260";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:135:"Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representation. (arXiv:2204.12260v1 [eess.AS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12260";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1902:"Recent general-purpose audio representations show state-of-the-art
performance on various audio tasks. These representations are pre-trained by
self-supervised learning methods that create training signals from the input.
For example, typical audio contrastive learning uses temporal relationships
among input sounds to create training signals, whereas some methods use a
difference among input views created by data augmentations. However, these
training signals do not provide information derived from the intact input
sound, which we think is suboptimal for learning representation that describes
the input as it is.

In this paper, we seek to learn audio representations from the input itself
as supervision using a pretext task of auto-encoding of masked spectrogram
patches, Masked Spectrogram Modeling (MSM, a variant of Masked Image Modeling
applied to audio spectrogram). To implement MSM, we use Masked Autoencoders
(MAE), an image self-supervised learning method. MAE learns to efficiently
encode the small number of visible patches into latent representations to carry
essential information for reconstructing a large number of masked patches.
While training, MAE minimizes the reconstruction error, which uses the input as
training signal, consequently achieving our goal.

We conducted experiments on our MSM using MAE (MSM-MAE) models under the
evaluation benchmark of the HEAR 2021 NeurIPS Challenge. Our MSM-MAE models
outperformed the HEAR 2021 Challenge results on seven out of 15 tasks (e.g.,
accuracies of 73.4% on CREMA-D and 85.8% on LibriCount), while showing top
performance on other tasks where specialized models perform better. We also
investigate how the design choices of MSM-MAE impact the performance and
conduct qualitative analysis of visualization outcomes to gain an understanding
of learned representations. We make our code available online.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:433:" <a href="http://arxiv.org/find/eess/1/au:+Niizumi_D/0/1/0/all/0/1">Daisuke Niizumi</a>, <a href="http://arxiv.org/find/eess/1/au:+Takeuchi_D/0/1/0/all/0/1">Daiki Takeuchi</a>, <a href="http://arxiv.org/find/eess/1/au:+Ohishi_Y/0/1/0/all/0/1">Yasunori Ohishi</a>, <a href="http://arxiv.org/find/eess/1/au:+Harada_N/0/1/0/all/0/1">Noboru Harada</a>, <a href="http://arxiv.org/find/eess/1/au:+Kashino_K/0/1/0/all/0/1">Kunio Kashino</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:176;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12261";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:70:"Managing Reliability Skew in DNA Storage. (arXiv:2204.12261v1 [cs.ET])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12261";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1926:"DNA is emerging as an increasingly attractive medium for data storage due to
a number of important and unique advantages it offers, most notably the
unprecedented durability and density. While the technology is evolving rapidly,
the prohibitive cost of reads and writes, the high frequency and the peculiar
nature of errors occurring in DNA storage pose a significant challenge to its
adoption. In this work we make a novel observation that the probability of
successful recovery of a given bit from any type of a DNA-based storage system
highly depends on its physical location within the DNA molecule. In other
words, when used as a storage medium, some parts of DNA molecules appear
significantly more reliable than others. We show that large differences in
reliability between different parts of DNA molecules lead to highly inefficient
use of error-correction resources, and that commonly used techniques such as
unequal error-correction cannot be used to bridge the reliability gap between
different locations in the context of DNA storage. We then propose two
approaches to address the problem. The first approach is general and applies to
any types of data; it stripes the data and ECC codewords across DNA molecules
in a particular fashion such that the effects of errors are spread out evenly
across different codewords and molecules, effectively de-biasing the underlying
storage medium. The second approach is application-specific, and seeks to
leverage the underlying reliability bias by using application-aware mapping of
data onto DNA molecules such that data that requires higher reliability is
stored in more reliable locations, whereas data that needs lower reliability is
stored in less reliable parts of DNA molecules. We show that the proposed data
mapping can be used to achieve graceful degradation in the presence of high
error rates, or to implement the concept of approximate storage in DNA.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:330:" <a href="http://arxiv.org/find/cs/1/au:+Lin_D/0/1/0/all/0/1">Dehui Lin</a>, <a href="http://arxiv.org/find/cs/1/au:+Tabatabaee_Y/0/1/0/all/0/1">Yasamin Tabatabaee</a>, <a href="http://arxiv.org/find/cs/1/au:+Pote_Y/0/1/0/all/0/1">Yash Pote</a>, <a href="http://arxiv.org/find/cs/1/au:+Jevdjic_D/0/1/0/all/0/1">Djordje Jevdjic</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:177;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12263";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:113:"Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking. (arXiv:2204.12263v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12263";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1271:"With the explosive growth of scientific publications, making the synthesis of
scientific knowledge and fact checking becomes an increasingly complex task. In
this paper, we propose a multi-task approach for verifying the scientific
questions based on a joint reasoning from facts and evidence in research
articles. We propose an intelligent combination of (1) an automatic information
summarization and (2) a Boolean Question Answering which allows to generate an
answer to a scientific question from only extracts obtained after
summarization. Thus on a given topic, our proposed approach conducts structured
content modeling based on paper abstracts to answer a scientific question while
highlighting texts from paper that discuss the topic. We based our final system
on an end-to-end Extractive Question Answering (EQA) combined with a three
outputs classification model to perform in-depth semantic understanding of a
question to illustrate the aggregation of multiple responses. With our light
and fast proposed architecture, we achieved an average error rate of 4% and a
F1-score of 95.6%. Our results are supported via experiments with two QA models
(BERT, RoBERTa) over 3 Million Open Access (OA) articles in the medical and
health domains on Europe PMC.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:357:" <a href="http://arxiv.org/find/cs/1/au:+Rakotoson_L/0/1/0/all/0/1">Lo&#xef;c Rakotoson</a>, <a href="http://arxiv.org/find/cs/1/au:+Letaillieur_C/0/1/0/all/0/1">Charles Letaillieur</a>, <a href="http://arxiv.org/find/cs/1/au:+Massip_S/0/1/0/all/0/1">Sylvain Massip</a>, <a href="http://arxiv.org/find/cs/1/au:+Laleye_F/0/1/0/all/0/1">Fr&#xe9;jus Laleye</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:178;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12264";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:112:"Energy Efficient Beamforming Optimization for Integrated Sensing and Communication. (arXiv:2204.12264v1 [cs.IT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12264";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:864:"This paper investigates the optimization of beamforming design in a system
with integrated sensing and communication (ISAC), where the base station (BS)
sends signals for simultaneous multiuser communication and radar sensing. We
aim at maximizing the energy efficiency (EE) of the multiuser communication
while guaranteeing the sensing requirement in terms of individual radar
beampattern gains. The problem is a complicated nonconvex fractional program
which is challenging to be solved. By appropriately reformulating the problem
and then applying the techniques of successive convex approximation (SCA) and
semidefinite relaxation (SDR), we propose an iterative algorithm to address
this problem. In theory, we prove that the introduced relaxation of the SDR is
rigorously tight. Numerical results validate the effectiveness of the proposed
algorithm.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:386:" <a href="http://arxiv.org/find/cs/1/au:+He_Z/0/1/0/all/0/1">Zhenyao He</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_W/0/1/0/all/0/1">Wei Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Shen_H/0/1/0/all/0/1">Hong Shen</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_Y/0/1/0/all/0/1">Yongming Huang</a>, <a href="http://arxiv.org/find/cs/1/au:+Xiao_H/0/1/0/all/0/1">Huahua Xiao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:179;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12266";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:94:"Attentive Fine-Grained Structured Sparsity for Image Restoration. (arXiv:2204.12266v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12266";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1174:"Image restoration tasks have witnessed great performance improvement in
recent years by developing large deep models. Despite the outstanding
performance, the heavy computation demanded by the deep models has restricted
the application of image restoration. To lift the restriction, it is required
to reduce the size of the networks while maintaining accuracy. Recently, N:M
structured pruning has appeared as one of the effective and practical pruning
approaches for making the model efficient with the accuracy constraint.
However, it fails to account for different computational complexities and
performance requirements for different layers of an image restoration network.
To further optimize the trade-off between the efficiency and the restoration
accuracy, we propose a novel pruning method that determines the pruning ratio
for N:M structured sparsity at each layer. Extensive experimental results on
super-resolution and deblurring tasks demonstrate the efficacy of our method
which outperforms previous pruning methods significantly. PyTorch
implementation for the proposed methods will be publicly available at
https://github.com/JungHunOh/SLS_CVPR2022.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:471:" <a href="http://arxiv.org/find/cs/1/au:+Oh_J/0/1/0/all/0/1">Junghun Oh</a>, <a href="http://arxiv.org/find/cs/1/au:+Kim_H/0/1/0/all/0/1">Heewon Kim</a>, <a href="http://arxiv.org/find/cs/1/au:+Nah_S/0/1/0/all/0/1">Seungjun Nah</a>, <a href="http://arxiv.org/find/cs/1/au:+Hong_C/0/1/0/all/0/1">Cheeun Hong</a>, <a href="http://arxiv.org/find/cs/1/au:+Choi_J/0/1/0/all/0/1">Jonghyun Choi</a>, <a href="http://arxiv.org/find/cs/1/au:+Lee_K/0/1/0/all/0/1">Kyoung Mu Lee</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:180;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12267";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"Sentiment Analysis of Cybersecurity Content on Twitter and Reddit. (arXiv:2204.12267v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12267";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1341:"Sentiment Analysis provides an opportunity to understand the subject(s),
especially in the digital age, due to an abundance of public data and effective
algorithms. Cybersecurity is a subject where opinions are plentiful and
differing in the public domain. This descriptive research analyzed
cybersecurity content on Twitter and Reddit to measure its sentiment, positive
or negative, or neutral. The data from Twitter and Reddit was amassed via
technology-specific APIs during a selected timeframe to create datasets, which
were then analyzed individually for their sentiment by VADER, an NLP (Natural
Language Processing) algorithm. A random sample of cybersecurity content (ten
tweets and posts) was also classified for sentiments by twenty human annotators
to evaluate the performance of VADER. Cybersecurity content on Twitter was at
least 48% positive, and Reddit was at least 26.5% positive. The positive or
neutral content far outweighed negative sentiments across both platforms. When
compared to human classification, which was considered the standard or source
of truth, VADER produced 60% accuracy for Twitter and 70% for Reddit in
assessing the sentiment; in other words, some agreement between algorithm and
human classifiers. Overall, the goal was to explore an uninhibited research
topic about cybersecurity sentiment

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:79:" <a href="http://arxiv.org/find/cs/1/au:+Thapa_B/0/1/0/all/0/1">Bipun Thapa</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:181;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12270";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:84:"Graph Neural Networks for Microbial Genome Recovery. (arXiv:2204.12270v1 [q-bio.GN])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12270";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1501:"Microbes have a profound impact on our health and environment, but our
understanding of the diversity and function of microbial communities is
severely limited. Through DNA sequencing of microbial communities
(metagenomics), DNA fragments (reads) of the individual microbes can be
obtained, which through assembly graphs can be combined into long contiguous
DNA sequences (contigs). Given the complexity of microbial communities, single
contig microbial genomes are rarely obtained. Instead, contigs are eventually
clustered into bins, with each bin ideally making up a full genome. This
process is referred to as metagenomic binning.

Current state-of-the-art techniques for metagenomic binning rely only on the
local features for the individual contigs. These techniques therefore fail to
exploit the similarities between contigs as encoded by the assembly graph, in
which the contigs are organized. In this paper, we propose to use Graph Neural
Networks (GNNs) to leverage the assembly graph when learning contig
representations for metagenomic binning. Our method, VaeG-Bin, combines
variational autoencoders for learning latent representations of the individual
contigs, with GNNs for refining these representations by taking into account
the neighborhood structure of the contigs in the assembly graph. We explore
several types of GNNs and demonstrate that VaeG-Bin recovers more high-quality
genomes than other state-of-the-art binners on both simulated and real-world
datasets.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:439:" <a href="http://arxiv.org/find/q-bio/1/au:+Lamurias_A/0/1/0/all/0/1">Andre Lamurias</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Tibo_A/0/1/0/all/0/1">Alessandro Tibo</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Hose_K/0/1/0/all/0/1">Katja Hose</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Albertsen_M/0/1/0/all/0/1">Mads Albertsen</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Nielsen_T/0/1/0/all/0/1">Thomas Dyhre Nielsen</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:182;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12274";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:135:"Socio-technical constraints and affordances of virtual collaboration -- A study of four online hackathons. (arXiv:2204.12274v1 [cs.HC])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12274";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1928:"Hackathons and similar time-bounded events have become a popular form of
collaboration. They are commonly organized as in-person events during which
teams engage in intense collaboration over a short period of time to complete a
project that is of interest to them. Most research to date has focused on
studying how teams collaborate in a co-located setting, pointing towards the
advantages of radical co-location. The global pandemic of 2020, however, has
led to many hackathons moving online, which challenges our current
understanding of how they function. In this paper, we address this gap by
presenting findings from a multiple-case study of 10 hackathon teams that
participated in 4 hackathons across two continents. By analyzing the collected
data, we found that teams merged synchronous and asynchronous means of
communication to maintain a common understanding of work progress as well as to
maintain awareness of each other's tasks. Task division was self-assigned based
on individual skills or interests, while leaders emerged from different
strategies (e.g., participant experience, the responsibility of registering the
team in an event). Some of the affordances of in-person hackathons, such as the
radical co-location of team members, could be partially reproduced in teams
that kept synchronous communication channels while working (i.e., shared audio
territories), in a sort of "radical virtual co-location". However, others, such
as interactions with other teams, easy access to mentors, and networking with
other participants, decreased. In addition, the technical constraints of the
different communication tools and platforms brought technical problems and were
overwhelming to participants. Our work contributes to understanding the virtual
collaboration of small teams in the context of online hackathons and how
technologies and event structures proposed by organizers imply this
collaboration.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:497:" <a href="http://arxiv.org/find/cs/1/au:+Mendes_W/0/1/0/all/0/1">Wendy Mendes</a>, <a href="http://arxiv.org/find/cs/1/au:+Richard_A/0/1/0/all/0/1">Albert Richard</a>, <a href="http://arxiv.org/find/cs/1/au:+Tillo_T/0/1/0/all/0/1">T&#xe4;he-Kai Tillo</a>, <a href="http://arxiv.org/find/cs/1/au:+Pinto_G/0/1/0/all/0/1">Gustavo Pinto</a>, <a href="http://arxiv.org/find/cs/1/au:+Gama_K/0/1/0/all/0/1">Kiev Gama</a>, <a href="http://arxiv.org/find/cs/1/au:+Nolte_A/0/1/0/all/0/1">Alexander Nolte</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:183;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12279";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:105:"Low-dimensional representation of infant and adult vocalization acoustics. (arXiv:2204.12279v1 [eess.AS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12279";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1418:"During the first years of life, infant vocalizations change considerably, as
infants develop the vocalization skills that enable them to produce speech
sounds. Characterizations based on specific acoustic features, protophone
categories, or phonetic transcription are able to provide a representation of
the sounds infants make at different ages and in different contexts but do not
fully describe how sounds are perceived by listeners, can be inefficient to
obtain at large scales, and are difficult to visualize in two dimensions
without additional statistical processing. Machine-learning-based approaches
provide the opportunity to complement these characterizations with purely
data-driven representations of infant sounds. Here, we use spectral features
extraction and unsupervised machine learning, specifically Uniform Manifold
Approximation (UMAP), to obtain a novel 2-dimensional spatial representation of
infant and caregiver vocalizations extracted from day-long home recordings.
UMAP yields a continuous and well-distributed space conducive to certain
analyses of infant vocal development. For instance, we found that the
dispersion of infant vocalization acoustics within the 2-D space over a day
increased from 3 to 9 months, and then decreased from 9 to 18 months. The
method also permits analysis of similarity between infant and adult
vocalizations, which also shows changes with infant age.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:366:" <a href="http://arxiv.org/find/eess/1/au:+Pagliarini_S/0/1/0/all/0/1">Silvia Pagliarini</a>, <a href="http://arxiv.org/find/eess/1/au:+Schneider_S/0/1/0/all/0/1">Sara Schneider</a>, <a href="http://arxiv.org/find/eess/1/au:+Kello_C/0/1/0/all/0/1">Christopher T. Kello</a>, <a href="http://arxiv.org/find/eess/1/au:+Warlaumont_A/0/1/0/all/0/1">Anne S. Warlaumont</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:184;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12280";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:87:"The variance-penalized stochastic shortest path problem. (arXiv:2204.12280v1 [math.OC])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12280";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:918:"The stochastic shortest path problem (SSPP) asks to resolve the
non-deterministic choices in a Markov decision process (MDP) such that the
expected accumulated weight before reaching a target state is maximized. This
paper addresses the optimization of the variance-penalized expectation (VPE) of
the accumulated weight, which is a variant of the SSPP in which a multiple of
the variance of accumulated weights is incurred as a penalty. It is shown that
the optimal VPE in MDPs with non-negative weights as well as an optimal
deterministic finite-memory scheduler can be computed in exponential space. The
threshold problem whether the maximal VPE exceeds a given rational is shown to
be EXPTIME-hard and to lie in NEXPTIME. Furthermore, a result of interest in
its own right obtained on the way is that a variance-minimal scheduler among
all expectation-optimal schedulers can be computed in polynomial time.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:257:" <a href="http://arxiv.org/find/math/1/au:+Piribauer_J/0/1/0/all/0/1">Jakob Piribauer</a>, <a href="http://arxiv.org/find/math/1/au:+Sankur_O/0/1/0/all/0/1">Ocan Sankur</a>, <a href="http://arxiv.org/find/math/1/au:+Baier_C/0/1/0/all/0/1">Christel Baier</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:185;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12281";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:61:"Data-Efficient Backdoor Attacks. (arXiv:2204.12281v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12281";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1119:"Recent studies have proven that deep neural networks are vulnerable to
backdoor attacks. Specifically, by mixing a small number of poisoned samples
into the training set, the behavior of the trained model can be maliciously
controlled. Existing attack methods construct such adversaries by randomly
selecting some clean data from the benign set and then embedding a trigger into
them. However, this selection strategy ignores the fact that each poisoned
sample contributes inequally to the backdoor injection, which reduces the
efficiency of poisoning. In this paper, we formulate improving the poisoned
data efficiency by the selection as an optimization problem and propose a
Filtering-and-Updating Strategy (FUS) to solve it. The experimental results on
CIFAR-10 and ImageNet-10 indicate that the proposed method is effective: the
same attack success rate can be achieved with only 47% to 75% of the poisoned
sample volume compared to the random selection strategy. More importantly, the
adversaries selected according to one setting can generalize well to other
settings, exhibiting strong transferability.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:303:" <a href="http://arxiv.org/find/cs/1/au:+Xia_P/0/1/0/all/0/1">Pengfei Xia</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_Z/0/1/0/all/0/1">Ziqiang Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_W/0/1/0/all/0/1">Wei Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_B/0/1/0/all/0/1">Bin Li</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:186;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12283";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:177:"A Novel Framework for Quantification of Immune Cell Density and Characterization of Tumor-Immune Spatial Relationships in Tumor Microenvironment. (arXiv:2204.12283v1 [q-bio.QM])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12283";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:885:"Understanding the impact of tumor biology on the composition of nearby cells
often requires characterizing the impact of biologically distinct tumor
regions. Biomarkers have been developed to label biologically distinct tumor
regions, but challenges arise because of differences in the spatial extent and
distribution of differentially labeled regions. In this work, we present a
framework for systematically investigating the impact of distinct tumor regions
on cells near the tumor borders, accounting their cross spatial distributions.
We apply the framework to multiplex immunohistochemistry (mIHC) studies of
pancreatic cancer and show its efficacy in demonstrating how biologically
different tumor regions impact the immune response in the tumor
microenvironment. Furthermore, we show that the proposed framework can be
extended to largescale whole slide image analysis.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:1052:" <a href="http://arxiv.org/find/q-bio/1/au:+Hasan_M/0/1/0/all/0/1">Mahmudul Hasan</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Kaczmarzyk_J/0/1/0/all/0/1">Jakub R. Kaczmarzyk</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Paredes_D/0/1/0/all/0/1">David Paredes</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Oblein_L/0/1/0/all/0/1">Lyanne Oblein</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Oentoro_J/0/1/0/all/0/1">Jaymie Oentoro</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Abousamra_S/0/1/0/all/0/1">Shahira Abousamra</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Horowitz_M/0/1/0/all/0/1">Michael Horowitz</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Samaras_D/0/1/0/all/0/1">Dimitris Samaras</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Chen_C/0/1/0/all/0/1">Chao Chen</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Kurc_T/0/1/0/all/0/1">Tahsin Kurc</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Shroyer_K/0/1/0/all/0/1">Kenneth R. Shroyer</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Saltz_J/0/1/0/all/0/1">Joel Saltz</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:187;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12284";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:97:"Federated Stochastic Primal-dual Learning with Differential Privacy. (arXiv:2204.12284v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12284";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1774:"Federated learning (FL) is a new paradigm that enables many clients to
jointly train a machine learning (ML) model under the orchestration of a
parameter server while keeping the local data not being exposed to any third
party. However, the training of FL is an interactive process between local
clients and the parameter server. Such process would cause privacy leakage
since adversaries may retrieve sensitive information by analyzing the overheard
messages. In this paper, we propose a new federated stochastic primal-dual
algorithm with differential privacy (FedSPD-DP). Compared to the existing
methods, the proposed FedSPD-DP incorporates local stochastic gradient descent
(local SGD) and partial client participation (PCP) for addressing the issues of
communication efficiency and straggler effects due to randomly accessed
clients. Our analysis shows that the data sampling strategy and PCP can enhance
the data privacy whereas the larger number of local SGD steps could increase
privacy leakage, revealing a non-trivial tradeoff between algorithm
communication efficiency and privacy protection. Specifically, we show that, by
guaranteeing $(\epsilon, \delta)$-DP for each client per communication round,
the proposed algorithm guarantees $(\mathcal{O}(q\epsilon \sqrt{p T}),
\delta)$-DP after $T$ communication rounds while maintaining an
$\mathcal{O}(1/\sqrt{pTQ})$ convergence rate for a convex and non-smooth
learning problem, where $Q$ is the number of local SGD steps, $p$ is the client
sampling probability, $q=\max_{i} q_i/\sqrt{1-q_i}$ and $q_i$ is the data
sampling probability of each client under PCP. Experiment results are presented
to evaluate the practical performance of the proposed algorithm and comparison
with state-of-the-art methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:316:" <a href="http://arxiv.org/find/cs/1/au:+Li_Y/0/1/0/all/0/1">Yiwei Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_S/0/1/0/all/0/1">Shuai Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Chang_T/0/1/0/all/0/1">Tsung-Hui Chang</a>, <a href="http://arxiv.org/find/cs/1/au:+Chi_C/0/1/0/all/0/1">Chong-Yung Chi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:188;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12288";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"Empowering Next POI Recommendation with Multi-Relational Modeling. (arXiv:2204.12288v1 [cs.IR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12288";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1365:"With the wide adoption of mobile devices and web applications, location-based
social networks (LBSNs) offer large-scale individual-level location-related
activities and experiences. Next point-of-interest (POI) recommendation is one
of the most important tasks in LBSNs, aiming to make personalized
recommendations of next suitable locations to users by discovering preferences
from users' historical activities. Noticeably, LBSNs have offered unparalleled
access to abundant heterogeneous relational information about users and POIs
(including user-user social relations, such as families or colleagues; and
user-POI visiting relations). Such relational information holds great potential
to facilitate the next POI recommendation. However, most existing methods
either focus on merely the user-POI visits, or handle different relations based
on over-simplified assumptions while neglecting relational heterogeneities. To
fill these critical voids, we propose a novel framework, MEMO, which
effectively utilizes the heterogeneous relations with a multi-network
representation learning module, and explicitly incorporates the inter-temporal
user-POI mutual influence with the coupled recurrent neural networks. Extensive
experiments on real-world LBSN data validate the superiority of our framework
over the state-of-the-art next POI recommendation methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:395:" <a href="http://arxiv.org/find/cs/1/au:+Huang_Z/0/1/0/all/0/1">Zheng Huang</a>, <a href="http://arxiv.org/find/cs/1/au:+Ma_J/0/1/0/all/0/1">Jing Ma</a>, <a href="http://arxiv.org/find/cs/1/au:+Dong_Y/0/1/0/all/0/1">Yushun Dong</a>, <a href="http://arxiv.org/find/cs/1/au:+Foutz_N/0/1/0/all/0/1">Natasha Zhang Foutz</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_J/0/1/0/all/0/1">Jundong Li</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:189;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12289";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:78:"A dynamic that evolves toward a Nash equilibrium. (arXiv:2204.12289v1 [cs.GT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12289";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:923:"In this paper, we study an exponentiated multiplicative weights dynamic based
on Hedge, a well-known algorithm in theoretical machine learning and
algorithmic game theory. The empirical average (arithmetic mean) of the
iterates Hedge generates is known to approach a minimax equilibrium in zero-sum
games. We generalize that result to show that a weighted version of the
empirical average converges to an equilibrium in the class of symmetric
bimatrix games for a diminishing learning rate parameter. Our dynamic is the
first dynamical system (whether continuous or discrete) shown to evolve toward
a Nash equilibrium without assuming monotonicity of the payoff structure or
that a potential function exists. Although our setting is somewhat restricted,
it is also general as the class of symmetric bimatrix games captures the entire
computational complexity of the PPAD class (even to approximate an
equilibrium).

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:95:" <a href="http://arxiv.org/find/cs/1/au:+Avramopoulos_I/0/1/0/all/0/1">Ioannis Avramopoulos</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:190;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12290";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations. (arXiv:2204.12290v1 [cs.SD])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12290";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:772:"Surrogate models are data-based approximations of computationally expensive
simulations that enable efficient exploration of the model's design space and
informed decision-making in many physical domains. The usage of surrogate
models in the vibroacoustic domain, however, is challenging due to the
non-smooth, complex behavior of wave phenomena. This paper investigates four
Machine Learning (ML) approaches in the modelling of surrogates of Sound
Transmission Loss (STL). Feature importance and feature engineering are used to
improve the models' accuracy while increasing their interpretability and
physical consistency. The transfer of the proposed techniques to other problems
in the vibroacoustic domain and possible limitations of the models are
discussed.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:457:" <a href="http://arxiv.org/find/cs/1/au:+Cunha_B/0/1/0/all/0/1">Barbara Cunha</a> (LTDS), <a href="http://arxiv.org/find/cs/1/au:+Zine_A/0/1/0/all/0/1">Abdel-Malek Zine</a> (ICJ), <a href="http://arxiv.org/find/cs/1/au:+Ichchou_M/0/1/0/all/0/1">Mohamed Ichchou</a> (ECL), <a href="http://arxiv.org/find/cs/1/au:+Droz_C/0/1/0/all/0/1">Christophe Droz</a> (COSYS-SII), <a href="http://arxiv.org/find/cs/1/au:+Foulard_S/0/1/0/all/0/1">St&#xe9;phane Foulard</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:191;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12293";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:96:"Contrastive Language-Action Pre-training for Temporal Localization. (arXiv:2204.12293v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12293";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1328:"Long-form video understanding requires designing approaches that are able to
temporally localize activities or language. End-to-end training for such tasks
is limited by the compute device memory constraints and lack of temporal
annotations at large-scale. These limitations can be addressed by pre-training
on large datasets of temporally trimmed videos supervised by class annotations.
Once the video encoder is pre-trained, it is common practice to freeze it
during fine-tuning. Therefore, the video encoder does not learn temporal
boundaries and unseen classes, causing a domain gap with respect to the
downstream tasks. Moreover, using temporally trimmed videos prevents to capture
the relations between different action categories and the background context in
a video clip which results in limited generalization capacity. To address these
limitations, we propose a novel post-pre-training approach without freezing the
video encoder which leverages language. We introduce a masked contrastive
learning loss to capture visio-linguistic relations between activities,
background video clips and language in the form of captions. Our experiments
show that the proposed approach improves the state-of-the-art on temporal
action localization, few-shot temporal action localization, and video language
grounding tasks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:497:" <a href="http://arxiv.org/find/cs/1/au:+Xu_M/0/1/0/all/0/1">Mengmeng Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Gundogdu_E/0/1/0/all/0/1">Erhan Gundogdu</a>, <a href="http://arxiv.org/find/cs/1/au:+Lapin_M/0/1/0/all/0/1">Maksim Lapin</a>, <a href="http://arxiv.org/find/cs/1/au:+Ghanem_B/0/1/0/all/0/1">Bernard Ghanem</a>, <a href="http://arxiv.org/find/cs/1/au:+Donoser_M/0/1/0/all/0/1">Michael Donoser</a>, <a href="http://arxiv.org/find/cs/1/au:+Bazzani_L/0/1/0/all/0/1">Loris Bazzani</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:192;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12294";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:108:"Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims. (arXiv:2204.12294v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12294";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:929:"False information has a significant negative influence on individuals as well
as on the whole society. Especially in the current COVID-19 era, we witness an
unprecedented growth of medical misinformation. To help tackle this problem
with machine learning approaches, we are publishing a feature-rich dataset of
approx. 317k medical news articles/blogs and 3.5k fact-checked claims. It also
contains 573 manually and more than 51k automatically labelled mappings between
claims and articles. Mappings consist of claim presence, i.e., whether a claim
is contained in a given article, and article stance towards the claim. We
provide several baselines for these two tasks and evaluate them on the manually
labelled part of the dataset. The dataset enables a number of additional tasks
related to medical misinformation, such as misinformation characterisation
studies or studies of misinformation diffusion between sources.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:583:" <a href="http://arxiv.org/find/cs/1/au:+Srba_I/0/1/0/all/0/1">Ivan Srba</a>, <a href="http://arxiv.org/find/cs/1/au:+Pecher_B/0/1/0/all/0/1">Branislav Pecher</a>, <a href="http://arxiv.org/find/cs/1/au:+Tomlein_M/0/1/0/all/0/1">Matus Tomlein</a>, <a href="http://arxiv.org/find/cs/1/au:+Moro_R/0/1/0/all/0/1">Robert Moro</a>, <a href="http://arxiv.org/find/cs/1/au:+Stefancova_E/0/1/0/all/0/1">Elena Stefancova</a>, <a href="http://arxiv.org/find/cs/1/au:+Simko_J/0/1/0/all/0/1">Jakub Simko</a>, <a href="http://arxiv.org/find/cs/1/au:+Bielikova_M/0/1/0/all/0/1">Maria Bielikova</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:193;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12296";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:111:"Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels. (arXiv:2204.12296v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12296";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:815:"In this paper, we propose an unsupervised method for hyperspectral remote
sensing image segmentation. The method exploits the mean-shift clustering
algorithm that takes as input a preliminary hyperspectral superpixels
segmentation together with the spectral pixel information. The proposed method
does not require the number of segmentation classes as input parameter, and it
does not exploit any a-priori knowledge about the type of land-cover or
land-use to be segmented (e.g. water, vegetation, building etc.). Experiments
on Salinas, SalinasA, Pavia Center and Pavia University datasets are carried
out. Performance are measured in terms of normalized mutual information,
adjusted Rand index and F1-score. Results demonstrate the validity of the
proposed method in comparison with the state of the art.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:355:" <a href="http://arxiv.org/find/cs/1/au:+Barbato_M/0/1/0/all/0/1">Mirko Paolo Barbato</a>, <a href="http://arxiv.org/find/cs/1/au:+Napoletano_P/0/1/0/all/0/1">Paolo Napoletano</a>, <a href="http://arxiv.org/find/cs/1/au:+Piccoli_F/0/1/0/all/0/1">Flavio Piccoli</a>, <a href="http://arxiv.org/find/cs/1/au:+Schettini_R/0/1/0/all/0/1">Raimondo Schettini</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:194;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12297";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:124:"Brain Tumor Detection and Classification Using a New Evolutionary Convolutional Neural Network. (arXiv:2204.12297v1 [cs.NE])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12297";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1485:"A definitive diagnosis of a brain tumour is essential for enhancing treatment
success and patient survival. However, it is difficult to manually evaluate
multiple magnetic resonance imaging (MRI) images generated in a clinic.
Therefore, more precise computer-based tumour detection methods are required.
In recent years, many efforts have investigated classical machine learning
methods to automate this process. Deep learning techniques have recently
sparked interest as a means of diagnosing brain tumours more accurately and
robustly. The goal of this study, therefore, is to employ brain MRI images to
distinguish between healthy and unhealthy patients (including tumour tissues).
As a result, an enhanced convolutional neural network is developed in this
paper for accurate brain image classification. The enhanced convolutional
neural network structure is composed of components for feature extraction and
optimal classification. Nonlinear L\'evy Chaotic Moth Flame Optimizer (NLCMFO)
optimizes hyperparameters for training convolutional neural network layers.
Using the BRATS 2015 data set and brain image datasets from Harvard Medical
School, the proposed model is assessed and compared with various optimization
techniques. The optimized CNN model outperforms other models from the
literature by providing 97.4% accuracy, 96.0% sensitivity, 98.6% specificity,
98.4% precision, and 96.6% F1-score, (the mean of the weighted harmonic value
of CNN precision and recall).

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:434:" <a href="http://arxiv.org/find/cs/1/au:+Dehkordi_A/0/1/0/all/0/1">Amin Abdollahi Dehkordi</a>, <a href="http://arxiv.org/find/cs/1/au:+Hashemi_M/0/1/0/all/0/1">Mina Hashemi</a>, <a href="http://arxiv.org/find/cs/1/au:+Neshat_M/0/1/0/all/0/1">Mehdi Neshat</a>, <a href="http://arxiv.org/find/cs/1/au:+Mirjalili_S/0/1/0/all/0/1">Seyedali Mirjalili</a>, <a href="http://arxiv.org/find/cs/1/au:+Sadiq_A/0/1/0/all/0/1">Ali Safaa Sadiq</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:195;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12298";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:112:"Linear TDOA-based Measurements for Distributed Estimation and Localized Tracking. (arXiv:2204.12298v1 [eess.SY])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12298";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1036:"We propose a linear time-difference-of-arrival (TDOA) measurement model to
improve \textit{distributed} estimation performance for localized target
tracking. We design distributed filters over sparse (possibly large-scale)
communication networks using consensus-based data-fusion techniques. The
proposed distributed and localized tracking protocols considerably reduce the
sensor network's required connectivity and communication rate. We, further,
consider $\kappa$-redundant observability and fault-tolerant design in case of
losing communication links or sensor nodes. We present the minimal conditions
on the remaining sensor network (after link/node removal) such that the
distributed observability is still preserved and, thus, the sensor network can
track the (single) maneuvering target. The motivation is to reduce the
communication load versus the processing load, as the computational units are,
in general, less costly than the communication devices. We evaluate the
tracking performance via simulations in MATLAB.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:213:" <a href="http://arxiv.org/find/eess/1/au:+Doostmohammadian_M/0/1/0/all/0/1">Mohammadreza Doostmohammadian</a>, <a href="http://arxiv.org/find/eess/1/au:+Charalambous_T/0/1/0/all/0/1">Themistoklis Charalambous</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:196;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12300";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:77:"Unified GCNs: Towards Connecting GCNs with CNNs. (arXiv:2204.12300v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12300";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1611:"Graph Convolutional Networks (GCNs) have been widely demonstrated their
powerful ability in graph data representation and learning. Existing graph
convolution layers are mainly designed based on graph signal processing and
transform aspect which usually suffer from some limitations, such as
over-smoothing, over-squashing and non-robustness, etc. As we all know that
Convolution Neural Networks (CNNs) have received great success in many computer
vision and machine learning. One main aspect is that CNNs leverage many
learnable convolution filters (kernels) to obtain rich feature descriptors and
thus can have high capacity to encode complex patterns in visual data analysis.
Also, CNNs are flexible in designing their network architecture, such as
MobileNet, ResNet, Xception, etc. Therefore, it is natural to arise a question:
can we design graph convolutional layer as flexibly as that in CNNs?
Innovatively, in this paper, we consider connecting GCNs with CNNs deeply from
a general perspective of depthwise separable convolution operation.
Specifically, we show that GCN and GAT indeed perform some specific depthwise
separable convolution operations. This novel interpretation enables us to
better understand the connections between GCNs (GCN, GAT) and CNNs and further
inspires us to design more Unified GCNs (UGCNs). As two showcases, we implement
two UGCNs, i.e., Separable UGCN (S-UGCN) and General UGCN (G-UGCN) for graph
data representation and learning. Promising experiments on several graph
representation benchmarks demonstrate the effectiveness and advantages of the
proposed UGCNs.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:230:" <a href="http://arxiv.org/find/cs/1/au:+Zhang_Z/0/1/0/all/0/1">Ziyan Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Jiang_B/0/1/0/all/0/1">Bo Jiang</a>, <a href="http://arxiv.org/find/cs/1/au:+Luo_B/0/1/0/all/0/1">Bin Luo</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:197;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12301";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"Designing Perceptual Puzzles by Differentiating Probabilistic Programs. (arXiv:2204.12301v1 [cs.GR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12301";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:526:"We design new visual illusions by finding "adversarial examples" for
principled models of human perception -- specifically, for probabilistic
models, which treat vision as Bayesian inference. To perform this search
efficiently, we design a differentiable probabilistic programming language,
whose API exposes MCMC inference as a first-class differentiable function. We
demonstrate our method by automatically creating illusions for three features
of human vision: color constancy, size constancy, and face perception.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:346:" <a href="http://arxiv.org/find/cs/1/au:+Chandra_K/0/1/0/all/0/1">Kartik Chandra</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_T/0/1/0/all/0/1">Tzu-Mao Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Tenenbaum_J/0/1/0/all/0/1">Joshua Tenenbaum</a>, <a href="http://arxiv.org/find/cs/1/au:+Ragan_Kelley_J/0/1/0/all/0/1">Jonathan Ragan-Kelley</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:198;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12302";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:151:"From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report). (arXiv:2204.12302v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12302";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1066:"Industry 4.0 offers opportunities to combine multiple sensor data sources
using IoT technologies for better utilization of raw material in production
lines. A common belief that data is readily available (the big data
phenomenon), is oftentimes challenged by the need to effectively acquire
quality data under severe constraints. In this paper we propose a design
methodology, using active learning to enhance learning capabilities, for
building a model of production outcome using a constrained amount of raw
material training data. The proposed methodology extends existing active
learning methods to effectively solve regression-based learning problems and
may serve settings where data acquisition requires excessive resources in the
physical world. We further suggest a set of qualitative measures to analyze
learners performance. The proposed methodology is demonstrated using an actual
application in the milk industry, where milk is gathered from multiple small
milk farms and brought to a dairy production plant to be processed into cottage
cheese.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:401:" <a href="http://arxiv.org/find/cs/1/au:+Shraga_R/0/1/0/all/0/1">Roee Shraga</a>, <a href="http://arxiv.org/find/cs/1/au:+Katz_G/0/1/0/all/0/1">Gil Katz</a>, <a href="http://arxiv.org/find/cs/1/au:+Badian_Y/0/1/0/all/0/1">Yael Badian</a>, <a href="http://arxiv.org/find/cs/1/au:+Calderon_N/0/1/0/all/0/1">Nitay Calderon</a>, <a href="http://arxiv.org/find/cs/1/au:+Gal_A/0/1/0/all/0/1">Avigdor Gal</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:199;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12303";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:70:"On converses to the polynomial method. (arXiv:2204.12303v1 [quant-ph])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12303";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1011:"A surprising 'converse to the polynomial method' of Aaronson et al. (CCC'16)
shows that any bounded quadratic polynomial can be computed exactly in
expectation by a 1-query algorithm up to a universal multiplicative factor
related to the famous Grothendieck constant. A natural question posed there
asks if bounded quartic polynomials can be approximated by $2$-query quantum
algorithms. Arunachalam, Palazuelos and the first author showed that there is
no direct analogue of the result of Aaronson et al. in this case. We improve on
this result in the following ways: First, we point out and fix a small error in
the construction that has to do with a translation from cubic to quartic
polynomials. Second, we give a completely explicit example based on techniques
from additive combinatorics. Third, we show that the result still holds when we
allow for a small additive error. For this, we apply an SDP characterization of
Gribling and Laurent (QIP'19) for the completely-bounded approximate degree.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:200:" <a href="http://arxiv.org/find/quant-ph/1/au:+Briet_J/0/1/0/all/0/1">Jop Bri&#xeb;t</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Gutierrez_F/0/1/0/all/0/1">Francisco Escudero Guti&#xe9;rrez</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:200;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12307";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Scheduling of Sensor Transmissions Based on Value of Information for Summary Statistics. (arXiv:2204.12307v1 [cs.NI])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12307";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:735:"The optimization of Value of Information (VoI) in sensor networks integrates
awareness of the measured process in the communication system. However, most
existing scheduling algorithms do not consider the specific needs of monitoring
applications, but define VoI as a generic Mean Square Error (MSE) of the whole
system state regardless of the relevance of individual components. In this
work, we consider different summary statistics, i.e., different functions of
the state, which can represent the useful information for a monitoring process,
particularly in safety and industrial applications. We propose policies that
minimize the estimation error for different summary statistics, showing
significant gains by simulation.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:430:" <a href="http://arxiv.org/find/cs/1/au:+Chiariotti_F/0/1/0/all/0/1">Federico Chiariotti</a>, <a href="http://arxiv.org/find/cs/1/au:+Kalor_A/0/1/0/all/0/1">Anders E. Kal&#xf8;r</a>, <a href="http://arxiv.org/find/cs/1/au:+Holm_J/0/1/0/all/0/1">Josefine Holm</a>, <a href="http://arxiv.org/find/cs/1/au:+Soret_B/0/1/0/all/0/1">Beatriz Soret</a>, <a href="http://arxiv.org/find/cs/1/au:+Popovski_P/0/1/0/all/0/1">Petar Popovski</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:201;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12308";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:106:"Supervised Attention in Sequence-to-Sequence Models for Speech Recognition. (arXiv:2204.12308v1 [eess.AS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12308";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:893:"Attention mechanism in sequence-to-sequence models is designed to model the
alignments between acoustic features and output tokens in speech recognition.
However, attention weights produced by models trained end to end do not always
correspond well with actual alignments, and several studies have further argued
that attention weights might not even correspond well with the relevance
attribution of frames. Regardless, visual similarity between attention weights
and alignments is widely used during training as an indicator of the models
quality. In this paper, we treat the correspondence between attention weights
and alignments as a learning problem by imposing a supervised attention loss.
Experiments have shown significant improved performance, suggesting that
learning the alignments well during training critically determines the
performance of sequence-to-sequence models.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:161:" <a href="http://arxiv.org/find/eess/1/au:+Yang_G/0/1/0/all/0/1">Gene-Ping Yang</a>, <a href="http://arxiv.org/find/eess/1/au:+Tang_H/0/1/0/all/0/1">Hao Tang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:202;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12309";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:149:"Information Retrieval in Friction Stir Welding of Aluminum Alloys by using Natural Language Processing based Algorithms. (arXiv:2204.12309v1 [cs.IR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12309";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1158:"Text summarization is a technique for condensing a big piece of text into a
few key elements that give a general impression of the content. When someone
requires a quick and precise summary of a large amount of information, it
becomes vital. If done manually, summarizing text can be costly and
time-consuming. Natural Language Processing (NLP) is the sub-division of
Artificial Intelligence that narrows down the gap between technology and human
cognition by extracting the relevant information from the pile of data. In the
present work, scientific information regarding the Friction Stir Welding of
Aluminum alloys was collected from the abstract of scholarly research papers.
For extracting the relevant information from these research abstracts four
Natural Language Processing based algorithms i.e. Latent Semantic Analysis
(LSA), Luhn Algorithm, Lex Rank Algorithm, and KL-Algorithm were used. In order
to evaluate the accuracy score of these algorithms, Recall-Oriented Understudy
for Gisting Evaluation (ROUGE) was used. The results showed that the Luhn
Algorithm resulted in the highest f1-Score of 0.413 in comparison to other
algorithms.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:84:" <a href="http://arxiv.org/find/cs/1/au:+Mishra_A/0/1/0/all/0/1">Akshansh Mishra</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:203;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12311";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"Formalizing a Diophantine Representation of the Set of Prime Numbers. (arXiv:2204.12311v1 [math.NT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12311";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:901:"The DPRM (Davis-Putnam-Robinson-Matiyasevich) theorem is the main step in the
negative resolution of Hilbert's 10th problem. Almost three decades of work on
the problem have resulted in several equally surprising results. These include
the existence of diophantine equations with a reduced number of variables, as
well as the explicit construction of polynomials that represent specific sets,
in particular the set of primes. In this work, we formalize these constructions
in the Mizar system. We focus on the set of prime numbers and its explicit
representation using 10 variables. It is the smallest representation known
today. For this, we show that the exponential function is diophantine, together
with the same properties for the binomial coefficient and factorial. This
formalization is the next step in the research on formal approaches to
diophantine sets following the DPRM theorem.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:172:" <a href="http://arxiv.org/find/math/1/au:+Pak_K/0/1/0/all/0/1">Karol P&#x105;k</a>, <a href="http://arxiv.org/find/math/1/au:+Kaliszyk_C/0/1/0/all/0/1">Cezary Kaliszyk</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:204;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12316";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:132:"Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective. (arXiv:2204.12316v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12316";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:914:"Metamorphic testing has recently been used to check the safety of neural NLP
models. Its main advantage is that it does not rely on a ground truth to
generate test cases. However, existing studies are mostly concerned with
robustness-like metamorphic relations, limiting the scope of linguistic
properties they can test. We propose three new classes of metamorphic
relations, which address the properties of systematicity, compositionality and
transitivity. Unlike robustness, our relations are defined over multiple source
inputs, thus increasing the number of test cases that we can produce by a
polynomial factor. With them, we test the internal consistency of
state-of-the-art NLP models, and show that they do not always behave according
to their expected linguistic properties. Lastly, we introduce a novel graphical
notation that efficiently summarises the inner structure of metamorphic
relations.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:426:" <a href="http://arxiv.org/find/cs/1/au:+Manino_E/0/1/0/all/0/1">Edoardo Manino</a>, <a href="http://arxiv.org/find/cs/1/au:+Rozanova_J/0/1/0/all/0/1">Julia Rozanova</a>, <a href="http://arxiv.org/find/cs/1/au:+Carvalho_D/0/1/0/all/0/1">Danilo Carvalho</a>, <a href="http://arxiv.org/find/cs/1/au:+Freitas_A/0/1/0/all/0/1">Andre Freitas</a>, <a href="http://arxiv.org/find/cs/1/au:+Cordeiro_L/0/1/0/all/0/1">Lucas Cordeiro</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:205;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12318";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:116:"Evaluating the Quality of a Synthesized Motion with the Fr\&#039;echet Motion Distance. (arXiv:2204.12318v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12318";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:90:"Evaluating the Quality of a Synthesized Motion with the Fr\'echet Motion
Distance

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:250:" <a href="http://arxiv.org/find/cs/1/au:+Maiorca_A/0/1/0/all/0/1">Antoine Maiorca</a>, <a href="http://arxiv.org/find/cs/1/au:+Yoon_Y/0/1/0/all/0/1">Youngwoo Yoon</a>, <a href="http://arxiv.org/find/cs/1/au:+Dutoit_T/0/1/0/all/0/1">Thierry Dutoit</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:206;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12322";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:105:"RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization. (arXiv:2204.12322v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12322";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1403:"We introduce a Power-of-Two post-training quantization( PTQ) method for deep
neural network that meets hardware requirements and does not call for long-time
retraining. PTQ requires a small set of calibration data and is easier for
deployment, but results in lower accuracy than Quantization-Aware Training(
QAT). Power-of-Two quantization can convert the multiplication introduced by
quantization and dequantization to bit-shift that is adopted by many efficient
accelerators. However, the Power-of-Two scale has fewer candidate values, which
leads to more rounding or clipping errors. We propose a novel Power-of-Two PTQ
framework, dubbed RAPQ, which dynamically adjusts the Power-of-Two scales of
the whole network instead of statically determining them layer by layer. It can
theoretically trade off the rounding error and clipping error of the whole
network. Meanwhile, the reconstruction method in RAPQ is based on the BN
information of every unit. Extensive experiments on ImageNet prove the
excellent performance of our proposed method. Without bells and whistles, RAPQ
can reach accuracy of 65% and 48% on ResNet-18 and MobileNetV2 respectively
with weight INT2 activation INT4. We are the first to propose PTQ for the more
constrained but hardware-friendly Power-of-Two quantization and prove that it
can achieve nearly the same accuracy as SOTA PTQ method. The code will be
released.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:464:" <a href="http://arxiv.org/find/cs/1/au:+Yao_H/0/1/0/all/0/1">Hongyi Yao</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_P/0/1/0/all/0/1">Pu Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Cao_J/0/1/0/all/0/1">Jian Cao</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_X/0/1/0/all/0/1">Xiangcheng Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Xie_C/0/1/0/all/0/1">Chenying Xie</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_B/0/1/0/all/0/1">Bingzhang Wang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:207;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12326";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:112:"Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering. (arXiv:2204.12326v1 [cs.IR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12326";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1543:"Recent years have witnessed the great accuracy performance of graph-based
Collaborative Filtering (CF) models for recommender systems. By taking the
user-item interaction behavior as a graph, these graph-based CF models borrow
the success of Graph Neural Networks (GNN), and iteratively perform
neighborhood aggregation to propagate the collaborative signals. While
conventional CF models are known for facing the challenges of the popularity
bias that favors popular items, one may wonder "Whether the existing
graph-based CF models alleviate or exacerbate popularity bias of recommender
systems?" To answer this question, we first investigate the two-fold
performances w.r.t. accuracy and novelty for existing graph-based CF methods.
The empirical results show that symmetric neighborhood aggregation adopted by
most existing graph-based CF models exacerbate the popularity bias and this
phenomenon becomes more serious as the depth of graph propagation increases.
Further, we theoretically analyze the cause of popularity bias for graph-based
CF. Then, we propose a simple yet effective plugin, namely r-AdjNorm, to
achieve an accuracy-novelty trade-off by controlling the normalization strength
in the neighborhood aggregation process. Meanwhile, r-AdjNorm can be smoothly
applied to the existing graph-based CF backbones without additional
computation. Finally, experimental results on three benchmark datasets show
that our proposed method can improve novelty without sacrificing accuracy under
various graph-based CF backbones.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:767:" <a href="http://arxiv.org/find/cs/1/au:+Zhao_M/0/1/0/all/0/1">Minghao Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_L/0/1/0/all/0/1">Le Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Liang_Y/0/1/0/all/0/1">Yile Liang</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_L/0/1/0/all/0/1">Lei Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_J/0/1/0/all/0/1">Jian Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Deng_Q/0/1/0/all/0/1">Qilin Deng</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_K/0/1/0/all/0/1">Kai Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Shen_X/0/1/0/all/0/1">Xudong Shen</a>, <a href="http://arxiv.org/find/cs/1/au:+Lv_T/0/1/0/all/0/1">Tangjie Lv</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_R/0/1/0/all/0/1">Runze Wu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:208;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12330";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:54:"Twin-width VII: groups. (arXiv:2204.12330v1 [math.GR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12330";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1067:"Twin-width is a recently introduced graph parameter with applications in
algorithmics, combinatorics, and finite model theory. For graphs of bounded
degree, finiteness of twin-width is preserved by quasi-isometry. Thus, through
Cayley graphs, it defines a group invariant. We prove that groups which are
abelian, hyperbolic, ordered, solvable, or with polynomial growth, have finite
twin-width. Twin-width can be characterised by excluding patterns in the
self-action by product of the group elements. Based on this characterisation,
we propose a strengthening called uniform twin-width, which is stable under
constructions such as group extensions, direct products, and direct limits.

