{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:OYHHA5QLGHGHGU6GFPFM6ZRBDT","short_pith_number":"pith:OYHHA5QL","canonical_record":{"source":{"id":"1905.04424","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-11T02:28:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5afef4d4c85424e60c5f4f37bee753fb58a8239deac8f8cdd53c1c38d4901352","abstract_canon_sha256":"e678e6901cfc9c64e322b5ab27461dec413d6e18c1e4966acf6fe035c2e7ea7b"},"schema_version":"1.0"},"canonical_sha256":"760e70760b31cc7353c62bcacf66211cd6f5fc01ff289e6423fbf5eb7249aace","source":{"kind":"arxiv","id":"1905.04424","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.04424","created_at":"2026-05-17T23:46:24Z"},{"alias_kind":"arxiv_version","alias_value":"1905.04424v1","created_at":"2026-05-17T23:46:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.04424","created_at":"2026-05-17T23:46:24Z"},{"alias_kind":"pith_short_12","alias_value":"OYHHA5QLGHGH","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"OYHHA5QLGHGHGU6G","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"OYHHA5QL","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:OYHHA5QLGHGHGU6GFPFM6ZRBDT","target":"record","payload":{"canonical_record":{"source":{"id":"1905.04424","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-11T02:28:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5afef4d4c85424e60c5f4f37bee753fb58a8239deac8f8cdd53c1c38d4901352","abstract_canon_sha256":"e678e6901cfc9c64e322b5ab27461dec413d6e18c1e4966acf6fe035c2e7ea7b"},"schema_version":"1.0"},"canonical_sha256":"760e70760b31cc7353c62bcacf66211cd6f5fc01ff289e6423fbf5eb7249aace","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:24.954652Z","signature_b64":"RhaJ2/trW1+3edy5bK49xJKffSqg7cD06nTat0l2dpHACfGoL5q00KyTCtQr20PPBAQniFym/eC4sj57kLQNBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"760e70760b31cc7353c62bcacf66211cd6f5fc01ff289e6423fbf5eb7249aace","last_reissued_at":"2026-05-17T23:46:24.953951Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:24.953951Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.04424","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e5J6wEaknSZqP6yQCC8apMM5sUdSHrVrRJduM3Pi4J/P/Hc4vQL+N6ZWO8YCFaRKx94j4NSbVgUR17W2GZzKAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T10:21:59.312723Z"},"content_sha256":"bfb976e5e1ac37c860d514f286aa046df0790cc817b4bb0ffc7034624186a956","schema_version":"1.0","event_id":"sha256:bfb976e5e1ac37c860d514f286aa046df0790cc817b4bb0ffc7034624186a956"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:OYHHA5QLGHGHGU6GFPFM6ZRBDT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Structured Discriminative Tensor Dictionary Learning for Unsupervised Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hao Tang, Jianjun Qian, Jian Zhang, Songsong Wu, Xiao-Yuan Jing, Yan Yan","submitted_at":"2019-05-11T02:28:04Z","abstract_excerpt":"Unsupervised Domain Adaptation (UDA) addresses the problem of performance degradation due to domain shift between training and testing sets, which is common in computer vision applications. Most existing UDA approaches are based on vector-form data although the typical format of data or features in visual applications is multi-dimensional tensor. Besides, current methods, including the deep network approaches, assume that abundant labeled source samples are provided for training. However, the number of labeled source samples are always limited due to expensive annotation cost in practice, maki"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.04424","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iy9CAhh2d/nSXmrIh7BjeMPxOQB4mPw2WT+JogDDicIKgsdhXCdcQrKzIHr9PSsp7cdrnX6ml5suSvKZ/x9VBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T10:21:59.313389Z"},"content_sha256":"70f8ad4944fef0d63277a79fb3406467bda7e910857acfcf8bbcad8716424a50","schema_version":"1.0","event_id":"sha256:70f8ad4944fef0d63277a79fb3406467bda7e910857acfcf8bbcad8716424a50"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OYHHA5QLGHGHGU6GFPFM6ZRBDT/bundle.json","state_url":"https://pith.science/pith/OYHHA5QLGHGHGU6GFPFM6ZRBDT