The existence of finitely generated groups with infinite twin-width is not
immediate. We construct one using a result of Osajda on embeddings of graphs
into groups. This implies the existence of a class of finite graphs with
unbounded twin-width but containing $2^{O(n)} \cdot n!$ graphs on vertex set
$\{1,\dots,n\}$, settling a question asked in a previous work.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:361:" <a href="http://arxiv.org/find/math/1/au:+Bonnet_E/0/1/0/all/0/1">&#xc9;douard Bonnet</a>, <a href="http://arxiv.org/find/math/1/au:+Geniet_C/0/1/0/all/0/1">Colin Geniet</a>, <a href="http://arxiv.org/find/math/1/au:+Tessera_R/0/1/0/all/0/1">Romain Tessera</a>, <a href="http://arxiv.org/find/math/1/au:+Thomasse_S/0/1/0/all/0/1">St&#xe9;phan Thomass&#xe9;</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:209;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12333";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:141:"An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes. (arXiv:2204.12333v1 [eess.IV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12333";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1853:"During the diagnosis of ischemic strokes, the Circle of Willis and its
surrounding vessels are the arteries of interest. Their visualization in case
of an acute stroke is often enabled by Computed Tomography Angiography (CTA).
Still, the identification and analysis of the cerebral arteries remain time
consuming in such scans due to a large number of peripheral vessels which may
disturb the visual impression. In previous work we proposed VirtualDSA++, an
algorithm designed to segment and label the cerebrovascular tree on CTA scans.
Especially with stroke patients, labeling is a delicate procedure, as in the
worst case whole hemispheres may not be present due to impeded perfusion.
Hence, we extended the labeling mechanism for the cerebral arteries to identify
occluded vessels. In the work at hand, we place the algorithm in a clinical
context by evaluating the labeling and occlusion detection on stroke patients,
where we have achieved labeling sensitivities comparable to other works between
92\,\% and 95\,\%. To the best of our knowledge, ours is the first work to
address labeling and occlusion detection at once, whereby a sensitivity of
67\,\% and a specificity of 81\,\% were obtained for the latter. VirtualDSA++
also automatically segments and models the intracranial system, which we
further used in a deep learning driven follow up work. We present the generic
concept of iterative systematic search for pathways on all nodes of said model,
which enables new interactive features. Exemplary, we derive in detail,
firstly, the interactive planning of vascular interventions like the mechanical
thrombectomy and secondly, the interactive suppression of vessel structures
that are not of interest in diagnosing strokes (like veins). We discuss both
features as well as further possibilities emerging from the proposed concept.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:600:" <a href="http://arxiv.org/find/eess/1/au:+Thamm_F/0/1/0/all/0/1">Florian Thamm</a>, <a href="http://arxiv.org/find/eess/1/au:+Jurgens_M/0/1/0/all/0/1">Markus J&#xfc;rgens</a>, <a href="http://arxiv.org/find/eess/1/au:+Taubmann_O/0/1/0/all/0/1">Oliver Taubmann</a>, <a href="http://arxiv.org/find/eess/1/au:+Thamm_A/0/1/0/all/0/1">Aleksandra Thamm</a>, <a href="http://arxiv.org/find/eess/1/au:+Rist_L/0/1/0/all/0/1">Leonhard Rist</a>, <a href="http://arxiv.org/find/eess/1/au:+Ditt_H/0/1/0/all/0/1">Hendrik Ditt</a>, <a href="http://arxiv.org/find/eess/1/au:+Maier_A/0/1/0/all/0/1">Andreas Maier</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:210;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12338";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:97:"Generating Topological Structure of Floorplans from Room Attributes. (arXiv:2204.12338v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12338";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:961:"Analysis of indoor spaces requires topological information. In this paper, we
propose to extract topological information from room attributes using what we
call Iterative and adaptive graph Topology Learning (ITL). ITL progressively
predicts multiple relations between rooms; at each iteration, it improves node
embeddings, which in turn facilitates generation of a better topological graph
structure. This notion of iterative improvement of node embeddings and
topological graph structure is in the same spirit as \cite{chen2020iterative}.
However, while \cite{chen2020iterative} computes the adjacency matrix based on
node similarity, we learn the graph metric using a relational decoder to
extract room correlations. Experiments using a new challenging indoor dataset
validate our proposed method. Qualitative and quantitative evaluation for
layout topology prediction and floorplan generation applications also
demonstrate the effectiveness of ITL.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:478:" <a href="http://arxiv.org/find/cs/1/au:+Yu_Y/0/1/0/all/0/1">Yin Yu</a>, <a href="http://arxiv.org/find/cs/1/au:+Will_H/0/1/0/all/0/1">Hutchcroft Will</a>, <a href="http://arxiv.org/find/cs/1/au:+Naji_K/0/1/0/all/0/1">Khosravan Naji</a>, <a href="http://arxiv.org/find/cs/1/au:+Ivaylo_B/0/1/0/all/0/1">Boyadzhiev Ivaylo</a>, <a href="http://arxiv.org/find/cs/1/au:+Yun_F/0/1/0/all/0/1">Fu Yun</a>, <a href="http://arxiv.org/find/cs/1/au:+Bing_K/0/1/0/all/0/1">Kang Sing Bing</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:211;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12340";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:76:"Lattices Without a Big Constant and With Noise. (arXiv:2204.12340v1 [cs.DS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12340";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:208:"We show how Frieze's analysis of subset sum solving using lattices can be
done with out any large constants and without flipping. We apply the variant
without the large constant to inputs with noise.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:169:" <a href="http://arxiv.org/find/cs/1/au:+Gortler_S/0/1/0/all/0/1">Steven J. Gortler</a>, <a href="http://arxiv.org/find/cs/1/au:+Theran_L/0/1/0/all/0/1">Louis Theran</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:212;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12344";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:84:"REDCHO: Robust Exact Dynamic Consensus of High Order. (arXiv:2204.12344v1 [eess.SY])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12344";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:794:"This article addresses the problem of average consensus in a multi-agent
system when the desired consensus quantity is a time varying signal. Recently,
the EDCHO protocol leveraged high order sliding modes to achieve exact
consensus under a constrained set of initial conditions, limiting its
applicability to static networks. In this work, we propose REDCHO, an extension
of the previous protocol which is robust to mismatch in the initial conditions,
making it suitable to use cases in which connection and disconnection of agents
is possible. The convergence properties of the protocol are formally explored.
Finally, the effectiveness and advantages of our proposal are shown with
concrete simulation examples showing the benefits of REDCHO against other
methods in the literature.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:295:" <a href="http://arxiv.org/find/eess/1/au:+Aldana_Lopez_R/0/1/0/all/0/1">Rodrigo Aldana-L&#xf3;pez</a>, <a href="http://arxiv.org/find/eess/1/au:+Aragues_R/0/1/0/all/0/1">Rosario Arag&#xfc;&#xe9;s</a>, <a href="http://arxiv.org/find/eess/1/au:+Sagues_C/0/1/0/all/0/1">Carlos Sag&#xfc;&#xe9;s</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:213;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12347";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"Restricted Black-box Adversarial Attack Against DeepFake Face Swapping. (arXiv:2204.12347v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12347";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1657:"DeepFake face swapping presents a significant threat to online security and
social media, which can replace the source face in an arbitrary photo/video
with the target face of an entirely different person. In order to prevent this
fraud, some researchers have begun to study the adversarial methods against
DeepFake or face manipulation. However, existing works focus on the white-box
setting or the black-box setting driven by abundant queries, which severely
limits the practical application of these methods. To tackle this problem, we
introduce a practical adversarial attack that does not require any queries to
the facial image forgery model. Our method is built on a substitute model
persuing for face reconstruction and then transfers adversarial examples from
the substitute model directly to inaccessible black-box DeepFake models.
Specially, we propose the Transferable Cycle Adversary Generative Adversarial
Network (TCA-GAN) to construct the adversarial perturbation for disrupting
unknown DeepFake systems. We also present a novel post-regularization module
for enhancing the transferability of generated adversarial examples. To
comprehensively measure the effectiveness of our approaches, we construct a
challenging benchmark of DeepFake adversarial attacks for future development.
Extensive experiments impressively show that the proposed adversarial attack
method makes the visual quality of DeepFake face images plummet so that they
are easier to be detected by humans and algorithms. Moreover, we demonstrate
that the proposed algorithm can be generalized to offer face image protection
against various face translation methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:313:" <a href="http://arxiv.org/find/cs/1/au:+Dong_J/0/1/0/all/0/1">Junhao Dong</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_Y/0/1/0/all/0/1">Yuan Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Lai_J/0/1/0/all/0/1">Jianhuang Lai</a>, <a href="http://arxiv.org/find/cs/1/au:+Xie_X/0/1/0/all/0/1">Xiaohua Xie</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:214;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12357";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"Direct 3D Printing of Soft Fluidic Actuators with Graded Porosity. (arXiv:2204.12357v1 [cs.RO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12357";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1689:"New additive manufacturing methods are needed to realize more complex soft
robots. One example is soft fluidic robotics, which exploits fluidic power and
stiffness gradients. Porous structures are an interesting type for this
approach, as they are flexible and allow for fluid transport. Within this work,
the Infill-Foam (InFoam) is proposed to print structures with graded porosity
by liquid rope coiling (LRC). By exploiting LRC, the InFoam method could
exploit the repeatable coiling patterns to print structures. To this end, only
the characterization of the relation between nozzle height and coil radius and
the extruded length were necessary (at a fixed temperature). Then by adjusting
the nozzle height and/or extrusion speed the porosity of the printed structure
could be set. The InFoam method was demonstrated by printing porous structures
using styrene-ethylene-butylene-styrene (SEBS) with porosities ranging from
46\% to 89\%. In compression tests, the cubes showed large changes in modulus
(more than 200 times), density (-89\% compared to bulk), and energy
dissipation. The InFoam method combined coiling and normal plotting to realize
a large range of porosity gradients. This grading was exploited to realize
rectangular structures with varying deformation patterns, which included
twisting, contraction, and bending. Furthermore, the InFoam method was shown to
be capable of programming the behavior of bending actuators by varying the
porosity. Both the output force and stroke showed correlations similar to those
of the cubes. Thus, the InFoam method can fabricate and program the mechanical
behavior of a soft fluidic (porous) actuator by grading porosity.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:261:" <a href="http://arxiv.org/find/cs/1/au:+Willemstein_N/0/1/0/all/0/1">Nick Willemstein</a>, <a href="http://arxiv.org/find/cs/1/au:+Kooij_H/0/1/0/all/0/1">Herman van der Kooij</a>, <a href="http://arxiv.org/find/cs/1/au:+Sadeghi_A/0/1/0/all/0/1">Ali Sadeghi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:215;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12358";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:69:"Polylogarithmic Sketches for Clustering. (arXiv:2204.12358v1 [cs.DS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12358";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1034:"Given $n$ points in $\ell_p^d$, we consider the problem of partitioning
points into $k$ clusters with associated centers. The cost of a clustering is
the sum of $p^{\text{th}}$ powers of distances of points to their cluster
centers. For $p \in [1,2]$, we design sketches of size
poly$(\log(nd),k,1/\epsilon)$ such that the cost of the optimal clustering can
be estimated to within factor $1+\epsilon$, despite the fact that the
compressed representation does not contain enough information to recover the
cluster centers or the partition into clusters. This leads to a streaming
algorithm for estimating the clustering cost with space
poly$(\log(nd),k,1/\epsilon)$. We also obtain a distributed memory algorithm,
where the $n$ points are arbitrarily partitioned amongst $m$ machines, each of
which sends information to a central party who then computes an approximation
of the clustering cost. Prior to this work, no such streaming or
distributed-memory algorithm was known with sublinear dependence on $d$ for $p
\in [1,2)$.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:174:" <a href="http://arxiv.org/find/cs/1/au:+Charikar_M/0/1/0/all/0/1">Moses Charikar</a>, <a href="http://arxiv.org/find/cs/1/au:+Waingarten_E/0/1/0/all/0/1">Erik Waingarten</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:216;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12363";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:76:"Causal Transportability for Visual Recognition. (arXiv:2204.12363v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12363";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1165:"Visual representations underlie object recognition tasks, but they often
contain both robust and non-robust features. Our main observation is that image
classifiers may perform poorly on out-of-distribution samples because spurious
correlations between non-robust features and labels can be changed in a new
environment. By analyzing procedures for out-of-distribution generalization
with a causal graph, we show that standard classifiers fail because the
association between images and labels is not transportable across settings.
However, we then show that the causal effect, which severs all sources of
confounding, remains invariant across domains. This motivates us to develop an
algorithm to estimate the causal effect for image classification, which is
transportable (i.e., invariant) across source and target environments. Without
observing additional variables, we show that we can derive an estimand for the
causal effect under empirical assumptions using representations in deep models
as proxies. Theoretical analysis, empirical results, and visualizations show
that our approach captures causal invariances and improves overall
generalization.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:563:" <a href="http://arxiv.org/find/cs/1/au:+Mao_C/0/1/0/all/0/1">Chengzhi Mao</a>, <a href="http://arxiv.org/find/cs/1/au:+Xia_K/0/1/0/all/0/1">Kevin Xia</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_J/0/1/0/all/0/1">James Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_H/0/1/0/all/0/1">Hao Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Yang_J/0/1/0/all/0/1">Junfeng Yang</a>, <a href="http://arxiv.org/find/cs/1/au:+Bareinboim_E/0/1/0/all/0/1">Elias Bareinboim</a>, <a href="http://arxiv.org/find/cs/1/au:+Vondrick_C/0/1/0/all/0/1">Carl Vondrick</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:217;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12365";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:106:"Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition. (arXiv:2204.12365v1 [stat.ML])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12365";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1277:"When a financial institution declines an application for credit, an adverse
action (AA) is said to occur. The applicant is then entitled to an explanation
for the negative decision. This paper focuses on credit decisions based on a
predictive model for probability of default and proposes a methodology for AA
explanation. The problem involves identifying the important predictors
responsible for the negative decision and is straightforward when the
underlying model is additive. However, it becomes non-trivial even for linear
models with interactions. We consider models with low-order interactions and
develop a simple and intuitive approach based on first principles. We then show
how the methodology generalizes to the well-known Shapely decomposition and the
recently proposed concept of Baseline Shapley (B-Shap). Unlike other Shapley
techniques in the literature for local interpretability of machine learning
results, B-Shap is computationally tractable since it involves just function
evaluations. An illustrative case study is used to demonstrate the usefulness
of the method. The paper also discusses situations with highly correlated
predictors and desirable properties of fitted models in the credit-lending
context, such as monotonicity and continuity.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:491:" <a href="http://arxiv.org/find/stat/1/au:+Nair_V/0/1/0/all/0/1">Vijayan N. Nair</a>, <a href="http://arxiv.org/find/stat/1/au:+Feng_T/0/1/0/all/0/1">Tianshu Feng</a>, <a href="http://arxiv.org/find/stat/1/au:+Hu_L/0/1/0/all/0/1">Linwei Hu</a>, <a href="http://arxiv.org/find/stat/1/au:+Zhang_Z/0/1/0/all/0/1">Zach Zhang</a>, <a href="http://arxiv.org/find/stat/1/au:+Chen_J/0/1/0/all/0/1">Jie Chen</a>, <a href="http://arxiv.org/find/stat/1/au:+Sudjianto_A/0/1/0/all/0/1">Agus Sudjianto</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:218;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12366";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"Robust Audio-Visual Instance Discrimination via Active Contrastive Set Mining. (arXiv:2204.12366v1 [cs.MM])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12366";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1199:"The recent success of audio-visual representation learning can be largely
attributed to their pervasive property of audio-visual synchronization, which
can be used as self-annotated supervision. As a state-of-the-art solution,
Audio-Visual Instance Discrimination (AVID) extends instance discrimination to
the audio-visual realm. Existing AVID methods construct the contrastive set by
random sampling based on the assumption that the audio and visual clips from
all other videos are not semantically related. We argue that this assumption is
rough, since the resulting contrastive sets have a large number of faulty
negatives. In this paper, we overcome this limitation by proposing a novel
Active Contrastive Set Mining (ACSM) that aims to mine the contrastive sets
with informative and diverse negatives for robust AVID. Moreover, we also
integrate a semantically-aware hard-sample mining strategy into our ACSM. The
proposed ACSM is implemented into two most recent state-of-the-art AVID methods
and significantly improves their performance. Extensive experiments conducted
on both action and sound recognition on multiple datasets show the remarkably
improved performance of our method.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:556:" <a href="http://arxiv.org/find/cs/1/au:+Xuan_H/0/1/0/all/0/1">Hanyu Xuan</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_Y/0/1/0/all/0/1">Yihong Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_S/0/1/0/all/0/1">Shuo Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_Z/0/1/0/all/0/1">Zhiliang Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Yang_J/0/1/0/all/0/1">Jian Yang</a>, <a href="http://arxiv.org/find/cs/1/au:+Yan_Y/0/1/0/all/0/1">Yan Yan</a>, <a href="http://arxiv.org/find/cs/1/au:+Alameda_Pineda_X/0/1/0/all/0/1">Xavier Alameda-Pineda</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:219;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12367";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:144:"ROMA: Cross-Domain Region Similarity Matching for Unpaired Nighttime Infrared to Daytime Visible Video Translation. (arXiv:2204.12367v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12367";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1912:"Infrared cameras are often utilized to enhance the night vision since the
visible light cameras exhibit inferior efficacy without sufficient
illumination. However, infrared data possesses inadequate color contrast and
representation ability attributed to its intrinsic heat-related imaging
principle. This makes it arduous to capture and analyze information for human
beings, meanwhile hindering its application. Although, the domain gaps between
unpaired nighttime infrared and daytime visible videos are even huger than
paired ones that captured at the same time, establishing an effective
translation mapping will greatly contribute to various fields. In this case,
the structural knowledge within nighttime infrared videos and semantic
information contained in the translated daytime visible pairs could be utilized
simultaneously. To this end, we propose a tailored framework ROMA that couples
with our introduced cRoss-domain regiOn siMilarity mAtching technique for
bridging the huge gaps. To be specific, ROMA could efficiently translate the
unpaired nighttime infrared videos into fine-grained daytime visible ones,
meanwhile maintain the spatiotemporal consistency via matching the cross-domain
region similarity. Furthermore, we design a multiscale region-wise
discriminator to distinguish the details from synthesized visible results and
real references. Extensive experiments and evaluations for specific
applications indicate ROMA outperforms the state-of-the-art methods. Moreover,
we provide a new and challenging dataset encouraging further research for
unpaired nighttime infrared and daytime visible video translation, named
InfraredCity. In particular, it consists of 9 long video clips including City,
Highway and Monitor scenarios. All clips could be split into 603,142 frames in
total, which are 20 times larger than the recently released daytime
infrared-to-visible dataset IRVI.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:466:" <a href="http://arxiv.org/find/cs/1/au:+Yu_Z/0/1/0/all/0/1">Zhenjie Yu</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_K/0/1/0/all/0/1">Kai Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_S/0/1/0/all/0/1">Shuang Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Han_B/0/1/0/all/0/1">Bingfeng Han</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_C/0/1/0/all/0/1">Chi Harold Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_S/0/1/0/all/0/1">Shuigen Wang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:220;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12368";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:79:"Coalgebraic Partition Refinement For All Functors. (arXiv:2204.12368v1 [cs.FL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12368";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1168:"Coalgebraic partition refinement generalizes classical transition system
minimization to general system types equipped with a coalgebraic equivalence
notion, subsuming strong, weighted, and probabilistic bisimilarity. The
asymptotically fastest algorithm requires an ad-hoc condition on the system
type and uses large amounts of memory, limiting the size of the transition
system that can be handled. A subsequent distributed algorithm is able to
handle larger systems by distributing the memory requirement over several
compute nodes, but this algorithm is asymptotically slower. We present an
algorithm that is applicable to all computable set functors, and runs in time
$O(k^2 n \log n)$, where n is the number of states and k is the number of
transitions per state. This algorithm is asymptotically slower than the fastest
algorithm by a factor of k, but asymptotically faster than the distributed
algorithm by a factor of n. In practice, our algorithm uses much less time and
memory on existing benchmarks. Transition systems that previously required half
an hour on HPC clusters can be minimized in seconds on a single core of a
laptop by our algorithm.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:174:" <a href="http://arxiv.org/find/cs/1/au:+Jacobs_J/0/1/0/all/0/1">Jules Jacobs</a>, <a href="http://arxiv.org/find/cs/1/au:+Wissmann_T/0/1/0/all/0/1">Thorsten Wi&#xdf;mann</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:221;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12371";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:132:"Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning. (arXiv:2204.12371v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12371";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1100:"How have individuals of social animals in nature evolved to learn from each
other, and what would be the optimal strategy for such learning in a specific
environment? Here, we address both problems by employing a deep reinforcement
learning model to optimize the social learning strategies (SLSs) of agents in a
cooperative game in a multi-dimensional landscape. Throughout the training for
maximizing the overall payoff, we find that the agent spontaneously learns
various concepts of social learning, such as copying, focusing on frequent and
well-performing neighbors, self-comparison, and the importance of balancing
between individual and social learning, without any explicit guidance or prior
knowledge about the system. The SLS from a fully trained agent outperforms all
of the traditional, baseline SLSs in terms of mean payoff. We demonstrate the
superior performance of the reinforcement learning agent in various
environments, including temporally changing environments and real social
networks, which also verifies the adaptability of our framework to different
social settings.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:160:" <a href="http://arxiv.org/find/cs/1/au:+Ha_S/0/1/0/all/0/1">Seungwoong Ha</a>, <a href="http://arxiv.org/find/cs/1/au:+Jeong_H/0/1/0/all/0/1">Hawoong Jeong</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:222;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12376";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:93:"Structural Rules and Algebraic Properties of Intersection Types. (arXiv:2204.12376v1 [cs.LO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12376";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:781:"In this paper we define several notions of term expansion, used to define
terms with less sharing, but with the same computational properties of terms
typable in an intersection type system. Expansion relates terms typed by
associative, commutative and idempotent intersections with terms typed in the
Curry type system and the relevant type system, terms typed by non-idempotent
intersections with terms typed in the affine and linear type systems and terms
typed by non-idempotent and non-commutative intersections with terms typed in
an ordered type system. Finally, we show how idempotent intersection is related
with the contraction rule, commutative intersection with the exchange rule and
associative intersection with the lack of structural rules in a type system.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:169:" <a href="http://arxiv.org/find/cs/1/au:+Alves_S/0/1/0/all/0/1">Sandra Alves</a>, <a href="http://arxiv.org/find/cs/1/au:+Florido_M/0/1/0/all/0/1">M&#xe1;rio Florido</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:223;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12378";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:110:"Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks. (arXiv:2204.12378v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12378";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1557:"Several areas have been improved with Deep Learning during the past years.
Implementing Deep Neural Networks (DNN) for non-safety related applications
have shown remarkable achievements over the past years; however, for using DNNs
in safety critical applications, we are missing approaches for verifying the
robustness of such models. A common challenge for DNNs occurs when exposed to
out-of-distribution samples that are outside of the scope of a DNN, but which
result in high confidence outputs despite no prior knowledge of such input.

In this paper, we analyze three methods that separate between in- and
out-of-distribution data, called supervisors, on four well-known DNN
architectures. We find that the outlier detection performance improves with the
quality of the model. We also analyse the performance of the particular
supervisors during the training procedure by applying the supervisor at a
predefined interval to investigate its performance as the training proceeds. We
observe that understanding the relationship between training results and
supervisor performance is crucial to improve the model's robustness and to
indicate, what input samples require further measures to improve the robustness
of a DNN. In addition, our work paves the road towards an instrument for safety
argumentation for safety critical applications. This paper is an extended
version of our previous work presented at 2019 SEAA (cf. [1]); here, we
elaborate on the used metrics, add an additional supervisor and test them on
two additional datasets.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:529:" <a href="http://arxiv.org/find/cs/1/au:+Henriksson_J/0/1/0/all/0/1">Jens Henriksson</a>, <a href="http://arxiv.org/find/cs/1/au:+Berger_C/0/1/0/all/0/1">Christian Berger</a>, <a href="http://arxiv.org/find/cs/1/au:+Borg_M/0/1/0/all/0/1">Markus Borg</a>, <a href="http://arxiv.org/find/cs/1/au:+Tornberg_L/0/1/0/all/0/1">Lars Tornberg</a>, <a href="http://arxiv.org/find/cs/1/au:+Sathyamoorthy_S/0/1/0/all/0/1">Sankar Raman Sathyamoorthy</a>, <a href="http://arxiv.org/find/cs/1/au:+Englund_C/0/1/0/all/0/1">Cristofer Englund</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:224;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12379";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:152:"An analytically divergence-free collocation method for the incompressible Navier-Stokes equations on the rotating sphere. (arXiv:2204.12379v1 [math.NA])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12379";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:846:"In this work, we develop a high-order collocation method using radial basis
function (RBF) for the incompressible Navier-Stokes equation (NSE) on the
rotating sphere. The method is based on solving the projection of the NSE on
the space of divergence-free functions. For that, we use matrix valued kernel
functions which allow an analytically divergence-free approximation of the
velocity field. Using kernel functions which lead to rotation-free
approximations, the pressure can be recovered by a simple kernel exchange in
one of the occurring approximations, without solving an additional Poisson
problem. We establish precise error estimates for the velocity and the pressure
functions for the semi-discretised solution. In the end, we give a short
estimate of the numerical cost and apply the new method to an experimental test
case.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:80:" <a href="http://arxiv.org/find/math/1/au:+Franz_T/0/1/0/all/0/1">Tino Franz</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:225;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12380";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:121:"Multi-task Learning for Concurrent Prediction of Thermal Comfort, Sensation, and Preference. (arXiv:2204.12380v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12380";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1890:"Indoor thermal comfort immensely impacts the health and performance of
occupants. Therefore, researchers and engineers have proposed numerous
computational models to estimate thermal comfort (TC). Given the impetus toward
energy efficiency, the current focus is on data-driven TC prediction solutions
that leverage state-of-the-art machine learning (ML) algorithms. However, an
indoor occupant's perception of indoor thermal comfort (TC) is subjective and
multi-dimensional. Different aspects of TC are represented by various standard
metrics/scales viz., thermal sensation (TSV), thermal comfort (TCV), and
thermal preference (TPV). The current ML-based TC prediction solutions adopt
the Single-task Learning approach, i.e., one prediction model per metric.
Consequently, solutions often focus on only one TC metric. Moreover, when
several metrics are considered, multiple TC models for a single indoor space
lead to conflicting predictions, making real-world deployment infeasible. This
work addresses these problems. With the vision toward energy conservation and
real-world application, naturally ventilated primary school classrooms are
considered. First, month-long field experiments are conducted in 5 schools and
14 classrooms, including 512 unique student participants. Further,
"DeepComfort," a Multi-task Learning inspired deep-learning model is proposed.
DeepComfort predicts multiple TC output metrics viz., TSV, TPV, and TCV,
simultaneously, through a single model. It demonstrates high F1-scores,
Accuracy (&gt;90%), and generalization capability when validated on the ASHRAE-II
database and the dataset created in this study. DeepComfort is also shown to
outperform 6 popular metric-specific single-task machine learning algorithms.
To the best of our knowledge, this work is the first application of Multi-task
Learning to thermal comfort prediction in classrooms.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:328:" <a href="http://arxiv.org/find/cs/1/au:+Lala_B/0/1/0/all/0/1">Betty Lala</a>, <a href="http://arxiv.org/find/cs/1/au:+Rizk_H/0/1/0/all/0/1">Hamada Rizk</a>, <a href="http://arxiv.org/find/cs/1/au:+Kala_S/0/1/0/all/0/1">Srikant Manas Kala</a>, <a href="http://arxiv.org/find/cs/1/au:+Hagishima_A/0/1/0/all/0/1">Aya Hagishima</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:226;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12384";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:92:"Qunity: A Unified Language for Quantum and Classical Computing. (arXiv:2204.12384v1 [cs.PL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12384";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1152:"We introduce Qunity, a new quantum programming language designed around the
central goal of treating quantum computing as a natural generalization of
classical computing. Qunity presents a unified syntax where familiar
programming constructs can have both quantum and classical effects. For
example, one can use sum types to implement the direct sum of linear operators,
exception handling syntax to implement projective measurements, and aliasing to
induce entanglement. Further, Qunity takes advantage of the overlooked BQP
subroutine theorem, allowing one to construct reversible subroutines from
irreversible quantum algorithms through the uncomputation of "garbage" outputs.
Unlike existing languages that enable quantum aspects with a separate add-on
(e.g., gates added to a classical language), we unify quantum and classical
computing through novel compositional semantics based on Kraus operators. We
present Qunity's syntax, type system, and denotational semantics, showing how
it can cleanly express several quantum algorithms. We also outline how Qunity
could be compiled to OpenQASM, demonstrating the realizability of our design.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:245:" <a href="http://arxiv.org/find/cs/1/au:+Voichick_F/0/1/0/all/0/1">Finn Voichick</a>, <a href="http://arxiv.org/find/cs/1/au:+Rand_R/0/1/0/all/0/1">Robert Rand</a>, <a href="http://arxiv.org/find/cs/1/au:+Hicks_M/0/1/0/all/0/1">Michael Hicks</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:227;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12386";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:119:"Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings. (arXiv:2204.12386v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12386";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1054:"Given multiple source word embeddings learnt using diverse algorithms and
lexical resources, meta word embedding learning methods attempt to learn more
accurate and wide-coverage word embeddings.

Prior work on meta-embedding has repeatedly discovered that simple vector
concatenation of the source embeddings to be a competitive baseline.

However, it remains unclear as to why and when simple vector concatenation
can produce accurate meta-embeddings.

We show that weighted concatenation can be seen as a spectrum matching
operation between each source embedding and the meta-embedding, minimising the
pairwise inner-product loss.

Following this theoretical analysis, we propose two \emph{unsupervised}
methods to learn the optimal concatenation weights for creating meta-embeddings
from a given set of source embeddings.

Experimental results on multiple benchmark datasets show that the proposed
weighted concatenated meta-embedding methods outperform previously proposed
meta-embedding learning methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:90:" <a href="http://arxiv.org/find/cs/1/au:+Bollegala_D/0/1/0/all/0/1">Danushka Bollegala</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:228;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12390";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Quantum-classical convolutional neural networks in radiological image classification. (arXiv:2204.12390v1 [quant-ph])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12390";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1061:"Quantum machine learning is receiving significant attention currently, but
its usefulness in comparison to classical machine learning techniques for
practical applications remains unclear. However, there are indications that
certain quantum machine learning algorithms might result in improved training
capabilities with respect to their classical counterparts - which might be
particularly beneficial in situations with little training data available. Such
situations naturally arise in medical classification tasks. Within this paper,
different hybrid quantum-classical convolutional neural networks (QCCNN) with
varying quantum circuit designs and encoding techniques are proposed. They are
applied to two- and three-dimensional medical imaging data, e.g. featuring
different, potentially malign, lesions in computed tomography scans. The
performance of these QCCNNs is already similar to the one of their classical
counterparts - therefore encouraging further studies towards the direction of
applying these algorithms within medical imaging tasks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:467:" <a href="http://arxiv.org/find/quant-ph/1/au:+Matic_A/0/1/0/all/0/1">Andrea Matic</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Monnet_M/0/1/0/all/0/1">Maureen Monnet</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Lorenz_J/0/1/0/all/0/1">Jeanette Miriam Lorenz</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Schachtner_B/0/1/0/all/0/1">Balthasar Schachtner</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Messerer_T/0/1/0/all/0/1">Thomas Messerer</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:229;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12393";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:97:"On Fragile Features and Batch Normalization in Adversarial Training. (arXiv:2204.12393v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12393";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1038:"Modern deep learning architecture utilize batch normalization (BN) to
stabilize training and improve accuracy. It has been shown that the BN layers
alone are surprisingly expressive. In the context of robustness against
adversarial examples, however, BN is argued to increase vulnerability. That is,
BN helps to learn fragile features. Nevertheless, BN is still used in
adversarial training, which is the de-facto standard to learn robust features.
In order to shed light on the role of BN in adversarial training, we
investigate to what extent the expressiveness of BN can be used to robustify
fragile features in comparison to random features. On CIFAR10, we find that
adversarially fine-tuning just the BN layers can result in non-trivial
adversarial robustness. Adversarially training only the BN layers from scratch,
in contrast, is not able to convey meaningful adversarial robustness. Our
results indicate that fragile features can be used to learn models with
moderate adversarial robustness, while random features cannot

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:252:" <a href="http://arxiv.org/find/cs/1/au:+Walter_N/0/1/0/all/0/1">Nils Philipp Walter</a>, <a href="http://arxiv.org/find/cs/1/au:+Stutz_D/0/1/0/all/0/1">David Stutz</a>, <a href="http://arxiv.org/find/cs/1/au:+Schiele_B/0/1/0/all/0/1">Bernt Schiele</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:230;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12397";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:76:"Tolerant Bipartiteness Testing in Dense Graphs. (arXiv:2204.12397v1 [cs.DS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12397";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1186:"Bipartite testing has been a central problem in the area of property testing
since its inception in the seminal work of Goldreich, Goldwasser and Ron
[FOCS'96 and JACM'98]. Though the non-tolerant version of bipartite testing has
been extensively studied in the literature, the tolerant variant is not well
understood. In this paper, we consider the following version of tolerant
bipartite testing: Given a parameter $\varepsilon \in (0,1)$ and access to the
adjacency matrix of a graph $G$, we can decide whether $G$ is
$\varepsilon$-close to being bipartite or $G$ is at least
$(2+\Omega(1))\varepsilon$-far from being bipartite, by performing
$\widetilde{\mathcal{O}}\left(\frac{1}{\varepsilon ^3}\right)$ queries and in
$2^{\widetilde{\mathcal{O}}(1/\varepsilon)}$ time. This improves upon the
state-of-the-art query and time complexities of this problem of
$\widetilde{\mathcal{O}}\left(\frac{1}{\varepsilon ^6}\right)$ and
$2^{\widetilde{\mathcal{O}}(1/\varepsilon^2)}$, respectively, from the work of
Alon, Fernandez de la Vega, Kannan and Karpinski (STOC'02 and JCSS'03), where
$\widetilde{\mathcal{O}}(\cdot)$ hides a factor polynomial in $\log
\frac{1}{\varepsilon}$.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:338:" <a href="http://arxiv.org/find/cs/1/au:+Ghosh_A/0/1/0/all/0/1">Arijit Ghosh</a>, <a href="http://arxiv.org/find/cs/1/au:+Mishra_G/0/1/0/all/0/1">Gopinath Mishra</a>, <a href="http://arxiv.org/find/cs/1/au:+Raychaudhury_R/0/1/0/all/0/1">Rahul Raychaudhury</a>, <a href="http://arxiv.org/find/cs/1/au:+Sen_S/0/1/0/all/0/1">Sayantan Sen</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:231;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12399";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:89:"Streaming Algorithms for High-Dimensional Robust Statistics. (arXiv:2204.12399v1 [cs.DS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12399";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1015:"We study high-dimensional robust statistics tasks in the streaming model. A
recent line of work obtained computationally efficient algorithms for a range
of high-dimensional robust estimation tasks. Unfortunately, all previous
algorithms require storing the entire dataset, incurring memory at least
quadratic in the dimension. In this work, we develop the first efficient
streaming algorithms for high-dimensional robust statistics with near-optimal
memory requirements (up to logarithmic factors). Our main result is for the
task of high-dimensional robust mean estimation in (a strengthening of) Huber's
contamination model. We give an efficient single-pass streaming algorithm for
this task with near-optimal error guarantees and space complexity nearly-linear
in the dimension. As a corollary, we obtain streaming algorithms with
near-optimal space complexity for several more complex tasks, including robust
covariance estimation, robust regression, and more generally robust stochastic
optimization.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:342:" <a href="http://arxiv.org/find/cs/1/au:+Diakonikolas_I/0/1/0/all/0/1">Ilias Diakonikolas</a>, <a href="http://arxiv.org/find/cs/1/au:+Kane_D/0/1/0/all/0/1">Daniel M. Kane</a>, <a href="http://arxiv.org/find/cs/1/au:+Pensia_A/0/1/0/all/0/1">Ankit Pensia</a>, <a href="http://arxiv.org/find/cs/1/au:+Pittas_T/0/1/0/all/0/1">Thanasis Pittas</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:232;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12402";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:109:"Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems. (arXiv:2204.12402v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12402";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1203:"Machine learning (ML)-enabled approaches are considered a substantial support
technique of detection and classification of obstacles of traffic participants
in self-driving vehicles. Major breakthroughs have been demonstrated the past
few years, even covering complete end-to-end data processing chain from sensory
inputs through perception and planning to vehicle control of acceleration,
breaking and steering. YOLO (you-only-look-once) is a state-of-the-art
perception neural network (NN) architecture providing object detection and
classification through bounding box estimations on camera images. As the NN is
trained on well annotated images, in this paper we study the variations of
confidence levels from the NN when tested on hand-crafted occlusion added to a
test set. We compare regular pedestrian detection to upper and lower body
detection. Our findings show that the two NN using only partial information
perform similarly well like the NN for the full body when the full body NN's
performance is 0.75 or better. Furthermore and as expected, the network, which
is only trained on the lower half body is least prone to disturbances from
occlusions of the upper half and vice versa.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:255:" <a href="http://arxiv.org/find/cs/1/au:+Henriksson_J/0/1/0/all/0/1">Jens Henriksson</a>, <a href="http://arxiv.org/find/cs/1/au:+Berger_C/0/1/0/all/0/1">Christian Berger</a>, <a href="http://arxiv.org/find/cs/1/au:+Ursing_S/0/1/0/all/0/1">Stig Ursing</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:233;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12404";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"Knowledge Transfer in Engineering Fleets: Hierarchical Bayesian Modelling for Multi-Task Learning. (arXiv:2204.12404v1 [stat.ML])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12404";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:845:"We propose a population-level analysis to address issues of data sparsity
when building predictive models of engineering infrastructure. By sharing
information between similar assets, hierarchical Bayesian modelling is used to
improve the survival analysis of a truck fleet (hazard curves) and power
prediction in a wind farm (power curves). In each example, a set of correlated
functions are learnt over the asset fleet, in a combined inference, to learn a
population model. Parameter estimation is improved when sub-fleets of assets
are allowed to share correlated information at different levels in the
hierarchy. In turn, groups with incomplete data automatically borrow
statistical strength from those that are data-rich. The correlations can be
inspected to inform which assets share information for which effect (i.e.
parameter).

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:582:" <a href="http://arxiv.org/find/stat/1/au:+Bull_L/0/1/0/all/0/1">L.A. Bull</a>, <a href="http://arxiv.org/find/stat/1/au:+Dhada_M/0/1/0/all/0/1">M. Dhada</a>, <a href="http://arxiv.org/find/stat/1/au:+Steinert_O/0/1/0/all/0/1">O. Steinert</a>, <a href="http://arxiv.org/find/stat/1/au:+Lindgren_T/0/1/0/all/0/1">T. Lindgren</a>, <a href="http://arxiv.org/find/stat/1/au:+Parlikad_A/0/1/0/all/0/1">A.K. Parlikad</a>, <a href="http://arxiv.org/find/stat/1/au:+Duncan_A/0/1/0/all/0/1">A.B. Duncan</a>, <a href="http://arxiv.org/find/stat/1/au:+Girolami_M/0/1/0/all/0/1">M. Girolami</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:234;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12406";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"A survey on attention mechanisms for medical applications: are we moving towards better algorithms?. (arXiv:2204.12406v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12406";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1499:"The increasing popularity of attention mechanisms in deep learning algorithms
for computer vision and natural language processing made these models
attractive to other research domains. In healthcare, there is a strong need for
tools that may improve the routines of the clinicians and the patients.
Naturally, the use of attention-based algorithms for medical applications
occurred smoothly. However, being healthcare a domain that depends on
high-stake decisions, the scientific community must ponder if these
high-performing algorithms fit the needs of medical applications. With this
motto, this paper extensively reviews the use of attention mechanisms in
machine learning (including Transformers) for several medical applications.
This work distinguishes itself from its predecessors by proposing a critical
analysis of the claims and potentialities of attention mechanisms presented in
the literature through an experimental case study on medical image
classification with three different use cases. These experiments focus on the
integrating process of attention mechanisms into established deep learning
architectures, the analysis of their predictive power, and a visual assessment
of their saliency maps generated by post-hoc explanation methods. This paper
concludes with a critical analysis of the claims and potentialities presented
in the literature about attention mechanisms and proposes future research lines
in medical applications that may benefit from these frameworks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:361:" <a href="http://arxiv.org/find/cs/1/au:+Goncalves_T/0/1/0/all/0/1">Tiago Gon&#xe7;alves</a>, <a href="http://arxiv.org/find/cs/1/au:+Rio_Torto_I/0/1/0/all/0/1">Isabel Rio-Torto</a>, <a href="http://arxiv.org/find/cs/1/au:+Teixeira_L/0/1/0/all/0/1">Lu&#xed;s F. Teixeira</a>, <a href="http://arxiv.org/find/cs/1/au:+Cardoso_J/0/1/0/all/0/1">Jaime S. Cardoso</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:235;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12408";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:119:"MILES: Visual BERT Pre-training with Injected Language Semantics for Video-text Retrieval. (arXiv:2204.12408v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12408";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1396:"Dominant pre-training work for video-text retrieval mainly adopt the
"dual-encoder" architectures to enable efficient retrieval, where two separate
encoders are used to contrast global video and text representations, but ignore
detailed local semantics. The recent success of image BERT pre-training with
masked visual modeling that promotes the learning of local visual context,
motivates a possible solution to address the above limitation. In this work, we
for the first time investigate masked visual modeling in video-text
pre-training with the "dual-encoder" architecture. We perform Masked visual
modeling with Injected LanguagE Semantics (MILES) by employing an extra
snapshot video encoder as an evolving "tokenizer" to produce reconstruction
targets for masked video patch prediction. Given the corrupted video, the video
encoder is trained to recover text-aligned features of the masked patches via
reasoning with the visible regions along the spatial and temporal dimensions,
which enhances the discriminativeness of local visual features and the
fine-grained cross-modality alignment. Our method outperforms state-of-the-art
methods for text-to-video retrieval on four datasets with both zero-shot and
fine-tune evaluation protocols. Our approach also surpasses the baseline models
significantly on zero-shot action recognition, which can be cast as
video-to-text retrieval.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:616:" <a href="http://arxiv.org/find/cs/1/au:+Ge_Y/0/1/0/all/0/1">Yuying Ge</a>, <a href="http://arxiv.org/find/cs/1/au:+Ge_Y/0/1/0/all/0/1">Yixiao Ge</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_X/0/1/0/all/0/1">Xihui Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_A/0/1/0/all/0/1">Alex Jinpeng Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_J/0/1/0/all/0/1">Jianping Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Shan_Y/0/1/0/all/0/1">Ying Shan</a>, <a href="http://arxiv.org/find/cs/1/au:+Qie_X/0/1/0/all/0/1">Xiaohu Qie</a>, <a href="http://arxiv.org/find/cs/1/au:+Luo_P/0/1/0/all/0/1">Ping Luo</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:236;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12409";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:85:"Distributed controller synthesis for deadlock avoidance. (arXiv:2204.12409v1 [cs.LO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12409";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:731:"We consider the distributed control synthesis problem for systems with locks.
The goal is to find local controllers so that the global system does not
deadlock. With no restriction this problem is undecidable even for three
processes each using a fixed number of locks. We propose two restrictions that
make distributed control decidable. The first one is to allow each process to
use at most two locks. The problem then becomes Sigma2P-complete, and even in
PTIME under some additional assumptions. The dining philosophers problem
satisfies these assumptions. The second restriction is a nested usage of locks.
In this case the synthesis problem is NEXPTIME-complete. The drinking
philosophers problem falls in this case.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:340:" <a href="http://arxiv.org/find/cs/1/au:+Gimbert_H/0/1/0/all/0/1">Hugo Gimbert</a>, <a href="http://arxiv.org/find/cs/1/au:+Mascle_C/0/1/0/all/0/1">Corto Mascle</a>, <a href="http://arxiv.org/find/cs/1/au:+Muscholl_A/0/1/0/all/0/1">Anca Muscholl</a>, <a href="http://arxiv.org/find/cs/1/au:+Walukiewicz_I/0/1/0/all/0/1">Igor Walukiewicz</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:237;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12415";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:124:"Automatic Monitoring of Fruit Ripening Rooms by UHF RFID Sensor Network and Machine Learning. (arXiv:2204.12415v1 [eess.SY])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12415";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:784:"Accelerated ripening through the exposure of fruits to controlled
environmental conditions and gases is nowadays one of the most assessed food
technologies, especially for climacteric and exotic products. However, a fine
granularity control of the process and consequently of the quality of the goods
is still missing, so the management of the ripening rooms is mainly based on
qualitative estimations only. Following the modern paradigms of Industry 4.0,
this contribution proposes a non-destructive RFID-based system for the
automatic evaluation of the live ripening of avocados. The system, coupled with
a properly trained automatic classification algorithm based on Support Vector
Machines (SVMs), can discriminate the stage of ripening with an accuracy
greater than 85%.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:901:" <a href="http://arxiv.org/find/eess/1/au:+Occhiuzzi_C/0/1/0/all/0/1">Cecilia Occhiuzzi</a>, <a href="http://arxiv.org/find/eess/1/au:+Camera_F/0/1/0/all/0/1">Francesca Camera</a>, <a href="http://arxiv.org/find/eess/1/au:+DOrazio_M/0/1/0/all/0/1">Michele D&#x27;Orazio</a>, <a href="http://arxiv.org/find/eess/1/au:+DUva_N/0/1/0/all/0/1">Nicola D&#x27;Uva</a>, <a href="http://arxiv.org/find/eess/1/au:+Amendola_S/0/1/0/all/0/1">Sara Amendola</a>, <a href="http://arxiv.org/find/eess/1/au:+Bianco_G/0/1/0/all/0/1">Giulio Maria Bianco</a>, <a href="http://arxiv.org/find/eess/1/au:+Miozzi_C/0/1/0/all/0/1">Carolina Miozzi</a>, <a href="http://arxiv.org/find/eess/1/au:+Garavaglia_L/0/1/0/all/0/1">Luigi Garavaglia</a>, <a href="http://arxiv.org/find/eess/1/au:+Martinelli_E/0/1/0/all/0/1">Eugenio Martinelli</a>, <a href="http://arxiv.org/find/eess/1/au:+Marrocco_G/0/1/0/all/0/1">Gaetano Marrocco</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:238;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12416";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:123:"XSS for the Masses: Integrating Security in a Web Programming Course using a Security Scanner. (arXiv:2204.12416v1 [cs.CR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12416";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1323:"Cybersecurity education is considered an important part of undergraduate
computing curricula, but many institutions teach it only in dedicated courses
or tracks. This optionality risks students graduating with limited exposure to
secure coding practices that are expected in industry. An alternative approach
is to integrate cybersecurity concepts across non-security courses, so as to
expose students to the interplay between security and other sub-areas of
computing. In this paper, we report on our experience of applying the security
integration approach to an undergraduate web programming course. In particular,
we added a practical introduction to secure coding, which highlighted the OWASP
Top 10 vulnerabilities by example, and demonstrated how to identify them using
out-of-the-box security scanner tools (e.g. ZAP). Furthermore, we incentivised
students to utilise these tools in their own course projects by offering bonus
marks. To assess the impact of this intervention, we scanned students' project
code over the last three years, finding a reduction in the number of
vulnerabilities. Finally, in focus groups and a survey, students shared that
our intervention helped to raise awareness, but they also highlighted the
importance of grading incentives and the need to teach security content
earlier.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:344:" <a href="http://arxiv.org/find/cs/1/au:+Shar_L/0/1/0/all/0/1">Lwin Khin Shar</a>, <a href="http://arxiv.org/find/cs/1/au:+Poskitt_C/0/1/0/all/0/1">Christopher M. Poskitt</a>, <a href="http://arxiv.org/find/cs/1/au:+Shim_K/0/1/0/all/0/1">Kyong Jin Shim</a>, <a href="http://arxiv.org/find/cs/1/au:+Wong_L/0/1/0/all/0/1">Li Ying Leonard Wong</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:239;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12418";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:112:"Bifrost: End-to-End Evaluation and Optimization of Reconfigurable DNN Accelerators. (arXiv:2204.12418v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12418";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1324:"Reconfigurable accelerators for deep neural networks (DNNs) promise to
improve performance such as inference latency. STONNE is the first
cycle-accurate simulator for reconfigurable DNN inference accelerators which
allows for the exploration of accelerator designs and configuration space.
However, preparing models for evaluation and exploring configuration space in
STONNE is a manual developer-timeconsuming process, which is a barrier for
research. This paper introduces Bifrost, an end-to-end framework for the
evaluation and optimization of reconfigurable DNN inference accelerators.
Bifrost operates as a frontend for STONNE and leverages the TVM deep learning
compiler stack to parse models and automate offloading of accelerated
computations. We discuss Bifrost's advantages over STONNE and other tools, and
evaluate the MAERI and SIGMA architectures using Bifrost. Additionally, Bifrost
introduces a module leveraging AutoTVM to efficiently explore accelerator
designs and dataflow mapping space to optimize performance. This is
demonstrated by tuning the MAERI architecture and generating efficient dataflow
mappings for AlexNet, obtaining an average speedup of $50\times$ for the
convolutional layers and $11\times$ for the fully connected layers. Our code is
available at www.github.com/gicLAB/bifrost.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:252:" <a href="http://arxiv.org/find/cs/1/au:+Stjerngren_A/0/1/0/all/0/1">Axel Stjerngren</a>, <a href="http://arxiv.org/find/cs/1/au:+Gibson_P/0/1/0/all/0/1">Perry Gibson</a>, <a href="http://arxiv.org/find/cs/1/au:+Cano_J/0/1/0/all/0/1">Jos&#xe9; Cano</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:240;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12420";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:124:"Interpretable Battery Cycle Life Range Prediction Using Early Degradation Data at Cell Level. (arXiv:2204.12420v1 [eess.SY])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12420";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1400:"Battery cycle life prediction using early degradation data has many potential
applications throughout the battery product life cycle. Various data-driven
methods have been proposed for point prediction of battery cycle life with
minimum knowledge of the battery degradation mechanisms. However, management of
batteries at end-of-life with lower economic and technical risk requires
prediction of cycle life with quantified uncertainty, which is still lacking.
The interpretability (i.e., the reason for high prediction accuracy) of these
advanced data-driven methods is also worthy of investigation. Here, a
physics-informed Quantile Regression Forest (QRF) model is introduced to make
cycle life range prediction with uncertainty quantified as the length of the
prediction interval, in addition to point predictions with high accuracy. The
hyperparameters of the QRF model are tuned with a proposed area-based
performance evaluation metric so that the coverage probabilities associated
with the prediction intervals are calibrated. The interpretability of the final
QRF model is explored with two global model-agnostic methods, namely
permutation importance, and partial dependence plot. The final QRF model
facilitates dual-criteria decision-making to select the high-cycle-life
charging protocol with consideration of both point predictions and uncertainty
associated with the prediction.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:403:" <a href="http://arxiv.org/find/eess/1/au:+Zhang_H/0/1/0/all/0/1">Huang Zhang</a>, <a href="http://arxiv.org/find/eess/1/au:+Su_Y/0/1/0/all/0/1">Yang Su</a>, <a href="http://arxiv.org/find/eess/1/au:+Altaf_F/0/1/0/all/0/1">Faisal Altaf</a>, <a href="http://arxiv.org/find/eess/1/au:+Wik_T/0/1/0/all/0/1">Torsten Wik</a>, <a href="http://arxiv.org/find/eess/1/au:+Gros_S/0/1/0/all/0/1">Sebastien Gros</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:241;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12421";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:128:"Disambiguation of morpho-syntactic features of African American English -- the case of habitual be. (arXiv:2204.12421v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12421";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:970:"Recent research has highlighted that natural language processing (NLP)
systems exhibit a bias against African American speakers. The bias errors are
often caused by poor representation of linguistic features unique to African
American English (AAE), due to the relatively low probability of occurrence of
many such features in training data. We present a workflow to overcome such
bias in the case of habitual "be". Habitual "be" is isomorphic, and therefore
ambiguous, with other forms of "be" found in both AAE and other varieties of
English. This creates a clear challenge for bias in NLP technologies. To
overcome the scarcity, we employ a combination of rule-based filters and data
augmentation that generate a corpus balanced between habitual and non-habitual
instances. With this balanced corpus, we train unbiased machine learning
classifiers, as demonstrated on a corpus of AAE transcribed texts, achieving
.65 F$_1$ score disambiguating habitual "be".

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:333:" <a href="http://arxiv.org/find/cs/1/au:+Santiago_H/0/1/0/all/0/1">Harrison Santiago</a>, <a href="http://arxiv.org/find/cs/1/au:+Martin_J/0/1/0/all/0/1">Joshua Martin</a>, <a href="http://arxiv.org/find/cs/1/au:+Moeller_S/0/1/0/all/0/1">Sarah Moeller</a>, <a href="http://arxiv.org/find/cs/1/au:+Tang_K/0/1/0/all/0/1">Kevin Tang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:242;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12423";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:121:"RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy. (arXiv:2204.12423v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12423";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1716:"The current cancer treatment practice collects multimodal data, such as
radiology images, histopathology slides, genomics and clinical data. The
importance of these data sources taken individually has fostered the recent
raise of radiomics and pathomics, i.e. the extraction of quantitative features
from radiology and histopathology images routinely collected to predict
clinical outcomes or to guide clinical decisions using artificial intelligence
algorithms. Nevertheless, how to combine them into a single multimodal
framework is still an open issue. In this work we therefore develop a
multimodal late fusion approach that combines hand-crafted features computed
from radiomics, pathomics and clinical data to predict radiation therapy
treatment outcomes for non-small-cell lung cancer patients. Within this
context, we investigate eight different late fusion rules (i.e. product,
maximum, minimum, mean, decision template, Dempster-Shafer, majority voting,
and confidence rule) and two patient-wise aggregation rules leveraging the
richness of information given by computer tomography images and whole-slide
scans. The experiments in leave-one-patient-out cross-validation on an in-house
cohort of 33 patients show that the proposed multimodal paradigm with an AUC
equal to $90.9\%$ outperforms each unimodal approach, suggesting that data
integration can advance precision medicine. As a further contribution, we also
compare the hand-crafted representations with features automatically computed
by deep networks, and the late fusion paradigm with early fusion, another
popular multimodal approach. In both cases, the experiments show that the
proposed multimodal approach provides the best results.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:669:" <a href="http://arxiv.org/find/cs/1/au:+Tortora_M/0/1/0/all/0/1">Matteo Tortora</a>, <a href="http://arxiv.org/find/cs/1/au:+Cordelli_E/0/1/0/all/0/1">Ermanno Cordelli</a>, <a href="http://arxiv.org/find/cs/1/au:+Sicilia_R/0/1/0/all/0/1">Rosa Sicilia</a>, <a href="http://arxiv.org/find/cs/1/au:+Nibid_L/0/1/0/all/0/1">Lorenzo Nibid</a>, <a href="http://arxiv.org/find/cs/1/au:+Ippolito_E/0/1/0/all/0/1">Edy Ippolito</a>, <a href="http://arxiv.org/find/cs/1/au:+Perrone_G/0/1/0/all/0/1">Giuseppe Perrone</a>, <a href="http://arxiv.org/find/cs/1/au:+Ramella_S/0/1/0/all/0/1">Sara Ramella</a>, <a href="http://arxiv.org/find/cs/1/au:+Soda_P/0/1/0/all/0/1">Paolo Soda</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:243;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12425";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:101:"Bioblox 2.5D -- Developing an Educational Game Based on Protein Docking. (arXiv:2204.12425v1 [cs.HC])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12425";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1067:"We present the development process of Bioblox2-5D, an educational biology
game aimed at teenagers. The game content refers to protein docking and aims to
improve learning about molecular shape complexity, the roles of charges in
molecular docking and the scoring function to calculate binding affinity. We
developed the game as part of a collaboration between the Computing Department
at Goldsmiths, University of London, and the Structural Bioinformatics group at
Imperial College London. The team at Imperial provided the content requirements
and validated the technical solution adopted in the game. The team at
Goldsmiths designed and implemented the content requirements into a fun and
stimulating educational puzzle game that supports teaching and motivates
students to engage with biology. We illustrate the game design choices, the
compromises and solutions that we applied to accomplish the desired learning
outcomes. This paper aims to illustrate useful insights and inspirations in the
context of educational game development for biology students.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:797:" <a href="http://arxiv.org/find/cs/1/au:+Leymarie_F/0/1/0/all/0/1">Frederic Fol Leymarie</a>, <a href="http://arxiv.org/find/cs/1/au:+Latham_W/0/1/0/all/0/1">William Latham</a>, <a href="http://arxiv.org/find/cs/1/au:+Sternberg_M/0/1/0/all/0/1">Michael J. E. Sternberg</a>, <a href="http://arxiv.org/find/cs/1/au:+Salimbeni_G/0/1/0/all/0/1">Guido Salimbeni</a>, <a href="http://arxiv.org/find/cs/1/au:+Islam_S/0/1/0/all/0/1">Suhail A. Islam</a>, <a href="http://arxiv.org/find/cs/1/au:+Reynolds_C/0/1/0/all/0/1">Christopher Reynolds</a>, <a href="http://arxiv.org/find/cs/1/au:+Cook_C/0/1/0/all/0/1">Charlie Cook</a>, <a href="http://arxiv.org/find/cs/1/au:+Suarez_L/0/1/0/all/0/1">Luis Armas Suarez</a>, <a href="http://arxiv.org/find/cs/1/au:+Leinfellner_R/0/1/0/all/0/1">Richard Leinfellner</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:244;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12426";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:86:"Time-triggered Federated Learning over Wireless Networks. (arXiv:2204.12426v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12426";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1329:"The newly emerging federated learning (FL) framework offers a new way to
train machine learning models in a privacy-preserving manner. However,
traditional FL algorithms are based on an event-triggered aggregation, which
suffers from stragglers and communication overhead issues. To address these
issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over
wireless networks, which is a generalized form of classic synchronous and
asynchronous FL. Taking the constrained resource and unreliable nature of
wireless communication into account, we jointly study the user selection and
bandwidth optimization problem to minimize the FL training loss. To solve this
joint optimization problem, we provide a thorough convergence analysis for
TT-Fed. Based on the obtained analytical convergence upper bound, the
optimization problem is decomposed into tractable sub-problems with respect to
each global aggregation round, and finally solved by our proposed online search
algorithm. Simulation results show that compared to asynchronous FL (FedAsync)
and FL with asynchronous user tiers (FedAT) benchmarks, our proposed TT-Fed
algorithm improves the converged test accuracy by up to 12.5% and 5%,
respectively, under highly imbalanced and non-IID data, while substantially
reducing the communication overhead.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:396:" <a href="http://arxiv.org/find/cs/1/au:+Zhou_X/0/1/0/all/0/1">Xiaokang Zhou</a>, <a href="http://arxiv.org/find/cs/1/au:+Deng_Y/0/1/0/all/0/1">Yansha Deng</a>, <a href="http://arxiv.org/find/cs/1/au:+Xia_H/0/1/0/all/0/1">Huiyun Xia</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_S/0/1/0/all/0/1">Shaochuan Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Bennis_M/0/1/0/all/0/1">Mehdi Bennis</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:245;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12430";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:88:"Federated Progressive Sparsification (Purge, Merge, Tune)+. (arXiv:2204.12430v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12430";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:948:"To improve federated training of neural networks, we develop FedSparsify, a
sparsification strategy based on progressive weight magnitude pruning. Our
method has several benefits. First, since the size of the network becomes
increasingly smaller, computation and communication costs during training are
reduced. Second, the models are incrementally constrained to a smaller set of
parameters, which facilitates alignment/merging of the local models and
improved learning performance at high sparsification rates. Third, the final
sparsified model is significantly smaller, which improves inference efficiency
and optimizes operations latency during encrypted communication. We show
experimentally that FedSparsify learns a subnetwork of both high sparsity and
learning performance. Our sparse models can reach a tenth of the size of the
original model with the same or better accuracy compared to existing pruning
and nonpruning baselines.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:339:" <a href="http://arxiv.org/find/cs/1/au:+Stripelis_D/0/1/0/all/0/1">Dimitris Stripelis</a>, <a href="http://arxiv.org/find/cs/1/au:+Gupta_U/0/1/0/all/0/1">Umang Gupta</a>, <a href="http://arxiv.org/find/cs/1/au:+Steeg_G/0/1/0/all/0/1">Greg Ver Steeg</a>, <a href="http://arxiv.org/find/cs/1/au:+Ambite_J/0/1/0/all/0/1">Jose Luis Ambite</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:246;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12432";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:147:"Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network. (arXiv:2204.12432v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12432";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1290:"Exercise testing has been available for more than a half-century and is a
remarkably versatile tool for diagnostic and prognostic information of patients
for a range of diseases, especially cardiovascular and pulmonary. With rapid
advancements in technology, wearables, and learning algorithm in the last
decade, its scope has evolved. Specifically, Cardiopulmonary exercise testing
(CPX) is one of the most commonly used laboratory tests for objective
evaluation of exercise capacity and performance levels in patients. CPX
provides a non-invasive, integrative assessment of the pulmonary,
cardiovascular, and skeletal muscle systems involving the measurement of gas
exchanges. However, its assessment is challenging, requiring the individual to
process multiple time series data points, leading to simplification to peak
values and slopes. But this simplification can discard the valuable trend
information present in these time series. In this work, we encode the time
series as images using the Gramian Angular Field and Markov Transition Field
and use it with a convolutional neural network and attention pooling approach
for the classification of heart failure and metabolic syndrome patients. Using
GradCAMs, we highlight the discriminative features identified by the model.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:249:" <a href="http://arxiv.org/find/cs/1/au:+Sharma_Y/0/1/0/all/0/1">Yash Sharma</a>, <a href="http://arxiv.org/find/cs/1/au:+Coronato_N/0/1/0/all/0/1">Nick Coronato</a>, <a href="http://arxiv.org/find/cs/1/au:+Brown_D/0/1/0/all/0/1">Donald E. Brown</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:247;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12433";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:119:"Equivalence and Duality of Polycyclic Codes Associated with Trinomials over Finite Fields. (arXiv:2204.12433v1 [cs.IT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12433";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:329:"In this paper, several conjectures proposed in [2] are studied, involving the
equivalence and duality of polycyclic codes associated with trinomials.
According to the results, we give methods to construct isodual and self-dual
polycyclic codes, and study the self-orthogonal and dual-containing polycyclic
codes over F2.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:306:" <a href="http://arxiv.org/find/cs/1/au:+Shi_M/0/1/0/all/0/1">Minjia Shi</a>, <a href="http://arxiv.org/find/cs/1/au:+Lu_H/0/1/0/all/0/1">Haodong Lu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhou_S/0/1/0/all/0/1">Shuang Zhou</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_J/0/1/0/all/0/1">Jiarui Xu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:248;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12436";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:104:"Incentives in Social Decision Schemes with Pairwise Comparison Preferences. (arXiv:2204.12436v1 [cs.GT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12436";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1227:"Social decision schemes (SDSs) map the preferences of individual voters over
multiple alternatives to a probability distribution over the alternatives. In
order to study properties such as efficiency, strategyproofness, and
participation for SDSs, preferences over alternatives are typically lifted to
preferences over lotteries using the notion of stochastic dominance (SD).
However, requiring strategyproofness or participation with respect to this
preference extension only leaves room for rather undesirable SDSs such as
random dictatorships. Hence, we focus on the natural but little understood
pairwise comparison (PC) preference extension, which postulates that one
lottery is preferred to another if the former is more likely to return a
preferred outcome. In particular, we settle three open questions raised by
Brandt (2017): (i) there is no Condorcet-consistent SDS that satisfies
PC-strategyproofness; (ii) there is no anonymous and neutral SDS that satisfies
PC-efficiency and PC-strategyproofness; and (iii) there is no anonymous and
neutral SDS that satisfies PC-efficiency and strict PC-participation. All three
impossibilities require m &gt;= 4 alternatives and turn into possibilities when m
&lt;= 3.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:257:" <a href="http://arxiv.org/find/cs/1/au:+Brandt_F/0/1/0/all/0/1">Felix Brandt</a>, <a href="http://arxiv.org/find/cs/1/au:+Lederer_P/0/1/0/all/0/1">Patrick Lederer</a>, <a href="http://arxiv.org/find/cs/1/au:+Suksompong_W/0/1/0/all/0/1">Warut Suksompong</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:249;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12440";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:124:"neuro2vec: Masked Fourier Spectrum Prediction for Neurophysiological Representation Learning. (arXiv:2204.12440v1 [eess.SP])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12440";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1317:"Extensive data labeling on neurophysiological signals is often prohibitively
expensive or impractical, as it may require particular infrastructure or domain
expertise. To address the appetite for data of deep learning methods, we
present for the first time a Fourier-based modeling framework for
self-supervised pre-training of neurophysiology signals. The intuition behind
our approach is simple: frequency and phase distribution of neurophysiology
signals reveal the underlying neurophysiological activities of the brain and
muscle. Our approach first randomly masks out a portion of the input signal and
then predicts the missing information from either spatiotemporal or the Fourier
domain. Pre-trained models can be potentially used for downstream tasks such as
sleep stage classification using electroencephalogram (EEG) signals and gesture
recognition using electromyography (EMG) signals. Unlike contrastive-based
methods, which strongly rely on carefully hand-crafted augmentations and
siamese structure, our approach works reasonably well with a simple transformer
encoder with no augmentation requirements. By evaluating our method on several
benchmark datasets, including both EEG and EMG, we show that our modeling
approach improves downstream neurophysiological related tasks by a large
margin.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:311:" <a href="http://arxiv.org/find/eess/1/au:+Wu_D/0/1/0/all/0/1">Di Wu</a>, <a href="http://arxiv.org/find/eess/1/au:+Li_S/0/1/0/all/0/1">Siyuan Li</a>, <a href="http://arxiv.org/find/eess/1/au:+Yang_J/0/1/0/all/0/1">Jie Yang</a>, <a href="http://arxiv.org/find/eess/1/au:+Sawan_M/0/1/0/all/0/1">Mohamad Sawan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:250;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12441";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:94:"Investigating the Optimal Neural Network Parameters for Decoding. (arXiv:2204.12441v1 [cs.IT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12441";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:312:"Neural Networks have been proved to work as decoders in telecommunications,
so the ways of making it efficient will be investigated in this thesis. The
different parameters to maximize the Neural Network Decoder's efficiency will
be investigated. The parameters will be tested for inversion errors only.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:94:" <a href="http://arxiv.org/find/cs/1/au:+Maumela_J/0/1/0/all/0/1">Joshua Tshifhiwa Maumela</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:251;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12442";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:91:"Multi-task Deep Neural Networks for Massive MIMO CSI Feedback. (arXiv:2204.12442v1 [cs.IT])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12442";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:953:"Deep learning has been widely applied for the channel state information (CSI)
feedback in frequency division duplexing (FDD) massive multiple-input
multiple-output (MIMO) system. For the typical supervised training of the
feedback model, the requirements of large amounts of task-specific labeled data
can hardly be satisfied, and the huge training costs and storage usage of the
model in multiple scenarios are hindrance for model application. In this
letter, a multi-task learning-based approach is proposed to improve the
feasibility of the feedback network. An encoder-shared feedback architecture
and the corresponding training scheme are further proposed to facilitate the
implementation of the multi-task learning approach. The experimental results
indicate that the proposed multi-task learning approach can achieve
comprehensive feedback performance with considerable reduction of training cost
and storage usage of the feedback model.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:386:" <a href="http://arxiv.org/find/cs/1/au:+Zhang_B/0/1/0/all/0/1">Boyuan Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_H/0/1/0/all/0/1">Haozhen Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Liang_X/0/1/0/all/0/1">Xin Liang</a>, <a href="http://arxiv.org/find/cs/1/au:+Gu_X/0/1/0/all/0/1">Xinyu Gu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_L/0/1/0/all/0/1">Lin Zhang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:252;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12443";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:99:"A review of Federated Learning in Intrusion Detection Systems for IoT. (arXiv:2204.12443v1 [cs.CR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12443";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1192:"Intrusion detection systems are evolving into intelligent systems that
perform data analysis searching for anomalies in their environment. The
development of deep learning technologies opened the door to build more complex
and effective threat detection models. However, training those models may be
computationally infeasible in most Internet of Things devices. Current
approaches rely on powerful centralized servers that receive data from all
their parties -- violating basic privacy constraints and substantially
affecting response times and operational costs due to the huge communication
overheads. To mitigate these issues, Federated Learning emerged as a promising
approach where different agents collaboratively train a shared model, neither
exposing training data to others nor requiring a compute-intensive centralized
infrastructure. This paper focuses on the application of Federated Learning
approaches in the field of Intrusion Detection. Both technologies are described
in detail and current scientific progress is reviewed and categorized. Finally,
the paper highlights the limitations present in recent works and presents some
future directions for this technology.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:262:" <a href="http://arxiv.org/find/cs/1/au:+Belenguer_A/0/1/0/all/0/1">Aitor Belenguer</a>, <a href="http://arxiv.org/find/cs/1/au:+Navaridas_J/0/1/0/all/0/1">Javier Navaridas</a>, <a href="http://arxiv.org/find/cs/1/au:+Pascual_J/0/1/0/all/0/1">Jose A. Pascual</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:253;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12445";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:112:"Assimilation of magnetic resonance elastography data in an in silico brain model. (arXiv:2204.12445v1 [math.NA])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12445";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1749:"This paper investigates a data assimilation approach for non-invasive
quantification of intracranial pressure from partial displacement data,
acquired through magnetic resonance elastography. Data assimilation is based on
a parametrized-background data weak methodology, in which the state of the
physical system -- tissue displacements and pressure fields -- is reconstructed
from partially available data assuming an underlying poroelastic biomechanics
model. For this purpose, a physics-informed manifold is built by sampling the
space of parameters describing the tissue model close to their physiological
ranges, to simulate the corresponding poroelastic problem, and compute a
reduced basis. Displacements and pressure reconstruction is sought in a reduced
space after solving a minimization problem that encompasses both the structure
of the reduced-order model and the available measurements. The proposed
pipeline is validated using synthetic data obtained after simulating the
poroelastic mechanics on a physiological brain. The numerical experiments
demonstrate that the framework can exhibit accurate joint reconstructions of
both displacement and pressure fields. The methodology can be formulated for an
arbitrary resolution of available displacement data from pertinent images. It
can also inherently handle uncertainty on the physical parameters of the
mechanical model by enlarging the physics-informed manifold accordingly.
Moreover, the framework can be used to characterize, in silico, biomarkers for
pathological conditions, by appropriately training the reduced-order model. A
first application for the estimation of ventricular pressure as an indicator of
abnormal intracranial pressure is shown in this contribution.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:717:" <a href="http://arxiv.org/find/math/1/au:+Galarce_F/0/1/0/all/0/1">Felipe Galarce</a>, <a href="http://arxiv.org/find/math/1/au:+Tabelown_K/0/1/0/all/0/1">Karsten Tabelown</a>, <a href="http://arxiv.org/find/math/1/au:+Polzehl_J/0/1/0/all/0/1">J&#xf6;rg Polzehl</a>, <a href="http://arxiv.org/find/math/1/au:+Papanikas_C/0/1/0/all/0/1">Christos Panagiotis Papanikas</a>, <a href="http://arxiv.org/find/math/1/au:+Vavourakis_V/0/1/0/all/0/1">Vasileios Vavourakis</a>, <a href="http://arxiv.org/find/math/1/au:+Lilaj_L/0/1/0/all/0/1">Ledia Lilaj</a>, <a href="http://arxiv.org/find/math/1/au:+Sack_I/0/1/0/all/0/1">Ingolf Sack</a>, <a href="http://arxiv.org/find/math/1/au:+Caiazzo_A/0/1/0/all/0/1">Alfonso Caiazzo</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:254;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12446";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:111:"Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD. (arXiv:2204.12446v1 [stat.ML])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12446";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1508:"We provide sharp path-dependent generalization and excess error guarantees
for the full-batch Gradient Decent (GD) algorithm for smooth losses (possibly
non-Lipschitz, possibly nonconvex). At the heart of our analysis is a novel
generalization error technique for deterministic symmetric algorithms, that
implies average output stability and a bounded expected gradient of the loss at
termination leads to generalization. This key result shows that small
generalization error occurs at stationary points, and allows us to bypass
Lipschitz assumptions on the loss prevalent in previous work. For nonconvex,
convex and strongly convex losses, we show the explicit dependence of the
generalization error in terms of the accumulated path-dependent optimization
error, terminal optimization error, number of samples, and number of
iterations. For nonconvex smooth losses, we prove that full-batch GD
efficiently generalizes close to any stationary point at termination, under the
proper choice of a decreasing step size. Further, if the loss is nonconvex but
the objective is PL, we derive vanishing bounds on the corresponding excess
risk. For convex and strongly-convex smooth losses, we prove that full-batch GD
generalizes even for large constant step sizes, and achieves a small excess
risk while training fast. Our full-batch GD generalization error and excess
risk bounds are significantly tighter than the existing bounds for (stochastic)
GD, when the loss is smooth (but possibly non-Lipschitz).

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:380:" <a href="http://arxiv.org/find/stat/1/au:+Nikolakakis_K/0/1/0/all/0/1">Konstantinos E. Nikolakakis</a>, <a href="http://arxiv.org/find/stat/1/au:+Haddadpour_F/0/1/0/all/0/1">Farzin Haddadpour</a>, <a href="http://arxiv.org/find/stat/1/au:+Karbasi_A/0/1/0/all/0/1">Amin Karbasi</a>, <a href="http://arxiv.org/find/stat/1/au:+Kalogerias_D/0/1/0/all/0/1">Dionysios S. Kalogerias</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:255;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12451";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:81:"Understanding The Robustness in Vision Transformers. (arXiv:2204.12451v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12451";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1079:"Recent studies show that Vision Transformers(ViTs) exhibit strong robustness
against various corruptions. Although this property is partly attributed to the
self-attention mechanism, there is still a lack of systematic understanding. In
this paper, we examine the role of self-attention in learning robust
representations. Our study is motivated by the intriguing properties of the
emerging visual grouping in Vision Transformers, which indicates that
self-attention may promote robustness through improved mid-level
representations. We further propose a family of fully attentional networks
(FANs) that strengthen this capability by incorporating an attentional channel
processing design. We validate the design comprehensively on various
hierarchical backbones. Our model achieves a state of-the-art 87.1% accuracy
and 35.8% mCE on ImageNet-1k and ImageNet-C with 76.8M parameters. We also
demonstrate state-of-the-art accuracy and robustness in two downstream tasks:
semantic segmentation and object detection. Code will be available at
https://github.com/NVlabs/FAN.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:564:" <a href="http://arxiv.org/find/cs/1/au:+Zhou_D/0/1/0/all/0/1">Daquan Zhou</a>, <a href="http://arxiv.org/find/cs/1/au:+Yu_Z/0/1/0/all/0/1">Zhiding Yu</a>, <a href="http://arxiv.org/find/cs/1/au:+Xie_E/0/1/0/all/0/1">Enze Xie</a>, <a href="http://arxiv.org/find/cs/1/au:+Xiao_C/0/1/0/all/0/1">Chaowei Xiao</a>, <a href="http://arxiv.org/find/cs/1/au:+Anandkumar_A/0/1/0/all/0/1">Anima Anandkumar</a>, <a href="http://arxiv.org/find/cs/1/au:+Feng_J/0/1/0/all/0/1">Jiashi Feng</a>, <a href="http://arxiv.org/find/cs/1/au:+Alvarez_J/0/1/0/all/0/1">Jose M. Alvarez</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:256;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12454";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:105:"Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images. (arXiv:2204.12454v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12454";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1006:"Multiple Instance Learning (MIL) methods have become increasingly popular for
classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology.
Most MIL methods operate at a single WSI magnification, by processing all the
tissue patches. Such a formulation induces high computational requirements, and
constrains the contextualization of the WSI-level representation to a single
scale. A few MIL methods extend to multiple scales, but are computationally
more demanding. In this paper, inspired by the pathological diagnostic process,
we propose ZoomMIL, a method that learns to perform multi-level zooming in an
end-to-end manner. ZoomMIL builds WSI representations by aggregating
tissue-context information from multiple magnifications. The proposed method
outperforms the state-of-the-art MIL methods in WSI classification on two large
datasets, while significantly reducing the computational demands with regard to
Floating-Point Operations (FLOPs) and processing time by up to 40x.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:593:" <a href="http://arxiv.org/find/cs/1/au:+Thandiackal_K/0/1/0/all/0/1">Kevin Thandiackal</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_B/0/1/0/all/0/1">Boqi Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Pati_P/0/1/0/all/0/1">Pushpak Pati</a>, <a href="http://arxiv.org/find/cs/1/au:+Jaume_G/0/1/0/all/0/1">Guillaume Jaume</a>, <a href="http://arxiv.org/find/cs/1/au:+Williamson_D/0/1/0/all/0/1">Drew F. K. Williamson</a>, <a href="http://arxiv.org/find/cs/1/au:+Gabrani_M/0/1/0/all/0/1">Maria Gabrani</a>, <a href="http://arxiv.org/find/cs/1/au:+Goksel_O/0/1/0/all/0/1">Orcun Goksel</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:257;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12456";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"Event Detection Explorer: An Interactive Tool for Event Detection Exploration. (arXiv:2204.12456v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12456";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1129:"Event Detection (ED) is an important task in natural language processing. In
the past few years, many datasets have been introduced for advancing ED machine
learning models. However, most of these datasets are under-explored because not
many tools are available for people to study events, trigger words, and event
mention instances systematically and efficiently. In this paper, we present an
interactive and easy-to-use tool, namely ED Explorer, for ED dataset and model
exploration. ED Explorer consists of an interactive web application, an API,
and an NLP toolkit, which can help both domain experts and non-experts to
better understand the ED task. We use ED Explorer to analyze a recent proposed
large-scale ED datasets (referred to as MAVEN), and discover several underlying
problems, including sparsity, label bias, label imbalance, and debatable
annotations, which provide us with directions to improve the MAVEN dataset. The
ED Explorer can be publicly accessed through <a href="http://edx.leafnlp.org/.">this http URL</a> The
demonstration video is available here
https://www.youtube.com/watch?v=6QPnxPwxg50.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:478:" <a href="http://arxiv.org/find/cs/1/au:+Zhang_W/0/1/0/all/0/1">Wenlong Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Ingale_B/0/1/0/all/0/1">Bhagyashree Ingale</a>, <a href="http://arxiv.org/find/cs/1/au:+Shabir_H/0/1/0/all/0/1">Hamza Shabir</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_T/0/1/0/all/0/1">Tianyi Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Shi_T/0/1/0/all/0/1">Tian Shi</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_P/0/1/0/all/0/1">Ping Wang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:258;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12458";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:92:"Learning Value Functions from Undirected State-only Experience. (arXiv:2204.12458v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12458";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1291:"This paper tackles the problem of learning value functions from undirected
state-only experience (state transitions without action labels i.e. (s,s',r)
tuples). We first theoretically characterize the applicability of Q-learning in
this setting. We show that tabular Q-learning in discrete Markov decision
processes (MDPs) learns the same value function under any arbitrary refinement
of the action space. This theoretical result motivates the design of Latent
Action Q-learning or LAQ, an offline RL method that can learn effective value
functions from state-only experience. Latent Action Q-learning (LAQ) learns
value functions using Q-learning on discrete latent actions obtained through a
latent-variable future prediction model. We show that LAQ can recover value
functions that have high correlation with value functions learned using ground
truth actions. Value functions learned using LAQ lead to sample efficient
acquisition of goal-directed behavior, can be used with domain-specific
low-level controllers, and facilitate transfer across embodiments. Our
experiments in 5 environments ranging from 2D grid world to 3D visual
navigation in realistic environments demonstrate the benefits of LAQ over
simpler alternatives, imitation learning oracles, and competing methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:243:" <a href="http://arxiv.org/find/cs/1/au:+Chang_M/0/1/0/all/0/1">Matthew Chang</a>, <a href="http://arxiv.org/find/cs/1/au:+Gupta_A/0/1/0/all/0/1">Arjun Gupta</a>, <a href="http://arxiv.org/find/cs/1/au:+Gupta_S/0/1/0/all/0/1">Saurabh Gupta</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:259;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12463";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:89:"Focal Sparse Convolutional Networks for 3D Object Detection. (arXiv:2204.12463v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12463";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1249:"Non-uniformed 3D sparse data, e.g., point clouds or voxels in different
spatial positions, make contribution to the task of 3D object detection in
different ways. Existing basic components in sparse convolutional networks
(Sparse CNNs) process all sparse data, regardless of regular or submanifold
sparse convolution. In this paper, we introduce two new modules to enhance the
capability of Sparse CNNs, both are based on making feature sparsity learnable
with position-wise importance prediction. They are focal sparse convolution
(Focals Conv) and its multi-modal variant of focal sparse convolution with
fusion, or Focals Conv-F for short. The new modules can readily substitute
their plain counterparts in existing Sparse CNNs and be jointly trained in an
end-to-end fashion. For the first time, we show that spatially learnable
sparsity in sparse convolution is essential for sophisticated 3D object
detection. Extensive experiments on the KITTI, nuScenes and Waymo benchmarks
validate the effectiveness of our approach. Without bells and whistles, our
results outperform all existing single-model entries on the nuScenes test
benchmark at the paper submission time. Code and models are at
https://github.com/dvlab-research/FocalsConv.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:386:" <a href="http://arxiv.org/find/cs/1/au:+Chen_Y/0/1/0/all/0/1">Yukang Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_Y/0/1/0/all/0/1">Yanwei Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_X/0/1/0/all/0/1">Xiangyu Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Sun_J/0/1/0/all/0/1">Jian Sun</a>, <a href="http://arxiv.org/find/cs/1/au:+Jia_J/0/1/0/all/0/1">Jiaya Jia</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:260;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12466";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Meta-free representation learning for few-shot learning via stochastic weight averaging. (arXiv:2204.12466v1 [cs.LG])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12466";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1376:"Recent studies on few-shot classification using transfer learning pose
challenges to the effectiveness and efficiency of episodic meta-learning
algorithms. Transfer learning approaches are a natural alternative, but they
are restricted to few-shot classification. Moreover, little attention has been
on the development of probabilistic models with well-calibrated uncertainty
from few-shot samples, except for some Bayesian episodic learning algorithms.
To tackle the aforementioned issues, we propose a new transfer learning method
to obtain accurate and reliable models for few-shot regression and
classification. The resulting method does not require episodic meta-learning
and is called meta-free representation learning (MFRL). MFRL first finds
low-rank representation generalizing well on meta-test tasks. Given the learned
representation, probabilistic linear models are fine-tuned with few-shot
samples to obtain models with well-calibrated uncertainty. The proposed method
not only achieves the highest accuracy on a wide range of few-shot learning
benchmark datasets but also correctly quantifies the prediction uncertainty. In
addition, weight averaging and temperature scaling are effective in improving
the accuracy and reliability of few-shot learning in existing meta-learning
algorithms with a wide range of learning paradigms and model architectures.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:157:" <a href="http://arxiv.org/find/cs/1/au:+Chen_K/0/1/0/all/0/1">Kuilin Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Lee_C/0/1/0/all/0/1">Chi-Guhn Lee</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:261;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12467";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:108:"A Model-Adaptive Clustering Method for Low-Carbon Energy System Optimization. (arXiv:2204.12467v1 [math.OC])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12467";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1103:"Intermittent renewable energy resources like wind and solar pose great
uncertainty of multiple time scales, from minutes to years, on the design and
operation of power systems. Energy system optimization models have been
developed to find the least-cost solution to matching the uncertainty with
flexibility resources. However, input data that capture such multi-time-scale
uncertainty are characterized with a long time horizon and bring great
difficulty to solving the optimization model. Here we propose an adaptive
clustering method based on the decision variables of optimization model to
alleviate the computational complexity, in which the energy system is optimized
over selected representative time periods instead of the full time horizon. The
proposed clustering method is adaptive to various energy system optimization
models or settings, because it extracts features from the optimization models.
Results show that the proposed clustering method can significantly lower the
error in approximating the solution with the full time horizon, compared to
traditional clustering methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:421:" <a href="http://arxiv.org/find/math/1/au:+Zhang_Y/0/1/0/all/0/1">Yuheng Zhang</a>, <a href="http://arxiv.org/find/math/1/au:+Cheng_V/0/1/0/all/0/1">Vivian Cheng</a>, <a href="http://arxiv.org/find/math/1/au:+Mallapragada_D/0/1/0/all/0/1">Dharik S. Mallapragada</a>, <a href="http://arxiv.org/find/math/1/au:+Song_J/0/1/0/all/0/1">Jie Song</a>, <a href="http://arxiv.org/find/math/1/au:+He_G/0/1/0/all/0/1">Guannan He</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:262;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12468";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"A Review of In-Memory Space-Efficient Data Structures for Temporal Graphs. (arXiv:2204.12468v1 [cs.DS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12468";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:708:"Temporal graphs model relationships among entities over time. Recent studies
applied temporal graphs to abstract complex systems such as continuous
communication among participants of social networks. Often, the amount of data
is larger than main memory, therefore, we need specialized structures that
balance space usage and query efficiency. In this paper, we review
space-efficient data structures that bring large temporal graphs from external
memory to primary memory and speed up specialized queries. We found a great
variety of studies using data compression techniques and self-indexed
compressed data structures. We point further research directions to improve the
current state-of-the-art.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:278:" <a href="http://arxiv.org/find/cs/1/au:+Brito_L/0/1/0/all/0/1">Luiz F. A. Brito</a>, <a href="http://arxiv.org/find/cs/1/au:+Travencolo_B/0/1/0/all/0/1">Bruno A. N. Traven&#xe7;olo</a>, <a href="http://arxiv.org/find/cs/1/au:+Albertini_M/0/1/0/all/0/1">Marcelo K. Albertini</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:263;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12471";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:76:"Coarse-to-fine Q-attention with Tree Expansion. (arXiv:2204.12471v1 [cs.RO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12471";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1021:"Coarse-to-fine Q-attention enables sample-efficient robot manipulation by
discretizing the translation space in a coarse-to-fine manner, where the
resolution gradually increases at each layer in the hierarchy. Although
effective, Q-attention suffers from "coarse ambiguity" - when voxelization is
significantly coarse, it is not feasible to distinguish similar-looking objects
without first inspecting at a finer resolution. To combat this, we propose to
envision Q-attention as a tree that can be expanded and used to accumulate
value estimates across the top-k voxels at each Q-attention depth. When our
extension, Q-attention with Tree Expansion (QTE), replaces standard Q-attention
in the Attention-driven Robot Manipulation (ARM) system, we are able to
accomplish a larger set of tasks; especially on those that suffer from "coarse
ambiguity". In addition to evaluating our approach across 12 RLBench tasks, we
also show that the improved performance is visible in a real-world task
involving small objects.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:164:" <a href="http://arxiv.org/find/cs/1/au:+James_S/0/1/0/all/0/1">Stephen James</a>, <a href="http://arxiv.org/find/cs/1/au:+Abbeel_P/0/1/0/all/0/1">Pieter Abbeel</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:264;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12477";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:88:"Digital Twins for Dynamic Management of Blockchain Systems. (arXiv:2204.12477v1 [cs.CR])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12477";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1038:"Blockchain systems are challenged by the so-called Trilemma tradeoff:
decentralization, scalability and security. Infrastructure and node
configuration, choice of the Consensus Protocol and complexity of the
application transactions are cited amongst the factors that affect the
tradeoffs balance. Given that Blockchains are complex, dynamic dynamic systems,
a dynamic approach to their management and reconfiguration at runtime is deemed
necessary to reflect the changes in the state of the infrastructure and
application. This paper introduces the utilisation of Digital Twins for this
purpose. The novel contribution of the paper is design of a framework and
conceptual architecture of a Digital Twin that can assist in maintaining the
Trilemma tradeoffs of time critical systems. The proposed Digital Twin is
illustrated via an innovative approach to dynamic selection of Consensus
Protocols. Simulations results show that the proposed framework can effectively
support the dynamic adaptation and management of the Blockchain

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:370:" <a href="http://arxiv.org/find/cs/1/au:+Diamantopoulos_G/0/1/0/all/0/1">Georgios Diamantopoulos</a>, <a href="http://arxiv.org/find/cs/1/au:+Tziritas_N/0/1/0/all/0/1">Nikos Tziritas</a>, <a href="http://arxiv.org/find/cs/1/au:+Bahsoon_R/0/1/0/all/0/1">Rami Bahsoon</a>, <a href="http://arxiv.org/find/cs/1/au:+Theodoropoulos_G/0/1/0/all/0/1">Georgios Theodoropoulos</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:265;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12481";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:78:"From Hyperbolic Geometry Back to Word Embeddings. (arXiv:2204.12481v1 [cs.CL])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12481";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:362:"We choose random points in the hyperbolic disc and claim that these points
are already word representations. However, it is yet to be uncovered which
point corresponds to which word of the human language of interest. This
correspondence can be approximately established using a pointwise mutual
information between words and recent alignment techniques.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:357:" <a href="http://arxiv.org/find/cs/1/au:+Nurmukhamedov_S/0/1/0/all/0/1">Sultan Nurmukhamedov</a>, <a href="http://arxiv.org/find/cs/1/au:+Mach_T/0/1/0/all/0/1">Thomas Mach</a>, <a href="http://arxiv.org/find/cs/1/au:+Sheverdin_A/0/1/0/all/0/1">Arsen Sheverdin</a>, <a href="http://arxiv.org/find/cs/1/au:+Assylbekov_Z/0/1/0/all/0/1">Zhenisbek Assylbekov</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:266;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12484";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation. (arXiv:2204.12484v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12484";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1197:"Recently, customized vision transformers have been adapted for human pose
estimation and have achieved superior performance with elaborate structures.
However, it is still unclear whether plain vision transformers can facilitate
pose estimation. In this paper, we take the first step toward answering the
question by employing a plain and non-hierarchical vision transformer together
with simple deconvolution decoders termed ViTPose for human pose estimation. We
demonstrate that a plain vision transformer with MAE pretraining can obtain
superior performance after finetuning on human pose estimation datasets.
ViTPose has good scalability with respect to model size and flexibility
regarding input resolution and token number. Moreover, it can be easily
pretrained using the unlabeled pose data without the need for large-scale
upstream ImageNet data. Our biggest ViTPose model based on the ViTAE-G backbone
with 1 billion parameters obtains the best 80.9 mAP on the MS COCO test-dev
set, while the ensemble models further set a new state-of-the-art for human
pose estimation, i.e., 81.1 mAP. The source code and models will be released at
https://github.com/ViTAE-Transformer/ViTPose.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:311:" <a href="http://arxiv.org/find/cs/1/au:+Xu_Y/0/1/0/all/0/1">Yufei Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_J/0/1/0/all/0/1">Jing Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_Q/0/1/0/all/0/1">Qiming Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Tao_D/0/1/0/all/0/1">Dacheng Tao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:267;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12486";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:159:"Measurement uncertainty and unicity of single number quantities describing the spatial decay of speech level in open-plan offices. (arXiv:2204.12486v1 [cs.SD])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12486";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1568:"The ISO 3382-3 standard (2012) defines single number quantities (SNQs) which
evaluate the acoustic quality of open-plan offices, but does not address the
issue of measurement uncertainties. This study focusses on the SNQs present in
this standard related to spatial decay of speech, i.e. D 2S , L pAS4m and r c.
The aim is to provide additional information to the limited literature on the
measurement uncertainties of these SNQs by use of both analytical developments
and a stochastic approach based on simulations. The accuracy of the analytical
developments was studied thanks to simulations of the sound propagation within
a series of offices (1 layout, 16 acoustic configurations with different screen
heights and different acoustic qualities of screens and ceiling). The SNQs
obtained in the simulations cover a wide range: D 2S between 3.4 and 7.5 dB(A),
L pAS4m between 40.6 and 51.9 dB(A) and r c between 2.5 and 14.7 m. Therefore,
the simulations are representative of a broad set of acoustic qualities.
Estimated uncertainties have a magnitude of 0.4 dB(A) for D 2S and vary between
0.4 and 0.7 dB(A) for L pAS4m and between 0.2 and 1.5 m for r c over a
measurement path comprising 7 measurement positions. The simulations also raise
the question of describing the acoustic quality of an office using a single
value for the indicators. The results of the simulations show that in some
cases, D 2S values significantly depend on the measurement path, leading to a
strong increase of its measurement uncertainty if a unique value is to be
considered.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:291:" <a href="http://arxiv.org/find/cs/1/au:+Lenne_L/0/1/0/all/0/1">Lucas Lenne</a> (INRS (Vandoeuvre l&#xe8;s Nancy)), <a href="http://arxiv.org/find/cs/1/au:+Chevret_P/0/1/0/all/0/1">Patrick Chevret</a>, <a href="http://arxiv.org/find/cs/1/au:+Parizet_E/0/1/0/all/0/1">&#xc9;tienne Parizet</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:268;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12488";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:96:"Distances Release with Differential Privacy in Tree and Grid Graph. (arXiv:2204.12488v1 [cs.DS])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12488";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1666:"Data about individuals may contain private and sensitive information. The
differential privacy (DP) was proposed to address the problem of protecting the
privacy of each individual while keeping useful information about a population.
Sealfon (2016) introduced a private graph model in which the graph topology is
assumed to be public while the weight information is assumed to be private.
That model can express hidden congestion patterns in a known transportation
system. In this paper, we revisit the problem of privately releasing
approximate distances between all pairs of vertices in (Sealfon 2016). Our goal
is to minimize the additive error, namely the difference between the released
distance and actual distance under private setting. We propose improved
solutions to that problem for several cases.

For the problem of privately releasing all-pairs distances, we show that for
tree with depth $h$, we can release all-pairs distances with additive error
$O(\log^{1.5} h \cdot \log^{1.5} V)$ for fixed privacy parameter where $V$ the
number of vertices in the tree, which improves the previous error bound
$O(\log^{2.5} V)$, since the size of $h$ can be as small as $O(\log V)$. Our
result implies that a $\log V$ factor is saved, and the additive error in tree
can be smaller than the error on array/path. Additionally, for the grid graph
with arbitrary edge weights, we also propose a method to release all-pairs
distances with additive error $\tilde O(V^{3/4}) $ for fixed privacy
parameters. On the application side, many cities like Manhattan are composed of
horizontal streets and vertical avenues, which can be modeled as a grid graph.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:151:" <a href="http://arxiv.org/find/cs/1/au:+Fan_C/0/1/0/all/0/1">Chenglin Fan</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_P/0/1/0/all/0/1">Ping Li</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:269;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12489";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:89:"Sound Localization by Self-Supervised Time Delay Estimation. (arXiv:2204.12489v1 [cs.CV])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12489";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:885:"Sounds reach one microphone in a stereo pair sooner than the other, resulting
in an interaural time delay that conveys their directions. Estimating a sound's
time delay requires finding correspondences between the signals recorded by
each microphone. We propose to learn these correspondences through
self-supervision, drawing on recent techniques from visual tracking. We adapt
the contrastive random walk of Jabri et al. to learn a cycle-consistent
representation from unlabeled stereo sounds, resulting in a model that performs
on par with supervised methods on "in the wild" internet recordings. We also
propose a multimodal contrastive learning model that solves a visually-guided
localization task: estimating the time delay for a particular person in a
multi-speaker mixture, given a visual representation of their face. Project
site: https://ificl.github.io/stereocrw/

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:244:" <a href="http://arxiv.org/find/cs/1/au:+Chen_Z/0/1/0/all/0/1">Ziyang Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Fouhey_D/0/1/0/all/0/1">David F. Fouhey</a>, <a href="http://arxiv.org/find/cs/1/au:+Owens_A/0/1/0/all/0/1">Andrew Owens</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:270;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.12490";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:141:"From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation. (arXiv:2204.12490v1 [cs.RO])";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.12490";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1181:"We propose to perform imitation learning for dexterous manipulation with
multi-finger robot hand from human demonstrations, and transfer the policy to
the real robot hand. We introduce a novel single-camera teleoperation system to
collect the 3D demonstrations efficiently with only an iPad and a computer. One
key contribution of our system is that we construct a customized robot hand for
each user in the physical simulator, which is a manipulator resembling the same
kinematics structure and shape of the operator's hand. This provides an
intuitive interface and avoid unstable human-robot hand retargeting for data
collection, leading to large-scale and high quality data. Once the data is
collected, the customized robot hand trajectories can be converted to different
specified robot hands (models that are manufactured) to generate training
demonstrations. With imitation learning using our data, we show large
improvement over baselines with multiple complex manipulation tasks.
Importantly, we show our learned policy is significantly more robust when
transferring to the real robot. More videos can be found in the
https://yzqin.github.io/dex-teleop-imitation .

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:228:" <a href="http://arxiv.org/find/cs/1/au:+Qin_Y/0/1/0/all/0/1">Yuzhe Qin</a>, <a href="http://arxiv.org/find/cs/1/au:+Su_H/0/1/0/all/0/1">Hao Su</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_X/0/1/0/all/0/1">Xiaolong Wang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:271;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/1906.01566";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:96:"GAMMA: A General Agent Motion Model for Autonomous Driving. (arXiv:1906.01566v6 [cs.RO] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/1906.01566";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1291:"This paper presents GAMMA, a general motion prediction model that enables
large-scale real-time simulation and planning for autonomous driving. GAMMA
models heterogeneous, interactive traffic agents. They operate under diverse
road conditions, with various geometric and kinematic constraints. GAMMA treats
the prediction task as constrained optimization in traffic agents' velocity
space. The objective is to optimize an agent's driving performance, while
obeying all the constraints resulting from the agent's kinematics, collision
avoidance with other agents, and the environmental context. Further, GAMMA
explicitly conditions the prediction on human behavioral states as parameters
of the optimization model, in order to account for versatile human behaviors.
We evaluated GAMMA on a set of real-world benchmark datasets. The results show
that GAMMA achieves high prediction accuracy on both homogeneous and
heterogeneous traffic datasets, with sub-millisecond execution time. Further,
the computational efficiency and the flexibility of GAMMA enable (i) simulation
of mixed urban traffic at many locations worldwide and (ii) planning for
autonomous driving in dense traffic with uncertain driver behaviors, both in
real-time. The open-source code of GAMMA is available online.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:306:" <a href="http://arxiv.org/find/cs/1/au:+Luo_Y/0/1/0/all/0/1">Yuanfu Luo</a>, <a href="http://arxiv.org/find/cs/1/au:+Cai_P/0/1/0/all/0/1">Panpan Cai</a>, <a href="http://arxiv.org/find/cs/1/au:+Lee_Y/0/1/0/all/0/1">Yiyuan Lee</a>, <a href="http://arxiv.org/find/cs/1/au:+Hsu_D/0/1/0/all/0/1">David Hsu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:272;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/1908.09881";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:115:"Consistently estimating network statistics using Aggregated Relational Data. (arXiv:1908.09881v3 [stat.ME] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/1908.09881";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:774:"Aggregated Relational Data, known as ARD, capture information about a social
network by asking a respondent questions of the form "How many people with
characteristic X do you know?" rather than asking about connections between
each pair of individuals directly. Despite widespread use and a growing
literature on ARD methodology, there is still no systematic understanding of
when and why ARD should accurately recover features of the unobserved network.
This paper provides such a characterization. First, we show that ARD provide
sufficient information to consistently estimate the parameters of a common
generative model for graphs. Then, we characterize conditions under which ARD
should recover individual and graph level statistics from the unobserved graph.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:438:" <a href="http://arxiv.org/find/stat/1/au:+Breza_E/0/1/0/all/0/1">Emily Breza</a>, <a href="http://arxiv.org/find/stat/1/au:+Chandrasekhar_A/0/1/0/all/0/1">Arun G. Chandrasekhar</a>, <a href="http://arxiv.org/find/stat/1/au:+Lubold_S/0/1/0/all/0/1">Shane Lubold</a>, <a href="http://arxiv.org/find/stat/1/au:+McCormick_T/0/1/0/all/0/1">Tyler H. McCormick</a>, <a href="http://arxiv.org/find/stat/1/au:+Pan_M/0/1/0/all/0/1">Mengjie Pan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:273;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/1909.03354";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:154:"Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Comparative Study. (arXiv:1909.03354v5 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/1909.03354";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1865:"Using deep learning models to diagnose cancer from histology data presents
several challenges. Cancer grading and localization of regions of interest
(ROIs) in these images normally relies on both image- and pixel-level labels,
the latter requiring a costly annotation process. Deep weakly-supervised object
localization (WSOL) methods provide different strategies for low-cost training
of deep learning models. Using only image-class annotations, these methods can
be trained to classify an image, and yield class activation maps (CAMs) for ROI
localization. This paper provides a review of state-of-art DL methods for WSOL.
We propose a taxonomy where these methods are divided into bottom-up and
top-down methods according to the information flow in models. Although the
latter have seen limited progress, recent bottom-up methods are currently
driving much progress with deep WSOL methods. Early works focused on designing
different spatial pooling functions. However, these methods reached limited
localization accuracy, and unveiled a major limitation -- the under-activation
of CAMs which leads to high false negative localization. Subsequent works aimed
to alleviate this issue and recover complete object. Representative methods
from our taxonomy are evaluated and compared in terms of classification and
localization accuracy on two challenging histology datasets. Overall, the
results indicate poor localization performance, particularly for generic
methods that were initially designed to process natural images. Methods
designed to address the challenges of histology data yielded good results.
However, all methods suffer from high false positive/negative localization.
Four key challenges are identified for the application of deep WSOL methods in
histology -- under/over activation of CAMs, sensitivity to thresholding, and
model selection.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:507:" <a href="http://arxiv.org/find/cs/1/au:+Rony_J/0/1/0/all/0/1">J&#xe9;r&#xf4;me Rony</a>, <a href="http://arxiv.org/find/cs/1/au:+Belharbi_S/0/1/0/all/0/1">Soufiane Belharbi</a>, <a href="http://arxiv.org/find/cs/1/au:+Dolz_J/0/1/0/all/0/1">Jose Dolz</a>, <a href="http://arxiv.org/find/cs/1/au:+Ayed_I/0/1/0/all/0/1">Ismail Ben Ayed</a>, <a href="http://arxiv.org/find/cs/1/au:+McCaffrey_L/0/1/0/all/0/1">Luke McCaffrey</a>, <a href="http://arxiv.org/find/cs/1/au:+Granger_E/0/1/0/all/0/1">Eric Granger</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:274;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2001.09046";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"PDE-based Group Equivariant Convolutional Neural Networks. (arXiv:2001.09046v5 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2001.09046";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1709:"We present a PDE-based framework that generalizes Group equivariant
Convolutional Neural Networks (G-CNNs). In this framework, a network layer is
seen as a set of PDE-solvers where geometrically meaningful PDE-coefficients
become the layer's trainable weights. Formulating our PDEs on homogeneous
spaces allows these networks to be designed with built-in symmetries such as
rotation in addition to the standard translation equivariance of CNNs.

Having all the desired symmetries included in the design obviates the need to
include them by means of costly techniques such as data augmentation. We will
discuss our PDE-based G-CNNs (PDE-G-CNNs) in a general homogeneous space
setting while also going into the specifics of our primary case of interest:
roto-translation equivariance.

We solve the PDE of interest by a combination of linear group convolutions
and non-linear morphological group convolutions with analytic kernel
approximations that we underpin with formal theorems. Our kernel approximations
allow for fast GPU-implementation of the PDE-solvers, we release our
implementation with this article in the form of the LieTorch extension to
PyTorch, available at https://gitlab.com/bsmetsjr/lietorch . Just like for
linear convolution a morphological convolution is specified by a kernel that we
train in our PDE-G-CNNs. In PDE-G-CNNs we do not use non-linearities such as
max/min-pooling and ReLUs as they are already subsumed by morphological
convolutions.