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OYHHA5QLGHGHGU6GFPFM6ZRBDT/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-31T10:21:59Z","links":{"resolver":"https://pith.science/pith/OYHHA5QLGHGHGU6GFPFM6ZRBDT","bundle":"https://pith.science/pith/OYHHA5QLGHGHGU6GFPFM6ZRBDT/bundle.json","state":"https://pith.science/pith/OYHHA5QLGHGHGU6GFPFM6ZRBDT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OYHHA5QLGHGHGU6GFPFM6ZRBDT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:OYHHA5QLGHGHGU6GFPFM6ZRBDT","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e678e6901cfc9c64e322b5ab27461dec413d6e18c1e4966acf6fe035c2e7ea7b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-11T02:28:04Z","title_canon_sha256":"5afef4d4c85424e60c5f4f37bee753fb58a8239deac8f8cdd53c1c38d4901352"},"schema_version":"1.0","source":{"id":"1905.04424","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.04424","created_at":"2026-05-17T23:46:24Z"},{"alias_kind":"arxiv_version","alias_value":"1905.04424v1","created_at":"2026-05-17T23:46:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.04424","created_at":"2026-05-17T23:46:24Z"},{"alias_kind":"pith_short_12","alias_value":"OYHHA5QLGHGH","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"OYHHA5QLGHGHGU6G","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"OYHHA5QL","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:70f8ad4944fef0d63277a79fb3406467bda7e910857acfcf8bbcad8716424a50","target":"graph","created_at":"2026-05-17T23:46:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Unsupervised Domain Adaptation (UDA) addresses the problem of performance degradation due to domain shift between training and testing sets, which is common in computer vision applications. Most existing UDA approaches are based on vector-form data although the typical format of data or features in visual applications is multi-dimensional tensor. Besides, current methods, including the deep network approaches, assume that abundant labeled source samples are provided for training. However, the number of labeled source samples are always limited due to expensive annotation cost in practice, maki","authors_text":"Hao Tang, Jianjun Qian, Jian Zhang, Songsong Wu, Xiao-Yuan Jing, Yan Yan","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-11T02:28:04Z","title":"Structured Discriminative Tensor Dictionary Learning for Unsupervised Domain Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.04424","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:bfb976e5e1ac37c860d514f286aa046df0790cc817b4bb0ffc7034624186a956","target":"record","created_at":"2026-05-17T23:46:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e678e6901cfc9c64e322b5ab27461dec413d6e18c1e4966acf6fe035c2e7ea7b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-11T02:28:04Z","title_canon_sha256":"5afef4d4c85424e60c5f4f37bee753fb58a8239deac8f8cdd53c1c38d4901352"},"schema_version":"1.0","source":{"id":"1905.04424","kind":"arxiv","version":1}},"canonical_sha256":"760e70760b31cc7353c62bcacf66211cd6f5fc01ff289e6423fbf5eb7249aace","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"760e70760b31cc7353c62bcacf66211cd6f5fc01ff289e6423fbf5eb7249aace","first_computed_at":"2026-05-17T23:46:24.953951Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:24.953951Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RhaJ2/trW1+3edy5bK49xJKffSqg7cD06nTat0l2dpHACfGoL5q00KyTCtQr20PPBAQniFym/eC4sj57kLQNBg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:24.954652Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.04424","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bfb976e5e1ac37c860d514f286aa046df0790cc817b4bb0ffc7034624186a956","sha256:70f8ad4944fef0d63277a79fb3406467bda7e910857acfcf8bbcad8716424a50"],"state_sha256":"c6fa725410407cc340897fe2313c31277e676a7cff020c4263c4063fb381fbdd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gR27EmGxC7dr5K2B0uELzJKgbtGkZ2GZWt2eVbt8P78PuZnuS7buWLZKuiE7T8iTDY1ENhUSU4Z6/3u+Q2lBDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T10:21:59.317101Z","bundle_sha256":"7ef49e2ee90fea99d200b1b70a42651697efefc6b955c80858919c76da9e88e1"}}