We present a set of experiments to demonstrate the strength of the proposed
PDE-G-CNNs in increasing the performance of deep learning based imaging
applications with far fewer parameters than traditional CNNs.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:327:" <a href="http://arxiv.org/find/cs/1/au:+Smets_B/0/1/0/all/0/1">Bart Smets</a>, <a href="http://arxiv.org/find/cs/1/au:+Portegies_J/0/1/0/all/0/1">Jim Portegies</a>, <a href="http://arxiv.org/find/cs/1/au:+Bekkers_E/0/1/0/all/0/1">Erik Bekkers</a>, <a href="http://arxiv.org/find/cs/1/au:+Duits_R/0/1/0/all/0/1">Remco Duits</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:275;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2001.11107";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"Hamiltonian neural networks for solving equations of motion. (arXiv:2001.11107v5 [physics.comp-ph] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2001.11107";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1118:"There has been a wave of interest in applying machine learning to study
dynamical systems. We present a Hamiltonian neural network that solves the
differential equations that govern dynamical systems. This is an
equation-driven machine learning method where the optimization process of the
network depends solely on the predicted functions without using any ground
truth data. The model learns solutions that satisfy, up to an arbitrarily small
error, Hamilton's equations and, therefore, conserve the Hamiltonian
invariants. The choice of an appropriate activation function drastically
improves the predictability of the network. Moreover, an error analysis is
derived and states that the numerical errors depend on the overall network
performance. The Hamiltonian network is then employed to solve the equations
for the nonlinear oscillator and the chaotic Henon-Heiles dynamical system. In
both systems, a symplectic Euler integrator requires two orders more evaluation
points than the Hamiltonian network in order to achieve the same order of the
numerical error in the predicted phase space trajectories.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:369:" <a href="http://arxiv.org/find/physics/1/au:+Mattheakis_M/0/1/0/all/0/1">Marios Mattheakis</a>, <a href="http://arxiv.org/find/physics/1/au:+Sondak_D/0/1/0/all/0/1">David Sondak</a>, <a href="http://arxiv.org/find/physics/1/au:+Dogra_A/0/1/0/all/0/1">Akshunna S. Dogra</a>, <a href="http://arxiv.org/find/physics/1/au:+Protopapas_P/0/1/0/all/0/1">Pavlos Protopapas</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:276;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2003.12841";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:90:"A Benchmark for Point Clouds Registration Algorithms. (arXiv:2003.12841v3 [cs.RO] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2003.12841";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1621:"Point clouds registration is a fundamental step of many point clouds
processing pipelines; however, most algorithms are tested on data that are
collected ad-hoc and not shared with the research community. These data often
cover only a very limited set of use cases; therefore, the results cannot be
generalised. Public datasets proposed until now, taken individually, cover only
a few kinds of environment and mostly a single sensor. For these reasons, we
developed a benchmark, for localization and mapping applications, using
multiple publicly available datasets. In this way, we are able to cover many
kinds of environment and many kinds of sensor that can produce point clouds.
Furthermore, the ground truth has been thoroughly inspected and evaluated to
ensure its quality. For some of the datasets, the accuracy of the ground truth
measuring system was not reported by the original authors, therefore we
estimated it with our own novel method, based on an iterative registration
algorithm. Along with the data, we provide a broad set of registration
problems, chosen to cover different types of initial misalignment, various
degrees of overlap, and different kinds of registration problems. Lastly, we
propose a metric to measure the performances of registration algorithms: it
combines the commonly used rotation and translation errors together, to allow
an objective comparison of the alignments. This work aims at encouraging
authors to use a public and shared benchmark, instead of data collected ad-hoc,
to ensure objectivity and repeatability, two fundamental characteristics in any
scientific field.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:447:" <a href="http://arxiv.org/find/cs/1/au:+Fontana_S/0/1/0/all/0/1">Simone Fontana</a>, <a href="http://arxiv.org/find/cs/1/au:+Cattaneo_D/0/1/0/all/0/1">Daniele Cattaneo</a>, <a href="http://arxiv.org/find/cs/1/au:+Ballardini_A/0/1/0/all/0/1">Augusto Luis Ballardini</a>, <a href="http://arxiv.org/find/cs/1/au:+Vaghi_M/0/1/0/all/0/1">Matteo Vaghi</a>, <a href="http://arxiv.org/find/cs/1/au:+Sorrenti_D/0/1/0/all/0/1">Domenico Giorgio Sorrenti</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:277;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2005.10878";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:147:"Multi-weight Nuclear Norm Minimization for Low-rank Matrix Recovery in Presence of Subspace Prior Information. (arXiv:2005.10878v2 [cs.IT] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2005.10878";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1233:"Weighted nuclear norm minimization has been recently recognized as a
technique for reconstruction of a low-rank matrix from compressively sampled
measurements when some prior information about the column and row subspaces of
the matrix is available. In this work, we study the recovery conditions and the
associated recovery guarantees of weighted nuclear norm minimization when
multiple weights are allowed. This setup might be used when one has access to
prior subspaces forming multiple angles with the column and row subspaces of
the ground-truth matrix. While existing works in this field use a single weight
to penalize all the angles, we propose a multi-weight problem which is designed
to penalize each angle independently using a distinct weight. Specifically, we
prove that our proposed multi-weight problem is stable and robust under weaker
conditions for the measurement operator than the analogous conditions for
single-weight scenario and standard nuclear norm minimization. Moreover, it
provides better reconstruction error than the state of the art methods. We
illustrate our results with extensive numerical experiments that demonstrate
the advantages of allowing multiple weights in the recovery procedure.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:263:" <a href="http://arxiv.org/find/cs/1/au:+Ardakani_H/0/1/0/all/0/1">Hamideh Sadat Fazael Ardakani</a>, <a href="http://arxiv.org/find/cs/1/au:+Daei_S/0/1/0/all/0/1">Sajad Daei</a>, <a href="http://arxiv.org/find/cs/1/au:+Haddadi_F/0/1/0/all/0/1">Farzan Haddadi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:278;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2006.07200";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:92:"Learning to Communicate Using Counterfactual Reasoning. (arXiv:2006.07200v4 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2006.07200";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1150:"Learning to communicate in order to share state information is an active
problem in the area of multi-agent reinforcement learning (MARL). The credit
assignment problem, the non-stationarity of the communication environment and
the creation of influenceable agents are major challenges within this research
field which need to be overcome in order to learn a valid communication
protocol. This paper introduces the novel multi-agent counterfactual
communication learning (MACC) method which adapts counterfactual reasoning in
order to overcome the credit assignment problem for communicating agents.
Secondly, the non-stationarity of the communication environment while learning
the communication Q-function is overcome by creating the communication
Q-function using the action policy of the other agents and the Q-function of
the action environment. Additionally, a social loss function is introduced in
order to create influenceable agents which is required to learn a valid
communication protocol. Our experiments show that MACC is able to outperform
the state-of-the-art baselines in four different scenarios in the Particle
environment.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:679:" <a href="http://arxiv.org/find/cs/1/au:+Vanneste_S/0/1/0/all/0/1">Simon Vanneste</a>, <a href="http://arxiv.org/find/cs/1/au:+Vanneste_A/0/1/0/all/0/1">Astrid Vanneste</a>, <a href="http://arxiv.org/find/cs/1/au:+Mets_K/0/1/0/all/0/1">Kevin Mets</a>, <a href="http://arxiv.org/find/cs/1/au:+Schepper_T/0/1/0/all/0/1">Tom De Schepper</a>, <a href="http://arxiv.org/find/cs/1/au:+Anwar_A/0/1/0/all/0/1">Ali Anwar</a>, <a href="http://arxiv.org/find/cs/1/au:+Mercelis_S/0/1/0/all/0/1">Siegfried Mercelis</a>, <a href="http://arxiv.org/find/cs/1/au:+Latre_S/0/1/0/all/0/1">Steven Latr&#xe9;</a>, <a href="http://arxiv.org/find/cs/1/au:+Hellinckx_P/0/1/0/all/0/1">Peter Hellinckx</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:279;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2008.03946";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:91:"A Large-Scale Chinese Short-Text Conversation Dataset. (arXiv:2008.03946v2 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2008.03946";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:913:"The advancements of neural dialogue generation models show promising results
on modeling short-text conversations. However, training such models usually
needs a large-scale high-quality dialogue corpus, which is hard to access. In
this paper, we present a large-scale cleaned Chinese conversation dataset,
LCCC, which contains a base version (6.8million dialogues) and a large version
(12.0 million dialogues). The quality of our dataset is ensured by a rigorous
data cleaning pipeline, which is built based on a set of rules and a classifier
that is trained on manually annotated 110K dialogue pairs. We also release
pre-training dialogue models which are trained on LCCC-base and LCCC-large
respectively. The cleaned dataset and the pre-training models will facilitate
the research of short-text conversation modeling. All the models and datasets
are available at https://github.com/thu-coai/CDial-GPT.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:546:" <a href="http://arxiv.org/find/cs/1/au:+Wang_Y/0/1/0/all/0/1">Yida Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Ke_P/0/1/0/all/0/1">Pei Ke</a>, <a href="http://arxiv.org/find/cs/1/au:+Zheng_Y/0/1/0/all/0/1">Yinhe Zheng</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_K/0/1/0/all/0/1">Kaili Huang</a>, <a href="http://arxiv.org/find/cs/1/au:+Jiang_Y/0/1/0/all/0/1">Yong Jiang</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhu_X/0/1/0/all/0/1">Xiaoyan Zhu</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_M/0/1/0/all/0/1">Minlie Huang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:280;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2010.06097";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"On Riemannian Gradient-Based Methods for Minimax Problems. (arXiv:2010.06097v4 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2010.06097";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1263:"In the paper, we study a class of useful minimax optimization problems on
Riemanian manifolds and propose a class of Riemanian gradient-based methods to
solve these minimax problems. Specifically, we propose a Riemannian gradient
descent ascent (RGDA) algorithm for the deterministic minimax optimization.
Moreover, we prove that our RGDA has a sample complexity of
$O(\kappa^2\epsilon^{-2})$ for finding an $\epsilon$-stationary point of the
nonconvex strongly-concave minimax problems, where $\kappa$ denotes the
condition number. At the same time, we introduce a Riemannian stochastic
gradient descent ascent (RSGDA) algorithm for the stochastic minimax
optimization. In the theoretical analysis, we prove that our RSGDA can achieve
a sample complexity of $O(\kappa^4\epsilon^{-4})$. To further reduce the sample
complexity, we propose an accelerated Riemannian stochastic gradient descent
ascent (Acc-RSGDA) algorithm based on the variance-reduced technique. We prove
that our Acc-RSGDA algorithm achieves a lower sample complexity of
$\tilde{O}(\kappa^{4}\epsilon^{-3})$. Extensive experimental results on the
robust distributional optimization and Deep Neural Networks (DNNs) training
over Stiefel manifold demonstrate efficiency of our algorithms.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:159:" <a href="http://arxiv.org/find/cs/1/au:+Huang_F/0/1/0/all/0/1">Feihu Huang</a>, <a href="http://arxiv.org/find/cs/1/au:+Gao_S/0/1/0/all/0/1">Shangqian Gao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:281;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2010.15776";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:93:"Quantum advantage for differential equation analysis. (arXiv:2010.15776v2 [quant-ph] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2010.15776";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1053:"Quantum algorithms for both differential equation solving and for machine
learning potentially offer an exponential speedup over all known classical
algorithms. However, there also exist obstacles to obtaining this potential
speedup in useful problem instances. The essential obstacle for quantum
differential equation solving is that outputting useful information may require
difficult post-processing, and the essential obstacle for quantum machine
learning is that inputting the training set is a difficult task just by itself.
In this paper, we demonstrate, when combined, these difficulties solve one
another. We show how the output of quantum differential equation solving can
serve as the input for quantum machine learning, allowing dynamical analysis in
terms of principal components, power spectra, and wavelet decompositions. To
illustrate this, we consider continuous time Markov processes on
epidemiological and social networks. These quantum algorithms provide an
exponential advantage over existing classical Monte Carlo methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:537:" <a href="http://arxiv.org/find/quant-ph/1/au:+Kiani_B/0/1/0/all/0/1">Bobak T. Kiani</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Palma_G/0/1/0/all/0/1">Giacomo De Palma</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Englund_D/0/1/0/all/0/1">Dirk Englund</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Kaminsky_W/0/1/0/all/0/1">William Kaminsky</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Marvian_M/0/1/0/all/0/1">Milad Marvian</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Lloyd_S/0/1/0/all/0/1">Seth Lloyd</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:282;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2011.11201";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:99:"Modular Action Concept Grounding in Semantic Video Prediction. (arXiv:2011.11201v4 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2011.11201";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1404:"Recent works in video prediction have mainly focused on passive forecasting
and low-level action-conditional prediction, which sidesteps the learning of
interaction between agents and objects. We introduce the task of semantic
action-conditional video prediction, which uses semantic action labels to
describe those interactions and can be regarded as an inverse problem of action
recognition. The challenge of this new task primarily lies in how to
effectively inform the model of semantic action information. Inspired by the
idea of Mixture of Experts, we embody each abstract label by a structured
combination of various visual concept learners and propose a novel video
prediction model, Modular Action Concept Network (MAC). Our method is evaluated
on two newly designed synthetic datasets, CLEVR-Building-Blocks and
Sapien-Kitchen, and one real-world dataset called Tower-Creation. Extensive
experiments demonstrate that MAC can correctly condition on given instructions
and generate corresponding future frames without need of bounding boxes. We
further show that the trained model can make out-of-distribution
generalization, be quickly adapted to new object categories and exploit its
learnt features for object detection, showing the progression towards
higher-level cognitive abilities. More visualizations can be found at
<a href="http://www.pair.toronto.edu/mac/.">this http URL</a>

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:401:" <a href="http://arxiv.org/find/cs/1/au:+Yu_W/0/1/0/all/0/1">Wei Yu</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_W/0/1/0/all/0/1">Wenxin Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Yin_S/0/1/0/all/0/1">Songhenh Yin</a>, <a href="http://arxiv.org/find/cs/1/au:+Easterbrook_S/0/1/0/all/0/1">Steve Easterbrook</a>, <a href="http://arxiv.org/find/cs/1/au:+Garg_A/0/1/0/all/0/1">Animesh Garg</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:283;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2011.12267";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:122:"A Framework for Fluid Motion Estimation using a Constraint-Based Refinement Approach. (arXiv:2011.12267v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2011.12267";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1025:"The goal of this paper is to formulate a general framework for fluid motion
estimation using a constraint-based refinement approach. We demonstrate that
for a particular choice of the constraint, our results closely approximate the
continuity equation based fluid flow. This closeness is theoretically justified
through a modified augmented Lagrangian method and validated numerically.
Further, along with the continuity constraint, our model can include other
geometric constraints as demonstrated. The mathematical well-posedness is
studied in the Hilbert space setting. Moreover, a special feature of our system
is the possibility of a diagonalization by the Cauchy-Riemann operator and
transforming it to a diffusion process on the curl and the divergence of the
flow. Using the theory of semigroups on the decoupled system, we show that our
approach preserves the spatial characteristics of the divergence and the
vorticities. We perform several numerical experiments and show the results on
different datasets.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:161:" <a href="http://arxiv.org/find/cs/1/au:+Doshi_H/0/1/0/all/0/1">Hirak Doshi</a>, <a href="http://arxiv.org/find/cs/1/au:+Kiran_N/0/1/0/all/0/1">N. Uday Kiran</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:284;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2011.13730";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:128:"Multiple Faults Estimation in Dynamical Systems: Tractable Design and Performance Bounds. (arXiv:2011.13730v3 [math.OC] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2011.13730";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1207:"In this article, we propose a tractable nonlinear fault isolation filter
along with explicit performance bounds for a class of nonlinear dynamical
systems. We consider the presence of additive and multiplicative faults,
occurring simultaneously and through an identical dynamical relationship, which
represents a relevant case in several application domains. The proposed filter
architecture combines tools from model-based approaches in the control
literature and regression techniques from machine learning. To this end, we
view the regression operator through a system-theoretic perspective to develop
operator bounds that are then utilized to derive performance bounds for the
proposed estimation filter. In the case of constant, simultaneously and
identically acting additive and multiplicative faults, it can be shown that the
estimation error converges to zero with an exponential rate. The performance of
the proposed estimation filter in the presence of incipient faults is validated
through an application on the lateral safety systems of SAE level 4 automated
vehicles. The numerical results show that the theoretical bounds of this study
are indeed close to the actual estimation error.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:367:" <a href="http://arxiv.org/find/math/1/au:+Ploeg_C/0/1/0/all/0/1">Chris van der Ploeg</a>, <a href="http://arxiv.org/find/math/1/au:+Alirezaei_M/0/1/0/all/0/1">Mohsen Alirezaei</a>, <a href="http://arxiv.org/find/math/1/au:+Wouw_N/0/1/0/all/0/1">Nathan van de Wouw</a>, <a href="http://arxiv.org/find/math/1/au:+Esfahani_P/0/1/0/all/0/1">Peyman Mohajerin Esfahani</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:285;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2101.02023";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:135:"Perfect domination, Roman domination and perfect Roman domination in lexicographic product graphs. (arXiv:2101.02023v3 [cs.DM] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2101.02023";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:690:"The aim of this paper is to obtain closed formulas for the perfect domination
number, the Roman domination number and the perfect Roman domination number of
lexicographic product graphs. We show that these formulas can be obtained
relatively easily for the case of the first two parameters. The picture is
quite different when it concerns the perfect Roman domination number. In this
case, we obtain general bounds and then we give sufficient and/or necessary
conditions for the bounds to be achieved. We also discuss the case of perfect
Roman graphs and we characterize the lexicographic product graphs where the
perfect Roman domination number equals the Roman domination number.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:289:" <a href="http://arxiv.org/find/cs/1/au:+Martinez_A/0/1/0/all/0/1">A. Cabrera Martinez</a>, <a href="http://arxiv.org/find/cs/1/au:+Garcia_Gomez_C/0/1/0/all/0/1">C. Garcia-Gomez</a>, <a href="http://arxiv.org/find/cs/1/au:+Rodriguez_Velazquez_J/0/1/0/all/0/1">J. A. Rodriguez-Velazquez</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:286;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2102.08146";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Nominal Unification and Matching of Higher Order Expressions with Recursive Let. (arXiv:2102.08146v4 [cs.LO] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2102.08146";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:795:"A sound and complete algorithm for nominal unification of higher-order
expressions with a recursive let is described, and shown to run in
nondeterministic polynomial time. We also explore specializations like nominal
letrec-matching for expressions, for DAGs, and for garbage-free expressions and
determine their complexity. We also provide a nominal unification algorithm for
higher-order expressions with recursive let and atom-variables, where we show
that it also runs in nondeterministic polynomial time. In addition we prove
that there is a guessing strategy for nominal unification with letrec and
atom-variable that is a trade-off between exponential growth and
non-determinism. Nominal matching with variables representing partial
letrec-environments is also shown to be in NP.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:429:" <a href="http://arxiv.org/find/cs/1/au:+Schmidt_Schauss_M/0/1/0/all/0/1">Manfred Schmidt-Schau&#xdf;</a>, <a href="http://arxiv.org/find/cs/1/au:+Kutsia_T/0/1/0/all/0/1">Temur Kutsia</a>, <a href="http://arxiv.org/find/cs/1/au:+Levy_J/0/1/0/all/0/1">Jordi Levy</a>, <a href="http://arxiv.org/find/cs/1/au:+Villaret_M/0/1/0/all/0/1">Mateu Villaret</a>, <a href="http://arxiv.org/find/cs/1/au:+Kutz_Y/0/1/0/all/0/1">Yunus Kutz</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:287;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2102.08577";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"DO-GAN: A Double Oracle Framework for Generative Adversarial Networks. (arXiv:2102.08577v2 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2102.08577";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1412:"In this paper, we propose a new approach to train Generative Adversarial
Networks (GANs) where we deploy a double-oracle framework using the generator
and discriminator oracles. GAN is essentially a two-player zero-sum game
between the generator and the discriminator. Training GANs is challenging as a
pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium
is difficult as GANs have a large-scale strategy space. In DO-GAN, we extend
the double oracle framework to GANs. We first generalize the players'
strategies as the trained models of generator and discriminator from the best
response oracles. We then compute the meta-strategies using a linear program.
For scalability of the framework where multiple generators and discriminator
best responses are stored in the memory, we propose two solutions: 1) pruning
the weakly-dominated players' strategies to keep the oracles from becoming
intractable; 2) applying continual learning to retain the previous knowledge of
the networks. We apply our framework to established GAN architectures such as
vanilla GAN, Deep Convolutional GAN, Spectral Normalization GAN and Stacked
GAN. Finally, we conduct experiments on MNIST, CIFAR-10 and CelebA datasets and
show that DO-GAN variants have significant improvements in both subjective
qualitative evaluation and quantitative metrics, compared with their respective
GAN architectures.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:479:" <a href="http://arxiv.org/find/cs/1/au:+Aung_A/0/1/0/all/0/1">Aye Phyu Phyu Aung</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_X/0/1/0/all/0/1">Xinrun Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Yu_R/0/1/0/all/0/1">Runsheng Yu</a>, <a href="http://arxiv.org/find/cs/1/au:+An_B/0/1/0/all/0/1">Bo An</a>, <a href="http://arxiv.org/find/cs/1/au:+Jayavelu_S/0/1/0/all/0/1">Senthilnath Jayavelu</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_X/0/1/0/all/0/1">Xiaoli Li</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:288;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2102.13086";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:68:"Simple multi-dataset detection. (arXiv:2102.13086v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2102.13086";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:943:"How do we build a general and broad object detection system? We use all
labels of all concepts ever annotated. These labels span diverse datasets with
potentially inconsistent taxonomies. In this paper, we present a simple method
for training a unified detector on multiple large-scale datasets. We use
dataset-specific training protocols and losses, but share a common detection
architecture with dataset-specific outputs. We show how to automatically
integrate these dataset-specific outputs into a common semantic taxonomy. In
contrast to prior work, our approach does not require manual taxonomy
reconciliation. Experiments show our learned taxonomy outperforms a
expert-designed taxonomy in all datasets. Our multi-dataset detector performs
as well as dataset-specific models on each training domain, and can generalize
to new unseen dataset without fine-tuning on them. Code is available at
https://github.com/xingyizhou/UniDet.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:264:" <a href="http://arxiv.org/find/cs/1/au:+Zhou_X/0/1/0/all/0/1">Xingyi Zhou</a>, <a href="http://arxiv.org/find/cs/1/au:+Koltun_V/0/1/0/all/0/1">Vladlen Koltun</a>, <a href="http://arxiv.org/find/cs/1/au:+Krahenbuhl_P/0/1/0/all/0/1">Philipp Kr&#xe4;henb&#xfc;hl</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:289;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2103.01648";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:120:"Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Prior. (arXiv:2103.01648v4 [stat.ML] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2103.01648";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1653:"In this work we address the problem of solving ill-posed inverse problems in
imaging where the prior is a variational autoencoder (VAE). Specifically we
consider the decoupled case where the prior is trained once and can be reused
for many different log-concave degradation models without retraining. Whereas
previous MAP-based approaches to this problem lead to highly non-convex
optimization algorithms, our approach computes the joint (space-latent) MAP
that naturally leads to alternate optimization algorithms and to the use of a
stochastic encoder to accelerate computations. The resulting technique (JPMAP)
performs Joint Posterior Maximization using an Autoencoding Prior. We show
theoretical and experimental evidence that the proposed objective function is
quite close to bi-convex. Indeed it satisfies a weak bi-convexity property
which is sufficient to guarantee that our optimization scheme converges to a
stationary point. We also highlight the importance of correctly training the
VAE using a denoising criterion, in order to ensure that the encoder
generalizes well to out-of-distribution images, without affecting the quality
of the generative model. This simple modification is key to providing
robustness to the whole procedure. Finally we show how our joint MAP
methodology relates to more common MAP approaches, and we propose a
continuation scheme that makes use of our JPMAP algorithm to provide more
robust MAP estimates. Experimental results also show the higher quality of the
solutions obtained by our JPMAP approach with respect to other non-convex MAP
approaches which more often get stuck in spurious local optima.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:264:" <a href="http://arxiv.org/find/stat/1/au:+Gonzalez_M/0/1/0/all/0/1">Mario Gonz&#xe1;lez</a>, <a href="http://arxiv.org/find/stat/1/au:+Almansa_A/0/1/0/all/0/1">Andr&#xe9;s Almansa</a>, <a href="http://arxiv.org/find/stat/1/au:+Tan_P/0/1/0/all/0/1">Pauline Tan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:290;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2103.01997";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Material Measurement Units for a Circular Economy: Foundations through a Review. (arXiv:2103.01997v3 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2103.01997";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:726:"Long-term availability of minerals and industrial materials is a necessary
condition for sustainable development as they are the constituents of any
manufacturing product. To enhance the efficiency of material management, we
define a computer-vision-enabled material measurement system and provide a
review of works relevant to its development with particular emphasis on the
foundations. A network of such systems for wide-area material stock monitoring
is also covered. Finally, challenges and future research directions are
discussed. As the first article bridging industrial ecology and advanced
computer vision, this review is intended to support both research communities
towards more sustainable manufacturing.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:253:" <a href="http://arxiv.org/find/cs/1/au:+Zocco_F/0/1/0/all/0/1">Federico Zocco</a>, <a href="http://arxiv.org/find/cs/1/au:+McLoone_S/0/1/0/all/0/1">Se&#xe1;n McLoone</a>, <a href="http://arxiv.org/find/cs/1/au:+Smyth_B/0/1/0/all/0/1">Beatrice Smyth</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:291;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2103.07615";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:134:"An Efficient Multitask Neural Network for Face Alignment, Head Pose Estimation and Face Tracking. (arXiv:2103.07615v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2103.07615";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1683:"While Convolutional Neural Networks (CNNs) have significantly boosted the
performance of face related algorithms, maintaining accuracy and efficiency
simultaneously in practical use remains challenging. The state-of-the-art
methods employ deeper networks for better performance, which makes it less
practical for mobile applications because of more parameters and higher
computational complexity. Therefore, we propose an efficient multitask neural
network, Alignment &amp; Tracking &amp; Pose Network (ATPN) for face alignment, face
tracking and head pose estimation. Specifically, to achieve better performance
with fewer layers for face alignment, we introduce a shortcut connection
between shallow-layer and deep-layer features. We find the shallow-layer
features are highly correspond to facial boundaries that can provide the
structural information of face and it is crucial for face alignment. Moreover,
we generate a cheap heatmap based on the face alignment result and fuse it with
features to improve the performance of the other two tasks. Based on the
heatmap, the network can utilize both geometric information of landmarks and
appearance information for head pose estimation. The heatmap also provides
attention clues for face tracking. The face tracking task also saves us the
face detection procedure for each frame, which also significantly boost the
real-time capability for video-based tasks. We experimentally validate ATPN on
four benchmark datasets, WFLW, 300VW, WIDER Face and 300W-LP. The experimental
results demonstrate that it achieves better performance with much less
parameters and lower computational complexity compared to other light models.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:384:" <a href="http://arxiv.org/find/cs/1/au:+Xia_J/0/1/0/all/0/1">Jiahao Xia</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_H/0/1/0/all/0/1">Haimin Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wen_S/0/1/0/all/0/1">Shiping Wen</a>, <a href="http://arxiv.org/find/cs/1/au:+Yang_S/0/1/0/all/0/1">Shuo Yang</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_M/0/1/0/all/0/1">Min Xu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:292;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2104.00462";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"SCALoss: Side and Corner Aligned Loss for Bounding Box Regression. (arXiv:2104.00462v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2104.00462";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1280:"Bounding box regression is an important component in object detection. Recent
work achieves promising performance by optimizing the Intersection over
Union~(IoU). However, IoU-based loss has the gradient vanish problem in the
case of low overlapping bounding boxes, and the model could easily ignore these
simple cases. In this paper, we propose Side Overlap~(SO) loss by maximizing
the side overlap of two bounding boxes, which puts more penalty for low
overlapping bounding box cases. Besides, to speed up the convergence, the
Corner Distance~(CD) is added into the objective function. Combining the Side
Overlap and Corner Distance, we get a new regression objective function,
\textit{Side and Corner Align Loss~(SCALoss)}. The SCALoss is well-correlated
with IoU loss, which also benefits the evaluation metric but produces more
penalty for low-overlapping cases. It can serve as a comprehensive similarity
measure, leading to better localization performance and faster convergence
speed. Experiments on COCO, PASCAL VOC, and LVIS benchmarks show that SCALoss
can bring consistent improvement and outperform $\ell_n$ loss and IoU based
loss with popular object detectors such as YOLOV3, SSD, Faster-RCNN. Code is
available at: \url{https://github.com/Turoad/SCALoss}.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:379:" <a href="http://arxiv.org/find/cs/1/au:+Zheng_T/0/1/0/all/0/1">Tu Zheng</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_S/0/1/0/all/0/1">Shuai Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_Y/0/1/0/all/0/1">Yang Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_Z/0/1/0/all/0/1">Zili Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Cai_D/0/1/0/all/0/1">Deng Cai</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:293;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2104.08559";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:114:"Abusing Cache Line Dirty States to Leak Information in Commercial Processors. (arXiv:2104.08559v2 [cs.CR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2104.08559";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1917:"Caches have been used to construct various types of covert and side channels
to leak information. Most existing cache channels exploit the timing difference
between cache hits and cache misses. However, we introduce a new and broader
classification of cache covert channel attacks: Hit+Miss, Hit+Hit, and
Miss+Miss. We highlight that cache misses for cache lines in different states
may have more significant time differences, and these can be used as timing
channels. Based on this classification, we propose a new stable and stealthy
Miss+Miss cache channel. Write-back caches are widely deployed in modern
processors. This paper presents in detail a way in which replacement latency
differences can be used to construct timing-based channels (called WB channels)
to leak information in a write-back cache. Any modification to a cache line by
a sender will set it to the dirty state, and the receiver can observe this
through measuring the latency of replacing this cache set. We also demonstrate
how senders could exploit a different number of dirty cache lines in a cache
set to improve transmission bandwidth with symbols encoding multiple bits. The
peak transmission bandwidths of the WB channels in commercial systems can vary
between 1300 and 4400~kbps per cache set in a hyper-threaded setting without
shared memory between the sender and the receiver. In contrast to most existing
cache channels, which always target specific memory addresses, the new WB
channels focus on the cache set and cache line states, making it difficult for
the channel to be disturbed by other processes on the core, and they can still
work in a cache using a random replacement policy. We also analyzed the
stealthiness of WB channels from the perspective of the number of cache loads
and cache miss rates. We discuss and evaluate possible defenses. The paper
finishes by discussing various forms of side-channel attack.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:229:" <a href="http://arxiv.org/find/cs/1/au:+Cui_Y/0/1/0/all/0/1">Yujie Cui</a>, <a href="http://arxiv.org/find/cs/1/au:+Yang_C/0/1/0/all/0/1">Chun Yang</a>, <a href="http://arxiv.org/find/cs/1/au:+Cheng_X/0/1/0/all/0/1">Xu Cheng</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:294;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2104.13137";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:143:"A non-singular boundary element method for interactions between acoustical field sources and structures. (arXiv:2104.13137v2 [math.NA] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2104.13137";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:653:"Localized point sources (monopoles) in an acoustical domain are implemented
to a three dimensional non-singular Helmholtz boundary element method in the
frequency domain. It allows for the straightforward use of higher order surface
elements on the boundaries of the problem. It will been shown that the effect
of the monopole sources ends up on the right hand side of the resulting matrix
system. Some carefully selected examples are studied, such as point sources
near and within a concentric spherical core-shell scatterer (with theoretical
verification), near a curved focusing surface and near a multi-scale and
multi-domain acoustic lens.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:77:" <a href="http://arxiv.org/find/math/1/au:+Sun_Q/0/1/0/all/0/1">Qiang Sun</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:295;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2104.14349";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:98:"Hand Gesture Recognition Based on a Nonconvex Regularization. (arXiv:2104.14349v3 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2104.14349";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:749:"Recognition of hand gestures is one of the most fundamental tasks in
human-robot interaction. Sparse representation based methods have been widely
used due to their efficiency and low demands on the training data. Recently,
nonconvex regularization techniques including the $\ell_{1-2}$ regularization
have been proposed in the image processing community to promote sparsity while
achieving efficient performance. In this paper, we propose a vision-based hand
gesture recognition model based on the $\ell_{1-2}$ regularization, which is
solved by the alternating direction method of multipliers (ADMM). Numerical
experiments on binary and gray-scale data sets have demonstrated the
effectiveness of this method in identifying hand gestures.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:233:" <a href="http://arxiv.org/find/cs/1/au:+Qin_J/0/1/0/all/0/1">Jing Qin</a>, <a href="http://arxiv.org/find/cs/1/au:+Ashley_J/0/1/0/all/0/1">Joshua Ashley</a>, <a href="http://arxiv.org/find/cs/1/au:+Xie_B/0/1/0/all/0/1">Biyun Xie</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:296;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2104.14750";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:90:"A Refined Inertial DC Algorithm for DC Programming. (arXiv:2104.14750v2 [math.OC] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2104.14750";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1102:"In this paper we consider the difference-of-convex (DC) programming problems,
whose objective function is the difference of two convex functions. The
classical DC Algorithm (DCA) is well-known for solving this kind of problems,
which generally returns a critical point. Recently, an inertial DC algorithm
(InDCA) equipped with heavy-ball inertial-force procedure was proposed in de
Oliveira et al. (Set-Valued and Variational Analysis 27(4):895--919, 2019),
which potentially helps to improve both the convergence speed and the solution
quality. Based on InDCA, we propose a refined inertial DC algorithm (RInDCA)
equipped with enlarged inertial step-size compared with InDCA. Empirically,
larger step-size accelerates the convergence. We demonstrate the subsequential
convergence of our refined version to a critical point. In addition, by
assuming the Kurdyka-{\L}ojasiewicz (KL) property of the objective function, we
establish the sequential convergence of RInDCA. Numerical simulations on
checking copositivity of matrices and image denoising problem show the benefit
of larger step-size.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:155:" <a href="http://arxiv.org/find/math/1/au:+You_Y/0/1/0/all/0/1">Yu You</a>, <a href="http://arxiv.org/find/math/1/au:+Niu_Y/0/1/0/all/0/1">Yi-Shuai Niu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:297;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2105.13634";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:128:"Saudi Parents&#039; Privacy Concerns about Their Children&#039;s Smart Device Applications. (arXiv:2105.13634v2 [cs.CR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2105.13634";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1722:"In this paper, we investigate Saudi parents' privacy concerns regarding their
children's smart device applications (apps). To this end, we conducted a survey
and analysed 119 responses. Our results show that Saudi parents expressed a
high level of concern regarding their children's privacy when using smart
device apps. However, they expressed higher concerns about apps' content than
privacy issues such as apps' requests to access sensitive data. Furthermore,
parents' concerns are not in line with most of the children's installed apps,
which contain apps inappropriate for their age, require parental guidance, and
request access to sensitive data such as location. We also discuss several
aspects of Saudi parents' practices and concerns compared to those reported by
Western (mainly from the UK) and Chinese parents in previous reports. We found
interesting patterns and established new relationships. For example, Saudi and
Western parents show higher levels of privacy concerns than Chinese parents.
Finally, we tested 14 privacy practices and concerns against high versus low
socioeconomic classes (parents' education, technical background, and income) to
find whether there are significant differences between high and low classes (we
denote these differences by "digital divide"). Out of 42 tests (14 properties x
3 classes) we found significant differences between high and low classes in 7
tests only. While this is a positive trend overall, it is important to work on
bridging these gaps. The results of this paper provide key findings to identify
areas of improvement and recommendations, especially for Saudis, which can be
used by parents, developers, researchers, regulators, and policy makers.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:176:" <a href="http://arxiv.org/find/cs/1/au:+Alashwali_E/0/1/0/all/0/1">Eman Alashwali</a>, <a href="http://arxiv.org/find/cs/1/au:+Alashwali_F/0/1/0/all/0/1">Fatimah Alashwali</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:298;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2105.13880";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:91:"Knowledge Inheritance for Pre-trained Language Models. (arXiv:2105.13880v2 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2105.13880";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1093:"Recent explorations of large-scale pre-trained language models (PLMs) have
revealed the power of PLMs with huge amounts of parameters, setting off a wave
of training ever-larger PLMs. However, it requires tremendous computational
resources to train a large-scale PLM, which may be practically unaffordable. In
addition, existing large-scale PLMs are mainly trained from scratch
individually, ignoring that many well-trained PLMs are available. To this end,
we explore the question how could existing PLMs benefit training large-scale
PLMs in future. Specifically, we introduce a pre-training framework named
"knowledge inheritance" (KI) and explore how could knowledge distillation serve
as auxiliary supervision during pre-training to efficiently learn larger PLMs.
Experimental results demonstrate the superiority of KI in training efficiency.
We also conduct empirical analyses to explore the effects of teacher PLMs'
pre-training settings, including model architecture, pre-training data, etc.
Finally, we show that KI could be applied to domain adaptation and knowledge
transfer.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:843:" <a href="http://arxiv.org/find/cs/1/au:+Qin_Y/0/1/0/all/0/1">Yujia Qin</a>, <a href="http://arxiv.org/find/cs/1/au:+Lin_Y/0/1/0/all/0/1">Yankai Lin</a>, <a href="http://arxiv.org/find/cs/1/au:+Yi_J/0/1/0/all/0/1">Jing Yi</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_J/0/1/0/all/0/1">Jiajie Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Han_X/0/1/0/all/0/1">Xu Han</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_Z/0/1/0/all/0/1">Zhengyan Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Su_Y/0/1/0/all/0/1">Yusheng Su</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_Z/0/1/0/all/0/1">Zhiyuan Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_P/0/1/0/all/0/1">Peng Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Sun_M/0/1/0/all/0/1">Maosong Sun</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhou_J/0/1/0/all/0/1">Jie Zhou</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:299;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2105.14435";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:82:"Convergence of Datalog over (Pre-) Semirings. (arXiv:2105.14435v3 [cs.DB] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2105.14435";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:853:"Recursive queries have been traditionally studied in the framework of
datalog, a language that restricts recursion to monotone queries over sets,
which is guaranteed to converge in polynomial time in the size of the input.
But modern big data systems require recursive computations beyond the Boolean
space. In this paper we study the convergence of datalog when it is interpreted
over an arbitrary semiring. We consider an ordered semiring, define the
semantics of a datalog program as a least fixpoint in this semiring, and study
the number of steps required to reach that fixpoint, if ever. We identify
algebraic properties of the semiring that correspond to certain convergence
properties of datalog programs. Finally, we describe a class of ordered
semirings on which one can use the semi-naive evaluation algorithm on any
datalog program.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:412:" <a href="http://arxiv.org/find/cs/1/au:+Khamis_M/0/1/0/all/0/1">Mahmoud Abo Khamis</a>, <a href="http://arxiv.org/find/cs/1/au:+Ngo_H/0/1/0/all/0/1">Hung Q. Ngo</a>, <a href="http://arxiv.org/find/cs/1/au:+Pichler_R/0/1/0/all/0/1">Reinhard Pichler</a>, <a href="http://arxiv.org/find/cs/1/au:+Suciu_D/0/1/0/all/0/1">Dan Suciu</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_Y/0/1/0/all/0/1">Yisu Remy Wang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:300;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2106.00186";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"Towards Light-weight and Real-time Line Segment Detection. (arXiv:2106.00186v3 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2106.00186";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1403:"Previous deep learning-based line segment detection (LSD) suffers from the
immense model size and high computational cost for line prediction. This
constrains them from real-time inference on computationally restricted
environments. In this paper, we propose a real-time and light-weight line
segment detector for resource-constrained environments named Mobile LSD
(M-LSD). We design an extremely efficient LSD architecture by minimizing the
backbone network and removing the typical multi-module process for line
prediction found in previous methods. To maintain competitive performance with
a light-weight network, we present novel training schemes: Segments of Line
segment (SoL) augmentation, matching and geometric loss. SoL augmentation
splits a line segment into multiple subparts, which are used to provide
auxiliary line data during the training process. Moreover, the matching and
geometric loss allow a model to capture additional geometric cues. Compared
with TP-LSD-Lite, previously the best real-time LSD method, our model
(M-LSD-tiny) achieves competitive performance with 2.5% of model size and an
increase of 130.5% in inference speed on GPU. Furthermore, our model runs at
56.8 FPS and 48.6 FPS on the latest Android and iPhone mobile devices,
respectively. To the best of our knowledge, this is the first real-time deep
LSD available on mobile devices. Our code is available.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:468:" <a href="http://arxiv.org/find/cs/1/au:+Gu_G/0/1/0/all/0/1">Geonmo Gu</a>, <a href="http://arxiv.org/find/cs/1/au:+Ko_B/0/1/0/all/0/1">Byungsoo Ko</a>, <a href="http://arxiv.org/find/cs/1/au:+Go_S/0/1/0/all/0/1">SeoungHyun Go</a>, <a href="http://arxiv.org/find/cs/1/au:+Lee_S/0/1/0/all/0/1">Sung-Hyun Lee</a>, <a href="http://arxiv.org/find/cs/1/au:+Lee_J/0/1/0/all/0/1">Jingeun Lee</a>, <a href="http://arxiv.org/find/cs/1/au:+Shin_M/0/1/0/all/0/1">Minchul Shin</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:301;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2106.00903";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:138:"Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation. (arXiv:2106.00903v2 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2106.00903";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1338:"Knowledge distillation (KD) is commonly used to construct synthetic data for
training non-autoregressive translation (NAT) models. However, there exists a
discrepancy on low-frequency words between the distilled and the original data,
leading to more errors on predicting low-frequency words. To alleviate the
problem, we directly expose the raw data into NAT by leveraging pretraining. By
analyzing directed alignments, we found that KD makes low-frequency source
words aligned with targets more deterministically but fails to align sufficient
low-frequency words from target to source. Accordingly, we propose reverse KD
to rejuvenate more alignments for low-frequency target words. To make the most
of authentic and synthetic data, we combine these complementary approaches as a
new training strategy for further boosting NAT performance. We conduct
experiments on five translation benchmarks over two advanced architectures.
Results demonstrate that the proposed approach can significantly and
universally improve translation quality by reducing translation errors on
low-frequency words. Encouragingly, our approach achieves 28.2 and 33.9 BLEU
points on the WMT14 English-German and WMT16 Romanian-English datasets,
respectively. Our code, data, and trained models are available at
\url{https://github.com/alphadl/RLFW-NAT}.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:469:" <a href="http://arxiv.org/find/cs/1/au:+Ding_L/0/1/0/all/0/1">Liang Ding</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_L/0/1/0/all/0/1">Longyue Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_X/0/1/0/all/0/1">Xuebo Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Wong_D/0/1/0/all/0/1">Derek F. Wong</a>, <a href="http://arxiv.org/find/cs/1/au:+Tao_D/0/1/0/all/0/1">Dacheng Tao</a>, <a href="http://arxiv.org/find/cs/1/au:+Tu_Z/0/1/0/all/0/1">Zhaopeng Tu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:302;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2106.07243";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:130:"Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networks. (arXiv:2106.07243v3 [math.OC] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2106.07243";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:845:"In this paper, we propose two communication efficient decentralized
optimization algorithms over a general directed multi-agent network. The first
algorithm, termed Compressed Push-Pull (CPP), combines the gradient tracking
Push-Pull method with communication compression. We show that CPP is applicable
to a general class of unbiased compression operators and achieves linear
convergence rate for strongly convex and smooth objective functions. The second
algorithm is a broadcast-like version of CPP (B-CPP), and it also achieves
linear convergence rate under the same conditions on the objective functions.
B-CPP can be applied in an asynchronous broadcast setting and further reduce
communication costs compared to CPP. Numerical experiments complement the
theoretical analysis and confirm the effectiveness of the proposed methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:309:" <a href="http://arxiv.org/find/math/1/au:+Song_Z/0/1/0/all/0/1">Zhuoqing Song</a>, <a href="http://arxiv.org/find/math/1/au:+Shi_L/0/1/0/all/0/1">Lei Shi</a>, <a href="http://arxiv.org/find/math/1/au:+Pu_S/0/1/0/all/0/1">Shi Pu</a>, <a href="http://arxiv.org/find/math/1/au:+Yan_M/0/1/0/all/0/1">Ming Yan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:303;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2106.13170";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:106:"A Characteristic Mapping Method for Tracer Transport on the Sphere. (arXiv:2106.13170v2 [math.NA] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2106.13170";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:991:"A semi-Lagrangian Characteristic Mapping method for the solution of the
tracer transport equations on the sphere is presented. The method solves for
the solution operator of the equations by approximating the inverse of the
diffeomorphism generated by a given velocity field. The evolution of any tracer
and mass density can then be computed via pullback with this map. We present a
novel spatial discretization of the manifold-valued map using a
projection-based approach with spherical spline interpolation. The numerical
scheme yields $C^1$ continuity for the map and global second-order accuracy for
the solution of the tracer transport equations. Error estimates are provided
and supported by convergence tests involving solid body rotation, moving
vortices, deformational, and compressible flows. Additionally, we illustrate
some unique features of computing the solution operator using a numerical
mixing test and the transport of a fractal set in a complex flow environment.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:172:" <a href="http://arxiv.org/find/math/1/au:+Taylor_S/0/1/0/all/0/1">Seth Taylor</a>, <a href="http://arxiv.org/find/math/1/au:+Nave_J/0/1/0/all/0/1">Jean-Christophe Nave</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:304;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2106.14104";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:93:"Can An Image Classifier Suffice For Action Recognition?. (arXiv:2106.14104v3 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2106.14104";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1000:"We explore a new perspective on video understanding by casting the video
recognition problem as an image recognition task. Our approach rearranges input
video frames into super images, which allow for training an image classifier
directly to fulfill the task of action recognition, in exactly the same way as
image classification. With such a simple idea, we show that transformer-based
image classifiers alone can suffice for action recognition. In particular, our
approach demonstrates strong and promising performance against SOTA methods on
several public datasets including Kinetics400, Moments In Time,
Something-Something V2 (SSV2), Jester and Diving48. We also experiment with the
prevalent ResNet image classifiers in computer vision to further validate our
idea. The results on both Kinetics400 and SSV2 are comparable to some of the
best-performed CNN approaches based on spatio-temporal modeling. Our source
codes and models are available at https://github.com/IBM/sifar-pytorch.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:314:" <a href="http://arxiv.org/find/cs/1/au:+Fan_Q/0/1/0/all/0/1">Quanfu Fan</a>, <a href="http://arxiv.org/find/cs/1/au:+Chun-Fu/0/1/0/all/0/1">Chun-Fu</a> (Richard) <a href="http://arxiv.org/find/cs/1/au:+Chen/0/1/0/all/0/1">Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Panda_R/0/1/0/all/0/1">Rameswar Panda</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:305;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2107.02288";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:125:"Performance Analysis of Regularized Convex Relaxation for Complex-Valued Data Detection. (arXiv:2107.02288v6 [cs.IT] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2107.02288";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:915:"In this work, we study complex-valued data detection performance in massive
multiple-input multiple-output (MIMO) systems. We focus on the problem of
recovering an $n$-dimensional signal whose entries are drawn from an arbitrary
constellation $\mathcal{K} \subset \mathbb{C}$ from $m$ noisy linear
measurements, with an independent and identically distributed (i.i.d.) complex
Gaussian channel. Since the optimal maximum likelihood (ML) detector is
computationally prohibitive for large dimensions, many convex relaxation
heuristic methods have been proposed to solve the detection problem. In this
paper, we consider a regularized version of this convex relaxation that we call
the regularized convex relaxation (RCR) detector and sharply derive asymptotic
expressions for its mean square error and symbol error probability. Monte-Carlo
simulations are provided to validate the derived analytical results.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:171:" <a href="http://arxiv.org/find/cs/1/au:+Alrashdi_A/0/1/0/all/0/1">Ayed M. Alrashdi</a>, <a href="http://arxiv.org/find/cs/1/au:+Sifaou_H/0/1/0/all/0/1">Houssem Sifaou</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:306;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2107.04438";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:128:"A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems. (arXiv:2107.04438v2 [cs.AI] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2107.04438";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:825:"In this paper, we study the problem of evaluating the addition of elements to
a set. This problem is difficult, because it can, in the general case, not be
reduced to unconditional preferences between the choices. Therefore, we model
preferences based on the context of the decision. We discuss and compare two
different Siamese network architectures for this task: a twin network that
compares the two sets resulting after the addition, and a triplet network that
models the contribution of each candidate to the existing set. We evaluate the
two settings on a real-world task; learning human card preferences for deck
building in the collectible card game Magic: The Gathering. We show that the
triplet approach achieves a better result than the twin network and that both
outperform previous results on this task.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:266:" <a href="http://arxiv.org/find/cs/1/au:+Bertram_T/0/1/0/all/0/1">Timo Bertram</a>, <a href="http://arxiv.org/find/cs/1/au:+Furnkranz_J/0/1/0/all/0/1">Johannes F&#xfc;rnkranz</a>, <a href="http://arxiv.org/find/cs/1/au:+Muller_M/0/1/0/all/0/1">Martin M&#xfc;ller</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:307;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2107.04642";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:125:"Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic Fairness. (arXiv:2107.04642v6 [cs.CY] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2107.04642";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1778:"As governments embrace algorithms, the burgeoning field of algorithmic
fairness provides an influential methodology for promoting equality-enhancing
reforms. However, even algorithms that satisfy mathematical fairness standards
can exacerbate oppression, causing critics to call for the field to shift its
focus from "fairness" to "justice." Yet any efforts to achieve algorithmic
justice in practice are constrained by a fundamental technical limitation: the
"impossibility of fairness" (an incompatibility between mathematical
definitions of fairness). The impossibility of fairness thus raises a central
question about algorithmic fairness: How can computer scientists support
equitable policy reforms with algorithms? In this article, I argue that
promoting justice with algorithms requires reforming the methodology of
algorithmic fairness. First, I diagnose why the current methodology for
algorithmic fairness--which I call "formal algorithmic fairness"--is flawed. I
demonstrate that the problems of algorithmic fairness--including the
impossibility of fairness--result from the methodology of the field, which
restricts analysis to isolated decision-making procedures. Second, I draw on
theories of substantive equality from law and philosophy to propose an
alternative methodology: "substantive algorithmic fairness." Because
substantive algorithmic fairness takes a more expansive scope to fairness, it
enables an escape from the impossibility of fairness and provides a rigorous
guide for alleviating injustice with algorithms. In sum, substantive
algorithmic fairness presents a new direction for algorithmic fairness: away
from formal mathematical models of "fairness" and toward substantive
evaluations of how algorithms can (and cannot) promote justice.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:77:" <a href="http://arxiv.org/find/cs/1/au:+Green_B/0/1/0/all/0/1">Ben Green</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:308;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2107.05131";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:97:"A dual approach for dynamic pricing in multi-demand markets. (arXiv:2107.05131v3 [cs.GT] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2107.05131";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1207:"Dynamic pricing schemes were introduced as an alternative to posted-price
mechanisms. In contrast to static models, the dynamic setting allows to update
the prices between buyer-arrivals based on the remaining sets of items and
buyers, and so it is capable of maximizing social welfare without the need for
a central coordinator. In this paper, we study the existence of optimal dynamic
pricing schemes in combinatorial markets. In particular, we concentrate on
multi-demand valuations, a natural extension of unit-demand valuations. The
proposed approach is based on computing an optimal dual solution of the maximum
social welfare problem with distinguished structural properties.

Our contribution is twofold. By relying on an optimal dual solution, we show
the existence of optimal dynamic prices in unit-demand markets and in
multi-demand markets up to three buyers, thus giving new interpretations of
results of Cohen-Addad et al. and Berger et al., respectively. Furthermore, we
provide an optimal dynamic pricing scheme for bi-demand valuations with an
arbitrary number of buyers. In all cases, our proofs also provide efficient
algorithms for determining the optimal dynamic prices.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:288:" <a href="http://arxiv.org/find/cs/1/au:+Berczi_K/0/1/0/all/0/1">Krist&#xf3;f B&#xe9;rczi</a>, <a href="http://arxiv.org/find/cs/1/au:+Berczi_Kovacs_E/0/1/0/all/0/1">Erika R. B&#xe9;rczi-Kov&#xe1;cs</a>, <a href="http://arxiv.org/find/cs/1/au:+Szogi_E/0/1/0/all/0/1">Evelin Sz&#xf6;gi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:309;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2107.05247";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:118:"INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering. (arXiv:2107.05247v2 [cs.IR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2107.05247";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1635:"Collaborative filtering is one of the most common scenarios and popular
research topics in recommender systems. Among existing methods, latent factor
models, i.e., learning a specific embedding for each user/item by
reconstructing the observed interaction matrix, have shown excellent
performances. However, such user-specific and item-specific embeddings are
intrinsically transductive, making it difficult to deal with new users and new
items unseen during training. Besides, the number of model parameters heavily
depends on the number of all users and items, restricting its scalability to
real-world applications. To solve the above challenges, in this paper, we
propose a novel model-agnostic and scalable Inductive Embedding Module for
collaborative filtering, namely INMO. INMO generates the inductive embeddings
for users (items) by characterizing their interactions with some template items
(template users), instead of employing an embedding lookup table. Under the
theoretical analysis, we further propose an effective indicator for the
selection of template users/items. Our proposed INMO can be attached to
existing latent factor models as a pre-module, inheriting the expressiveness of
backbone models, while bringing the inductive ability and reducing model
parameters. We validate the generality of INMO by attaching it to both Matrix
Factorization (MF) and LightGCN, which are two representative latent factor
models for collaborative filtering. Extensive experiments on three public
benchmarks demonstrate the effectiveness and efficiency of INMO in both
transductive and inductive recommendation scenarios.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:385:" <a href="http://arxiv.org/find/cs/1/au:+Wu_Y/0/1/0/all/0/1">Yunfan Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Cao_Q/0/1/0/all/0/1">Qi Cao</a>, <a href="http://arxiv.org/find/cs/1/au:+Shen_H/0/1/0/all/0/1">Huawei Shen</a>, <a href="http://arxiv.org/find/cs/1/au:+Tao_S/0/1/0/all/0/1">Shuchang Tao</a>, <a href="http://arxiv.org/find/cs/1/au:+Cheng_X/0/1/0/all/0/1">Xueqi Cheng</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:310;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2107.06433";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:138:"A New Parallel Algorithm for Sinkhorn Word-Movers Distance and Its Performance on PIUMA and Xeon CPU. (arXiv:2107.06433v3 [cs.DC] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2107.06433";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1772:"The Word Movers Distance (WMD) measures the semantic dissimilarity between
two text documents by computing the cost of optimally moving all words of a
source/query document to the most similar words of a target document. Computing
WMD between two documents is costly because it requires solving an
$O(V^3log(V))$ optimization problem where $V$ is the number of unique words in
the document. Fortunately, WMD can be framed as an Earth Mover's Distance (EMD)
for which the algorithmic complexity can be reduced to $O(V^2)$ by adding an
entropy penalty to the optimization problem and solving it using the
Sinkhorn-Knopp algorithm. Additionally, the computation can be made highly
parallel by adopting a batching approach, i.e., computing the WMD of a single
query document against multiple target documents at once.

Sinkhorn WMD is a key kernel used in many ML/NLP applications. and usually
gets implemented in Python. However, a straightforward Python implementation
may leave significant performance on the table even though it may internally
call optimized C++ BLAS routines. We present a new sparse {P}arallel
{A}lgorithm for {S}inkhorn-Knopp {W}ord-movers {D}istance to compute the
semantic distance of one document to many other documents by adopting the
$O(V^2)$ EMD algorithm. We algorithmically transform $O(V^2)$ dense
compute-heavy EMD version into an equivalent sparse one using new fused
SDDMM-SpMM (sparse selection of dense-dense matrix-, sparse-dense
matrix-multiplication) kernels. We implemented and optimized this algorithm for
two very different architectures -- the new Intel Programmable Integrated
Unified Memory Architecture (PIUMA) and Intel Xeon CPUs. We show that we were
able to reach close to peak performance on both platforms.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:173:" <a href="http://arxiv.org/find/cs/1/au:+Tithi_J/0/1/0/all/0/1">Jesmin Jahan Tithi</a>, <a href="http://arxiv.org/find/cs/1/au:+Petrini_F/0/1/0/all/0/1">Fabrizio Petrini</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:311;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2108.00941";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:88:"A Survey of Human-in-the-loop for Machine Learning. (arXiv:2108.00941v3 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2108.00941";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1170:"Human-in-the-loop aims to train an accurate prediction model with minimum
cost by integrating human knowledge and experience. Humans can provide training
data for machine learning applications and directly accomplish tasks that are
hard for computers in the pipeline with the help of machine-based approaches.
In this paper, we survey existing works on human-in-the-loop from a data
perspective and classify them into three categories with a progressive
relationship: (1) the work of improving model performance from data processing,
(2) the work of improving model performance through interventional model
training, and (3) the design of the system independent human-in-the-loop. Using
the above categorization, we summarize major approaches in the field; along
with their technical strengths/ weaknesses, we have simple classification and
discussion in natural language processing, computer vision, and others.
Besides, we provide some open challenges and opportunities. This survey intends
to provide a high-level summarization for human-in-the-loop and motivates
interested readers to consider approaches for designing effective
human-in-the-loop solutions.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:464:" <a href="http://arxiv.org/find/cs/1/au:+Wu_X/0/1/0/all/0/1">Xingjiao Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Xiao_L/0/1/0/all/0/1">Luwei Xiao</a>, <a href="http://arxiv.org/find/cs/1/au:+Sun_Y/0/1/0/all/0/1">Yixuan Sun</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_J/0/1/0/all/0/1">Junhang Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Ma_T/0/1/0/all/0/1">Tianlong Ma</a>, <a href="http://arxiv.org/find/cs/1/au:+He_L/0/1/0/all/0/1">Liang He</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:312;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2108.05761";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:153:"Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer&#039;s disease classification. (arXiv:2108.05761v3 [stat.ME] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2108.05761";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:987:"Multi-view data refers to a setting where features are divided into feature
sets, for example because they correspond to different sources. Stacked
penalized logistic regression (StaPLR) is a recently introduced method that can
be used for classification and automatically selecting the views that are most
important for prediction. We introduce an extension of this method to a setting
where the data has a hierarchical multi-view structure. We also introduce a new
view importance measure for StaPLR, which allows us to compare the importance
of views at any level of the hierarchy. We apply our extended StaPLR algorithm
to Alzheimer's disease classification where different MRI measures have been
calculated from three scan types: structural MRI, diffusion-weighted MRI, and
resting-state fMRI. StaPLR can identify which scan types and which derived MRI
measures are most important for classification, and it outperforms elastic net
regression in classification performance.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:594:" <a href="http://arxiv.org/find/stat/1/au:+Loon_W/0/1/0/all/0/1">Wouter van Loon</a>, <a href="http://arxiv.org/find/stat/1/au:+Vos_F/0/1/0/all/0/1">Frank de Vos</a>, <a href="http://arxiv.org/find/stat/1/au:+Fokkema_M/0/1/0/all/0/1">Marjolein Fokkema</a>, <a href="http://arxiv.org/find/stat/1/au:+Szabo_B/0/1/0/all/0/1">Botond Szabo</a>, <a href="http://arxiv.org/find/stat/1/au:+Koini_M/0/1/0/all/0/1">Marisa Koini</a>, <a href="http://arxiv.org/find/stat/1/au:+Schmidt_R/0/1/0/all/0/1">Reinhold Schmidt</a>, <a href="http://arxiv.org/find/stat/1/au:+Rooij_M/0/1/0/all/0/1">Mark de Rooij</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:313;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2108.09498";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:173:"Active User Detection and Channel Estimation for Spatial-based Random Access in Crowded Massive MIMO Systems via Blind Super-resolution. (arXiv:2108.09498v2 [cs.IT] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2108.09498";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1764:"This work presents a novel framework for random access in crowded scenarios
of multiple-input multiple-output(MIMO) systems. A multi-antenna base station
(BS) and multiple single-antenna users are considered in these systems. A huge
portion of the system resources is dedicated as orthogonal pilots for accurate
channel estimation which imposes a huge training overhead. This overhead can be
highly mitigated by exploiting intrinsic angular domain sparsity of massive
MIMO channels and the sporadic traffic of users, i.e., few number of users are
active to sent or receive data in each coherence interval. In fact, the angles
of arrivals (AoAs) coming from active users are continuous parameters and can
take any arbitrary values. Besides, the AoAs corresponding to each active user
are alongside each other forming a specific cluster. This work revolves around
exploiting these features. Specifically, a blind clustering-based algorithm is
proposed that not only recovers the transmitted data by users in grant free
random access and primary pilots in random access blocks of coherent
transmission, but also provides accurate channel estimation. Our approach is
based on transforming the unknown variables into a higher dimensional space
with matrix variables. An off-grid atomic norm minimization is then proposed to
obtain the unknown matrix from only a few observed arrays at the BS. Then, a
clustering-based approach is employed to identify which AoAs correspond to
which active users. After identifying active users and their AoAs, an
alternating-based approach is performed to obtain the channels and data or
primary pilots of active users. Simulation results demonstrate the
effectiveness of our approach in AoA detection as well as data recovery.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:255:" <a href="http://arxiv.org/find/cs/1/au:+Afshar_A/0/1/0/all/0/1">Abolghasem Afshar</a>, <a href="http://arxiv.org/find/cs/1/au:+Vakili_V/0/1/0/all/0/1">Vahid Tabataba Vakili</a>, <a href="http://arxiv.org/find/cs/1/au:+Daei_S/0/1/0/all/0/1">Sajad Daei</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:314;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2109.04517";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"Prediction and Prevention of Pandemics via Graphical Model Inference and Convex Programming. (arXiv:2109.04517v2 [cs.SI] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2109.04517";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1793:"Hard-to-predict bursts of COVID-19 pandemic revealed significance of
statistical modeling which would resolve spatio-temporal correlations over
geographical areas, for example spread of the infection over a city with census
tract granularity. In this manuscript, we provide algorithmic answers to the
following two inter-related public health challenges. (1) Inference Challenge:
assuming that there are $N$ census blocks (nodes) in the city, and given an
initial infection at any set of nodes, what is the probability for a subset of
census blocks to become infected by the time the spread of the infection burst
is stabilized? (2) Prevention Challenge: What is the minimal control action one
can take to minimize the infected part of the stabilized state footprint? To
answer the challenges, we build a Graphical Model of pandemic of the attractive
Ising (pair-wise, binary) type, where each node represents a census track and
each edge factor represents the strength of the pairwise interaction between a
pair of nodes. We show that almost all attractive Ising Models on dense graphs
result in either of the two modes for the most probable state: either all nodes
which were not infected initially became infected, or all the initially
uninfected nodes remain uninfected. This bi-modal solution of the Inference
Challenge allows us to re-state the Prevention Challenge as the following
tractable convex programming: for the bare Ising Model with pair-wise and bias
factors representing the system without prevention measures, such that the MAP
state is fully infected for at least one of the initial infection patterns,
find the closest, in $l_1$ norm, set of factors resulting in all the MAP states
of the Ising model, with the optimal prevention measures applied, to become
safe.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:455:" <a href="http://arxiv.org/find/cs/1/au:+Krechetov_M/0/1/0/all/0/1">Mikhail Krechetov</a>, <a href="http://arxiv.org/find/cs/1/au:+Sikaroudi_A/0/1/0/all/0/1">Amir Mohammad Esmaieeli Sikaroudi</a>, <a href="http://arxiv.org/find/cs/1/au:+Efrat_A/0/1/0/all/0/1">Alon Efrat</a>, <a href="http://arxiv.org/find/cs/1/au:+Polishchuk_V/0/1/0/all/0/1">Valentin Polishchuk</a>, <a href="http://arxiv.org/find/cs/1/au:+Chertkov_M/0/1/0/all/0/1">Michael Chertkov</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:315;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2109.05067";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:110:"The Flaws of Policies Requiring Human Oversight of Government Algorithms. (arXiv:2109.05067v3 [cs.HC] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2109.05067";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1755:"As algorithms become an influential component of government decision-making
around the world, policymakers have debated how governments can attain the
benefits of algorithms while preventing the harms of algorithms. One mechanism
that has become a centerpiece of global efforts to regulate government
algorithms is to require human oversight of algorithmic decisions. Despite the
widespread turn to human oversight, these policies rest on an uninterrogated
assumption: that people are able to effectively oversee algorithmic
decision-making. In this article, I survey 41 policies that prescribe human
oversight of government algorithms and find that they suffer from two
significant flaws. First, evidence suggests that people are unable to perform
the desired oversight functions. Second, as a result of the first flaw, human
oversight policies legitimize government uses of faulty and controversial
algorithms without addressing the fundamental issues with these tools. Thus,
rather than protect against the potential harms of algorithmic decision-making
in government, human oversight policies provide a false sense of security in
adopting algorithms and enable vendors and agencies to shirk accountability for
algorithmic harms. In light of these flaws, I propose a shift from human
oversight to institutional oversight as the central mechanism for regulating
government algorithms. This institutional approach operates in two stages.
First, agencies must justify that it is appropriate to incorporate an algorithm
into decision-making and that any proposed forms of human oversight are
supported by empirical evidence. Second, these justifications must receive
democratic public review and approval before the agency can adopt the
algorithm.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:77:" <a href="http://arxiv.org/find/cs/1/au:+Green_B/0/1/0/all/0/1">Ben Green</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:316;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2109.08377";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"Benchmarking Feature-based Algorithm Selection Systems for Black-box Numerical Optimization. (arXiv:2109.08377v4 [cs.NE] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2109.08377";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1549:"Feature-based algorithm selection aims to automatically find the best one
from a portfolio of optimization algorithms on an unseen problem based on its
landscape features. Feature-based algorithm selection has recently received
attention in the research field of black-box numerical optimization. However,
there is still room for analysis of algorithm selection for black-box
optimization. Most previous studies have focused only on whether an algorithm
selection system can outperform the single-best solver in a portfolio. In
addition, a benchmarking methodology for algorithm selection systems has not
been well investigated in the literature. In this context, this paper analyzes
algorithm selection systems on the 24 noiseless black-box optimization
benchmarking functions. First, we demonstrate that the first successful
performance measure is more reliable than the expected runtime measure for
benchmarking algorithm selection systems. Then, we examine the influence of
randomness on the performance of algorithm selection systems. We also show that
the performance of algorithm selection systems can be significantly improved by
using sequential least squares programming as a pre-solver. We point out that
the difficulty of outperforming the single-best solver depends on algorithm
portfolios, cross-validation methods, and dimensions. Finally, we demonstrate
that the effectiveness of algorithm portfolios depends on various factors.
These findings provide fundamental insights for algorithm selection for
black-box optimization.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:81:" <a href="http://arxiv.org/find/cs/1/au:+Tanabe_R/0/1/0/all/0/1">Ryoji Tanabe</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:317;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2109.09374";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:137:"Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection. (arXiv:2109.09374v2 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2109.09374";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1851:"Despite impressive state-of-the-art performance on a wide variety of machine
learning tasks, deep learning methods can produce over-confident predictions,
particularly with limited training data. Therefore, quantifying uncertainty is
particularly important in critical applications such as lesion detection and
clinical diagnosis, where a realistic assessment of uncertainty is essential in
determining surgical margins, disease status and appropriate treatment. In this
work, we propose a novel approach that uses quantile regression for quantifying
aleatoric uncertainty in both supervised and unsupervised lesion detection
problems. The resulting confidence intervals can be used for lesion detection
and segmentation. In the unsupervised setting, we combine quantile regression
with the Variational AutoEncoder (VAE). Here we address the problem of
quantifying uncertainty in the images that are reconstructed by the VAE as the
basis for principled outlier or lesion detection. The VAE models the output as
a conditionally independent Gaussian characterized by its mean and variance.
Unfortunately, joint optimization of both mean and variance in the VAE leads to
the well-known problem of shrinkage or underestimation of variance. Here we
describe an alternative Quantile-Regression VAE (QR-VAE) that avoids this
variance shrinkage problem by directly estimating conditional quantiles for the
input image. Using the estimated quantiles, we compute the conditional mean and
variance for the input image from which we then detect outliers by thresholding
at a false-discovery-rate corrected p-value. In the supervised setting, we
develop binary quantile regression (BQR) for the supervised lesion segmentation
task. We show how BQR can be used to capture uncertainty in lesion boundaries
in a manner that characterizes expert disagreement.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:326:" <a href="http://arxiv.org/find/cs/1/au:+Akrami_H/0/1/0/all/0/1">Haleh Akrami</a>, <a href="http://arxiv.org/find/cs/1/au:+Joshi_A/0/1/0/all/0/1">Anand Joshi</a>, <a href="http://arxiv.org/find/cs/1/au:+Aydore_S/0/1/0/all/0/1">Sergul Aydore</a>, <a href="http://arxiv.org/find/cs/1/au:+Leahy_R/0/1/0/all/0/1">Richard Leahy</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:318;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2109.10756";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:155:"Constrained multi-agent ergodic area surveying control based on finite element approximation of the potential field. (arXiv:2109.10756v3 [math.OC] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2109.10756";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1731:"Heat Equation Driven Area Coverage (HEDAC) is a state-of-the-art multi-agent
ergodic motion control guided by a gradient of a potential field. A finite
element method is hereby implemented to obtain a solution of Helmholtz partial
differential equation, which models the potential field for surveying motion
control. This allows us to survey arbitrarily shaped domains and to include
obstacles in an elegant and robust manner intrinsic to HEDAC's fundamental
idea. For a simple kinematic motion, the obstacles and boundary avoidance
constraints are successfully handled by directing the agent motion with the
gradient of the potential. However, including additional constraints, such as
the minimal clearance dsitance from stationary and moving obstacles and the
minimal path curvature radius, requires further alternations of the control
algorithm. We introduce a relatively simple yet robust approach for handling
these constraints by formulating a straightforward optimization problem based
on collision-free escapes route maneuvers. This approach provides a guaranteed
collision avoidance mechanism, while being computationally inexpensive as a
result of the optimization problem partitioning. The proposed motion control is
evaluated in three realistic surveying scenarios simulations, showing the
effectiveness of the surveying and the robustness of the control algorithm.
Furthermore, potential maneuvering difficulties due to improperly defined
surveying scenarios are highlighted and we provide guidelines on how to
overpass them. The results are promising and indiacate real-world applicability
of proposed constrained multi-agent motion control for autonomous surveying and
potentially other HEDAC utilizations.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:267:" <a href="http://arxiv.org/find/math/1/au:+Ivic_S/0/1/0/all/0/1">Stefan Ivi&#x107;</a>, <a href="http://arxiv.org/find/math/1/au:+Sikirica_A/0/1/0/all/0/1">Ante Sikirica</a>, <a href="http://arxiv.org/find/math/1/au:+Crnkovic_B/0/1/0/all/0/1">Bojan Crnkovi&#x107;</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:319;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2109.11224";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:115:"A Novel Open Set Energy-based Flow Classifier for Network Intrusion Detection. (arXiv:2109.11224v2 [cs.CR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2109.11224";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1548:"Network intrusion detection systems (NIDS) are one of many solutions that
make up a computer security system. Several machine learning-based NIDS have
been proposed in recent years, but most of them were developed and evaluated
under the assumption that the training context is similar to the test context.
In real networks, this assumption is false, given the emergence of new attacks
and variants of known attacks. To deal with this reality, the open set
recognition field, which is the most general task of recognizing classes not
seen during training in any domain, began to gain importance in NIDS research.
Yet, existing solutions are often bounded to high temporal complexities and
performance bottlenecks. In this work, we propose an algorithm to be used in
NIDS that performs open set recognition. Our proposal is an adaptation of the
single-class Energy-based Flow Classifier (EFC), which proved to be an
algorithm with strong generalization capability and low computational cost. The
new version of EFC correctly classifies not only known attacks, but also
unknown ones, and differs from other proposals from the literature by
presenting a single layer with low temporal complexity. Our proposal was
evaluated against well-established multi-class algorithms and as an open set
classifier. It proved to be an accurate classifier in both evaluations, similar
to the state of the art. As a conclusion of our work, we consider EFC a
promising algorithm to be used in NIDS for its high performance and
applicability in real networks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:510:" <a href="http://arxiv.org/find/cs/1/au:+Souza_M/0/1/0/all/0/1">Manuela M. C. Souza</a>, <a href="http://arxiv.org/find/cs/1/au:+Pontes_C/0/1/0/all/0/1">Camila Pontes</a>, <a href="http://arxiv.org/find/cs/1/au:+Gondim_J/0/1/0/all/0/1">Joao Gondim</a>, <a href="http://arxiv.org/find/cs/1/au:+Garcia_L/0/1/0/all/0/1">Luis P. F. Garcia</a>, <a href="http://arxiv.org/find/cs/1/au:+DaSilva_L/0/1/0/all/0/1">Luiz DaSilva</a>, <a href="http://arxiv.org/find/cs/1/au:+Marotta_M/0/1/0/all/0/1">Marcelo A. Marotta</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:320;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2109.12384";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:165:"Joint Progressive and Coarse-to-fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion. (arXiv:2109.12384v3 [eess.IV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2109.12384";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1747:"Registration of brain MRI images requires to solve a deformation field, which
is extremely difficult in aligning intricate brain tissues, e.g., subcortical
nuclei, etc. Existing efforts resort to decomposing the target deformation
field into intermediate sub-fields with either tiny motions, i.e., progressive
registration stage by stage, or lower resolutions, i.e., coarse-to-fine
estimation of the full-size deformation field. In this paper, we argue that
those efforts are not mutually exclusive, and propose a unified framework for
robust brain MRI registration in both progressive and coarse-to-fine manners
simultaneously. Specifically, building on a dual-encoder U-Net, the
fixed-moving MRI pair is encoded and decoded into multi-scale deformation
sub-fields from coarse to fine. Each decoding block contains two proposed novel
modules: i) in Deformation Field Integration (DFI), a single integrated
sub-field is calculated, warping by which is equivalent to warping
progressively by sub-fields from all previous decoding blocks, and ii) in
Non-rigid Feature Fusion (NFF), features of the fixed-moving pair are aligned
by DFI-integrated sub-field, and then fused to predict a finer sub-field.
Leveraging both DFI and NFF, the target deformation field is factorized into
multi-scale sub-fields, where the coarser fields alleviate the estimate of a
finer one and the finer field learns to make up those misalignments insolvable
by previous coarser ones. The extensive and comprehensive experimental results
on both private and public datasets demonstrate a superior registration
performance of brain MRI images over progressive registration only and
coarse-to-fine estimation only, with an increase by at most 8% in the average
Dice.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:557:" <a href="http://arxiv.org/find/eess/1/au:+Lv_J/0/1/0/all/0/1">Jinxin Lv</a>, <a href="http://arxiv.org/find/eess/1/au:+Wang_Z/0/1/0/all/0/1">Zhiwei Wang</a>, <a href="http://arxiv.org/find/eess/1/au:+Shi_H/0/1/0/all/0/1">Hongkuan Shi</a>, <a href="http://arxiv.org/find/eess/1/au:+Zhang_H/0/1/0/all/0/1">Haobo Zhang</a>, <a href="http://arxiv.org/find/eess/1/au:+Wang_S/0/1/0/all/0/1">Sheng Wang</a>, <a href="http://arxiv.org/find/eess/1/au:+Wang_Y/0/1/0/all/0/1">Yilang Wang</a>, <a href="http://arxiv.org/find/eess/1/au:+Li_Q/0/1/0/all/0/1">Qiang Li</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:321;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2110.00909";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:85:"Design and Evaluate Recomposited OR-AND-XOR-PUF. (arXiv:2110.00909v3 [cs.CR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2110.00909";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1921:"Physical Unclonable Function (PUF) is a hardware security primitive with a
desirable feature of low-cost. Based on the space of challenge-response pairs
(CRPs), it has two categories:weak PUF and strong PUF. Though designing a
reliable and secure lightweight strong PUF is challenging, there is continuing
efforts to fulfill this gap due to wide range of applications enabled by strong
PUF. It was prospected that the combination of MAX and MIN bit-wise operation
is promising for improving the modeling resilience when MAX and MIN are
employed in the PUF recomposition. The main rationale lies on the fact that
each bit-wise might be mainly vulnerable to one specific type of modeling
attack, combining them can have an improved holistic resilience. This work is
to first evaluate the main PUF performance, in particular,uniformity and
reliability of the OR-AND-XOR-PUF(OAX-PUF)-(x, y, z)-OAX-PUF. Compared with the
most used l-XOR-PUF, the (x, y, z)-OAX-PUF eventually exhibits better
reliability given l=x+y+z without degrading the uniformity retaining to be 50%.
We further examine the modeling resilience of the (x, y, z)-OAX-PUF with four
powerful attacking strategies to date, which are Logistic Regression (LR)
attack, reliability assisted CMA-ES attack, multilayer perceptron (MLP) attack,
and the most recent hybrid LR-reliability attack. In comparison with the
XOR-APUF, the OAX-APUF successfully defeats the CAM-ES attack. However, it
shows no notable modeling accuracy drop against other three attacks, though the
attacking times have been greatly prolonged to LR and hybrid LR-reliability
attacks. Overall, the OAX recomposition could be an alternative lightweight
recomposition method compared to XOR towards constructing strong PUFs if the
underlying PUF, e.g., FF-APUF, has exhibited improved resilience to modeling
attack, because the OAX incurs smaller reliability degradation compared to XOR.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:462:" <a href="http://arxiv.org/find/cs/1/au:+Yao_J/0/1/0/all/0/1">Jianrong Yao</a>, <a href="http://arxiv.org/find/cs/1/au:+Pang_L/0/1/0/all/0/1">Lihui Pang</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_Z/0/1/0/all/0/1">Zhi Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Yang_W/0/1/0/all/0/1">Wei Yang</a>, <a href="http://arxiv.org/find/cs/1/au:+Fu_A/0/1/0/all/0/1">Anmin Fu</a>, <a href="http://arxiv.org/find/cs/1/au:+Gao_Y/0/1/0/all/0/1">Yansong Gao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:322;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2110.01931";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:98:"Anchor-free Oriented Proposal Generator for Object Detection. (arXiv:2110.01931v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2110.01931";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1403:"Oriented object detection is a practical and challenging task in remote
sensing image interpretation. Nowadays, oriented detectors mostly use
horizontal boxes as intermedium to derive oriented boxes from them. However,
the horizontal boxes are inclined to get small Intersection-over-Unions (IoUs)
with ground truths, which may have some undesirable effects, such as
introducing redundant noise, mismatching with ground truths, detracting from
the robustness of detectors, etc. In this paper, we propose a novel Anchor-free
Oriented Proposal Generator (AOPG) that abandons horizontal box-related
operations from the network architecture. AOPG first produces coarse oriented
boxes by a Coarse Location Module (CLM) in an anchor-free manner and then
refines them into high-quality oriented proposals. After AOPG, we apply a Fast
R-CNN head to produce the final detection results. Furthermore, the shortage of
large-scale datasets is also a hindrance to the development of oriented object
detection. To alleviate the data insufficiency, we release a new dataset on the
basis of our DIOR dataset and name it DIOR-R. Massive experiments demonstrate
the effectiveness of AOPG. Particularly, without bells and whistles, we achieve
the accuracy of 64.41%, 75.24% and 96.22% mAP on the DIOR-R, DOTA and HRSC2016
datasets respectively. Code and models are available at
https://github.com/jbwang1997/AOPG.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:541:" <a href="http://arxiv.org/find/cs/1/au:+Cheng_G/0/1/0/all/0/1">Gong Cheng</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_J/0/1/0/all/0/1">Jiabao Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_K/0/1/0/all/0/1">Ke Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Xie_X/0/1/0/all/0/1">Xingxing Xie</a>, <a href="http://arxiv.org/find/cs/1/au:+Lang_C/0/1/0/all/0/1">Chunbo Lang</a>, <a href="http://arxiv.org/find/cs/1/au:+Yao_Y/0/1/0/all/0/1">Yanqing Yao</a>, <a href="http://arxiv.org/find/cs/1/au:+Han_J/0/1/0/all/0/1">Junwei Han</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:323;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2110.02007";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:96:"Empowering Local Communities Using Artificial Intelligence. (arXiv:2110.02007v3 [cs.AI] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2110.02007";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1010:"Artificial Intelligence (AI) is increasingly used to analyze large amounts of
data in various practices, such as object recognition. We are specifically
interested in using AI-powered systems to engage local communities in
developing plans or solutions for pressing societal and environmental concerns.
Such local contexts often involve multiple stakeholders with different and even
contradictory agendas, resulting in mismatched expectations of these systems'
behaviors and desired outcomes. There is a need to investigate if AI models and
pipelines can work as expected in different contexts through co-creation and
field deployment. Based on case studies in co-creating AI-powered systems with
local people, we explain challenges that require more attention and provide
viable paths to bridge AI research with citizen needs. We advocate for
developing new collaboration approaches and mindsets that are needed to
co-create AI-powered systems in multi-stakeholder contexts to address local
concerns.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:522:" <a href="http://arxiv.org/find/cs/1/au:+Hsu_Y/0/1/0/all/0/1">Yen-Chia Hsu</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_T/0/1/0/all/0/1">Ting-Hao &#x27;Kenneth&#x27; Huang</a>, <a href="http://arxiv.org/find/cs/1/au:+Verma_H/0/1/0/all/0/1">Himanshu Verma</a>, <a href="http://arxiv.org/find/cs/1/au:+Mauri_A/0/1/0/all/0/1">Andrea Mauri</a>, <a href="http://arxiv.org/find/cs/1/au:+Nourbakhsh_I/0/1/0/all/0/1">Illah Nourbakhsh</a>, <a href="http://arxiv.org/find/cs/1/au:+Bozzon_A/0/1/0/all/0/1">Alessandro Bozzon</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:324;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2110.03684";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:91:"Cross-Domain Imitation Learning via Optimal Transport. (arXiv:2110.03684v3 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2110.03684";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:897:"Cross-domain imitation learning studies how to leverage expert demonstrations
of one agent to train an imitation agent with a different embodiment or
morphology. Comparing trajectories and stationary distributions between the
expert and imitation agents is challenging because they live on different
systems that may not even have the same dimensionality. We propose
Gromov-Wasserstein Imitation Learning (GWIL), a method for cross-domain
imitation that uses the Gromov-Wasserstein distance to align and compare states
between the different spaces of the agents. Our theory formally characterizes
the scenarios where GWIL preserves optimality, revealing its possibilities and
limitations. We demonstrate the effectiveness of GWIL in non-trivial continuous
control domains ranging from simple rigid transformation of the expert domain
to arbitrary transformation of the state-action space.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:334:" <a href="http://arxiv.org/find/cs/1/au:+Fickinger_A/0/1/0/all/0/1">Arnaud Fickinger</a>, <a href="http://arxiv.org/find/cs/1/au:+Cohen_S/0/1/0/all/0/1">Samuel Cohen</a>, <a href="http://arxiv.org/find/cs/1/au:+Russell_S/0/1/0/all/0/1">Stuart Russell</a>, <a href="http://arxiv.org/find/cs/1/au:+Amos_B/0/1/0/all/0/1">Brandon Amos</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:325;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2110.03855";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:109:"Hardware Functional Obfuscation With Ferroelectric Active Interconnects. (arXiv:2110.03855v2 [cs.ET] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2110.03855";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1793:"Camouflaging gate techniques are typically used in hardware security to
prevent reverse engineering. Layout level camouflaging by adding dummy contacts
ensures some level of protection against extracting the correct netlist.
Threshold voltage manipulation for multi-functional logic with identical
layouts has also been introduced for functional obfuscation. All these
techniques are implemented at the expense of circuit-complexity and with
significant area, energy, and delay penalty. In this paper, we propose an
efficient hardware encryption technique with minimal complexity and overheads
based on ferroelectric field-effect transistor (FeFET) active interconnects.
The active interconnect provides run-time reconfigurable inverter-buffer logic
by utilizing the threshold voltage programmability of the FeFETs. Our method
utilizes only two FeFETs and an inverter to realize the masking function
compared to recent reconfigurable logic gate implementations using several
FeFETs and complex differential logic. We fabricate the proposed circuit and
demonstrate the functionality. Judicious placement of the proposed logic in the
IC makes it acts as a hardware encryption key and enables encoding and decoding
of the functional output without affecting the critical path timing delay.
Also, we achieve comparable encryption probability with a limited number of
encryption units. In addition, we show a peripheral programming scheme for
reconfigurable logic by reusing the existing scan chain logic, hence obviating
the need for specialized programming logic and circuitry for keybit
distribution. Our analysis shows an average encryption probability of 97.43%
with an increase of 2.24%/ 3.67% delay for the most critical path/ sum of 100
critical paths delay for ISCAS85 benchmarks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:880:" <a href="http://arxiv.org/find/cs/1/au:+Yu_T/0/1/0/all/0/1">Tonggunag Yu</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_Y/0/1/0/all/0/1">Yixin Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Deng_S/0/1/0/all/0/1">Shan Deng</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_Z/0/1/0/all/0/1">Zijian Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Jao_N/0/1/0/all/0/1">Nicolas Jao</a>, <a href="http://arxiv.org/find/cs/1/au:+Kim_Y/0/1/0/all/0/1">You Sung Kim</a>, <a href="http://arxiv.org/find/cs/1/au:+Duenkel_S/0/1/0/all/0/1">Stefan Duenkel</a>, <a href="http://arxiv.org/find/cs/1/au:+Beyer_S/0/1/0/all/0/1">Sven Beyer</a>, <a href="http://arxiv.org/find/cs/1/au:+Ni_K/0/1/0/all/0/1">Kai Ni</a>, <a href="http://arxiv.org/find/cs/1/au:+George_S/0/1/0/all/0/1">Sumitha George</a>, <a href="http://arxiv.org/find/cs/1/au:+Narayanan_V/0/1/0/all/0/1">Vijaykrishnan Narayanan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:326;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2110.06354";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation. (arXiv:2110.06354v3 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2110.06354";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1609:"Recent years have witnessed the dramatic growth of paper volumes with plenty
of new research papers published every day, especially in the area of computer
science. How to glean papers worth reading from the massive literature to do a
quick survey or keep up with the latest advancement about a specific research
topic has become a challenging task. Existing academic search engines such as
Google Scholar return relevant papers by individually calculating the relevance
between each paper and query. However, such systems usually omit the
prerequisite chains of a research topic and cannot form a meaningful reading
path. In this paper, we introduce a new task named Reading Path Generation
(RPG) which aims at automatically producing a path of papers to read for a
given query. To serve as a research benchmark, we further propose SurveyBank, a
dataset consisting of large quantities of survey papers in the field of
computer science as well as their citation relationships. Each survey paper
contains key phrases extracted from its title and multi-level reading lists
inferred from its references. Furthermore, we propose a
graph-optimization-based approach for reading path generation which takes the
relationship between papers into account. Extensive evaluations demonstrate
that our approach outperforms other baselines. A Real-time Reading Path
Generation System (RePaGer) has been also implemented with our designed model.
To the best of our knowledge, we are the first to target this important
research problem. Our source code of RePaGer system and SurveyBank dataset can
be found on here.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:626:" <a href="http://arxiv.org/find/cs/1/au:+Ding_J/0/1/0/all/0/1">Jiayuan Ding</a>, <a href="http://arxiv.org/find/cs/1/au:+Xiang_T/0/1/0/all/0/1">Tong Xiang</a>, <a href="http://arxiv.org/find/cs/1/au:+Ou_Z/0/1/0/all/0/1">Zijing Ou</a>, <a href="http://arxiv.org/find/cs/1/au:+Zuo_W/0/1/0/all/0/1">Wangyang Zuo</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_R/0/1/0/all/0/1">Ruihui Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Lin_C/0/1/0/all/0/1">Chenghua Lin</a>, <a href="http://arxiv.org/find/cs/1/au:+Zheng_Y/0/1/0/all/0/1">Yefeng Zheng</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_B/0/1/0/all/0/1">Bang Liu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:327;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2110.07855";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:86:"Hierarchical Curriculum Learning for AMR Parsing. (arXiv:2110.07855v5 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2110.07855";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:970:"Abstract Meaning Representation (AMR) parsing aims to translate sentences to
semantic representation with a hierarchical structure, and is recently
empowered by pretrained sequence-to-sequence models. However, there exists a
gap between their flat training objective (i.e., equally treats all output
tokens) and the hierarchical AMR structure, which limits the model
generalization. To bridge this gap, we propose a Hierarchical Curriculum
Learning (HCL) framework with Structure-level (SC) and Instance-level Curricula
(IC). SC switches progressively from core to detail AMR semantic elements while
IC transits from structure-simple to -complex AMR instances during training.
Through these two warming-up processes, HCL reduces the difficulty of learning
complex structures, thus the flat model can better adapt to the AMR hierarchy.
Extensive experiments on AMR2.0, AMR3.0, structure-complex and
out-of-distribution situations verify the effectiveness of HCL.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:543:" <a href="http://arxiv.org/find/cs/1/au:+Wang_P/0/1/0/all/0/1">Peiyi Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_L/0/1/0/all/0/1">Liang Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_T/0/1/0/all/0/1">Tianyu Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Dai_D/0/1/0/all/0/1">Damai Dai</a>, <a href="http://arxiv.org/find/cs/1/au:+Cao_Y/0/1/0/all/0/1">Yunbo Cao</a>, <a href="http://arxiv.org/find/cs/1/au:+Chang_B/0/1/0/all/0/1">Baobao Chang</a>, <a href="http://arxiv.org/find/cs/1/au:+Sui_Z/0/1/0/all/0/1">Zhifang Sui</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:328;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2110.07957";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:123:"Don&#039;t speak too fast: The impact of data bias on self-supervised speech models. (arXiv:2110.07957v3 [eess.AS] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2110.07957";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:716:"Self-supervised Speech Models (S3Ms) have been proven successful in many
speech downstream tasks, like ASR. However, how pre-training data affects S3Ms'
downstream behavior remains an unexplored issue. In this paper, we study how
pre-training data affects S3Ms by pre-training models on biased datasets
targeting different factors of speech, including gender, content, and prosody,
and evaluate these pre-trained S3Ms on selected downstream tasks in SUPERB
Benchmark. Our experiments show that S3Ms have tolerance toward gender bias.
Moreover, we find that the content of speech has little impact on the
performance of S3Ms across downstream tasks, but S3Ms do show a preference
toward a slower speech rate.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:318:" <a href="http://arxiv.org/find/eess/1/au:+Meng_Y/0/1/0/all/0/1">Yen Meng</a>, <a href="http://arxiv.org/find/eess/1/au:+Chou_Y/0/1/0/all/0/1">Yi-Hui Chou</a>, <a href="http://arxiv.org/find/eess/1/au:+Liu_A/0/1/0/all/0/1">Andy T. Liu</a>, <a href="http://arxiv.org/find/eess/1/au:+Lee_H/0/1/0/all/0/1">Hung-yi Lee</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:329;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2110.10106";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:98:"Subframework-Based Rigidity Control in Multirobot Networks. (arXiv:2110.10106v2 [eess.SY] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2110.10106";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:851:"This paper presents an alternative approach to the study of distance rigidity
in networks of mobile agents, based on a subframework scheme. The advantage of
the proposed strategy lies in expressing framework rigidity, which is
inherently global, as a set of local properties. Also, we show that a
framework's normalized rigidity eigenvalue degrades as the graph's diameter
increases. Thus, the rigidity eigenvalue associated to each subframework arise
naturally as a local rigidity metric. A decentralized subframework-based
controller for maintaining rigidity using only range measurements is developed,
which is also aimed to minimize the network's communication load. Finally, we
show that the information exchange required by the controller is completed in a
finite number of iterations, indicating the convenience of the proposed scheme.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:364:" <a href="http://arxiv.org/find/eess/1/au:+Presenza_J/0/1/0/all/0/1">Juan F. Presenza</a>, <a href="http://arxiv.org/find/eess/1/au:+Alvarez_Hamelin_J/0/1/0/all/0/1">J. Ignacio Alvarez-Hamelin</a>, <a href="http://arxiv.org/find/eess/1/au:+Mas_I/0/1/0/all/0/1">Ignacio Mas</a>, <a href="http://arxiv.org/find/eess/1/au:+Giribet_J/0/1/0/all/0/1">Juan I. Giribet</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:330;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2110.11996";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:130:"The Eigenvectors of Single-spiked Complex Wishart Matrices: Finite and Asymptotic Analyses. (arXiv:2110.11996v2 [math.PR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2110.11996";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1596:"Let $\mathbf{W}\in\mathbb{C}^{n\times n}$ be a {\it single-spiked} Wishart
matrix in the class $\mathbf{W}\sim \mathcal{CW}_n(m,\mathbf{I}_n+ \theta
\mathbf{v}\mathbf{v}^\dagger) $ with $m\geq n$, where $\mathbf{I}_n$ is the
$n\times n$ identity matrix, $\mathbf{v}\in\mathbb{C}^{n\times 1}$ is an
arbitrary vector with unit Euclidean norm, $\theta\geq 0$ is a non-random
parameter, and $(\cdot)^\dagger$ represents the conjugate-transpose operator.
Let $\mathbf{u}_1$ and $\mathbf{u}_n$ denote the eigenvectors corresponding to
the samllest and the largest eigenvalues of $\mathbf{W}$, respectively. This
paper investigates the probability density function (p.d.f.) of the random
quantity
$Z_{\ell}^{(n)}=\left|\mathbf{v}^\dagger\mathbf{u}_\ell\right|^2\in(0,1)$ for
$\ell=1,n$. In particular, we derive a finite dimensional closed-form p.d.f.
for $Z_{1}^{(n)}$ which is amenable to asymptotic analysis as $m,n$ diverges
with $m-n$ fixed. It turns out that, in this asymptotic regime, the scaled
random variable $nZ_{1}^{(n)}$ converges in distribution to
$\chi^2_2/2(1+\theta)$, where $\chi_2^2$ denotes a chi-squared random variable
with two degrees of freedom. This reveals that $\mathbf{u}_1$ can be used to
infer information about the spike. On the other hand, the finite dimensional
p.d.f. of $Z_{n}^{(n)}$ is expressed as a double integral in which the
integrand contains a determinant of a square matrix of dimension $(n-2)$.
Although a simple solution to this double integral seems intractable, for
special configurations of $n=2,3$, and $4$, we obtain closed-form expressions.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:275:" <a href="http://arxiv.org/find/math/1/au:+Dharmawansa_P/0/1/0/all/0/1">Prathapasinghe Dharmawansa</a>, <a href="http://arxiv.org/find/math/1/au:+Dissanayake_P/0/1/0/all/0/1">Pasan Dissanayake</a>, <a href="http://arxiv.org/find/math/1/au:+Chen_Y/0/1/0/all/0/1">Yang Chen</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:331;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2110.12194";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:106:"RPT++: Customized Feature Representation for Siamese Visual Tracking. (arXiv:2110.12194v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2110.12194";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1328:"While recent years have witnessed remarkable progress in the feature
representation of visual tracking, the problem of feature misalignment between
the classification and regression tasks is largely overlooked. The approaches
of feature extraction make no difference for these two tasks in most of
advanced trackers. We argue that the performance gain of visual tracking is
limited since features extracted from the salient area provide more
recognizable visual patterns for classification, while these around the
boundaries contribute to accurately estimating the target state.

We address this problem by proposing two customized feature extractors, named
polar pooling and extreme pooling to capture task-specific visual patterns.
Polar pooling plays the role of enriching information collected from the
semantic keypoints for stronger classification, while extreme pooling
facilitates explicit visual patterns of the object boundary for accurate target
state estimation. We demonstrate the effectiveness of the task-specific feature
representation by integrating it into the recent and advanced tracker RPT.
Extensive experiments on several benchmarks show that our Customized Features
based RPT (RPT++) achieves new state-of-the-art performances on OTB-100,
VOT2018, VOT2019, GOT-10k, TrackingNet and LaSOT.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:308:" <a href="http://arxiv.org/find/cs/1/au:+Ma_Z/0/1/0/all/0/1">Ziang Ma</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_H/0/1/0/all/0/1">Haitao Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_L/0/1/0/all/0/1">Linyuan Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Yin_J/0/1/0/all/0/1">Jun Yin</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:332;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2110.12899";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:118:"No One Representation to Rule Them All: Overlapping Features of Training Methods. (arXiv:2110.12899v3 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2110.12899";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1458:"Despite being able to capture a range of features of the data, high accuracy
models trained with supervision tend to make similar predictions. This
seemingly implies that high-performing models share similar biases regardless
of training methodology, which would limit ensembling benefits and render
low-accuracy models as having little practical use. Against this backdrop,
recent work has developed quite different training techniques, such as
large-scale contrastive learning, yielding competitively high accuracy on
generalization and robustness benchmarks. This motivates us to revisit the
assumption that models necessarily learn similar functions. We conduct a
large-scale empirical study of models across hyper-parameters, architectures,
frameworks, and datasets. We find that model pairs that diverge more in
training methodology display categorically different generalization behavior,
producing increasingly uncorrelated errors. We show these models specialize in
subdomains of the data, leading to higher ensemble performance: with just 2
models (each with ImageNet accuracy ~76.5%), we can create ensembles with 83.4%
(+7% boost). Surprisingly, we find that even significantly low-accuracy models
can be used to improve high-accuracy models. Finally, we show diverging
training methodology yield representations that capture overlapping (but not
supersetting) feature sets which, when combined, lead to increased downstream
performance.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:262:" <a href="http://arxiv.org/find/cs/1/au:+Gontijo_Lopes_R/0/1/0/all/0/1">Raphael Gontijo-Lopes</a>, <a href="http://arxiv.org/find/cs/1/au:+Dauphin_Y/0/1/0/all/0/1">Yann Dauphin</a>, <a href="http://arxiv.org/find/cs/1/au:+Cubuk_E/0/1/0/all/0/1">Ekin D. Cubuk</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:333;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2110.13721";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:105:"Geometric Transformer for End-to-End Molecule Properties Prediction. (arXiv:2110.13721v3 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2110.13721";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1014:"Transformers have become methods of choice in many applications thanks to
their ability to represent complex interactions between elements. However,
extending the Transformer architecture to non-sequential data such as molecules
and enabling its training on small datasets remains a challenge. In this work,
we introduce a Transformer-based architecture for molecule property prediction,
which is able to capture the geometry of the molecule. We modify the classical
positional encoder by an initial encoding of the molecule geometry, as well as
a learned gated self-attention mechanism. We further suggest an augmentation
scheme for molecular data capable of avoiding the overfitting induced by the
overparameterized architecture. The proposed framework outperforms the
state-of-the-art methods while being based on pure machine learning solely,
i.e. the method does not incorporate domain knowledge from quantum chemistry
and does not use extended geometric inputs besides the pairwise atomic
distances.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:163:" <a href="http://arxiv.org/find/cs/1/au:+Choukroun_Y/0/1/0/all/0/1">Yoni Choukroun</a>, <a href="http://arxiv.org/find/cs/1/au:+Wolf_L/0/1/0/all/0/1">Lior Wolf</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:334;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.00134";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:93:"Context Meta-Reinforcement Learning via Neuromodulation. (arXiv:2111.00134v3 [cs.NE] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.00134";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1221:"Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt
quickly to tasks from few samples in dynamic environments. Such a feat is
achieved through dynamic representations in an agent's policy network (obtained
via reasoning about task context, model parameter updates, or both). However,
obtaining rich dynamic representations for fast adaptation beyond simple
benchmark problems is challenging due to the burden placed on the policy
network to accommodate different policies. This paper addresses the challenge
by introducing neuromodulation as a modular component to augment a standard
policy network that regulates neuronal activities in order to produce efficient
dynamic representations for task adaptation. The proposed extension to the
policy network is evaluated across multiple discrete and continuous control
environments of increasing complexity. To prove the generality and benefits of
the extension in meta-RL, the neuromodulated network was applied to two
state-of-the-art meta-RL algorithms (CAVIA and PEARL). The result demonstrates
that meta-RL augmented with neuromodulation produces significantly better
result and richer dynamic representations in comparison to the baselines.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:433:" <a href="http://arxiv.org/find/cs/1/au:+Ben_Iwhiwhu_E/0/1/0/all/0/1">Eseoghene Ben-Iwhiwhu</a>, <a href="http://arxiv.org/find/cs/1/au:+Dick_J/0/1/0/all/0/1">Jeffery Dick</a>, <a href="http://arxiv.org/find/cs/1/au:+Ketz_N/0/1/0/all/0/1">Nicholas A. Ketz</a>, <a href="http://arxiv.org/find/cs/1/au:+Pilly_P/0/1/0/all/0/1">Praveen K. Pilly</a>, <a href="http://arxiv.org/find/cs/1/au:+Soltoggio_A/0/1/0/all/0/1">Andrea Soltoggio</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:335;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.04473";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"Senatus -- A Fast and Accurate Code-to-Code Recommendation Engine. (arXiv:2111.04473v2 [cs.SE] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.04473";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1824:"Machine learning on source code (MLOnCode) is a popular research field that
has been driven by the availability of large-scale code repositories and the
development of powerful probabilistic and deep learning models for mining
source code. Code-to-code recommendation is a task in MLOnCode that aims to
recommend relevant, diverse and concise code snippets that usefully extend the
code currently being written by a developer in their development environment
(IDE). Code-to-code recommendation engines hold the promise of increasing
developer productivity by reducing context switching from the IDE and
increasing code-reuse. Existing code-to-code recommendation engines do not
scale gracefully to large codebases, exhibiting a linear growth in query time
as the code repository increases in size. In addition, existing code-to-code
recommendation engines fail to account for the global statistics of code
repositories in the ranking function, such as the distribution of code snippet
lengths, leading to sub-optimal retrieval results. We address both of these
weaknesses with \emph{Senatus}, a new code-to-code recommendation engine. At
the core of Senatus is \emph{De-Skew} LSH a new locality sensitive hashing
(LSH) algorithm that indexes the data for fast (sub-linear time) retrieval
while also counteracting the skewness in the snippet length distribution using
novel abstract syntax tree-based feature scoring and selection algorithms. We
evaluate Senatus and find the recommendations to be of higher quality than
competing baselines, while achieving faster search. For example on the
CodeSearchNet dataset Senatus improves performance by 31.21\% F1 and
147.9\emph{x} faster query time compared to Facebook Aroma. Senatus also
outperforms standard MinHash LSH by 29.2\% F1 and 51.02\emph{x} faster query
time.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:419:" <a href="http://arxiv.org/find/cs/1/au:+Silavong_F/0/1/0/all/0/1">Fran Silavong</a>, <a href="http://arxiv.org/find/cs/1/au:+Moran_S/0/1/0/all/0/1">Sean Moran</a>, <a href="http://arxiv.org/find/cs/1/au:+Georgiadis_A/0/1/0/all/0/1">Antonios Georgiadis</a>, <a href="http://arxiv.org/find/cs/1/au:+Saphal_R/0/1/0/all/0/1">Rohan Saphal</a>, <a href="http://arxiv.org/find/cs/1/au:+Otter_R/0/1/0/all/0/1">Robert Otter</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:336;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.04476";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:121:"An Open Access Dataset of Tweets related to Exoskeletons and 100 Research Questions. (arXiv:2111.04476v2 [cs.CY] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.04476";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1539:"The exoskeleton technology has been rapidly advancing in the recent past due
to its multitude of applications in different fields. With the projected
increase in the diverse uses of exoskeletons in the next few years in these
application domains and beyond, it is crucial to study, interpret, and analyze
user perspectives, opinions, and feedback related to exoskeletons, for which a
comprehensive dataset of communications related to exoskeletons is necessary.
The Internet of Everything style of today's living, characterized by people
spending more time on the Internet than ever before, holds the potential for
developing such a dataset by the mining of relevant web behavior data from
social media communications, which have increased exponentially in the last few
years. Twitter, one such social media platform, is highly popular amongst all
age groups, who communicate on diverse topics via tweets while sharing their
views, opinions, perspectives, and feedback towards the same. To address this
research challenge, this paper makes multiple scientific contributions to this
field. First, it presents a novel approach of mining tweets that is not bound
by any restrictions on the number of days during which the tweets can be mined.
Second, by using this approach, it presents an open-access dataset of
approximately 20,000 tweets related to exoskeletons, that were posted over a
period of 231 days. Finally, based on a review of 108 emerging works in this
field, the paper discusses multiple interdisciplinary applications

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:162:" <a href="http://arxiv.org/find/cs/1/au:+Thakur_N/0/1/0/all/0/1">Nirmalya Thakur</a>, <a href="http://arxiv.org/find/cs/1/au:+Han_C/0/1/0/all/0/1">Chia Y. Han</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:337;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.04877";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:97:"Papaya: Practical, Private, and Scalable Federated Learning. (arXiv:2111.04877v2 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.04877";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1250:"Cross-device Federated Learning (FL) is a distributed learning paradigm with
several challenges that differentiate it from traditional distributed learning,
variability in the system characteristics on each device, and millions of
clients coordinating with a central server being primary ones. Most FL systems
described in the literature are synchronous - they perform a synchronized
aggregation of model updates from individual clients. Scaling synchronous FL is
challenging since increasing the number of clients training in parallel leads
to diminishing returns in training speed, analogous to large-batch training.
Moreover, stragglers hinder synchronous FL training. In this work, we outline a
production asynchronous FL system design. Our work tackles the aforementioned
issues, sketches of some of the system design challenges and their solutions,
and touches upon principles that emerged from building a production FL system
for millions of clients. Empirically, we demonstrate that asynchronous FL
converges faster than synchronous FL when training across nearly one hundred
million devices. In particular, in high concurrency settings, asynchronous FL
is 5x faster and has nearly 8x less communication overhead than synchronous FL.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:1148:" <a href="http://arxiv.org/find/cs/1/au:+Huba_D/0/1/0/all/0/1">Dzmitry Huba</a>, <a href="http://arxiv.org/find/cs/1/au:+Nguyen_J/0/1/0/all/0/1">John Nguyen</a>, <a href="http://arxiv.org/find/cs/1/au:+Malik_K/0/1/0/all/0/1">Kshitiz Malik</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhu_R/0/1/0/all/0/1">Ruiyu Zhu</a>, <a href="http://arxiv.org/find/cs/1/au:+Rabbat_M/0/1/0/all/0/1">Mike Rabbat</a>, <a href="http://arxiv.org/find/cs/1/au:+Yousefpour_A/0/1/0/all/0/1">Ashkan Yousefpour</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_C/0/1/0/all/0/1">Carole-Jean Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhan_H/0/1/0/all/0/1">Hongyuan Zhan</a>, <a href="http://arxiv.org/find/cs/1/au:+Ustinov_P/0/1/0/all/0/1">Pavel Ustinov</a>, <a href="http://arxiv.org/find/cs/1/au:+Srinivas_H/0/1/0/all/0/1">Harish Srinivas</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_K/0/1/0/all/0/1">Kaikai Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Shoumikhin_A/0/1/0/all/0/1">Anthony Shoumikhin</a>, <a href="http://arxiv.org/find/cs/1/au:+Min_J/0/1/0/all/0/1">Jesik Min</a>, <a href="http://arxiv.org/find/cs/1/au:+Malek_M/0/1/0/all/0/1">Mani Malek</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:338;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.06343";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:138:"Reliability Function of Quantum Information Decoupling via the Sandwiched R\&#039;enyi Divergence. (arXiv:2111.06343v2 [quant-ph] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.06343";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1041:"Quantum information decoupling is a fundamental quantum information
processing task, which also serves as a crucial tool in a diversity of topics
in quantum physics. In this paper, we characterize the reliability function of
catalytic quantum information decoupling, that is, the best exponential rate
under which perfect decoupling is asymptotically approached. We have obtained
the exact formula when the decoupling cost is below a critical value. In the
situation of high cost, we provide upper and lower bounds. This result is then
applied to quantum state merging, exploiting its inherent connection to
decoupling. In addition, as technical tools, we derive the exact exponents for
the smoothing of the conditional min-entropy and max-information, and we prove
a novel bound for the convex-split lemma.

Our results are given in terms of the sandwiched R\'enyi divergence,
providing it with a new type of operational meaning in characterizing how fast
the performance of quantum information tasks approaches the perfect.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:162:" <a href="http://arxiv.org/find/quant-ph/1/au:+Li_K/0/1/0/all/0/1">Ke Li</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Yao_Y/0/1/0/all/0/1">Yongsheng Yao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:339;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.08284";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:97:"Few-Shot Self-Rationalization with Natural Language Prompts. (arXiv:2111.08284v2 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.08284";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1121:"Self-rationalization models that predict task labels and generate free-text
elaborations for their predictions could enable more intuitive interaction with
NLP systems. These models are, however, currently trained with a large amount
of human-written free-text explanations for each task which hinders their
broader usage. We propose to study a more realistic setting of
self-rationalization using few training examples. We present FEB -- a
standardized collection of four existing English-language datasets and
associated metrics. We identify the right prompting approach by extensively
exploring natural language prompts on FEB. Then, by using this prompt and
scaling the model size, we demonstrate that making progress on few-shot
self-rationalization is possible. We show there is still ample room for
improvement in this task: the average plausibility of generated explanations
assessed by human annotators is at most 51% (with GPT-3), while plausibility of
human explanations is 76%. We hope that FEB and our proposed approach will spur
the community to take on the few-shot self-rationalization challenge.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:340:" <a href="http://arxiv.org/find/cs/1/au:+Marasovic_A/0/1/0/all/0/1">Ana Marasovi&#x107;</a>, <a href="http://arxiv.org/find/cs/1/au:+Beltagy_I/0/1/0/all/0/1">Iz Beltagy</a>, <a href="http://arxiv.org/find/cs/1/au:+Downey_D/0/1/0/all/0/1">Doug Downey</a>, <a href="http://arxiv.org/find/cs/1/au:+Peters_M/0/1/0/all/0/1">Matthew E. Peters</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:340;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.08516";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:54:"Multiset Neurons. (arXiv:2111.08516v2 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.08516";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1465:"The present work reports a comparative performance of artificial neurons
obtained in terms of the real-valued Jaccard and coincidence similarity indices
and respectively derived functionals. The interiority index and classic
cross-correlation are also included for comparison purposes. After presenting
the basic concepts related to real-valued multisets and the adopted similarity
metrics, including the generalization of the real-valued Jaccard and
coincidence indices to higher orders, we proceed to studying the response of a
single neuron, not taking into account the output non-linearity (e.g.~sigmoid),
respectively to the detection of gaussian two-dimensional stimulus in presence
of displacement, magnification, intensity variation, noise and interference
from additional patterns. It is shown that the real-valued Jaccard and
coincidence approaches are substantially more robust and effective than the
interiority index and the classic cross-correlation. The coincidence-based
neurons are shown to have the best overall performance respectively to the
considered type of data and perturbations. The potential of the multiset
neurons is further illustrated with respect to the challenging problem of image
segmentation, leading to impressive cost/benefit performance. The reported
concepts, methods, and results, have substantial implications not only for
pattern recognition and machine learning, but also regarding neurobiology and
neuroscience.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:87:" <a href="http://arxiv.org/find/cs/1/au:+Costa_L/0/1/0/all/0/1">Luciano da F. Costa</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:341;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.09388";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:134:"High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metrics. (arXiv:2111.09388v3 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.09388";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1011:"In Neural Machine Translation, it is typically assumed that the sentence with
the highest estimated probability should also be the translation with the
highest quality as measured by humans. In this work, we question this
assumption and show that model estimates and translation quality only vaguely
correlate. We apply Minimum Bayes Risk (MBR) decoding on unbiased samples to
optimize diverse automated metrics of translation quality as an alternative
inference strategy to beam search. Instead of targeting the hypotheses with the
highest model probability, MBR decoding extracts the hypotheses with the
highest estimated quality. Our experiments show that the combination of a
neural translation model with a neural reference-based metric, BLEURT, results
in significant improvement in human evaluations. This improvement is obtained
with translations different from classical beam-search output: these
translations have much lower model likelihood and are less favored by surface
metrics like BLEU.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:326:" <a href="http://arxiv.org/find/cs/1/au:+Freitag_M/0/1/0/all/0/1">Markus Freitag</a>, <a href="http://arxiv.org/find/cs/1/au:+Grangier_D/0/1/0/all/0/1">David Grangier</a>, <a href="http://arxiv.org/find/cs/1/au:+Tan_Q/0/1/0/all/0/1">Qijun Tan</a>, <a href="http://arxiv.org/find/cs/1/au:+Liang_B/0/1/0/all/0/1">Bowen Liang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:342;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.09996";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:66:"LOLNeRF: Learn from One Look. (arXiv:2111.09996v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.09996";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:997:"We present a method for learning a generative 3D model based on neural
radiance fields, trained solely from data with only single views of each
object. While generating realistic images is no longer a difficult task,
producing the corresponding 3D structure such that they can be rendered from
different views is non-trivial. We show that, unlike existing methods, one does
not need multi-view data to achieve this goal. Specifically, we show that by
reconstructing many images aligned to an approximate canonical pose with a
single network conditioned on a shared latent space, you can learn a space of
radiance fields that models shape and appearance for a class of objects. We
demonstrate this by training models to reconstruct object categories using
datasets that contain only one view of each subject without depth or geometry
information. Our experiments show that we achieve state-of-the-art results in
novel view synthesis and high-quality results for monocular depth prediction.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:421:" <a href="http://arxiv.org/find/cs/1/au:+Rebain_D/0/1/0/all/0/1">Daniel Rebain</a>, <a href="http://arxiv.org/find/cs/1/au:+Matthews_M/0/1/0/all/0/1">Mark Matthews</a>, <a href="http://arxiv.org/find/cs/1/au:+Yi_K/0/1/0/all/0/1">Kwang Moo Yi</a>, <a href="http://arxiv.org/find/cs/1/au:+Lagun_D/0/1/0/all/0/1">Dmitry Lagun</a>, <a href="http://arxiv.org/find/cs/1/au:+Tagliasacchi_A/0/1/0/all/0/1">Andrea Tagliasacchi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:343;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.10367";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:126:"SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech. (arXiv:2111.10367v2 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.10367";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1434:"Progress in speech processing has been facilitated by shared datasets and
benchmarks. Historically these have focused on automatic speech recognition
(ASR), speaker identification, or other lower-level tasks. Interest has been
growing in higher-level spoken language understanding tasks, including using
end-to-end models, but there are fewer annotated datasets for such tasks. At
the same time, recent work shows the possibility of pre-training generic
representations and then fine-tuning for several tasks using relatively little
labeled data. We propose to create a suite of benchmark tasks for Spoken
Language Understanding Evaluation (SLUE) consisting of limited-size labeled
training sets and corresponding evaluation sets. This resource would allow the
research community to track progress, evaluate pre-trained representations for
higher-level tasks, and study open questions such as the utility of pipeline
versus end-to-end approaches. We present the first phase of the SLUE benchmark
suite, consisting of named entity recognition, sentiment analysis, and ASR on
the corresponding datasets. We focus on naturally produced (not read or
synthesized) speech, and freely available datasets. We provide new
transcriptions and annotations on subsets of the VoxCeleb and VoxPopuli
datasets, evaluation metrics and results for baseline models, and an
open-source toolkit to reproduce the baselines and evaluate new models.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:554:" <a href="http://arxiv.org/find/cs/1/au:+Shon_S/0/1/0/all/0/1">Suwon Shon</a>, <a href="http://arxiv.org/find/cs/1/au:+Pasad_A/0/1/0/all/0/1">Ankita Pasad</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_F/0/1/0/all/0/1">Felix Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Brusco_P/0/1/0/all/0/1">Pablo Brusco</a>, <a href="http://arxiv.org/find/cs/1/au:+Artzi_Y/0/1/0/all/0/1">Yoav Artzi</a>, <a href="http://arxiv.org/find/cs/1/au:+Livescu_K/0/1/0/all/0/1">Karen Livescu</a>, <a href="http://arxiv.org/find/cs/1/au:+Han_K/0/1/0/all/0/1">Kyu J. Han</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:344;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.12860";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:139:"Applied Exoskeleton Technology: A Comprehensive Review of Physical and Cognitive Human-Robot-Interfac. (arXiv:2111.12860v6 [cs.RO] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.12860";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1576:"Exoskeletons and orthoses are wearable mobile systems providing mechanical
benefits to the users. Despite significant improvements in the last decades,
the technology is not fully mature to be adopted for strenuous and
non-programmed tasks. To accommodate this insufficiency, different aspects of
this technology need to be analysed and improved. Numerous studies have been
trying to address some aspects of exoskeletons, e.g. mechanism design, intent
prediction, and control scheme. However, most works have focused on a specific
element of design or application without providing a comprehensive review
framework. This study aims to analyse and survey the contributing aspects to
the improvement and broad adoption of this technology. To address this, after
introducing assistive devices and exoskeletons, the main design criteria will
be investigated from a physical Human-Robot Interface (HRI) perspective. The
study will be further developed by outlining several examples of known
assistive devices in different categories. In order to establish an intelligent
HRI strategy and enabling intuitive control for users, cognitive HRI will be
investigated. Various approaches to this strategy will be reviewed, and a model
for intent prediction will be proposed. This model is utilised to predict the
gate phase from a single Electromyography (EMG) channel input. The outcomes of
modelling show the potential use of single-channel input in low-power assistive
devices. Furthermore, the proposed model can provide redundancy in devices with
a complex control strategy.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:429:" <a href="http://arxiv.org/find/cs/1/au:+Nazari_F/0/1/0/all/0/1">Farhad Nazari</a>, <a href="http://arxiv.org/find/cs/1/au:+Mohajer_N/0/1/0/all/0/1">Navid Mohajer</a>, <a href="http://arxiv.org/find/cs/1/au:+Nahavandi_D/0/1/0/all/0/1">Darius Nahavandi</a>, <a href="http://arxiv.org/find/cs/1/au:+Khosravi_A/0/1/0/all/0/1">Abbas Khosravi</a>, <a href="http://arxiv.org/find/cs/1/au:+Nahavandi_S/0/1/0/all/0/1">Saeid Nahavandi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:345;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.14055";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:106:"ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. (arXiv:2111.14055v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.14055";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1397:"Fast stereo based 3D object detectors have made great progress recently.
However, they lag far behind high-precision stereo based methods in accuracy.
We argue that the main reason is due to the poor geometry-aware feature
representation in 3D space. To solve this problem, we propose an efficient
stereo geometry network (ESGN). The key in our ESGN is an efficient
geometry-aware feature generation (EGFG) module. Our EGFG module first uses a
stereo correlation and reprojection module to construct multi-scale stereo
volumes in camera frustum space, second employs a multi-scale BEV projection
and fusion module to generate multiple geometry-aware features. In these two
steps, we adopt deep multi-scale information fusion for discriminative
geometry-aware feature generation, without any complex aggregation networks. In
addition, we introduce a deep geometry-aware feature distillation scheme to
guide stereo feature learning with a LiDAR-based detector. The experiments are
performed on the classical KITTI dataset. On KITTI test set, our ESGN
outperforms the fast state-of-art-art detector YOLOStereo3D by 5.14\% on
mAP$_{3d}$ at 62$ms$. To the best of our knowledge, our ESGN achieves a best
trade-off between accuracy and speed. We hope that our efficient stereo
geometry network can provide more possible directions for fast 3D object
detection. Our source code will be released.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:380:" <a href="http://arxiv.org/find/cs/1/au:+Gao_A/0/1/0/all/0/1">Aqi Gao</a>, <a href="http://arxiv.org/find/cs/1/au:+Pang_Y/0/1/0/all/0/1">Yanwei Pang</a>, <a href="http://arxiv.org/find/cs/1/au:+Nie_J/0/1/0/all/0/1">Jing Nie</a>, <a href="http://arxiv.org/find/cs/1/au:+Cao_J/0/1/0/all/0/1">Jiale Cao</a>, <a href="http://arxiv.org/find/cs/1/au:+Guo_Y/0/1/0/all/0/1">Yishun Guo</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:346;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.14705";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:119:"Exponential integrators for second-order in time partial differential equations. (arXiv:2111.14705v2 [math.NA] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.14705";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:419:"Two types of second-order in time partial differential equations (PDEs),
namely semilinear wave equations and semilinear beam equations are considered.
To solve these equations with exponential integrators, we present an approach
to compute efficiently the action of the matrix exponential as well as those of
related matrix functions. Various numerical simulations are presented that
illustrate this approach.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:171:" <a href="http://arxiv.org/find/math/1/au:+Ostermann_A/0/1/0/all/0/1">Alexander Ostermann</a>, <a href="http://arxiv.org/find/math/1/au:+Phan_D/0/1/0/all/0/1">Duy Phan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:347;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2111.15367";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"A Review on Graph Neural Network Methods in Financial Applications. (arXiv:2111.15367v2 [q-fin.ST] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2111.15367";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:908:"With multiple components and relations, financial data are often presented as
graph data, since it could represent both the individual features and the
complicated relations. Due to the complexity and volatility of the financial
market, the graph constructed on the financial data is often heterogeneous or
time-varying, which imposes challenges on modeling technology. Among the graph
modeling technologies, graph neural network (GNN) models are able to handle the
complex graph structure and achieve great performance and thus could be used to
solve financial tasks. In this work, we provide a comprehensive review of GNN
models in recent financial context. We first categorize the commonly-used
financial graphs and summarize the feature processing step for each node. Then
we summarize the GNN methodology for each graph type, application in each area,
and propose some potential research areas.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:327:" <a href="http://arxiv.org/find/q-fin/1/au:+Wang_J/0/1/0/all/0/1">Jianian Wang</a>, <a href="http://arxiv.org/find/q-fin/1/au:+Zhang_S/0/1/0/all/0/1">Sheng Zhang</a>, <a href="http://arxiv.org/find/q-fin/1/au:+Xiao_Y/0/1/0/all/0/1">Yanghua Xiao</a>, <a href="http://arxiv.org/find/q-fin/1/au:+Song_R/0/1/0/all/0/1">Rui Song</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:348;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2112.00913";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:140:"CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicing. (arXiv:2112.00913v3 [eess.IV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2112.00913";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1365:"Deep learning based methods hold state-of-the-art results in low-level image
processing tasks, but remain difficult to interpret due to their black-box
construction. Unrolled optimization networks present an interpretable
alternative to constructing deep neural networks by deriving their architecture
from classical iterative optimization methods without use of tricks from the
standard deep learning tool-box. So far, such methods have demonstrated
performance close to that of state-of-the-art models while using their
interpretable construction to achieve a comparably low learned parameter count.
In this work, we propose an unrolled convolutional dictionary learning network
(CDLNet) and demonstrate its competitive denoising and joint denoising and
demosaicing (JDD) performance both in low and high parameter count regimes.
Specifically, we show that the proposed model outperforms state-of-the-art
fully convolutional denoising and JDD models when scaled to a similar parameter
count. In addition, we leverage the model's interpretable construction to
propose a noise-adaptive parameterization of thresholds in the network that
enables state-of-the-art blind denoising performance, and near perfect
generalization on noise-levels unseen during training. Furthermore, we show
that such performance extends to the JDD task and unsupervised learning.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:296:" <a href="http://arxiv.org/find/eess/1/au:+Janjusevic_N/0/1/0/all/0/1">Nikola Janju&#x161;evi&#x107;</a>, <a href="http://arxiv.org/find/eess/1/au:+Khalilian_Gourtani_A/0/1/0/all/0/1">Amirhossein Khalilian-Gourtani</a>, <a href="http://arxiv.org/find/eess/1/au:+Wang_Y/0/1/0/all/0/1">Yao Wang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:349;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2112.02731";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:105:"Detecting DeFi Securities Violations from Token Smart Contract Code. (arXiv:2112.02731v2 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2112.02731";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1639:"Decentralized Finance (DeFi) is a system of financial products and services
built and delivered through smart contracts on various blockchains. In the past
year, DeFi has gained popularity and market capitalization. However, it has
also been connected to crime, in particular, various types of securities
violations. The lack of Know Your Customer requirements in DeFi poses
challenges to governments trying to mitigate potential offending in this space.
This study aims to uncover whether this problem is suited to a machine learning
approach, namely, whether we can identify DeFi projects potentially engaging in
securities violations based on their tokens' smart contract code. We adapt
prior work on detecting specific types of securities violations across
Ethereum, building a random forest classifier based on features extracted from
DeFi projects' tokens' smart contract code. The final classifier achieves a
98.6% F1-score. From further feature-level analysis, we find a single feature
makes this a highly detectable problem. The high reliance on a single feature
means that, at this stage, a complex machine learning model may not be
necessary or desirable for this problem. However, this may change as DeFi
securities violations become more sophisticated. Another contribution of our
study is a new dataset, comprised of (a) a verified ground truth dataset for
tokens involved in securities violations and (b) a set of legitimate tokens
from a reputable DeFi aggregator. This paper further discusses the potential
use of a model like ours by prosecutors in enforcement efforts and connects it
to the wider legal context.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:254:" <a href="http://arxiv.org/find/cs/1/au:+Trozze_A/0/1/0/all/0/1">Arianna Trozze</a>, <a href="http://arxiv.org/find/cs/1/au:+Kleinberg_B/0/1/0/all/0/1">Bennett Kleinberg</a>, <a href="http://arxiv.org/find/cs/1/au:+Davies_T/0/1/0/all/0/1">Toby Davies</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:350;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2112.06147";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:144:"Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identification. (arXiv:2112.06147v3 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2112.06147";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1931:"RGB-Infrared person re-identification (RGB-IR ReID) aims to associate people
across disjoint RGB and IR camera views. Currently, state-of-the-art
performance of RGB-IR ReID is not as impressive as that of conventional ReID.
Much of that is due to the notorious modality bias training issue brought by
the single-modality ImageNet pre-training, which might yield RGB-biased
representations that severely hinder the cross-modality image retrieval. This
paper makes first attempt to tackle the task from a pre-training perspective.
We propose a self-supervised pre-training solution, named Modality-Aware
Multiple Granularity Learning (MMGL), which directly trains models from scratch
only on multi-modal ReID datasets, but achieving competitive results against
ImageNet pre-training, without using any external data or sophisticated tuning
tricks. First, we develop a simple-but-effective 'permutation recovery' pretext
task that globally maps shuffled RGB-IR images into a shared latent permutation
space, providing modality-invariant global representations for downstream ReID
tasks. Second, we present a part-aware cycle-contrastive (PCC) learning
strategy that utilizes cross-modality cycle-consistency to maximize agreement
between semantically similar RGB-IR image patches. This enables contrastive
learning for the unpaired multi-modal scenarios, further improving the
discriminability of local features without laborious instance augmentation.
Based on these designs, MMGL effectively alleviates the modality bias training
problem. Extensive experiments demonstrate that it learns better
representations (+8.03% Rank-1 accuracy) with faster training speed (converge
only in few hours) and higher data efficiency (&lt;5% data size) than ImageNet
pre-training. The results also suggest it generalizes well to various existing
models, losses and has promising transferability across datasets. The code will
be released.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:470:" <a href="http://arxiv.org/find/cs/1/au:+Wan_L/0/1/0/all/0/1">Lin Wan</a>, <a href="http://arxiv.org/find/cs/1/au:+Jing_Q/0/1/0/all/0/1">Qianyan Jing</a>, <a href="http://arxiv.org/find/cs/1/au:+Sun_Z/0/1/0/all/0/1">Zongyuan Sun</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_C/0/1/0/all/0/1">Chuang Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_Z/0/1/0/all/0/1">Zhihang Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_Y/0/1/0/all/0/1">Yehansen Chen</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:351;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2112.06377";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"Surfer100: Generating Surveys From Web Resources, Wikipedia-style. (arXiv:2112.06377v3 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2112.06377";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:842:"Fast-developing fields such as Artificial Intelligence (AI) often outpace the
efforts of encyclopedic sources such as Wikipedia, which either do not
completely cover recently-introduced topics or lack such content entirely. As a
result, methods for automatically producing content are valuable tools to
address this information overload. We show that recent advances in pretrained
language modeling can be combined for a two-stage extractive and abstractive
approach for Wikipedia lead paragraph generation. We extend this approach to
generate longer Wikipedia-style summaries with sections and examine how such
methods struggle in this application through detailed studies with 100
reference human-collected surveys. This is the first study on utilizing web
resources for long Wikipedia-style summaries to the best of our knowledge.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:799:" <a href="http://arxiv.org/find/cs/1/au:+Li_I/0/1/0/all/0/1">Irene Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Fabbri_A/0/1/0/all/0/1">Alexander Fabbri</a>, <a href="http://arxiv.org/find/cs/1/au:+Kawamura_R/0/1/0/all/0/1">Rina Kawamura</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_Y/0/1/0/all/0/1">Yixin Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Tang_X/0/1/0/all/0/1">Xiangru Tang</a>, <a href="http://arxiv.org/find/cs/1/au:+Tae_J/0/1/0/all/0/1">Jaesung Tae</a>, <a href="http://arxiv.org/find/cs/1/au:+Shen_C/0/1/0/all/0/1">Chang Shen</a>, <a href="http://arxiv.org/find/cs/1/au:+Ma_S/0/1/0/all/0/1">Sally Ma</a>, <a href="http://arxiv.org/find/cs/1/au:+Mizutani_T/0/1/0/all/0/1">Tomoe Mizutani</a>, <a href="http://arxiv.org/find/cs/1/au:+Radev_D/0/1/0/all/0/1">Dragomir Radev</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:352;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2112.06455";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:111:"Self-Paced Deep Regression Forests with Consideration on Ranking Fairness. (arXiv:2112.06455v5 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2112.06455";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1602:"Deep discriminative models (DDMs), such as deep regression forests, deep
neural decision forests, have been extensively studied recently to solve
problems like facial age estimation, head pose estimation, gaze estimation and
so forth. Such problems are challenging in part because a large amount of
effective training data without noise and bias is often not available. While
some progress has been achieved through learning more discriminative features,
or reweighting samples, we argue what is more desirable is to learn gradually
to discriminate like human beings. Then, we resort to self-paced learning
(SPL). But a natural question arises: can self-paced regime lead DDMs to
achieve more robust and less biased solutions? A serious problem with SPL,
which is firstly discussed by this work, is it tends to aggravate the bias of
solutions, especially for obvious imbalanced data. To this end, this paper
proposes a new self-paced paradigm for deep discriminative model, which
distinguishes noisy and underrepresented examples according to the output
likelihood and entropy associated with each example, and tackle the fundamental
ranking problem in SPL from a new perspective: fairness. This paradigm is
fundamental, and could be easily combined with a variety of DDMs. Extensive
experiments on three computer vision tasks, such as facial age estimation, head
pose estimation and gaze estimation, demonstrate the efficacy of our paradigm.
To the best of our knowledge, our work is the first paper in the literature of
SPL that considers ranking fairness for self-paced regime construction.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:387:" <a href="http://arxiv.org/find/cs/1/au:+Pan_L/0/1/0/all/0/1">Lili Pan</a>, <a href="http://arxiv.org/find/cs/1/au:+Meng_M/0/1/0/all/0/1">Mingming Meng</a>, <a href="http://arxiv.org/find/cs/1/au:+Ren_Y/0/1/0/all/0/1">Yazhou Ren</a>, <a href="http://arxiv.org/find/cs/1/au:+Zheng_Y/0/1/0/all/0/1">Yali Zheng</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_Z/0/1/0/all/0/1">Zenglin Xu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:353;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2112.07428";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:105:"Obtaining Calibrated Probabilities with Personalized Ranking Models. (arXiv:2112.07428v2 [cs.IR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2112.07428";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1164:"For personalized ranking models, the well-calibrated probability of an item
being preferred by a user has great practical value. While existing work shows
promising results in image classification, probability calibration has not been
much explored for personalized ranking. In this paper, we aim to estimate the
calibrated probability of how likely a user will prefer an item. We investigate
various parametric distributions and propose two parametric calibration
methods, namely Gaussian calibration and Gamma calibration. Each proposed
method can be seen as a post-processing function that maps the ranking scores
of pre-trained models to well-calibrated preference probabilities, without
affecting the recommendation performance. We also design the unbiased empirical
risk minimization framework that guides the calibration methods to learning of
true preference probability from the biased user-item interaction dataset.
Extensive evaluations with various personalized ranking models on real-world
datasets show that both the proposed calibration methods and the unbiased
empirical risk minimization significantly improve the calibration performance.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:235:" <a href="http://arxiv.org/find/cs/1/au:+Kweon_W/0/1/0/all/0/1">Wonbin Kweon</a>, <a href="http://arxiv.org/find/cs/1/au:+Kang_S/0/1/0/all/0/1">SeongKu Kang</a>, <a href="http://arxiv.org/find/cs/1/au:+Yu_H/0/1/0/all/0/1">Hwanjo Yu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:354;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2112.08908";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:146:"Effective highly accurate integrators for linear Klein-Gordon equations from low to high frequency regimes. (arXiv:2112.08908v2 [math.NA] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2112.08908";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:713:"We introduce an efficient class of numerical schemes for the Klein--Gordon
equation which are highly accurate from slowly varying up to highly oscillatory
regimes. Their construction is based on Magnus expansions tailored to the
structure of the input term which allows us to resolve the oscillations in the
system up to second order convergence in time uniformly in all frequencies
$\omega_n$. Depending on the nature of the oscillatory term, the proposed
methods even show superior convergence, reaching up to fourth-order
convergence, while maintaining high efficiency and small error constants.
Numerical experiments highlight our theoretical findings and underline the
efficiency of the new schemes.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:279:" <a href="http://arxiv.org/find/math/1/au:+Kropielnicka_K/0/1/0/all/0/1">Karolina Kropielnicka</a>, <a href="http://arxiv.org/find/math/1/au:+Lademann_K/0/1/0/all/0/1">Karolina Lademann</a>, <a href="http://arxiv.org/find/math/1/au:+Schratz_K/0/1/0/all/0/1">Katharina Schratz</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:355;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2112.10601";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:131:"Error estimates for a splitting integrator for abstract semilinear boundary coupled systems. (arXiv:2112.10601v2 [math.NA] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2112.10601";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:677:"We derive a numerical method, based on operator splitting, to abstract
parabolic semilinear boundary coupled systems. The method decouples the linear
components which describe the coupling and the dynamics in the bulk and on the
surface, and treats the nonlinear terms by approximating the integral in the
variation of constants formula. The convergence proof is based on estimates for
a recursive formulation of the error, using the parabolic smoothing property of
analytic semigroups and a careful comparison of the exact and approximate
flows. Numerical experiments, including problems with dynamic boundary
conditions, reporting on convergence rates are presented.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:273:" <a href="http://arxiv.org/find/math/1/au:+Csomos_P/0/1/0/all/0/1">Petra Csom&#xf3;s</a>, <a href="http://arxiv.org/find/math/1/au:+Farkas_B/0/1/0/all/0/1">B&#xe1;lint Farkas</a>, <a href="http://arxiv.org/find/math/1/au:+Kovacs_B/0/1/0/all/0/1">Bal&#xe1;zs Kov&#xe1;cs</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:356;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2112.11635";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:139:"Breaking the Rate-Loss Bound of Quantum Key Distribution with Asynchronous Two-Photon Interference. (arXiv:2112.11635v3 [quant-ph] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2112.11635";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1468:"Twin-field quantum key distribution can overcome the secret key capacity of
repeaterless quantum key distribution via single-photon interference. However,
to compensate for the channel fluctuations and lock the laser fluctuations, the
techniques of phase tracking and phase locking are indispensable in experiment,
which drastically increase experimental complexity and hinder free-space
realization. Inspired by the duality in entanglement, we herein present an
asynchronous measurement-device-independent quantum key distribution protocol
that can surpass the secret key capacity even without phase tracking and phase
locking. Leveraging the concept of time multiplexing, asynchronous two-photon
Bell-state measurement is realized by postmatching two interference detection
events. For a 1 GHz system, the new protocol reaches a transmission distance of
450 km without phase tracking. After further removing phase locking, our
protocol is still capable of breaking the capacity at 270 km. Intriguingly,
when using the same experimental techniques, our protocol has a higher key rate
than the phase-matching-type twin-field protocol. In the presence of imperfect
intensity modulation, it also has a significant advantage in terms of the
transmission distance over the sending-or-not-sending type twin-field protocol.
With high key rates and accessible technology, our work provides a promising
candidate for practical scalable quantum communication networks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:835:" <a href="http://arxiv.org/find/quant-ph/1/au:+Xie_Y/0/1/0/all/0/1">Yuan-Mei Xie</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Lu_Y/0/1/0/all/0/1">Yu-Shuo Lu</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Weng_C/0/1/0/all/0/1">Chen-Xun Weng</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Cao_X/0/1/0/all/0/1">Xiao-Yu Cao</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Jia_Z/0/1/0/all/0/1">Zhao-Ying Jia</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Bao_Y/0/1/0/all/0/1">Yu Bao</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Wang_Y/0/1/0/all/0/1">Yang Wang</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Fu_Y/0/1/0/all/0/1">Yao Fu</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Yin_H/0/1/0/all/0/1">Hua-Lei Yin</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Chen_Z/0/1/0/all/0/1">Zeng-Bing Chen</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:357;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2112.12021";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:122:"Community Detection in Medical Image Datasets: Using Wavelets and Spectral Methods. (arXiv:2112.12021v2 [eess.IV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2112.12021";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1643:"Medical image datasets can have large number of images representing patients
with different health conditions and various disease severity. When dealing
with raw unlabeled image datasets, the large number of samples often makes it
hard for experts and non-experts to understand the variety of images present in
a dataset. Supervised learning methods rely on labeled images which requires a
considerable effort by medical experts to first understand the communities of
images present in the data and then labeling the images. Here, we propose an
algorithm to facilitate the automatic identification of communities in medical
image datasets. We further demonstrate that such analysis can be insightful in
a supervised setting when the images are already labeled. Such insights are
useful because, health and disease severity can be considered a continuous
spectrum, and within each class, there usually are finer communities worthy of
investigation, especially when they have similarities to communities in other
classes. In our approach, we use wavelet decomposition of images in tandem with
spectral methods. We show that the eigenvalues of a graph Laplacian can reveal
the number of notable communities in an image dataset. Moreover, analyzing the
similarities may be used to infer a spectrum representing the severity of the
disease. In our experiments, we use a dataset of images labeled with different
conditions for COVID patients. We detect 25 communities in the dataset and then
observe that only 6 of those communities contain patients with pneumonia. We
also investigate the contents of a colorectal cancer histology dataset.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:95:" <a href="http://arxiv.org/find/eess/1/au:+Yousefzadeh_R/0/1/0/all/0/1">Roozbeh Yousefzadeh</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:358;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2112.13168";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands. (arXiv:2112.13168v3 [q-bio.QM] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2112.13168";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1358:"Identifying novel drug-target interactions (DTI) is a critical and rate
limiting step in drug discovery. While deep learning models have been proposed
to accelerate the identification process, we show that state-of-the-art models
fail to generalize to novel (i.e., never-before-seen) structures. We first
unveil the mechanisms responsible for this shortcoming, demonstrating how
models rely on shortcuts that leverage the topology of the protein-ligand
bipartite network, rather than learning the node features. Then, we introduce
AI-Bind, a pipeline that combines network-based sampling strategies with
unsupervised pre-training, allowing us to limit the annotation imbalance and
improve binding predictions for novel proteins and ligands. We illustrate the
value of AI-Bind by predicting drugs and natural compounds with binding
affinity to SARS-CoV-2 viral proteins and the associated human proteins. We
also validate these predictions via auto-docking simulations and comparison
with recent experimental evidence, and step up the process of interpreting
machine learning prediction of protein-ligand binding by identifying potential
active binding sites on the amino acid sequence. Overall, AI-Bind offers a
powerful high-throughput approach to identify drug-target combinations, with
the potential of becoming a powerful tool in drug discovery.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:893:" <a href="http://arxiv.org/find/q-bio/1/au:+Chatterjee_A/0/1/0/all/0/1">Ayan Chatterjee</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Walters_R/0/1/0/all/0/1">Robin Walters</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Shafi_Z/0/1/0/all/0/1">Zohair Shafi</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Ahmed_O/0/1/0/all/0/1">Omair Shafi Ahmed</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Sebek_M/0/1/0/all/0/1">Michael Sebek</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Gysi_D/0/1/0/all/0/1">Deisy Gysi</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Yu_R/0/1/0/all/0/1">Rose Yu</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Eliassi_Rad_T/0/1/0/all/0/1">Tina Eliassi-Rad</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Barabasi_A/0/1/0/all/0/1">Albert-L&#xe1;szl&#xf3; Barab&#xe1;si</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Menichetti_G/0/1/0/all/0/1">Giulia Menichetti</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:359;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2201.03413";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:89:"Systems Challenges for Trustworthy Embodied Systems. (arXiv:2201.03413v2 [cs.AI] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2201.03413";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1066:"A new generation of increasingly autonomous and self-learning embodied
systems is about to be developed. When deploying embodied systems into a
real-life context we face various engineering challenges, as it is crucial to
coordinate the behavior of embodied systems in a beneficial manner, ensure
their compatibility with our human-centered social values, and design
verifiably safe and reliable human-machine interaction. We are arguing that
traditional systems engineering is coming to a climacteric from embedded to
embodied systems, and with assuring the trustworthiness of dynamic federations
of situationally aware, intent-driven, explorative, ever-evolving, largely
non-predictable, and increasingly autonomous embodied systems in uncertain,
complex, and unpredictable real-world contexts. We are therefore identifying a
number of urgent systems challenges for trustworthy embodied systems, including
robust and human-centric AI, cognitive architectures, uncertainty
quantification, trustworthy self-integration, and continual analysis and
assurance.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:84:" <a href="http://arxiv.org/find/cs/1/au:+Ruess_H/0/1/0/all/0/1">Harald Rue&#xdf;</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:360;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2201.04196";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:135:"A polynomial-time approximation scheme for parallel two-stage flowshops under makespan constraint. (arXiv:2201.04196v2 [cs.DS] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2201.04196";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:885:"As a hybrid of the Parallel Two-stage Flowshop problem and the Multiple
Knapsack problem, we investigate the scheduling of parallel two-stage flowshops
under makespan constraint, which was motivated by applications in cloud
computing and introduced by Chen et al. [3] recently. A set of two-stage jobs
are selected and scheduled on parallel two-stage flowshops to achieve the
maximum total profit while maintaining the given makespan constraint. We give a
positive answer to an open question about its approximability proposed by Chen
et al. [3]. More specifically, based on guessing strategies and rounding
techniques for linear programs, we present a polynomial-time approximation
scheme (PTAS) for the case when the number of flowshops is a fixed constant. As
the case with two flowshops is already strongly NP-hard, our PTAS achieves the
best possible approximation ratio.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:231:" <a href="http://arxiv.org/find/cs/1/au:+Tong_W/0/1/0/all/0/1">Weitian Tong</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_Y/0/1/0/all/0/1">Yao Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_H/0/1/0/all/0/1">Huili Zhang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:361;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2201.07265";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:123:"EP-PQM: Efficient Parametric Probabilistic Quantum Memory with Fewer Qubits and Gates. (arXiv:2201.07265v2 [cs.ET] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2201.07265";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1958:"Machine learning (ML) classification tasks can be carried out on a quantum
computer (QC) using Probabilistic Quantum Memory (PQM) and its extension,
Parameteric PQM (P-PQM) by calculating the Hamming distance between an input
pattern and a database of $r$ patterns containing $z$ features with $a$
distinct attributes.

For accurate computations, the feature must be encoded using one-hot
encoding, which is memory-intensive for multi-attribute datasets with $a&gt;2$. We
can easily represent multi-attribute data more compactly on a classical
computer by replacing one-hot encoding with label encoding. However, replacing
these encoding schemes on a QC is not straightforward as PQM and P-PQM operate
at the quantum bit level.

We present an enhanced P-PQM, called EP-PQM, that allows label encoding of
data stored in a PQM data structure and reduces the circuit depth of the data
storage and retrieval procedures. We show implementations for an ideal QC and a
noisy intermediate-scale quantum (NISQ) device.

Our complexity analysis shows that the EP-PQM approach requires $O\left(z
\log_2(a)\right)$ qubits as opposed to $O(za)$ qubits for P-PQM. EP-PQM also
requires fewer gates, reducing gate count from $O\left(rza\right)$ to
$O\left(rz\log_2(a)\right)$.

For five datasets, we demonstrate that training an ML classification model
using EP-PQM requires 48% to 77% fewer qubits than P-PQM for datasets with
$a&gt;2$. EP-PQM reduces circuit depth in the range of 60% to 96%, depending on
the dataset. The depth decreases further with a decomposed circuit, ranging
between 94% and 99%.

EP-PQM requires less space; thus, it can train on and classify larger
datasets than previous PQM implementations on NISQ devices. Furthermore,
reducing the number of gates speeds up the classification and reduces the noise
associated with deep quantum circuits. Thus, EP-PQM brings us closer to
scalable ML on a NISQ device.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:342:" <a href="http://arxiv.org/find/cs/1/au:+Khan_M/0/1/0/all/0/1">Mushahid Khan</a>, <a href="http://arxiv.org/find/cs/1/au:+Faye_J/0/1/0/all/0/1">Jean Paul Latyr Faye</a>, <a href="http://arxiv.org/find/cs/1/au:+Mendes_U/0/1/0/all/0/1">Udson C. Mendes</a>, <a href="http://arxiv.org/find/cs/1/au:+Miranskyy_A/0/1/0/all/0/1">Andriy Miranskyy</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:362;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2201.09367";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:110:"Sketch2PQ: Freeform Planar Quadrilateral Mesh Design via a Single Sketch. (arXiv:2201.09367v4 [cs.GR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2201.09367";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1469:"The freeform architectural modeling process often involves two important
stages: concept design and digital modeling. In the first stage, architects
usually sketch the overall 3D shape and the panel layout on a physical or
digital paper briefly. In the second stage, a digital 3D model is created using
the sketch as a reference. The digital model needs to incorporate geometric
requirements for its components, such as the planarity of panels due to
consideration of construction costs, which can make the modeling process more
challenging. In this work, we present a novel sketch-based system to bridge the
concept design and digital modeling of freeform roof-like shapes represented as
planar quadrilateral (PQ) meshes. Our system allows the user to sketch the
surface boundary and contour lines under axonometric projection and supports
the sketching of occluded regions. In addition, the user can sketch feature
lines to provide directional guidance to the PQ mesh layout. Given the 2D
sketch input, we propose a deep neural network to infer in real-time the
underlying surface shape along with a dense conjugate direction field, both of
which are used to extract the final PQ mesh. To train and validate our network,
we generate a large synthetic dataset that mimics architect sketching of
freeform quadrilateral patches. The effectiveness and usability of our system
are demonstrated with quantitative and qualitative evaluation as well as user
studies.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:463:" <a href="http://arxiv.org/find/cs/1/au:+Deng_Z/0/1/0/all/0/1">Zhi Deng</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_Y/0/1/0/all/0/1">Yang Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Pan_H/0/1/0/all/0/1">Hao Pan</a>, <a href="http://arxiv.org/find/cs/1/au:+Jabi_W/0/1/0/all/0/1">Wassim Jabi</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_J/0/1/0/all/0/1">Juyong Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Deng_B/0/1/0/all/0/1">Bailin Deng</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:363;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2201.09419";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:154:"Automated machine learning for secure key rate in discrete-modulated continuous-variable quantum key distribution. (arXiv:2201.09419v2 [quant-ph] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2201.09419";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1440:"Continuous-variable quantum key distribution (CV QKD) with discrete
modulation has attracted increasing attention due to its experimental
simplicity, lower-cost implementation and compatibility with classical optical
communication. Correspondingly, some novel numerical methods have been proposed
to analyze the security of these protocols against collective attacks, which
promotes key rates over one hundred kilometers of fiber distance. However,
numerical methods are limited by their calculation time and resource
consumption, for which they cannot play more roles on mobile platforms in
quantum networks. To improve this issue, a neural network model predicting key
rates in nearly real time has been proposed previously. Here, we go further and
show a neural network model combined with Bayesian optimization. This model
automatically designs the best architecture of neural network computing key
rates in real time. We demonstrate our model with two variants of CV QKD
protocols with quaternary modulation. The results show high reliability with
secure probability as high as $99.15\%-99.59\%$, considerable tightness and
high efficiency with speedup of approximately $10^7$ in both cases. This
inspiring model enables the real-time computation of unstructured quantum key
distribution protocols' key rate more automatically and efficiently, which has
met the growing needs of implementing QKD protocols on moving platforms.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:588:" <a href="http://arxiv.org/find/quant-ph/1/au:+Liu_Z/0/1/0/all/0/1">Zhi-Ping Liu</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Zhou_M/0/1/0/all/0/1">Min-Gang Zhou</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Liu_W/0/1/0/all/0/1">Wen-Bo Liu</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Li_C/0/1/0/all/0/1">Chen-Long Li</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Gu_J/0/1/0/all/0/1">Jie Gu</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Yin_H/0/1/0/all/0/1">Hua-Lei Yin</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Chen_Z/0/1/0/all/0/1">Zeng-Bing Chen</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:364;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2201.11329";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:118:"Quantum algorithm for dense and full-rank kernels using hierarchical matrices. (arXiv:2201.11329v2 [quant-ph] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2201.11329";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1189:"Kernel matrices, which arise from discretizing a kernel function $k(x,x')$,
have a variety of applications in mathematics and engineering. Classically, the
celebrated fast multipole method was designed to perform matrix multiplication
on kernel matrices of dimension $N$ in time almost linear in $N$ by using
techniques later generalized into the linear algebraic framework of
hierarchical matrices. In light of this success, we propose a quantum algorithm
for efficiently performing matrix operations on hierarchical matrices by
implementing a quantum block-encoding of the hierarchical matrix structure.
When applied to many physical kernel matrices, our quantum algorithm can solve
quantum linear systems of dimension $N$ in time $O(\kappa
\operatorname{polylog}(\frac{N}{\varepsilon}))$, where $\kappa$ and
$\varepsilon$ are the condition number and error bound of the matrix operation.
This runtime is near-optimal and, in terms of $N$, exponentially improves over
prior quantum linear systems algorithms for dense and full-rank kernel
matrices. We discuss possible applications of our algorithm in solving integral
equations and accelerating computations in N-body problems.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:264:" <a href="http://arxiv.org/find/quant-ph/1/au:+Nguyen_Q/0/1/0/all/0/1">Quynh T. Nguyen</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Kiani_B/0/1/0/all/0/1">Bobak T. Kiani</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Lloyd_S/0/1/0/all/0/1">Seth Lloyd</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:365;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2201.11410";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence. (arXiv:2201.11410v4 [cs.IT] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2201.11410";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1288:"Mobile edge computing (MEC) is considered a novel paradigm for
computation-intensive and delay-sensitive tasks in fifth generation (5G)
networks and beyond. However, its uncertainty, referred to as dynamic and
randomness, from the mobile device, wireless channel, and edge network sides,
results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization
problems. Thanks to the evolved reinforcement learning (RL), upon iteratively
interacting with the dynamic and random environment, its trained agent can
intelligently obtain the optimal policy in MEC. Furthermore, its evolved
versions, such as deep RL (DRL), can achieve higher convergence speed
efficiency and learning accuracy based on the parametric approximation for the
large-scale state-action space. This paper provides a comprehensive research
review on RL-enabled MEC and offers insight for development in this area. More
importantly, associated with free mobility, dynamic channels, and distributed
services, the MEC challenges that can be solved by different kinds of RL
algorithms are identified, followed by how they can be solved by RL solutions
in diverse mobile applications. Finally, the open challenges are discussed to
provide helpful guidance for future research in RL training and learning MEC.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:603:" <a href="http://arxiv.org/find/cs/1/au:+Wei_P/0/1/0/all/0/1">Peng Wei</a>, <a href="http://arxiv.org/find/cs/1/au:+Guo_K/0/1/0/all/0/1">Kun Guo</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_Y/0/1/0/all/0/1">Ye Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_J/0/1/0/all/0/1">Jue Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Feng_W/0/1/0/all/0/1">Wei Feng</a>, <a href="http://arxiv.org/find/cs/1/au:+Jin_S/0/1/0/all/0/1">Shi Jin</a>, <a href="http://arxiv.org/find/cs/1/au:+Ge_N/0/1/0/all/0/1">Ning Ge</a>, <a href="http://arxiv.org/find/cs/1/au:+Liang_Y/0/1/0/all/0/1">Ying-Chang Liang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:366;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2201.11965";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:148:"Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints. (arXiv:2201.11965v2 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2201.11965";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1306:"We consider primal-dual-based reinforcement learning (RL) in episodic
constrained Markov decision processes (CMDPs) with non-stationary objectives
and constraints, which play a central role in ensuring the safety of RL in
time-varying environments. In this problem, the reward/utility functions and
the state transition functions are both allowed to vary arbitrarily over time
as long as their cumulative variations do not exceed certain known variation
budgets. Designing safe RL algorithms in time-varying environments is
particularly challenging because of the need to integrate the constraint
violation reduction, safe exploration, and adaptation to the non-stationarity.
To this end, we propose a Periodically Restarted Optimistic Primal-Dual
Proximal Policy Optimization (PROPD-PPO) algorithm that features three
mechanisms: periodic-restart-based policy improvement, dual update with dual
regularization, and periodic-restart-based optimistic policy evaluation. We
establish a dynamic regret bound and a constraint violation bound for the
proposed algorithm in both the linear kernel CMDP function approximation
setting and the tabular CMDP setting under two alternative assumptions. This
paper provides the first provably efficient algorithm for non-stationary CMDPs
with safe exploration.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:159:" <a href="http://arxiv.org/find/cs/1/au:+Ding_Y/0/1/0/all/0/1">Yuhao Ding</a>, <a href="http://arxiv.org/find/cs/1/au:+Lavaei_J/0/1/0/all/0/1">Javad Lavaei</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:367;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2201.11968";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:110:"Training invariances and the low-rank phenomenon: beyond linear networks. (arXiv:2201.11968v2 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2201.11968";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1115:"The implicit bias induced by the training of neural networks has become a
topic of rigorous study. In the limit of gradient flow and gradient descent
with appropriate step size, it has been shown that when one trains a deep
linear network with logistic or exponential loss on linearly separable data,
the weights converge to rank-1 matrices. In this paper, we extend this
theoretical result to the last few linear layers of the much wider class of
nonlinear ReLU-activated feedforward networks containing fully-connected layers
and skip connections. Similar to the linear case, the proof relies on specific
local training invariances, sometimes referred to as alignment, which we show
to hold for submatrices where neurons are stably-activated in all training
examples, and it reflects empirical results in the literature. We also show
this is not true in general for the full matrix of ReLU fully-connected layers.
Our proof relies on a specific decomposition of the network into a multilinear
function and another ReLU network whose weights are constant under a certain
parameter directional convergence.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:160:" <a href="http://arxiv.org/find/cs/1/au:+Le_T/0/1/0/all/0/1">Thien Le</a>, <a href="http://arxiv.org/find/cs/1/au:+Jegelka_S/0/1/0/all/0/1">Stefanie Jegelka</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:368;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2201.12409";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:115:"A Unified Approach to Entity-Centric Context Tracking in Social Conversations. (arXiv:2201.12409v2 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2201.12409";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1219:"In human-human conversations, Context Tracking deals with identifying
important entities and keeping track of their properties and relationships.
This is a challenging problem that encompasses several subtasks such as slot
tagging, coreference resolution, resolving plural mentions and entity linking.
We approach this problem as an end-to-end modeling task where the
conversational context is represented by an entity repository containing the
entity references mentioned so far, their properties and the relationships
between them. The repository is updated turn-by-turn, thus making training and
inference computationally efficient even for long conversations. This paper
lays the groundwork for an investigation of this framework in two ways. First,
we release Contrack, a large scale human-human conversation corpus for context
tracking with people and location annotations. It contains over 7000
conversations with an average of 11.8 turns, 5.8 entities and 15.2 references
per conversation. Second, we open-source a neural network architecture for
context tracking. Finally we compare this network to state-of-the-art
approaches for the subtasks it subsumes and report results on the involved
tradeoffs.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:433:" <a href="http://arxiv.org/find/cs/1/au:+Ruckert_U/0/1/0/all/0/1">Ulrich R&#xfc;ckert</a>, <a href="http://arxiv.org/find/cs/1/au:+Sunkara_S/0/1/0/all/0/1">Srinivas Sunkara</a>, <a href="http://arxiv.org/find/cs/1/au:+Rastogi_A/0/1/0/all/0/1">Abhinav Rastogi</a>, <a href="http://arxiv.org/find/cs/1/au:+Prakash_S/0/1/0/all/0/1">Sushant Prakash</a>, <a href="http://arxiv.org/find/cs/1/au:+Khaitan_P/0/1/0/all/0/1">Pranav Khaitan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:369;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2202.06594";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:126:"A formal algebraic approach for the quantitative modeling of connectors in architectures. (arXiv:2202.06594v3 [cs.LO] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2202.06594";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:962:"In this paper we propose an algebraic formalization of connectors in the
quantitative setting in order to address the performance issues related with
the architectures of component-based systems. For this, we firstly introduce a
weighted Algebra of Interactions over a set of ports and a commutative and
idempotent semiring. The algebra serves sufficiently for modeling well-known
coordination schemes in the weighted setup. In turn, we introduce and study a
weighted Algebra of Connectors over a set of ports and a commutative and
idempotent semiring, which extends the weighted Algebra of Interactions with
types that express two different modes of synchronization, in particular,
Rendezvous and Broadcast. Moreover, we show the expressiveness of the algebra
by modeling several weighted connectors. Finally, we introduce a congruence
relation for weighted connectors and provide conditions for proving congruence
between distinct weighted connectors.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:193:" <a href="http://arxiv.org/find/cs/1/au:+Fountoukidou_C/0/1/0/all/0/1">Christina Chrysovalanti Fountoukidou</a>, <a href="http://arxiv.org/find/cs/1/au:+Pittou_M/0/1/0/all/0/1">Maria Pittou</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:370;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2202.09008";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:80:"On Variance Estimation of Random Forests. (arXiv:2202.09008v2 [stat.ML] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2202.09008";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1163:"Ensemble methods, such as random forests, are popular in applications due to
their high predictive accuracy. Existing literature views a random forest
prediction as an infinite-order incomplete U-statistic to quantify its
uncertainty. However, these methods focus on a small subsampling size of each
tree, which is theoretically valid but practically limited. This paper develops
an unbiased variance estimator based on incomplete U-statistics, which allows
the tree size to be comparable with the overall sample size, making statistical
inference possible in a broader range of real applications. Simulation results
demonstrate that our estimators enjoy lower bias and a more accurate coverage
rate without additional computational costs. We also propose a local smoothing
procedure to reduce the variation of our estimator, which shows improved
numerical performance when the number of trees is relatively small. Further, we
investigate the ratio consistency of our proposed variance estimator under
specific scenarios. In particular, we develop a new "double U-statistic"
formulation to analyze the Hoeffding decomposition of the estimator's variance.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:241:" <a href="http://arxiv.org/find/stat/1/au:+Xu_T/0/1/0/all/0/1">Tianning Xu</a>, <a href="http://arxiv.org/find/stat/1/au:+Zhu_R/0/1/0/all/0/1">Ruoqing Zhu</a>, <a href="http://arxiv.org/find/stat/1/au:+Shao_X/0/1/0/all/0/1">Xiaofeng Shao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:371;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2202.09028";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:104:"On the Implicit Bias Towards Minimal Depth of Deep Neural Networks. (arXiv:2202.09028v6 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2202.09028";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1057:"We study the implicit bias of stochastic gradient descent to favor low-depth
solutions when training deep neural networks. Recent results in the literature
suggest that penultimate layer representations learned by a classifier over
multiple classes exhibit a clustering property, called neural collapse. First,
we empirically show that neural collapse generally strengthens when increasing
the number of layers. In addition, we demonstrate that neural collapse extends
beyond the penultimate layer and emerges in intermediate layers as well, making
the higher layers essentially redundant. We characterize a notion of effective
depth which measures the minimal layer that enjoys neural collapse. In this
regard, we hypothesize and empirically show that gradient descent implicitly
selects neural networks of small effective depths. Finally, we theoretically
and empirically show that the effective depth of a trained neural network
monotonically increases when training with extended portions of random labels
and connecting it with generalization.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:167:" <a href="http://arxiv.org/find/cs/1/au:+Galanti_T/0/1/0/all/0/1">Tomer Galanti</a>, <a href="http://arxiv.org/find/cs/1/au:+Galanti_L/0/1/0/all/0/1">Liane Galanti</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:372;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2202.09637";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:121:"Decision Problems in a Logic for Reasoning about Reconfigurable Distributed Systems. (arXiv:2202.09637v2 [cs.LO] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2202.09637";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:788:"We consider a logic used to describe sets of configurations of distributed
systems, whose network topologies can be changed at runtime, by reconfiguration
programs. The logic uses inductive definitions to describe networks with an
unbounded number of components and interactions, written using a multiplicative
conjunction, reminiscent of Bunched Implications and Separation Logic. We study
the complexity of the satisfiability and entailment problems for the
configuration logic under consideration. Additionally, we consider robustness
properties, such as tightness (are all interactions entirely connected to
components?) and degree boundedness (is every component involved in a bounded
number of interactions?), the latter being an ingredient for decidability of
entailments.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:239:" <a href="http://arxiv.org/find/cs/1/au:+Bozga_M/0/1/0/all/0/1">Marius Bozga</a>, <a href="http://arxiv.org/find/cs/1/au:+Bueri_L/0/1/0/all/0/1">Lucas Bueri</a>, <a href="http://arxiv.org/find/cs/1/au:+Iosif_R/0/1/0/all/0/1">Radu Iosif</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:373;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2202.09971";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:83:"Deep Feature based Cross-slide Registration. (arXiv:2202.09971v5 [eess.IV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2202.09971";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1704:"Cross-slide image analysis provides additional information by analysing the
expression of different biomarkers as compared to a single slide analysis.
These biomarker stained slides are analysed side by side, revealing unknown
relations between them. During the slide preparation, a tissue section may be
placed at an arbitrary orientation as compared to other sections of the same
tissue block. The problem is compounded by the fact that tissue contents are
likely to change from one section to the next and there may be unique artefacts
on some of the slides. This makes registration of each section to a reference
section of the same tissue block an important pre-requisite task before any
cross-slide analysis. We propose a deep feature based registration (DFBR)
method which utilises data-driven features to estimate the rigid
transformation. We adopted a multi-stage strategy for improving the quality of
registration. We also developed a visualisation tool to view registered pairs
of WSIs at different magnifications. With the help of this tool, one can apply
a transformation on the fly without the need to generate transformed source WSI
in a pyramidal form. We compared the performance of data-driven features with
that of hand-crafted features on the COMET dataset. Our approach can align the
images with low registration errors. Generally, the success of non-rigid
registration is dependent on the quality of rigid registration. To evaluate the
efficacy of the DFBR method, the first two steps of the ANHIR winner's
framework are replaced with our DFBR to register challenge provided image
pairs. The modified framework produces comparable results to that of challenge
winning team.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:418:" <a href="http://arxiv.org/find/eess/1/au:+Awan_R/0/1/0/all/0/1">Ruqayya Awan</a>, <a href="http://arxiv.org/find/eess/1/au:+Raza_S/0/1/0/all/0/1">Shan E Ahmed Raza</a>, <a href="http://arxiv.org/find/eess/1/au:+Lotz_J/0/1/0/all/0/1">Johannes Lotz</a>, <a href="http://arxiv.org/find/eess/1/au:+Weiss_N/0/1/0/all/0/1">Nick Weiss</a>, <a href="http://arxiv.org/find/eess/1/au:+Rajpoot_N/0/1/0/all/0/1">Nasir Rajpoot</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:374;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2202.11149";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:149:"Incorporating social norms into a configurable agent-based model of the decision to perform commuting behaviour. (arXiv:2202.11149v2 [cs.MA] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2202.11149";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1942:"Introduction: Active commuting has been recommended as a method to increase
population physical activity, but evidence is mixed. Social norms related to
travel behaviour may influence the uptake of active commuting interventions but
are rarely considered in the design and evaluation of interventions.

Methods: We developed an agent-based model that incorporates social norms
related to travel behaviour and demonstrate the utility of this through
implementing car-free Wednesdays. A synthetic population of Waltham Forest,
London, UK was generated using a microsimulation approach with data from the UK
Census 2011 and UK HLS datasets. An agent-based model was created using this
synthetic population which modelled how the actions of peers and neighbours,
subculture, habit, weather, bicycle ownership, car ownership, environmental
supportiveness, and congestion affect the decision to travel between four
modes: walking, cycling, driving, and taking public transport.

Results: In the control scenario, the odds of active travel were plausible at
0.091 (89% HPDI: [0.091, 0.091]). Compared to the control scenario, the odds of
active travel were increased by 70.3% (89% HPDI: [70.3%, 70.3%]), in the
intervention scenario, on non-car-free days; the effect of the intervention is
sustained to non-car-free days.

Discussion: While these results demonstrate the utility of our agent-based
model, rather than aim to make accurate predictions, they do suggest that by
there being a 'nudge' of car-free days, there may be a sustained change in
active commuting behaviour. The model is a useful tool for investigating the
effect of how social networks and social norms influence the effectiveness of
various interventions. If configured using real-world built environment data,
it may be useful for investigating how social norms interact with the built
environment to cause the emergence of commuting conventions.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:410:" <a href="http://arxiv.org/find/cs/1/au:+Greener_R/0/1/0/all/0/1">Robert Greener</a>, <a href="http://arxiv.org/find/cs/1/au:+Lewis_D/0/1/0/all/0/1">Daniel Lewis</a>, <a href="http://arxiv.org/find/cs/1/au:+Reades_J/0/1/0/all/0/1">Jon Reades</a>, <a href="http://arxiv.org/find/cs/1/au:+Miles_S/0/1/0/all/0/1">Simon Miles</a>, <a href="http://arxiv.org/find/cs/1/au:+Cummins_S/0/1/0/all/0/1">Steven Cummins</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:375;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2202.11902";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:83:"A PTAS for Packing Hypercubes into a Knapsack. (arXiv:2202.11902v2 [cs.DS] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2202.11902";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1447:"We study the d-dimensional hypercube knapsack problem where we are given a
set of d-dimensional hypercubes with associated profits, and a knapsack which
is a unit d-dimensional hypercube. The goal is to find an axis-aligned
non-overlapping packing of a subset of hypercubes such that the profit of the
packed hypercubes is maximized. For this problem, Harren (ICALP'06) gave an
algorithm with an approximation ratio of (1+1/2^d+epsilon). For d=2, Jansen and
Solis-Oba (IPCO'08) showed that the problem admits a polynomial-time
approximation scheme (PTAS); Heydrich and Wiese (SODA'17) further improved the
running time and gave an efficient polynomial-time approximation scheme
(EPTAS). Both the results use structural properties of 2-D packing, which do
not generalize to higher dimensions. For d&gt;2, it remains open to obtain a PTAS,
and in fact, there has been no improvement since Harren's result.

We settle the problem by providing a PTAS. Our main technical contribution is
a structural lemma which shows that any packing of hypercubes can be converted
into another structured packing such that a high profitable subset of
hypercubes is packed into a constant number of special hypercuboids, called
V-Boxes and N-Boxes. As a side result, we give an almost optimal algorithm for
a variant of the strip packing problem in higher dimensions. This might have
applications for other multidimensional geometric packing problems.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:331:" <a href="http://arxiv.org/find/cs/1/au:+Jansen_K/0/1/0/all/0/1">Klaus Jansen</a>, <a href="http://arxiv.org/find/cs/1/au:+Khan_A/0/1/0/all/0/1">Arindam Khan</a>, <a href="http://arxiv.org/find/cs/1/au:+Lira_M/0/1/0/all/0/1">Marvin Lira</a>, <a href="http://arxiv.org/find/cs/1/au:+Sreenivas_K/0/1/0/all/0/1">K. V. N. Sreenivas</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:376;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2202.13898";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:110:"DistAD: Software Anomaly Detection Based on Execution Trace Distribution. (arXiv:2202.13898v2 [cs.SE] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2202.13898";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1647:"Modern software systems have become increasingly complex, which makes them
difficult to test and validate. Detecting software partial anomalies in complex
systems at runtime can assist with handling unintended software behaviors,
avoiding catastrophic software failures and improving software runtime
availability. These detection techniques aim to identify the manifestation of
faults (anomalies) before they ultimately lead to unavoidable failures, thus,
supporting the following runtime fault-tolerant techniques. In this work, we
propose a novel anomaly detection method named DistAD, which is based on the
distribution of software runtime dynamic execution traces. Unlike other
existing works using key performance indicators, the execution trace is
collected during runtime via intrusive instrumentation. Instrumentation are
controlled following a sampling mechanism to avoid excessive overheads.
Bi-directional Long Short-Term Memory (Bi-LSTM), an architecture of Recurrent
Neural Network (RNN) is used to achieve the anomaly detection. The whole
framework is constructed under a One-Class Neural Network (OCNN) learning mode
which can help eliminate the limits of lacking for enough labeled samples and
the data imbalance issues. A series of controlled experiments are conducted on
a widely used database system named Cassandra to prove the validity and
feasibility of the proposed method. Overheads brought about by the intrusive
probing are also evaluated. The results show that DistAD can achieve more than
70% accuracy and 90% recall (in normal states) with no more than 2 times
overheads compared with unmonitored executions.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:385:" <a href="http://arxiv.org/find/cs/1/au:+Kong_S/0/1/0/all/0/1">Shiyi Kong</a>, <a href="http://arxiv.org/find/cs/1/au:+Ai_J/0/1/0/all/0/1">Jun Ai</a>, <a href="http://arxiv.org/find/cs/1/au:+Lu_M/0/1/0/all/0/1">Minyan Lu</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_S/0/1/0/all/0/1">Shuguang Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wong_W/0/1/0/all/0/1">W. Eric Wong</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:377;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2203.00449";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Deep Learning based Prediction of MSI using MMR Markers in Colorectal Cancer. (arXiv:2203.00449v3 [q-bio.QM] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2203.00449";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1728:"The accurate diagnosis and molecular profiling of colorectal cancers are
critical for planning the best treatment options for patients. Microsatellite
instability (MSI) or mismatch repair (MMR) status plays a vital role in
appropriate treatment selection, has prognostic implications and is used to
investigate the possibility of patients having underlying genetic disorders
(Lynch syndrome). NICE recommends that all CRC patients should be offered
MMR/MSI testing. Immunohistochemistry is commonly used to assess MMR status
with subsequent molecular testing performed as required. This incurs
significant extra costs and requires additional resources. The introduction of
automated methods that can predict MSI or MMR status from a target image could
substantially reduce the cost associated with MMR testing. Unlike previous
studies on MSI prediction involving training a CNN using coarse labels (MSI vs
Microsatellite Stable (MSS)), we have utilised fine-grain MMR labels for
training purposes. In this paper, we present our work on predicting MSI status
in a two-stage process using a single target slide either stained with CK8/18
or H&amp;E. First, we trained a multi-headed convolutional neural network model
where each head was responsible for predicting one of the MMR protein
expressions. To this end, we performed the registration of MMR stained slides
to the target slide as a pre-processing step. In the second stage, statistical
features computed from the MMR prediction maps were used for the final MSI
prediction. Our results demonstrated that MSI classification can be improved by
incorporating fine-grained MMR labels in comparison to the previous approaches
in which only coarse labels were utilised.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:684:" <a href="http://arxiv.org/find/q-bio/1/au:+Awan_R/0/1/0/all/0/1">Ruqayya Awan</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Nimir_M/0/1/0/all/0/1">Mohammed Nimir</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Raza_S/0/1/0/all/0/1">Shan E Ahmed Raza</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Bilal_M/0/1/0/all/0/1">Mohsin Bilal</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Lotz_J/0/1/0/all/0/1">Johannes Lotz</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Snead_D/0/1/0/all/0/1">David Snead</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Robinson_A/0/1/0/all/0/1">Andrew Robinson</a>, <a href="http://arxiv.org/find/q-bio/1/au:+Rajpoot_N/0/1/0/all/0/1">Nasir Rajpoot</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:378;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2203.01360";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:124:"Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations. (arXiv:2203.01360v2 [math.NA] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2203.01360";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1591:"Deep neural networks have been shown to provide accurate function
approximations in high dimensions. However, fitting network parameters requires
training data that may not be available beforehand, which is particularly
challenging in science and engineering applications where often it is even
unclear how to collect new informative training data in the first place. This
work proposes Neural Galerkin schemes based on deep learning that generate
training data samples with active learning for numerically solving
high-dimensional partial differential equations. Neural Galerkin schemes train
networks by minimizing the residual sequentially over time, which enables
adaptively collecting new training data in a self-informed manner that is
guided by the dynamics described by the partial differential equations, which
is in stark contrast to many other machine learning methods that aim to fit
network parameters globally in time without taking into account training data
acquisition. Our finding is that the active form of gathering training data of
the proposed Neural Galerkin schemes is key for numerically realizing the
expressive power of networks in high dimensions. Numerical experiments
demonstrate that Neural Galerkin schemes have the potential to enable
simulating phenomena and processes with many variables for which traditional
and other deep-learning-based solvers fail, especially when features of the
solutions evolve locally such as in high-dimensional wave propagation problems
and interacting particle systems described by Fokker-Planck and kinetic
equations.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:278:" <a href="http://arxiv.org/find/math/1/au:+Bruna_J/0/1/0/all/0/1">Joan Bruna</a>, <a href="http://arxiv.org/find/math/1/au:+Peherstorfer_B/0/1/0/all/0/1">Benjamin Peherstorfer</a>, <a href="http://arxiv.org/find/math/1/au:+Vanden_Eijnden_E/0/1/0/all/0/1">Eric Vanden-Eijnden</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:379;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2203.02077";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:110:"User-Level Membership Inference Attack against Metric Embedding Learning. (arXiv:2203.02077v2 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2203.02077";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:946:"Membership inference (MI) determines if a sample was part of a victim model
training set. Recent development of MI attacks focus on record-level membership
inference which limits their application in many real-world scenarios. For
example, in the person re-identification task, the attacker (or investigator)
is interested in determining if a user's images have been used during training
or not. However, the exact training images might not be accessible to the
attacker. In this paper, we develop a user-level MI attack where the goal is to
find if any sample from the target user has been used during training even when
no exact training sample is available to the attacker. We focus on metric
embedding learning due to its dominance in person re-identification, where
user-level MI attack is more sensible. We conduct an extensive evaluation on
several datasets and show that our approach achieves high accuracy on
user-level MI task.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:232:" <a href="http://arxiv.org/find/cs/1/au:+Li_G/0/1/0/all/0/1">Guoyao Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Rezaei_S/0/1/0/all/0/1">Shahbaz Rezaei</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_X/0/1/0/all/0/1">Xin Liu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:380;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2203.03634";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:114:"BPM-Net: non-contact blood pressure measuring network based on face videos. (arXiv:2203.03634v2 [eess.IV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2203.03634";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1309:"Blood pressure indicates cardiac function and peripheral vascular resistance
and is critical for disease diagnosis. Traditionally, blood pressure data are
mainly acquired through contact sensors, which require high maintenance and may
be inconvenient and unfriendly to some people (e.g., burn patients). In this
paper, we proposed an efficient non-contact blood pressure measurement network
based on face videos. First, an innovative oversampling training strategy is
proposed to handle the unbalanced data distribution. The input video sequences
are first normalized and converted to our proposed YUVT color space. Then the
spatio-temporal slicer encodes it into a multi-domain spatio-temporal mapping.
Finally, the feature extractor composed of a series backbone network and LSTM
fits the high-dimensional feature, which is fed into blood pressure classifier
to locates the blood pressure interval. The blood pressure calculator combines
the results of the feature extractor and the blood pressure classifier to
output the final blood pressure value. We tested BPM-Net on MMSE-HR dataset,
the MAE of systolic blood pressure reached 12.35 mmHg and that of diastolic
blood pressure reached 9.5 mmHg. Experimental results on MMSE-HR show that the
network outperforms existing state-of-the-art methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:324:" <a href="http://arxiv.org/find/eess/1/au:+Zhuang_J/0/1/0/all/0/1">Jialiang Zhuang</a>, <a href="http://arxiv.org/find/eess/1/au:+Li_B/0/1/0/all/0/1">Bin Li</a>, <a href="http://arxiv.org/find/eess/1/au:+Zhang_Y/0/1/0/all/0/1">Yun Zhang</a>, <a href="http://arxiv.org/find/eess/1/au:+Zheng_X/0/1/0/all/0/1">Xiujuan Zheng</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:381;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2203.04180";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:154:"Tuning-free multi-coil compressed sensing MRI with Parallel Variable Density Approximate Message Passing (P-VDAMP). (arXiv:2203.04180v2 [math.NA] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2203.04180";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1658:"Magnetic Resonance Imaging (MRI) has excellent soft tissue contrast but is
hindered by an inherently slow data acquisition process. Compressed sensing,
which reconstructs sparse signals from incoherently sampled data, has been
widely applied to accelerate MRI acquisitions. Compressed sensing MRI requires
one or more model parameters to be tuned, which is usually done by hand, giving
sub-optimal tuning in general. To address this issue, we build on previous work
by the authors on the single-coil Variable Density Approximate Message Passing
(VDAMP) algorithm, extending the framework to multiple receiver coils to
propose the Parallel VDAMP (P-VDAMP) algorithm. For Bernoulli random variable
density sampling, P-VDAMP obeys a "state evolution", where the intermediate
per-iteration image estimate is distributed according to the ground truth
corrupted by a zero-mean Gaussian vector with approximately known covariance.
To our knowledge, P-VDAMP is the first algorithm for multi-coil MRI data that
obeys a state evolution with accurately tracked parameters. We leverage state
evolution to automatically tune sparse parameters on-the-fly with Stein's
Unbiased Risk Estimate (SURE). P-VDAMP is evaluated on brain, knee and
angiogram datasets and compared with four variants of the Fast Iterative
Shrinkage-Thresholding algorithm (FISTA), including two tuning-free variants
from the literature. The proposed method is found to have a similar
reconstruction quality and time to convergence as FISTA with an optimally tuned
sparse weighting and offers substantial robustness and reconstruction quality
improvements over competing tuning-free methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:419:" <a href="http://arxiv.org/find/math/1/au:+Millard_C/0/1/0/all/0/1">Charles Millard</a>, <a href="http://arxiv.org/find/math/1/au:+Chiew_M/0/1/0/all/0/1">Mark Chiew</a>, <a href="http://arxiv.org/find/math/1/au:+Tanner_J/0/1/0/all/0/1">Jared Tanner</a>, <a href="http://arxiv.org/find/math/1/au:+Hess_A/0/1/0/all/0/1">Aaron T. Hess</a>, <a href="http://arxiv.org/find/math/1/au:+Mailhe_B/0/1/0/all/0/1">Boris Mailhe</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:382;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2203.05400";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:126:"Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation. (arXiv:2203.05400v2 [math.ST] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2203.05400";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:968:"It is common to model a deterministic response function, such as the output
of a computer experiment, as a Gaussian process with a Mat\'ern covariance
kernel. The smoothness parameter of a Mat\'ern kernel determines many important
properties of the model in the large data limit, such as the rate of
convergence of the conditional mean to the response function. We prove that the
maximum likelihood and cross-validation estimates of the smoothness parameter
cannot asymptotically undersmooth the truth when the data are obtained on a
fixed bounded subset of $\mathbb{R}^d$. That is, if the data-generating
response function has Sobolev smoothness $\nu_0 + d/2$, then the smoothness
parameter estimates cannot remain below $\nu_0$ as more data are obtained. The
results are based on approximation theory in Sobolev spaces and a general
theorem, proved using reproducing kernel Hilbert space techniques, about sets
of values the parameter estimates cannot take.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:86:" <a href="http://arxiv.org/find/math/1/au:+Karvonen_T/0/1/0/all/0/1">Toni Karvonen</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:383;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2203.08073";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:84:"Can A Neural Network Hear the Shape of A Drum?. (arXiv:2203.08073v2 [cs.SD] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2203.08073";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:757:"We have developed a deep neural network that reconstructs the shape of a
polygonal domain given the first hundred of its Laplacian eigenvalues. Having
an encoder-decoder structure, the network maps input spectra to a latent space
and then predicts the discretized image of the domain on a square grid. We
tested this network on randomly generated pentagons. The prediction accuracy is
high and the predictions obey the Laplacian scaling rule. The network recovers
the continuous rotational degree of freedom beyond the symmetry of the grid.
The variation of the latent variables under the scaling transformation shows
they are strongly correlated with Weyl' s parameters (area, perimeter, and a
certain function of the angles) of the test polygons.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:164:" <a href="http://arxiv.org/find/cs/1/au:+Zhao_Y/0/1/0/all/0/1">Yueqi Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Fogler_M/0/1/0/all/0/1">Michael M. Fogler</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:384;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2203.10077";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:122:"Tight Vector Bin Packing with Few Small Items via Fast Exact Matching in Multigraphs. (arXiv:2203.10077v2 [cs.DS] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2203.10077";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1265:"We solve the Bin Packing problem in $O^*(2^k)$ time, where $k$ is the number
of items less or equal to one third of the bin capacity. This parameter
measures the distance from the polynomially solvable case of only large (i.e.,
greater than one third) items. Our algorithm is actually designed to work for a
more general Vector Bin Packing problem, in which items are multidimensional
vectors. We improve over the previous fastest $O^*(k! \cdot 4^k)$ time
algorithm.

Our algorithm works by reducing the problem to finding an exact weight
perfect matching in a (multi-)graph with $O^*(2^k)$ edges, whose weights are
integers of the order of $O^*(2^k)$. To solve the matching problem in the
desired time, we give a variant of the classic Mulmuley-Vazirani-Vazirani
algorithm with only a linear dependence on the edge weights and the number of
edges, which may be of independent interest.

Moreover, we give a tight lower bound, under the Strong Exponential Time
Hypothesis (SETH), showing that the constant $2$ in the base of the exponent
cannot be further improved for Vector Bin Packing.

Our techniques also lead to improved algorithms for Vector Multiple Knapsack,
Vector Bin Covering, and Perfect Matching with Hitting Constraints.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:269:" <a href="http://arxiv.org/find/cs/1/au:+Lassota_A/0/1/0/all/0/1">Alexandra Lassota</a>, <a href="http://arxiv.org/find/cs/1/au:+Lukasiewicz_A/0/1/0/all/0/1">Aleksander &#x141;ukasiewicz</a>, <a href="http://arxiv.org/find/cs/1/au:+Polak_A/0/1/0/all/0/1">Adam Polak</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:385;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2203.10992";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:109:"Spoofing-Aware Speaker Verification with Unsupervised Domain Adaptation. (arXiv:2203.10992v2 [cs.SD] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2203.10992";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:967:"In this paper, we initiate the concern of enhancing the spoofing robustness
of the automatic speaker verification (ASV) system, without the primary
presence of a separate countermeasure module. We start from the standard ASV
framework of the ASVspoof 2019 baseline and approach the problem from the
back-end classifier based on probabilistic linear discriminant analysis. We
employ three unsupervised domain adaptation techniques to optimize the back-end
using the audio data in the training partition of the ASVspoof 2019 dataset. We
demonstrate notable improvements on both logical and physical access scenarios,
especially on the latter where the system is attacked by replayed audios, with
a maximum of 36.1% and 5.3% relative improvement on bonafide and spoofed cases,
respectively. We perform additional studies such as per-attack breakdown
analysis, data composition, and integration with a countermeasure system at
score-level with Gaussian back-end.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:249:" <a href="http://arxiv.org/find/cs/1/au:+Liu_X/0/1/0/all/0/1">Xuechen Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Sahidullah_M/0/1/0/all/0/1">Md Sahidullah</a>, <a href="http://arxiv.org/find/cs/1/au:+Kinnunen_T/0/1/0/all/0/1">Tomi Kinnunen</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:386;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2203.13209";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:72:"Direct parsing to sentiment graphs. (arXiv:2203.13209v2 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2203.13209";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:298:"This paper demonstrates how a graph-based semantic parser can be applied to
the task of structured sentiment analysis, directly predicting sentiment graphs
from text. We advance the state of the art on 4 out of 5 standard benchmark
sets. We release the source code, models and predictions.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:498:" <a href="http://arxiv.org/find/cs/1/au:+Samuel_D/0/1/0/all/0/1">David Samuel</a>, <a href="http://arxiv.org/find/cs/1/au:+Barnes_J/0/1/0/all/0/1">Jeremy Barnes</a>, <a href="http://arxiv.org/find/cs/1/au:+Kurtz_R/0/1/0/all/0/1">Robin Kurtz</a>, <a href="http://arxiv.org/find/cs/1/au:+Oepen_S/0/1/0/all/0/1">Stephan Oepen</a>, <a href="http://arxiv.org/find/cs/1/au:+Ovrelid_L/0/1/0/all/0/1">Lilja &#xd8;vrelid</a>, <a href="http://arxiv.org/find/cs/1/au:+Velldal_E/0/1/0/all/0/1">Erik Velldal</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:387;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2203.13250";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:66:"Global Tracking Transformers. (arXiv:2203.13250v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2203.13250";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1180:"We present a novel transformer-based architecture for global multi-object
tracking. Our network takes a short sequence of frames as input and produces
global trajectories for all objects. The core component is a global tracking
transformer that operates on objects from all frames in the sequence. The
transformer encodes object features from all frames, and uses trajectory
queries to group them into trajectories. The trajectory queries are object
features from a single frame and naturally produce unique trajectories. Our
global tracking transformer does not require intermediate pairwise grouping or
combinatorial association, and can be jointly trained with an object detector.
It achieves competitive performance on the popular MOT17 benchmark, with 75.3
MOTA and 59.1 HOTA. More importantly, our framework seamlessly integrates into
state-of-the-art large-vocabulary detectors to track any objects. Experiments
on the challenging TAO dataset show that our framework consistently improves
upon baselines that are based on pairwise association, outperforming published
works by a significant 7.7 tracking mAP. Code is available at
https://github.com/xingyizhou/GTR.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:342:" <a href="http://arxiv.org/find/cs/1/au:+Zhou_X/0/1/0/all/0/1">Xingyi Zhou</a>, <a href="http://arxiv.org/find/cs/1/au:+Yin_T/0/1/0/all/0/1">Tianwei Yin</a>, <a href="http://arxiv.org/find/cs/1/au:+Koltun_V/0/1/0/all/0/1">Vladlen Koltun</a>, <a href="http://arxiv.org/find/cs/1/au:+Krahenbuhl_P/0/1/0/all/0/1">Philipp Kr&#xe4;henb&#xfc;hl</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:388;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2203.14003";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:135:"Optical Wireless Transmissions over Multi-layer Underwater Channels with Generalized Gamma Fading. (arXiv:2203.14003v2 [cs.IT] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2203.14003";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1395:"Underwater optical communication (UWOC) is a potential solution for broadband
connectivity in oceans and seas for underwater applications providing high data
rate transmission with low latency and high reliability. Recent measurement
campaigns suggest generalized Gamma distribution as a viable model for oceanic
turbulence. In this paper, we analyze the performance of a UWOC system by
modeling the vertical underwater link as a multi-layer cascaded channel, each
distributed according to independent but not identically distributed (i.ni.d.)
generalized Gamma random variables and considering the zero bore-sight model
for pointing errors. We derive analytical expressions for probability density
function (PDF) and cumulative distribution function (CDF) for the
signal-to-noise ratios (SNR) of the combined channel and develop performance
metrics of the considered UWOC system using outage probability, average bit
error rate (BER), and ergodic capacity. We also derive the asymptotic
expressions for outage probability and average BER to determine the diversity
order of the proposed system for a better insight into the system performance.
We use Monte-Carlo simulation results to validate our exact and asymptotic
expressions and demonstrate the performance of the considered underwater UWOC
system using measurement-based parametric data available for turbulent oceanic
channels.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:249:" <a href="http://arxiv.org/find/cs/1/au:+Das_S/0/1/0/all/0/1">Suhrid Das</a>, <a href="http://arxiv.org/find/cs/1/au:+Rahman_Z/0/1/0/all/0/1">Ziyaur Rahman</a>, <a href="http://arxiv.org/find/cs/1/au:+Zafaruddin_S/0/1/0/all/0/1">S. M. Zafaruddin</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:389;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2203.16401";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"Recognition of polar lows in Sentinel-1 SAR images with deep learning. (arXiv:2203.16401v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2203.16401";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1474:"In this paper, we explore the possibility of detecting polar lows in C-band
SAR images by means of deep learning. Specifically, we introduce a novel
dataset consisting of Sentinel-1 images labeled as positive; representing a
maritime mesocyclone, or negative; representing a normal sea state. The dataset
is constructed using the ERA5 dataset as baseline and it consists of 2004
annotated images. To our knowledge, this is the first dataset of its kind to be
publicly released. The dataset is used to train a deep learning model to
classify the labeled images. Evaluated on an independent test set, the model
yields an F-1 score of 0.95, indicating that polar lows can be consistently
detected from SAR images. Interpretability techniques applied to the deep
learning model reveal that atmospheric fronts and cyclonic eyes are key
features in the classification. Moreover, experimental results show that the
model is accurate even if: (i) such features are significantly cropped due to
the limited swath width of the SAR, (ii) the features are partly covered by sea
ice and (iii) land is covering significant parts of the images. By evaluating
the model performance on multiple input image resolutions (pixel sizes of 500m,
1km and 2km), it is found that higher resolution yield the best performance.
This emphasises the potential of using high resolution sensors like SAR for
detecting polar lows, as compared to conventionally used sensors such as
scatterometers.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:171:" <a href="http://arxiv.org/find/cs/1/au:+Grahn_J/0/1/0/all/0/1">Jakob Grahn</a>, <a href="http://arxiv.org/find/cs/1/au:+Bianchi_F/0/1/0/all/0/1">Filippo Maria Bianchi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:390;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.00716";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:140:"CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos. (arXiv:2204.00716v2 [cs.IR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.00716";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1822:"Current dense retrievers are not robust to out-of-domain and outlier queries,
i.e. their effectiveness on these queries is much poorer than what one would
expect. In this paper, we consider a specific instance of such queries: queries
that contain typos. We show that a small character level perturbation in
queries (as caused by typos) highly impacts the effectiveness of dense
retrievers. We then demonstrate that the root cause of this resides in the
input tokenization strategy employed by BERT. In BERT, tokenization is
performed using the BERT's WordPiece tokenizer and we show that a token with a
typo will significantly change the token distributions obtained after
tokenization. This distribution change translates to changes in the input
embeddings passed to the BERT-based query encoder of dense retrievers. We then
turn our attention to devising dense retriever methods that are robust to such
queries with typos, while still being as performant as previous methods on
queries without typos. For this, we use CharacterBERT as the backbone encoder
and an efficient yet effective training method, called Self-Teaching (ST), that
distills knowledge from queries without typos into the queries with typos.
Experimental results show that CharacterBERT in combination with ST achieves
significantly higher effectiveness on queries with typos compared to previous
methods. Along with these results and the open-sourced implementation of the
methods, we also provide a new passage retrieval dataset consisting of
real-world queries with typos and associated relevance assessments on the MS
MARCO corpus, thus supporting the research community in the investigation of
effective and robust dense retrievers. Code, experimental results and dataset
are made available at https://github.com/ielab/CharacterBERT-DR.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:166:" <a href="http://arxiv.org/find/cs/1/au:+Zhuang_S/0/1/0/all/0/1">Shengyao Zhuang</a>, <a href="http://arxiv.org/find/cs/1/au:+Zuccon_G/0/1/0/all/0/1">Guido Zuccon</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:391;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.01172";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:111:"PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models. (arXiv:2204.01172v2 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.01172";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1281:"Current methods for few-shot fine-tuning of pretrained masked language models
(PLMs) require carefully engineered prompts and verbalizers for each new task
to convert examples into a cloze-format that the PLM can score. In this work,
we propose PERFECT, a simple and efficient method for few-shot fine-tuning of
PLMs without relying on any such handcrafting, which is highly effective given
as few as 32 data points. PERFECT makes two key design choices: First, we show
that manually engineered task prompts can be replaced with task-specific
adapters that enable sample-efficient fine-tuning and reduce memory and storage
costs by roughly factors of 5 and 100, respectively. Second, instead of using
handcrafted verbalizers, we learn new multi-token label embeddings during
fine-tuning, which are not tied to the model vocabulary and which allow us to
avoid complex auto-regressive decoding. These embeddings are not only learnable
from limited data but also enable nearly 100x faster training and inference.
Experiments on a wide range of few-shot NLP tasks demonstrate that PERFECT,
while being simple and efficient, also outperforms existing state-of-the-art
few-shot learning methods. Our code is publicly available at
https://github.com/facebookresearch/perfect.git.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:614:" <a href="http://arxiv.org/find/cs/1/au:+Mahabadi_R/0/1/0/all/0/1">Rabeeh Karimi Mahabadi</a>, <a href="http://arxiv.org/find/cs/1/au:+Zettlemoyer_L/0/1/0/all/0/1">Luke Zettlemoyer</a>, <a href="http://arxiv.org/find/cs/1/au:+Henderson_J/0/1/0/all/0/1">James Henderson</a>, <a href="http://arxiv.org/find/cs/1/au:+Saeidi_M/0/1/0/all/0/1">Marzieh Saeidi</a>, <a href="http://arxiv.org/find/cs/1/au:+Mathias_L/0/1/0/all/0/1">Lambert Mathias</a>, <a href="http://arxiv.org/find/cs/1/au:+Stoyanov_V/0/1/0/all/0/1">Veselin Stoyanov</a>, <a href="http://arxiv.org/find/cs/1/au:+Yazdani_M/0/1/0/all/0/1">Majid Yazdani</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:392;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.02255";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:116:"Sufficient Reasons for A Zero-Day Intrusion Detection Artificial Immune System. (arXiv:2204.02255v2 [cs.AI] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.02255";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:941:"The Internet is the most complex machine humankind has ever built, and how to
defense it from intrusions is even more complex. With the ever increasing of
new intrusions, intrusion detection task rely on Artificial Intelligence more
and more. Interpretability and transparency of the machine learning model is
the foundation of trust in AI-driven intrusion detection results. Current
interpretation Artificial Intelligence technologies in intrusion detection are
heuristic, which is neither accurate nor sufficient. This paper proposed a
rigorous interpretable Artificial Intelligence driven intrusion detection
approach, based on artificial immune system. Details of rigorous interpretation
calculation process for a decision tree model is presented. Prime implicant
explanation for benign traffic flow are given in detail as rule for negative
selection of the cyber immune system. Experiments are carried out in real-life
traffic.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:475:" <a href="http://arxiv.org/find/cs/1/au:+Zhou_Q/0/1/0/all/0/1">Qianru Zhou</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_R/0/1/0/all/0/1">Rongzhen Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_L/0/1/0/all/0/1">Lei Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Nallanathan_A/0/1/0/all/0/1">Arumugam Nallanathan</a>, <a href="http://arxiv.org/find/cs/1/au:+Yanga_J/0/1/0/all/0/1">Jian Yanga</a>, <a href="http://arxiv.org/find/cs/1/au:+Fu_A/0/1/0/all/0/1">Anmin Fu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:393;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.06173";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"Task-Driven Data Augmentation for Vision-Based Robotic Control. (arXiv:2204.06173v2 [cs.RO] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.06173";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1486:"Today's robots often interface data-driven perception and planning models
with classical model-based controllers. For example, drones often use computer
vision models to estimate navigation waypoints that are tracked by model
predictive control (MPC). Often, such learned perception/planning models
produce erroneous waypoint predictions on out-of-distribution (OoD) or even
adversarial visual inputs, which increase control cost. However, today's
methods to train robust perception models are largely task-agnostic - they
augment a dataset using random image transformations or adversarial examples
targeted at the vision model in isolation. As such, they often introduce pixel
perturbations that are ultimately benign for control, while missing those that
are most adversarial. In contrast to prior work that synthesizes adversarial
examples for single-step vision tasks, our key contribution is to efficiently
synthesize adversarial scenarios for multi-step, model-based control. To do so,
we leverage differentiable MPC methods to calculate the sensitivity of a
model-based controller to errors in state estimation, which in turn guides how
we synthesize adversarial inputs. We show that re-training vision models on
these adversarial datasets improves control performance on OoD test scenarios
by up to 28.2% compared to standard task-agnostic data augmentation. Our system
is tested on examples of robotic navigation and vision-based control of an
autonomous air vehicle.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:181:" <a href="http://arxiv.org/find/cs/1/au:+Agarwal_S/0/1/0/all/0/1">Shubhankar Agarwal</a>, <a href="http://arxiv.org/find/cs/1/au:+Chinchali_S/0/1/0/all/0/1">Sandeep P. Chinchali</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:394;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.06466";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:115:"Grand canonical ensembles of sparse networks and Bayesian inference. (arXiv:2204.06466v2 [cond-mat.dis-nn] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.06466";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1147:"Maximum entropy network ensembles have been very successful in modelling
sparse network topologies and in solving challenging inference problems.
However the sparse maximum entropy network models proposed so far have fixed
number of nodes and are typically not exchangeable. Here we consider
hierarchical models for exchangeable networks in the sparse limit, i.e. with
the total number of links scaling linearly with the total number of nodes. The
approach is grand canonical, i.e. the number of nodes of the network is not
fixed a priori: it is finite but can be arbitrarily large. In this way the
grand canonical network ensembles circumvent the difficulties in treating
infinite sparse exchangeable networks which according to the Aldous-Hoover
theorem must vanish. The approach can treat networks with given degree
distribution or networks with given distribution of latent variables. When only
a subgraph induced by a subset of nodes is known, this model allows a Bayesian
estimation of the network size and the degree sequence (or the sequence of
latent variables) of the entire network which can be used for network
reconstruction.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:94:" <a href="http://arxiv.org/find/cond-mat/1/au:+Bianconi_G/0/1/0/all/0/1">Ginestra Bianconi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:395;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.07341";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:86:"LaMemo: Language Modeling with Look-Ahead Memory. (arXiv:2204.07341v2 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.07341";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1091:"Although Transformers with fully connected self-attentions are powerful to
model long-term dependencies, they are struggling to scale to long texts with
thousands of words in language modeling. One of the solutions is to equip the
model with a recurrence memory. However, existing approaches directly reuse
hidden states from the previous segment that encodes contexts in a
uni-directional way. As a result, this prohibits the memory to dynamically
interact with the current context that provides up-to-date information for
token prediction. To remedy this issue, we propose Look-Ahead Memory (LaMemo)
that enhances the recurrence memory by incrementally attending to the
right-side tokens, and interpolating with the old memory states to maintain
long-term information in the history. LaMemo embraces bi-directional attention
and segment recurrence with an additional computation overhead only linearly
proportional to the memory length. Experiments on widely used language modeling
benchmarks demonstrate its superiority over the baselines equipped with
different types of memory.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:394:" <a href="http://arxiv.org/find/cs/1/au:+Ji_H/0/1/0/all/0/1">Haozhe Ji</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_R/0/1/0/all/0/1">Rongsheng Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Yang_Z/0/1/0/all/0/1">Zhenyu Yang</a>, <a href="http://arxiv.org/find/cs/1/au:+Hu_Z/0/1/0/all/0/1">Zhipeng Hu</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_M/0/1/0/all/0/1">Minlie Huang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:396;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.07352";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:182:"A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations. (arXiv:2204.07352v2 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.07352";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1339:"We propose a novel federated learning paradigm to model data variability
among heterogeneous clients in multi-centric studies. Our method is expressed
through a hierarchical Bayesian latent variable model, where client-specific
parameters are assumed to be realization from a global distribution at the
master level, which is in turn estimated to account for data bias and
variability across clients. We show that our framework can be effectively
optimized through expectation maximization (EM) over latent master's
distribution and clients' parameters. We also introduce formal differential
privacy (DP) guarantees compatibly with our EM optimization scheme. We tested
our method on the analysis of multi-modal medical imaging data and clinical
scores from distributed clinical datasets of patients affected by Alzheimer's
disease. We demonstrate that our method is robust when data is distributed
either in iid and non-iid manners, even when local parameters perturbation is
included to provide DP guarantees. Moreover, the variability of data, views and
centers can be quantified in an interpretable manner, while guaranteeing
high-quality data reconstruction as compared to state-of-the-art autoencoding
models and federated learning schemes. The code is available at
https://gitlab.inria.fr/epione/federated-multi-views-ppca.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:250:" <a href="http://arxiv.org/find/cs/1/au:+Balelli_I/0/1/0/all/0/1">Irene Balelli</a>, <a href="http://arxiv.org/find/cs/1/au:+Silva_S/0/1/0/all/0/1">Santiago Silva</a>, <a href="http://arxiv.org/find/cs/1/au:+Lorenzi_M/0/1/0/all/0/1">Marco Lorenzi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:397;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.07462";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:81:"Two new families of bivariate APN functions. (arXiv:2204.07462v2 [cs.IT] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.07462";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:642:"In this work, we present two new families of quadratic APN functions. The
first one (F1) is constructed via biprojective polynomials. This family
includes one of the two APN families introduced by G\"olo\v{g}lu in 2022. Then,
following a similar approach as in Li \emph{et al.} (2022), we give another
family (F2) obtained by adding certain terms to F1. As a byproduct, this second
family includes one of the two families introduced by Li \emph{et al.} (2022).
Moreover, we show that for $n=12$, from our constructions, we can obtain APN
functions that are CCZ-inequivalent to any other known APN function over
$\mathbb{F}_{2^{12}}$.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:244:" <a href="http://arxiv.org/find/cs/1/au:+Calderini_M/0/1/0/all/0/1">Marco Calderini</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_K/0/1/0/all/0/1">Kangquan Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Villa_I/0/1/0/all/0/1">Irene Villa</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:398;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.07570";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:128:"TreeStep: Tree Search for Vector Perturbation Precoding under per-Antenna Power Constraint. (arXiv:2204.07570v2 [cs.NI] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.07570";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:980:"Vector Perturbation Precoding (VPP) can speed up downlink data transmissions
in Large and Massive Multi-User MIMO systems but is known to be NP-hard. While
there are several algorithms in the literature for VPP under total power
constraint, they are not applicable for VPP under per-antenna power constraint.
This paper proposes a novel, parallel tree search algorithm for VPP under
per-antenna power constraint, called \emph{\textbf{TreeStep}}, to find good
quality solutions to the VPP problem with practical computational complexity.
We show that our method can provide huge performance gain over simple linear
precoding like Regularised Zero Forcing. We evaluate TreeStep for several large
MIMO~($16\times16$ and $24\times24$) and massive MIMO~($16\times32$ and
$24\times 48$) and demonstrate that TreeStep outperforms the popular
polynomial-time VPP algorithm, the Fixed Complexity Sphere Encoder, by
achieving the extremely low BER of $10^{-6}$ at a much lower SNR.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:173:" <a href="http://arxiv.org/find/cs/1/au:+Singh_A/0/1/0/all/0/1">Abhishek Kumar Singh</a>, <a href="http://arxiv.org/find/cs/1/au:+Jamieson_K/0/1/0/all/0/1">Kyle Jamieson</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:399;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.08479";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:125:"Inductive Biases for Object-Centric Representations in the Presence of Complex Textures. (arXiv:2204.08479v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.08479";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:756:"Understanding which inductive biases could be helpful for the unsupervised
learning of object-centric representations of natural scenes is challenging. We
use neural style transfer to generate datasets where objects have complex
textures while still retaining ground-truth annotations. We find that methods
that use a single module to reconstruct both the shape and visual appearance of
each object learn more useful representations and achieve better object
separation. In addition, we observe that adjusting the latent space size is not
sufficient to improve segmentation performance. Finally, the downstream
usefulness of the representations is significantly more strongly correlated
with segmentation quality than with reconstruction accuracy.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:246:" <a href="http://arxiv.org/find/cs/1/au:+Papa_S/0/1/0/all/0/1">Samuele Papa</a>, <a href="http://arxiv.org/find/cs/1/au:+Winther_O/0/1/0/all/0/1">Ole Winther</a>, <a href="http://arxiv.org/find/cs/1/au:+Dittadi_A/0/1/0/all/0/1">Andrea Dittadi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:400;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.08714";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:88:"NAFSSR: Stereo Image Super-Resolution Using NAFNet. (arXiv:2204.08714v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.08714";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1223:"Stereo image super-resolution aims at enhancing the quality of
super-resolution results by utilizing the complementary information provided by
binocular systems. To obtain reasonable performance, most methods focus on
finely designing modules, loss functions, and etc. to exploit information from
another viewpoint. This has the side effect of increasing system complexity,
making it difficult for researchers to evaluate new ideas and compare methods.
This paper inherits a strong and simple image restoration model, NAFNet, for
single-view feature extraction and extends it by adding cross attention modules
to fuse features between views to adapt to binocular scenarios. The proposed
baseline for stereo image super-resolution is noted as NAFSSR. Furthermore,
training/testing strategies are proposed to fully exploit the performance of
NAFSSR. Extensive experiments demonstrate the effectiveness of our method. In
particular, NAFSSR outperforms the state-of-the-art methods on the KITTI 2012,
KITTI 2015, Middlebury, and Flickr1024 datasets. With NAFSSR, we won 1st place
in the NTIRE 2022 Stereo Image Super-resolution Challenge. Codes and models
will be released at https://github.com/megvii-research/NAFNet.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:233:" <a href="http://arxiv.org/find/cs/1/au:+Chu_X/0/1/0/all/0/1">Xiaojie Chu</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_L/0/1/0/all/0/1">Liangyu Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Yu_W/0/1/0/all/0/1">Wenqing Yu</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:401;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.09007";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:93:"Opal: Multimodal Image Generation for News Illustration. (arXiv:2204.09007v2 [cs.HC] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.09007";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1035:"Multimodal AI advancements have presented people with powerful ways to create
images from text. Recent work has shown that text-to-image generations are able
to represent a broad range of subjects and artistic styles. However,
translating text prompts into visual messages is difficult. In this paper, we
address this challenge with Opal, a system that produces text-to-image
generations for editorial illustration. Given an article text, Opal guides
users through a structured search for visual concepts and provides pipelines
allowing users to illustrate based on an article's tone, subjects, and intended
illustration style. Our evaluation shows that Opal efficiently generates
diverse sets of editorial illustrations, graphic assets, and concept ideas.
Users with Opal were more efficient at generation and generated over two times
more usable results than users without. We conclude on a discussion of how
structured and rapid exploration can help users better understand the
capabilities of human AI co-creative systems.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:236:" <a href="http://arxiv.org/find/cs/1/au:+Liu_V/0/1/0/all/0/1">Vivian Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Qiao_H/0/1/0/all/0/1">Han Qiao</a>, <a href="http://arxiv.org/find/cs/1/au:+Chilton_L/0/1/0/all/0/1">Lydia Chilton</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:402;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.09110";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:123:"Councils in Action: Automating the Curation of Municipal Governance Data for Research. (arXiv:2204.09110v2 [cs.DL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.09110";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1007:"Large scale comparative research into municipal governance is often
prohibitively difficult due to a lack of high-quality data. But, recent
advances in speech-to-text algorithms and natural language processing has made
it possible to more easily collect and analyze data about municipal
governments. In this paper, we introduce an open-source platform, the Council
Data Project (CDP), to curate novel datasets for research into municipal
governance. The contribution of this work is two-fold: 1. We demonstrate that
CDP, as an infrastructure, can be used to assemble reliable comparative data on
municipal governance; 2. We provide exploratory analysis of three
municipalities to show how CDP data can be used to gain insight into how
municipal governments perform over time. We conclude by describing future
directions for research on and with CDP such as the development of machine
learning models for speaker annotation, outline generation, and named entity
recognition for improved linked data.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:173:" <a href="http://arxiv.org/find/cs/1/au:+Brown_J/0/1/0/all/0/1">Jackson Maxfield Brown</a>, <a href="http://arxiv.org/find/cs/1/au:+Weber_N/0/1/0/all/0/1">Nicholas Weber</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:403;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.09409";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:106:"Video Moment Retrieval from Text Queries via Single Frame Annotation. (arXiv:2204.09409v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.09409";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1349:"Video moment retrieval aims at finding the start and end timestamps of a
moment (part of a video) described by a given natural language query. Fully
supervised methods need complete temporal boundary annotations to achieve
promising results, which is costly since the annotator needs to watch the whole
moment. Weakly supervised methods only rely on the paired video and query, but
the performance is relatively poor. In this paper, we look closer into the
annotation process and propose a new paradigm called "glance annotation". This
paradigm requires the timestamp of only one single random frame, which we refer
to as a "glance", within the temporal boundary of the fully supervised
counterpart. We argue this is beneficial because comparing to weak supervision,
trivial cost is added yet more potential in performance is provided. Under the
glance annotation setting, we propose a method named as Video moment retrieval
via Glance Annotation (ViGA) based on contrastive learning. ViGA cuts the input
video into clips and contrasts between clips and queries, in which glance
guided Gaussian distributed weights are assigned to all clips. Our extensive
experiments indicate that ViGA achieves better results than the
state-of-the-art weakly supervised methods by a large margin, even comparable
to fully supervised methods in some cases.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:630:" <a href="http://arxiv.org/find/cs/1/au:+Cui_R/0/1/0/all/0/1">Ran Cui</a>, <a href="http://arxiv.org/find/cs/1/au:+Qian_T/0/1/0/all/0/1">Tianwen Qian</a>, <a href="http://arxiv.org/find/cs/1/au:+Peng_P/0/1/0/all/0/1">Pai Peng</a>, <a href="http://arxiv.org/find/cs/1/au:+Daskalaki_E/0/1/0/all/0/1">Elena Daskalaki</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_J/0/1/0/all/0/1">Jingjing Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Wei_D/0/1/0/all/0/1">De Wei</a>, <a href="http://arxiv.org/find/cs/1/au:+Sun_H/0/1/0/all/0/1">Huyang Sun</a>, <a href="http://arxiv.org/find/cs/1/au:+Jiang_Y/0/1/0/all/0/1">Yu-Gang Jiang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:404;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.09649";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:123:"BliMe: Verifiably Secure Outsourced Computation with Hardware-Enforced Taint Tracking. (arXiv:2204.09649v3 [cs.CR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.09649";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1238:"We present Blinded Memory (BliMe), a way to realize efficient and secure
outsourced computation. BliMe consists of a novel and minimal set of ISA
extensions that uses taint tracking to ensure the confidentiality of sensitive
(client) data even in the presence of server malware, run-time attacks, and
side-channel attacks.

To secure outsourced computation, the BliMe extensions can be used together
with an attestable, fixed-function trusted execution environment (TEE) and an
encryption engine that provides atomic decrypt-and-taint and
encrypt-and-untaint operations. The TEE engages in an attestation and key
agreement protocol with the client. It provides the resulting client-specific
keys to the encryption engine. Clients rely on remote attestation to ensure
that their data will always be protected by BliMe's taint tracking policy after
decryption.

We provide a machine-checked security proof and an FPGA implementation
(BliMe-Ibex) of BliMe's taint tracking policy. We show that BliMe-Ibex does not
reduce performance relative to the unmodified core, and incurs only minor
increases in resource consumption in terms of power (${\approx}2.1\%$), LUTs
(${\approx}1.0\%$), and registers (${\approx}2.3\%$).

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:337:" <a href="http://arxiv.org/find/cs/1/au:+ElAtali_H/0/1/0/all/0/1">Hossam ElAtali</a>, <a href="http://arxiv.org/find/cs/1/au:+Gunn_L/0/1/0/all/0/1">Lachlan J. Gunn</a>, <a href="http://arxiv.org/find/cs/1/au:+Liljestrand_H/0/1/0/all/0/1">Hans Liljestrand</a>, <a href="http://arxiv.org/find/cs/1/au:+Asokan_N/0/1/0/all/0/1">N. Asokan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:405;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.10022";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:146:"Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions. (arXiv:2204.10022v2 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.10022";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1590:"Estimating the effects of continuous-valued interventions from observational
data is critically important in fields such as climate science, healthcare, and
economics. Recent work focuses on designing neural-network architectures and
regularization functions to allow for scalable estimation of average and
individual-level dose response curves from high-dimensional, large-sample data.
Such methodologies assume ignorability (all confounding variables are observed)
and positivity (all levels of treatment can be observed for every unit
described by a given covariate value), which are especially challenged in the
continuous treatment regime. Developing scalable sensitivity and uncertainty
analyses that allow us to understand the ignorance induced in our estimates
when these assumptions are relaxed receives less attention. Here, we develop a
continuous treatment-effect marginal sensitivity model (CMSM) and derive bounds
that agree with both the observed data and a researcher-defined level of hidden
confounding. We introduce a scalable algorithm to derive the bounds and
uncertainty-aware deep models to efficiently estimate these bounds for
high-dimensional, large-sample observational data. We validate our methods
using both synthetic and real-world experiments. For the latter, we work in
concert with climate scientists interested in evaluating the climatological
impacts of human emissions on cloud properties using satellite observations
from the past 15 years: a finite-data problem known to be complicated by the
presence of a multitude of unobserved confounders.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:588:" <a href="http://arxiv.org/find/cs/1/au:+Jesson_A/0/1/0/all/0/1">Andrew Jesson</a>, <a href="http://arxiv.org/find/cs/1/au:+Douglas_A/0/1/0/all/0/1">Alyson Douglas</a>, <a href="http://arxiv.org/find/cs/1/au:+Manshausen_P/0/1/0/all/0/1">Peter Manshausen</a>, <a href="http://arxiv.org/find/cs/1/au:+Meinshausen_N/0/1/0/all/0/1">Nicolai Meinshausen</a>, <a href="http://arxiv.org/find/cs/1/au:+Stier_P/0/1/0/all/0/1">Philip Stier</a>, <a href="http://arxiv.org/find/cs/1/au:+Gal_Y/0/1/0/all/0/1">Yarin Gal</a>, <a href="http://arxiv.org/find/cs/1/au:+Shalit_U/0/1/0/all/0/1">Uri Shalit</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:406;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.10072";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:94:"Detecting Topology Attacks against Graph Neural Networks. (arXiv:2204.10072v2 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.10072";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1053:"Graph neural networks (GNNs) have been widely used in many real applications,
and recent studies have revealed their vulnerabilities against topology
attacks. To address this issue, existing efforts have mainly been dedicated to
improving the robustness of GNNs, while little attention has been paid to the
detection of such attacks. In this work, we study the victim node detection
problem under topology attacks against GNNs. Our approach is built upon the key
observation rooted in the intrinsic message passing nature of GNNs. That is,
the neighborhood of a victim node tends to have two competing group forces,
pushing the node classification results towards the original label and the
targeted label, respectively. Based on this observation, we propose to detect
victim nodes by deliberately designing an effective measurement of the
neighborhood variance for each node. Extensive experimental results on four
real-world datasets and five existing topology attacks show the effectiveness
and efficiency of the proposed detection approach.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:457:" <a href="http://arxiv.org/find/cs/1/au:+Xu_S/0/1/0/all/0/1">Senrong Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Yao_Y/0/1/0/all/0/1">Yuan Yao</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_L/0/1/0/all/0/1">Liangyue Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Yang_W/0/1/0/all/0/1">Wei Yang</a>, <a href="http://arxiv.org/find/cs/1/au:+Xu_F/0/1/0/all/0/1">Feng Xu</a>, <a href="http://arxiv.org/find/cs/1/au:+Tong_H/0/1/0/all/0/1">Hanghang Tong</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:407;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.10160";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:113:"A Multi-Person Video Dataset Annotation Method of Spatio-Temporally Actions. (arXiv:2204.10160v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.10160";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:973:"Spatio-temporal action detection is an important and challenging problem in
video understanding. However, the application of the existing large-scale
spatio-temporal action datasets in specific fields is limited, and there is
currently no public tool for making spatio-temporal action datasets, it takes a
lot of time and effort for researchers to customize the spatio-temporal action
datasets, so we propose a multi-Person video dataset Annotation Method of
spatio-temporally actions.First, we use ffmpeg to crop the videos and frame the
videos; then use yolov5 to detect human in the video frame, and then use deep
sort to detect the ID of the human in the video frame. By processing the
detection results of yolov5 and deep sort, we can get the annotation file of
the spatio-temporal action dataset to complete the work of customizing the
spatio-temporal action dataset.
https://github.com/Whiffe/Custom-ava-dataset_Custom-Spatio-Temporally-Action-Video-Dataset

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:75:" <a href="http://arxiv.org/find/cs/1/au:+Yang_F/0/1/0/all/0/1">Fan Yang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:408;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.10169";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:88:"A computational study of Gomory-Hu tree algorithms. (arXiv:2204.10169v2 [cs.DS] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.10169";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:543:"We present an experimental study of algorithms for computing the Gomory-Hu
tree (aka cut tree) in undirected weighted graphs. We develop a new
implementation based on two popular maxflow algorithms, IBFS and BK. We compare
it with the algorithms from the previous experimental study by Goldberg and
Tsioutsiouliklis (2001) and with the more recent algorithm by Akibo et al.
(2016) (designed for unweighted simple graphs). Results indicate that on some
classes of problems new implementation significantly outperforms previous
methods.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:92:" <a href="http://arxiv.org/find/cs/1/au:+Kolmogorov_V/0/1/0/all/0/1">Vladimir Kolmogorov</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:409;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.10178";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:124:"Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation. (arXiv:2204.10178v2 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.10178";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:676:"We introduce Doctor XAvIer, a BERT-based diagnostic system that extracts
relevant clinical data from transcribed patient-doctor dialogues and explains
predictions using feature attribution methods. We present a novel performance
plot and evaluation metric for feature attribution methods: Feature Attribution
Dropping (FAD) curve and its Normalized Area Under the Curve (N-AUC). FAD curve
analysis shows that integrated gradients outperforms Shapley values in
explaining diagnosis classification. Doctor XAvIer outperforms the baseline
with 0.97 F1-score in named entity recognition and symptom pertinence
classification and 0.91 F1-score in diagnosis classification.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:163:" <a href="http://arxiv.org/find/cs/1/au:+Ngai_H/0/1/0/all/0/1">Hillary Ngai</a>, <a href="http://arxiv.org/find/cs/1/au:+Rudzicz_F/0/1/0/all/0/1">Frank Rudzicz</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:410;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.10479";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:102:"Analysis of Temporal Difference Learning: Linear System Approach. (arXiv:2204.10479v2 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.10479";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:881:"The goal of this technical note is to introduce a new finite-time convergence
analysis of temporal difference (TD) learning based on stochastic linear system
models. TD-learning is a fundamental reinforcement learning (RL) to evaluate a
given policy by estimating the corresponding value function for a Markov
decision process. While there has been a series of successful works in
theoretical analysis of TDlearning, it was not until recently that researchers
found some guarantees on its statistical efficiency by developing finite-time
error bounds. In this paper, we propose a simple control theoretic finite-time
analysis of TD-learning, which exploits linear system models and standard
notions in linear system communities. The proposed work provides new simple
templets for RL analysis, and additional insights on TD-learning and RL based
on ideas in control theory.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:155:" <a href="http://arxiv.org/find/cs/1/au:+Lee_D/0/1/0/all/0/1">Donghwan Lee</a>, <a href="http://arxiv.org/find/cs/1/au:+Kim_D/0/1/0/all/0/1">Do Wan Kim</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:411;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.10584";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:90:"Non-Uniformly Terminating Chase: Size and Complexity. (arXiv:2204.10584v2 [cs.DB] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.10584";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1135:"The chase procedure, originally introduced for checking implication of
database constraints, and later on used for computing data exchange solutions,
has recently become a central algorithmic tool in rule-based ontological
reasoning. In this context, a key problem is non-uniform chase termination:
does the chase of a database w.r.t. a rule-based ontology terminate? And if
this is the case, what is the size of the result of the chase? We focus on
guarded tuple-generating dependencies (TGDs), which form a robust rule-based
ontology language, and study the above central questions for the semi-oblivious
version of the chase. One of our main findings is that non-uniform
semi-oblivious chase termination for guarded TGDs is feasible in polynomial
time w.r.t. the database, and the size of the result of the chase (whenever is
finite) is linear w.r.t. the database. Towards our results concerning
non-uniform chase termination, we show that basic techniques such as
simplification and linearization, originally introduced in the context of
ontological query answering, can be safely applied to the chase termination
problem.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:253:" <a href="http://arxiv.org/find/cs/1/au:+Calautti_M/0/1/0/all/0/1">Marco Calautti</a>, <a href="http://arxiv.org/find/cs/1/au:+Gottlob_G/0/1/0/all/0/1">Georg Gottlob</a>, <a href="http://arxiv.org/find/cs/1/au:+Pieris_A/0/1/0/all/0/1">Andreas Pieris</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:412;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.10836";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:108:"Federated Learning Enables Big Data for Rare Cancer Boundary Detection. (arXiv:2204.10836v2 [cs.LG] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.10836";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1465:"Although machine learning (ML) has shown promise in numerous domains, there
are concerns about generalizability to out-of-sample data. This is currently
addressed by centrally sharing ample, and importantly diverse, data from
multiple sites. However, such centralization is challenging to scale (or even
not feasible) due to various limitations. Federated ML (FL) provides an
alternative to train accurate and generalizable ML models, by only sharing
numerical model updates. Here we present findings from the largest FL study
to-date, involving data from 71 healthcare institutions across 6 continents, to
generate an automatic tumor boundary detector for the rare disease of
glioblastoma, utilizing the largest dataset of such patients ever used in the
literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33%
improvement over a publicly trained model to delineate the surgically
targetable tumor, and 23% improvement over the tumor's entire extent. We
anticipate our study to: 1) enable more studies in healthcare informed by large
and diverse data, ensuring meaningful results for rare diseases and
underrepresented populations, 2) facilitate further quantitative analyses for
glioblastoma via performance optimization of our consensus model for eventual
public release, and 3) demonstrate the effectiveness of FL at such scale and
task complexity as a paradigm shift for multi-site collaborations, alleviating
the need for data sharing.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:4284:" <a href="http://arxiv.org/find/cs/1/au:+Pati_S/0/1/0/all/0/1">Sarthak Pati</a>, <a href="http://arxiv.org/find/cs/1/au:+Baid_U/0/1/0/all/0/1">Ujjwal Baid</a>, <a href="http://arxiv.org/find/cs/1/au:+Edwards_B/0/1/0/all/0/1">Brandon Edwards</a>, <a href="http://arxiv.org/find/cs/1/au:+Sheller_M/0/1/0/all/0/1">Micah Sheller</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_S/0/1/0/all/0/1">Shih-Han Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Reina_G/0/1/0/all/0/1">G Anthony Reina</a>, <a href="http://arxiv.org/find/cs/1/au:+Foley_P/0/1/0/all/0/1">Patrick Foley</a>, <a href="http://arxiv.org/find/cs/1/au:+Gruzdev_A/0/1/0/all/0/1">Alexey Gruzdev</a>, <a href="http://arxiv.org/find/cs/1/au:+Karkada_D/0/1/0/all/0/1">Deepthi Karkada</a>, <a href="http://arxiv.org/find/cs/1/au:+Davatzikos_C/0/1/0/all/0/1">Christos Davatzikos</a>, <a href="http://arxiv.org/find/cs/1/au:+Sako_C/0/1/0/all/0/1">Chiharu Sako</a>, <a href="http://arxiv.org/find/cs/1/au:+Ghodasara_S/0/1/0/all/0/1">Satyam Ghodasara</a>, <a href="http://arxiv.org/find/cs/1/au:+Bilello_M/0/1/0/all/0/1">Michel Bilello</a>, <a href="http://arxiv.org/find/cs/1/au:+Mohan_S/0/1/0/all/0/1">Suyash Mohan</a>, <a href="http://arxiv.org/find/cs/1/au:+Vollmuth_P/0/1/0/all/0/1">Philipp Vollmuth</a>, <a href="http://arxiv.org/find/cs/1/au:+Brugnara_G/0/1/0/all/0/1">Gianluca Brugnara</a>, <a href="http://arxiv.org/find/cs/1/au:+Preetha_C/0/1/0/all/0/1">Chandrakanth J Preetha</a>, <a href="http://arxiv.org/find/cs/1/au:+Sahm_F/0/1/0/all/0/1">Felix Sahm</a>, <a href="http://arxiv.org/find/cs/1/au:+Maier_Hein_K/0/1/0/all/0/1">Klaus Maier-Hein</a>, <a href="http://arxiv.org/find/cs/1/au:+Zenk_M/0/1/0/all/0/1">Maximilian Zenk</a>, <a href="http://arxiv.org/find/cs/1/au:+Bendszus_M/0/1/0/all/0/1">Martin Bendszus</a>, <a href="http://arxiv.org/find/cs/1/au:+Wick_W/0/1/0/all/0/1">Wolfgang Wick</a>, <a href="http://arxiv.org/find/cs/1/au:+Calabrese_E/0/1/0/all/0/1">Evan Calabrese</a>, <a href="http://arxiv.org/find/cs/1/au:+Rudie_J/0/1/0/all/0/1">Jeffrey Rudie</a>, <a href="http://arxiv.org/find/cs/1/au:+Villanueva_Meyer_J/0/1/0/all/0/1">Javier Villanueva-Meyer</a>, <a href="http://arxiv.org/find/cs/1/au:+Cha_S/0/1/0/all/0/1">Soonmee Cha</a>, <a href="http://arxiv.org/find/cs/1/au:+Ingalhalikar_M/0/1/0/all/0/1">Madhura Ingalhalikar</a>, <a href="http://arxiv.org/find/cs/1/au:+Jadhav_M/0/1/0/all/0/1">Manali Jadhav</a>, <a href="http://arxiv.org/find/cs/1/au:+Pandey_U/0/1/0/all/0/1">Umang Pandey</a>, <a href="http://arxiv.org/find/cs/1/au:+Saini_J/0/1/0/all/0/1">Jitender Saini</a>, <a href="http://arxiv.org/find/cs/1/au:+Garrett_J/0/1/0/all/0/1">John Garrett</a>, <a href="http://arxiv.org/find/cs/1/au:+Larson_M/0/1/0/all/0/1">Matthew Larson</a>, <a href="http://arxiv.org/find/cs/1/au:+Jeraj_R/0/1/0/all/0/1">Robert Jeraj</a>, <a href="http://arxiv.org/find/cs/1/au:+Currie_S/0/1/0/all/0/1">Stuart Currie</a>, <a href="http://arxiv.org/find/cs/1/au:+Frood_R/0/1/0/all/0/1">Russell Frood</a>, <a href="http://arxiv.org/find/cs/1/au:+Fatania_K/0/1/0/all/0/1">Kavi Fatania</a>, <a href="http://arxiv.org/find/cs/1/au:+Huang_R/0/1/0/all/0/1">Raymond Y Huang</a>, <a href="http://arxiv.org/find/cs/1/au:+Chang_K/0/1/0/all/0/1">Ken Chang</a>, <a href="http://arxiv.org/find/cs/1/au:+Balana_C/0/1/0/all/0/1">Carmen Balana</a>, <a href="http://arxiv.org/find/cs/1/au:+Capellades_J/0/1/0/all/0/1">Jaume Capellades</a>, <a href="http://arxiv.org/find/cs/1/au:+Puig_J/0/1/0/all/0/1">Josep Puig</a>, <a href="http://arxiv.org/find/cs/1/au:+Trenkler_J/0/1/0/all/0/1">Johannes Trenkler</a>, <a href="http://arxiv.org/find/cs/1/au:+Pichler_J/0/1/0/all/0/1">Josef Pichler</a>, <a href="http://arxiv.org/find/cs/1/au:+Necker_G/0/1/0/all/0/1">Georg Necker</a>, <a href="http://arxiv.org/find/cs/1/au:+Haunschmidt_A/0/1/0/all/0/1">Andreas Haunschmidt</a>, <a href="http://arxiv.org/find/cs/1/au:+Meckel_S/0/1/0/all/0/1">Stephan Meckel</a>, <a href="http://arxiv.org/find/cs/1/au:+Shukla_G/0/1/0/all/0/1">Gaurav Shukla</a>, <a href="http://arxiv.org/find/cs/1/au:+Liem_S/0/1/0/all/0/1">Spencer Liem</a>, <a href="http://arxiv.org/find/cs/1/au:+Alexander_G/0/1/0/all/0/1">Gregory S Alexander</a>, <a href="http://arxiv.org/find/cs/1/au:+Lombardo_J/0/1/0/all/0/1">Joseph Lombardo</a>, et al. (229 additional authors not shown)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:413;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.10869";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:92:"Identity Preserving Loss for Learned Image Compression. (arXiv:2204.10869v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.10869";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1731:"Deep learning model inference on embedded devices is challenging due to the
limited availability of computation resources. A popular alternative is to
perform model inference on the cloud, which requires transmitting images from
the embedded device to the cloud. Image compression techniques are commonly
employed in such cloud-based architectures to reduce transmission latency over
low bandwidth networks. This work proposes an end-to-end image compression
framework that learns domain-specific features to achieve higher compression
ratios than standard HEVC/JPEG compression techniques while maintaining
accuracy on downstream tasks (e.g., recognition). Our framework does not
require fine-tuning of the downstream task, which allows us to drop-in any
off-the-shelf downstream task model without retraining. We choose faces as an
application domain due to the ready availability of datasets and off-the-shelf
recognition models as representative downstream tasks. We present a novel
Identity Preserving Reconstruction (IPR) loss function which achieves
Bits-Per-Pixel (BPP) values that are ~38% and ~42% of CRF-23 HEVC compression
for LFW (low-resolution) and CelebA-HQ (high-resolution) datasets,
respectively, while maintaining parity in recognition accuracy. The superior
compression ratio is achieved as the model learns to retain the domain-specific
features (e.g., facial features) while sacrificing details in the background.
Furthermore, images reconstructed by our proposed compression model are robust
to changes in downstream model architectures. We show at-par recognition
performance on the LFW dataset with an unseen recognition model while retaining
a lower BPP value of ~38% of CRF-23 HEVC compression.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:429:" <a href="http://arxiv.org/find/cs/1/au:+Xiao_J/0/1/0/all/0/1">Jiuhong Xiao</a>, <a href="http://arxiv.org/find/cs/1/au:+Aggarwal_L/0/1/0/all/0/1">Lavisha Aggarwal</a>, <a href="http://arxiv.org/find/cs/1/au:+Banerjee_P/0/1/0/all/0/1">Prithviraj Banerjee</a>, <a href="http://arxiv.org/find/cs/1/au:+Aggarwal_M/0/1/0/all/0/1">Manoj Aggarwal</a>, <a href="http://arxiv.org/find/cs/1/au:+Medioni_G/0/1/0/all/0/1">Gerard Medioni</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:414;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.10893";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:116:"Locally Aggregated Feature Attribution on Natural Language Model Understanding. (arXiv:2204.10893v2 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.10893";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1213:"With the growing popularity of deep-learning models, model understanding
becomes more important. Much effort has been devoted to demystify deep neural
networks for better interpretability. Some feature attribution methods have
shown promising results in computer vision, especially the gradient-based
methods where effectively smoothing the gradients with reference data is key to
a robust and faithful result. However, direct application of these
gradient-based methods to NLP tasks is not trivial due to the fact that the
input consists of discrete tokens and the "reference" tokens are not explicitly
defined. In this work, we propose Locally Aggregated Feature Attribution
(LAFA), a novel gradient-based feature attribution method for NLP models.
Instead of relying on obscure reference tokens, it smooths gradients by
aggregating similar reference texts derived from language model embeddings. For
evaluation purpose, we also design experiments on different NLP tasks including
Entity Recognition and Sentiment Analysis on public datasets as well as key
feature detection on a constructed Amazon catalogue dataset. The superior
performance of the proposed method is demonstrated through experiments.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:312:" <a href="http://arxiv.org/find/cs/1/au:+Zhang_S/0/1/0/all/0/1">Sheng Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_J/0/1/0/all/0/1">Jin Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Jiang_H/0/1/0/all/0/1">Haitao Jiang</a>, <a href="http://arxiv.org/find/cs/1/au:+Song_R/0/1/0/all/0/1">Rui Song</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:415;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.10938";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:110:"A Multi-level Alignment Training Scheme for Video-and-Language Grounding. (arXiv:2204.10938v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.10938";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1372:"To solve video-and-language grounding tasks, the key is for the network to
understand the connection between the two modalities. For a pair of video and
language description, their semantic relation is reflected by their encodings'
similarity. A good multi-modality encoder should be able to well capture both
inputs' semantics and encode them in the shared feature space where embedding
distance gets properly translated into their semantic similarity. In this work,
we focused on this semantic connection between video and language, and
developed a multi-level alignment training scheme to directly shape the
encoding process. Global and segment levels of video-language alignment pairs
were designed, based on the information similarity ranging from high-level
context to fine-grained semantics. The contrastive loss was used to contrast
the encodings' similarities between the positive and negative alignment pairs,
and to ensure the network is trained in such a way that similar information is
encoded closely in the shared feature space while information of different
semantics is kept apart. Our multi-level alignment training can be applied to
various video-and-language grounding tasks. Together with the task-specific
training loss, our framework achieved comparable performance to previous
state-of-the-arts on multiple video QA and retrieval datasets.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:318:" <a href="http://arxiv.org/find/cs/1/au:+Zhang_Y/0/1/0/all/0/1">Yubo Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Niu_F/0/1/0/all/0/1">Feiyang Niu</a>, <a href="http://arxiv.org/find/cs/1/au:+Ping_Q/0/1/0/all/0/1">Qing Ping</a>, <a href="http://arxiv.org/find/cs/1/au:+Thattai_G/0/1/0/all/0/1">Govind Thattai</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:416;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11025";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"GAMORRA: An API-Level Workload Model for Rasterization-based Graphics Pipeline Architecture. (arXiv:2204.11025v2 [cs.GR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11025";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1606:"The performance of applications that require frame rendering time estimation
or dynamic frequency scaling, rely on the accuracy of the workload model that
is utilized within these applications. Existing models lack sufficient accuracy
in their core model. Hence, they require changes to the target application or
the hardware to produce accurate results. This paper introduces a mathematical
workload model for a rasterization-based graphics Application Programming
Interface (API) pipeline, named GAMORRA, which works based on the load and
complexity of each stage of the pipeline. Firstly, GAMORRA models each stage of
the pipeline based on their operation complexity and the input data size. Then,
the calculated workloads of the stages are fed to a Multiple Linear Regression
(MLR) model as explanatory variables. A hybrid offline/online training scheme
is proposed as well to train the model. A suite of benchmarks is also designed
to tune the model parameters based on the performance of the target system. The
experiments were performed on Direct3D 11 and on two different rendering
platforms comparing GAMORRA to an AutoRegressive (AR) model, a Frame Complexity
Model (FCM) and a frequency-based (FRQ) model. The experiments show an average
of 1.27 ms frame rendering time estimation error (9.45%) compared to an average
of 1.87 ms error (13.23%) for FCM which is the best method among the three
chosen methods. However, this comes at the cost of 0.54 ms (4.58%) increase in
time complexity compared to FCM. Furthermore, GAMMORA improves frametime
underestimations by 1.1% compared to FCM.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:274:" <a href="http://arxiv.org/find/cs/1/au:+Mohammadi_I/0/1/0/all/0/1">Iman Soltani Mohammadi</a>, <a href="http://arxiv.org/find/cs/1/au:+Ghanbari_M/0/1/0/all/0/1">Mohammad Ghanbari</a>, <a href="http://arxiv.org/find/cs/1/au:+Hashemi_M/0/1/0/all/0/1">Mahmoud Reza Hashemi</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:417;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11368";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:140:"Enhancing the STIX Representation of MITRE ATT&amp;CK for Group Filtering and Technique Prioritization. (arXiv:2204.11368v2 [cs.CR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11368";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:542:"In this paper, we enhance the machine-readable representation of the ATT&amp;CK
Groups knowledge base provided by MITRE in STIX 2.1 format to make available
and queryable additional types of contextual information. Such information
includes the motivations of activity groups, the countries they have originated
from, and the sectors and countries they have targeted. We demonstrate how to
utilize the enhanced model to construct intelligible queries to filter activity
groups of interest and retrieve relevant tactical intelligence.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:173:" <a href="http://arxiv.org/find/cs/1/au:+Zych_M/0/1/0/all/0/1">Mateusz Zych</a>, <a href="http://arxiv.org/find/cs/1/au:+Mavroeidis_V/0/1/0/all/0/1">Vasileios Mavroeidis</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:418;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11382";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction. (arXiv:2204.11382v2 [cs.SD] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11382";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1368:"Speech emotion recognition systems have high prediction latency because of
the high computational requirements for deep learning models and low
generalizability mainly because of the poor reliability of emotional
measurements across multiple corpora. To solve these problems, we present a
speech emotion recognition system based on a reductionist approach of
decomposing and analyzing syllable-level features. Mel-spectrogram of an audio
stream is decomposed into syllable-level components, which are then analyzed to
extract statistical features. The proposed method uses formant attention,
noise-gate filtering, and rolling normalization contexts to increase feature
processing speed and tolerance to adversity. A set of syllable-level formant
features is extracted and fed into a single hidden layer neural network that
makes predictions for each syllable as opposed to the conventional approach of
using a sophisticated deep learner to make sentence-wide predictions. The
syllable level predictions help to achieve the real-time latency and lower the
aggregated error in utterance level cross-corpus predictions. The experiments
on IEMOCAP (IE), MSP-Improv (MI), and RAVDESS (RA) databases show that the
method archives real-time latency while predicting with state-of-the-art
cross-corpus unweighted accuracy of 47.6% for IE to MI and 56.2% for MI to IE.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:395:" <a href="http://arxiv.org/find/cs/1/au:+Rehman_A/0/1/0/all/0/1">Abdul Rehman</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_Z/0/1/0/all/0/1">Zhen-Tao Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_M/0/1/0/all/0/1">Min Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Cao_W/0/1/0/all/0/1">Wei-Hua Cao</a>, <a href="http://arxiv.org/find/cs/1/au:+Jiang_C/0/1/0/all/0/1">Cheng-Shan Jiang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:419;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11411";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:105:"Road Traffic Law Adaptive Decision-making for Self-Driving Vehicles. (arXiv:2204.11411v2 [cs.RO] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11411";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1831:"Self-driving vehicles have their own intelligence to drive on open roads.
However, vehicle managers, e.g., government or industrial companies, still need
a way to tell these self-driving vehicles what behaviors are encouraged or
forbidden. Unlike human drivers, current self-driving vehicles cannot
understand the traffic laws, thus rely on the programmers manually writing the
corresponding principles into the driving systems. It would be less efficient
and hard to adapt some temporary traffic laws, especially when the vehicles use
data-driven decision-making algorithms. Besides, current self-driving vehicle
systems rarely take traffic law modification into consideration. This work aims
to design a road traffic law adaptive decision-making method. The
decision-making algorithm is designed based on reinforcement learning, in which
the traffic rules are usually implicitly coded in deep neural networks. The
main idea is to supply the adaptability to traffic laws of self-driving
vehicles by a law-adaptive backup policy. In this work, the natural
language-based traffic laws are first translated into a logical expression by
the Linear Temporal Logic method. Then, the system will try to monitor in
advance whether the self-driving vehicle may break the traffic laws by
designing a long-term RL action space. Finally, a sample-based planning method
will re-plan the trajectory when the vehicle may break the traffic rules. The
method is validated in a Beijing Winter Olympic Lane scenario and an overtaking
case, built in CARLA simulator. The results show that by adopting this method,
the self-driving vehicles can comply with new issued or updated traffic laws
effectively. This method helps self-driving vehicles governed by digital
traffic laws, which is necessary for the wide adoption of autonomous driving.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:694:" <a href="http://arxiv.org/find/cs/1/au:+Liu_J/0/1/0/all/0/1">Jiaxin Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhou_W/0/1/0/all/0/1">Wenhui Zhou</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_H/0/1/0/all/0/1">Hong Wang</a>, <a href="http://arxiv.org/find/cs/1/au:+Cao_Z/0/1/0/all/0/1">Zhong Cao</a>, <a href="http://arxiv.org/find/cs/1/au:+Yu_W/0/1/0/all/0/1">Wenhao Yu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_C/0/1/0/all/0/1">Chengxiang Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_D/0/1/0/all/0/1">Ding Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Yang_D/0/1/0/all/0/1">Diange Yang</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_J/0/1/0/all/0/1">Jun Li</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:420;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11582";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Graph-DETR3D: Rethinking Overlapping Regions for Multi-View 3D Object Detection. (arXiv:2204.11582v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11582";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1803:"3D object detection from multiple image views is a fundamental and
challenging task for visual scene understanding. Due to its low cost and high
efficiency, multi-view 3D object detection has demonstrated promising
application prospects. However, accurately detecting objects through
perspective views in the 3D space is extremely difficult due to the lack of
depth information. Recently, DETR3D introduces a novel 3D-2D query paradigm in
aggregating multi-view images for 3D object detection and achieves
state-of-the-art performance. In this paper, with intensive pilot experiments,
we quantify the objects located at different regions and find that the
"truncated instances" (i.e., at the border regions of each image) are the main
bottleneck hindering the performance of DETR3D. Although it merges multiple
features from two adjacent views in the overlapping regions, DETR3D still
suffers from insufficient feature aggregation, thus missing the chance to fully
boost the detection performance. In an effort to tackle the problem, we propose
Graph-DETR3D to automatically aggregate multi-view imagery information through
graph structure learning (GSL). It constructs a dynamic 3D graph between each
object query and 2D feature maps to enhance the object representations,
especially at the border regions. Besides, Graph-DETR3D benefits from a novel
depth-invariant multi-scale training strategy, which maintains the visual depth
consistency by simultaneously scaling the image size and the object depth.
Extensive experiments on the nuScenes dataset demonstrate the effectiveness and
efficiency of our Graph-DETR3D. Notably, our best model achieves 49.5 NDS on
the nuScenes test leaderboard, achieving new state-of-the-art in comparison
with various published image-view 3D object detectors.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:473:" <a href="http://arxiv.org/find/cs/1/au:+Chen_Z/0/1/0/all/0/1">Zehui Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_Z/0/1/0/all/0/1">Zhenyu Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_S/0/1/0/all/0/1">Shiquan Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Fang_L/0/1/0/all/0/1">Liangji Fang</a>, <a href="http://arxiv.org/find/cs/1/au:+Jiang_Q/0/1/0/all/0/1">Qinhong Jiang</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_F/0/1/0/all/0/1">Feng Zhao</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:421;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11587";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:139:"Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction. (arXiv:2204.11587v2 [cs.IR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11587";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1188:"Rich user behavior information is of great importance for capturing and
understanding user interest in click-through rate (CTR) prediction. To improve
the richness, collecting long-term behaviors becomes a typical approach in
academy and industry but at the cost of increasing online storage and latency.
Recently, researchers have proposed several approaches to shorten long-term
behavior sequence and then model user interests. These approaches reduce online
cost efficiently but do not well handle the noisy information in long-term user
behavior, which may deteriorate the performance of CTR prediction
significantly. To obtain better cost/performance trade-off, we propose a novel
Adversarial Filtering Model (ADFM) to model long-term user behavior. ADFM uses
a hierarchical aggregation representation to compress raw behavior sequence and
then learns to remove useless behavior information with an adversarial
filtering mechanism. The selected user behaviors are fed into interest
extraction module for CTR prediction. Experimental results on public datasets
and industrial dataset demonstrate that our method achieves significant
improvements over state-of-the-art models.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:388:" <a href="http://arxiv.org/find/cs/1/au:+Li_X/0/1/0/all/0/1">Xiaochen Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhong_R/0/1/0/all/0/1">Rui Zhong</a>, <a href="http://arxiv.org/find/cs/1/au:+Liang_J/0/1/0/all/0/1">Jian Liang</a>, <a href="http://arxiv.org/find/cs/1/au:+Liu_X/0/1/0/all/0/1">Xialong Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_Y/0/1/0/all/0/1">Yu Zhang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:422;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11639";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:118:"Investigating Black-Box Function Recognition Using Hardware Performance Counters. (arXiv:2204.11639v2 [cs.CR] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11639";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1674:"We present new methods and results for discovering information about
black-box program functions using hardware performance counters (HPC), where an
investigator can only invoke and measure the results of function calls.
Important use cases include analysing compiled libraries, e.g. static and
dynamic link libraries, and trusted execution environment (TEE) applications.
Drawing inspiration from recent literature on malware classification, we
develop and evaluate a machine learning-based approach using information from
HPCs for function recognition. We use this to classify a comprehensive set of
HPC events, including L1 instruction cache accesses, TLB misses, and
instruction retirements, to recognise functions from standard benchmarking and
cryptographic libraries. This includes various ciphers in different modes of
operation, e.g. AES-CTR vs. AES-ECB; signing, verification, and hashing
algorithms; and more. Three major architectures are evaluated using
off-the-shelf Intel/X86-64, ARM, and RISC-V CPUs under various compilation
assumptions. Following this, we develop and evaluate two novel use cases.
Firstly, we show that several known CVE-numbered OpenSSL vulnerabilities can be
detected using HPC differences between patched and unpatched library versions.
Secondly, we develop a proof-of-concept for recognising standardised
cryptographic functions within ARM TrustZone TEE applications using the
open-source OP-TEE framework. In all cases, HPCs could be used with significant
accuracy (86.22-99.83%) depending on the target architecture and application.
Lastly, we discuss mitigations, outstanding challenges, and directions for
future research.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:273:" <a href="http://arxiv.org/find/cs/1/au:+Shepherd_C/0/1/0/all/0/1">Carlton Shepherd</a>, <a href="http://arxiv.org/find/cs/1/au:+Semal_B/0/1/0/all/0/1">Benjamin Semal</a>, <a href="http://arxiv.org/find/cs/1/au:+Markantonakis_K/0/1/0/all/0/1">Konstantinos Markantonakis</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:423;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11797";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution. (arXiv:2204.11797v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11797";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1359:"3D neural networks are widely used in real-world applications (e.g., AR/VR
headsets, self-driving cars). They are required to be fast and accurate;
however, limited hardware resources on edge devices make these requirements
rather challenging. Previous work processes 3D data using either voxel-based or
point-based neural networks, but both types of 3D models are not
hardware-efficient due to the large memory footprint and random memory access.
In this paper, we study 3D deep learning from the efficiency perspective. We
first systematically analyze the bottlenecks of previous 3D methods. We then
combine the best from point-based and voxel-based models together and propose a
novel hardware-efficient 3D primitive, Point-Voxel Convolution (PVConv). We
further enhance this primitive with the sparse convolution to make it more
effective in processing large (outdoor) scenes. Based on our designed 3D
primitive, we introduce 3D Neural Architecture Search (3D-NAS) to explore the
best 3D network architecture given a resource constraint. We evaluate our
proposed method on six representative benchmark datasets, achieving
state-of-the-art performance with 1.8-23.7x measured speedup. Furthermore, our
method has been deployed to the autonomous racing vehicle of MIT Driverless,
achieving larger detection range, higher accuracy and lower latency.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:390:" <a href="http://arxiv.org/find/cs/1/au:+Liu_Z/0/1/0/all/0/1">Zhijian Liu</a>, <a href="http://arxiv.org/find/cs/1/au:+Tang_H/0/1/0/all/0/1">Haotian Tang</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhao_S/0/1/0/all/0/1">Shengyu Zhao</a>, <a href="http://arxiv.org/find/cs/1/au:+Shao_K/0/1/0/all/0/1">Kevin Shao</a>, <a href="http://arxiv.org/find/cs/1/au:+Han_S/0/1/0/all/0/1">Song Han</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:424;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11817";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:88:"Translation between Molecules and Natural Language. (arXiv:2204.11817v2 [cs.CL] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11817";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1578:"Joint representations between images and text have been deeply investigated
in the literature. In computer vision, the benefits of incorporating natural
language have become clear for enabling semantic-level control of images. In
this work, we present $\textbf{MolT5}-$a self-supervised learning framework for
pretraining models on a vast amount of unlabeled natural language text and
molecule strings. $\textbf{MolT5}$ allows for new, useful, and challenging
analogs of traditional vision-language tasks, such as molecule captioning and
text-based de novo molecule generation (altogether: translation between
molecules and language), which we explore for the first time. Furthermore,
since $\textbf{MolT5}$ pretrains models on single-modal data, it helps overcome
the chemistry domain shortcoming of data scarcity. Additionally, we consider
several metrics, including a new cross-modal embedding-based metric, to
evaluate the tasks of molecule captioning and text-based molecule generation.
By interfacing molecules with natural language, we enable a higher semantic
level of control over molecule discovery and understanding--a critical task for
scientific domains such as drug discovery and material design. Our results show
that $\textbf{MolT5}$-based models are able to generate outputs, both molecule
and text, which in many cases are high quality and match the input modality. On
molecule generation, our best model achieves 30% exact matching test accuracy
(i.e., it generates the correct structure for about one-third of the captions
in our held-out test set).

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:388:" <a href="http://arxiv.org/find/cs/1/au:+Edwards_C/0/1/0/all/0/1">Carl Edwards</a>, <a href="http://arxiv.org/find/cs/1/au:+Lai_T/0/1/0/all/0/1">Tuan Lai</a>, <a href="http://arxiv.org/find/cs/1/au:+Ros_K/0/1/0/all/0/1">Kevin Ros</a>, <a href="http://arxiv.org/find/cs/1/au:+Honke_G/0/1/0/all/0/1">Garrett Honke</a>, <a href="http://arxiv.org/find/cs/1/au:+Ji_H/0/1/0/all/0/1">Heng Ji</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:425;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11824";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:74:"Retrieval-Augmented Diffusion Models. (arXiv:2204.11824v2 [cs.CV] UPDATED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11824";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1272:"Generative image synthesis with diffusion models has recently achieved
excellent visual quality in several tasks such as text-based or
class-conditional image synthesis. Much of this success is due to a dramatic
increase in the computational capacity invested in training these models. This
work presents an alternative approach: inspired by its successful application
in natural language processing, we propose to complement the diffusion model
with a retrieval-based approach and to introduce an explicit memory in the form
of an external database. During training, our diffusion model is trained with
similar visual features retrieved via CLIP and from the neighborhood of each
training instance. By leveraging CLIP's joint image-text embedding space, our
model achieves highly competitive performance on tasks for which it has not
been explicitly trained, such as class-conditional or text-image synthesis, and
can be conditioned on both text and image embeddings. Moreover, we can apply
our approach to unconditional generation, where it achieves state-of-the-art
performance. Our approach incurs low computational and memory overheads and is
easy to implement. We discuss its relationship to concurrent work and will
publish code and pretrained models soon.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:337:" <a href="http://arxiv.org/find/cs/1/au:+Blattmann_A/0/1/0/all/0/1">Andreas Blattmann</a>, <a href="http://arxiv.org/find/cs/1/au:+Rombach_R/0/1/0/all/0/1">Robin Rombach</a>, <a href="http://arxiv.org/find/cs/1/au:+Oktay_K/0/1/0/all/0/1">Kaan Oktay</a>, <a href="http://arxiv.org/find/cs/1/au:+Ommer_B/0/1/0/all/0/1">Bj&#xf6;rn Ommer</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:426;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2202.04915";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"Quantum advantage using high-dimensional twisted photons as quantum finite automata. (arXiv:2202.04915v1 [quant-ph] CROSS LISTED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2202.04915";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1007:"Quantum finite automata (QFA) are basic computational devices that make
binary decisions using quantum operations. They are known to be exponentially
memory efficient compared to their classical counterparts. Here, we demonstrate
an experimental implementation of multi-qubit QFAs using the orbital angular
momentum (OAM) of single photons. We implement different high-dimensional QFAs
encoded on a single photon, where multiple qubits operate in parallel without
the need for complicated multi-partite operations. Using two to eight OAM
quantum states to implement up to four parallel qubits, we show that a
high-dimensional QFA is able to detect the prime numbers 5 and 11 while
outperforming classical finite automata in terms of the required memory. Our
work benefits from the ease of encoding, manipulating, and deciphering
multi-qubit states encoded in the OAM degree of freedom of single photons,
demonstrating the advantages structured photons provide for complex quantum
information tasks.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:395:" <a href="http://arxiv.org/find/quant-ph/1/au:+Plachta_S/0/1/0/all/0/1">Stephen Z. D. Plachta</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Hiekkamaki_M/0/1/0/all/0/1">Markus Hiekkam&#xe4;ki</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Yakaryilmaz_A/0/1/0/all/0/1">Abuzer Yakary&#x131;lmaz</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Fickler_R/0/1/0/all/0/1">Robert Fickler</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:427;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11188";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:86:"M2N: Mesh Movement Networks for PDE Solvers. (arXiv:2204.11188v1 [cs.LG] CROSS LISTED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11188";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1457:"Mainstream numerical Partial Differential Equation (PDE) solvers require
discretizing the physical domain using a mesh. Mesh movement methods aim to
improve the accuracy of the numerical solution by increasing mesh resolution
where the solution is not well-resolved, whilst reducing unnecessary resolution
elsewhere. However, mesh movement methods, such as the Monge-Ampere method,
require the solution of auxiliary equations, which can be extremely expensive
especially when the mesh is adapted frequently. In this paper, we propose to
our best knowledge the first learning-based end-to-end mesh movement framework
for PDE solvers. Key requirements of learning-based mesh movement methods are
alleviating mesh tangling, boundary consistency, and generalization to mesh
with different resolutions. To achieve these goals, we introduce the neural
spline model and the graph attention network (GAT) into our models
respectively. While the Neural-Spline based model provides more flexibility for
large deformation, the GAT based model can handle domains with more complicated
shapes and is better at performing delicate local deformation. We validate our
methods on stationary and time-dependent, linear and non-linear equations, as
well as regularly and irregularly shaped domains. Compared to the traditional
Monge-Ampere method, our approach can greatly accelerate the mesh adaptation
process, whilst achieving comparable numerical error reduction.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:886:" <a href="http://arxiv.org/find/cs/1/au:+Song_W/0/1/0/all/0/1">Wenbin Song</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_M/0/1/0/all/0/1">Mingrui Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Wallwork_J/0/1/0/all/0/1">Joseph G. Wallwork</a>, <a href="http://arxiv.org/find/cs/1/au:+Gao_J/0/1/0/all/0/1">Junpeng Gao</a>, <a href="http://arxiv.org/find/cs/1/au:+Tian_Z/0/1/0/all/0/1">Zheng Tian</a>, <a href="http://arxiv.org/find/cs/1/au:+Sun_F/0/1/0/all/0/1">Fanglei Sun</a>, <a href="http://arxiv.org/find/cs/1/au:+Piggott_M/0/1/0/all/0/1">Matthew D. Piggott</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_J/0/1/0/all/0/1">Junqing Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Shi_Z/0/1/0/all/0/1">Zuoqiang Shi</a>, <a href="http://arxiv.org/find/cs/1/au:+Chen_X/0/1/0/all/0/1">Xiang Chen</a>, <a href="http://arxiv.org/find/cs/1/au:+Wang_J/0/1/0/all/0/1">Jun Wang</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:428;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11338";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"Taming Hybrid-Cloud Fast and Scalable Graph Analytics at Twitter. (arXiv:2204.11338v1 [cs.DB] CROSS LISTED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11338";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1239:"We have witnessed a boosted demand for graph analytics at Twitter in recent
years, and graph analytics has become one of the key parts of Twitter's
large-scale data analytics and machine learning for driving engagement, serving
the most relevant content, and promoting healthier conversations. However,
infrastructure for graph analytics has historically not been an area of
investment at Twitter, resulting in a long timeline and huge engineering effort
for each project to deal with graphs at the Twitter scale. How do we build a
unified graph analytics user experience to fulfill modern data analytics on
various graph scales spanning from thousands to hundreds of billions of
vertices and edges?

To bring fast and scalable graph analytics capability into production, we
investigate the challenges we are facing in large-scale graph analytics at
Twitter and propose a unified graph analytics platform for efficient, scalable,
and reliable graph analytics across on-premises and cloud, to fulfill the
requirements of diverse graph use cases and challenging scales. We also conduct
quantitative benchmarking on Twitter's production-level graph use cases between
popular graph analytics frameworks to certify our solution.

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:9:"parseType";s:7:"Literal";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:988:" <a href="http://arxiv.org/find/cs/1/au:+Tang_C/0/1/0/all/0/1">Chunxu Tang</a>, <a href="http://arxiv.org/find/cs/1/au:+Li_Y/0/1/0/all/0/1">Yao Li</a>, <a href="http://arxiv.org/find/cs/1/au:+Luo_Z/0/1/0/all/0/1">Zhenxiao Luo</a>, <a href="http://arxiv.org/find/cs/1/au:+Ghosh_M/0/1/0/all/0/1">Mainak Ghosh</a>, <a href="http://arxiv.org/find/cs/1/au:+Wu_H/0/1/0/all/0/1">Huijun Wu</a>, <a href="http://arxiv.org/find/cs/1/au:+Zhang_L/0/1/0/all/0/1">Lu Zhang</a>, <a href="http://arxiv.org/find/cs/1/au:+Lu_A/0/1/0/all/0/1">Anneliese Lu</a>, <a href="http://arxiv.org/find/cs/1/au:+Kabra_R/0/1/0/all/0/1">Ruchin Kabra</a>, <a href="http://arxiv.org/find/cs/1/au:+Navadiya_N/0/1/0/all/0/1">Nikhil Kantibhai Navadiya</a>, <a href="http://arxiv.org/find/cs/1/au:+Mishra_P/0/1/0/all/0/1">Prachi Mishra</a>, <a href="http://arxiv.org/find/cs/1/au:+Mukhedkar_P/0/1/0/all/0/1">Prateek Mukhedkar</a>, <a href="http://arxiv.org/find/cs/1/au:+Channapattan_V/0/1/0/all/0/1">Vrushali Channapattan</a>";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:429;a:6:{s:4:"data";s:5:"

";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:31:"http://arxiv.org/abs/2204.11593";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:2:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:136:"Scaling Cross-Domain Content-Based Image Retrieval for E-commerce Snap and Search Application. (arXiv:2204.11593v1 [cs.IR] CROSS LISTED)";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:31:"http://arxiv.org/abs/2204.11593";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:549:"In this industry talk at ECIR 2022, we illustrate how we approach the main
challenges from large scale cross-domain content-based image retrieval using a
cascade method and a combination of our visual search and classification
capabilities. Specifically, we present a system that is able to handle the
scale of the data for e-commerce usage and the cross-domain nature of the query
and gallery image pools. We showcase the approach applied in real-world
e-commerce snap and search use case and its impact on ranking and latency
performance.

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