{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:6QVYAUQIOD6P74G2AG6AXF3X2Y","short_pith_number":"pith:6QVYAUQI","canonical_record":{"source":{"id":"1604.04574","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-15T17:20:01Z","cross_cats_sorted":[],"title_canon_sha256":"ab2bc8fb19578af14bebec53f2fdbad5149715d5ae2b0673240b621cdd62e2bd","abstract_canon_sha256":"d92db6db7fbea28634c26a2a5c455f0aadd91ce99e4b42dda963c9155fd95684"},"schema_version":"1.0"},"canonical_sha256":"f42b80520870fcfff0da01bc0b9777d63d7d8b0c256c5dfb34fbf180512a9b60","source":{"kind":"arxiv","id":"1604.04574","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.04574","created_at":"2026-05-18T01:17:01Z"},{"alias_kind":"arxiv_version","alias_value":"1604.04574v1","created_at":"2026-05-18T01:17:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.04574","created_at":"2026-05-18T01:17:01Z"},{"alias_kind":"pith_short_12","alias_value":"6QVYAUQIOD6P","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_16","alias_value":"6QVYAUQIOD6P74G2","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_8","alias_value":"6QVYAUQI","created_at":"2026-05-18T12:30:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:6QVYAUQIOD6P74G2AG6AXF3X2Y","target":"record","payload":{"canonical_record":{"source":{"id":"1604.04574","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-15T17:20:01Z","cross_cats_sorted":[],"title_canon_sha256":"ab2bc8fb19578af14bebec53f2fdbad5149715d5ae2b0673240b621cdd62e2bd","abstract_canon_sha256":"d92db6db7fbea28634c26a2a5c455f0aadd91ce99e4b42dda963c9155fd95684"},"schema_version":"1.0"},"canonical_sha256":"f42b80520870fcfff0da01bc0b9777d63d7d8b0c256c5dfb34fbf180512a9b60","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:17:01.810835Z","signature_b64":"52OvZnrrpnfL4N0cl/F9+2u/6CYWIG2Oksr21tWnr0dP0CsWvQilL0yWPYpGNWl/SWo+UUm+kwcTp3WBeMpuAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f42b80520870fcfff0da01bc0b9777d63d7d8b0c256c5dfb34fbf180512a9b60","last_reissued_at":"2026-05-18T01:17:01.810144Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:17:01.810144Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1604.04574","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":"2026-05-18T01:17:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PQHxntPsiNcADOyVQhKBxcrvHXMnrODzxyediM+K9LYog/hfCqphP8LWbceMe4AnWG0Uh1tGY3LQ+CZ25c/0Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T19:14:27.754658Z"},"content_sha256":"e125497f666360c9958be6a06d5561f700eeb6de2f4bc64f8bc50803d87f2cf5","schema_version":"1.0","event_id":"sha256:e125497f666360c9958be6a06d5561f700eeb6de2f4bc64f8bc50803d87f2cf5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:6QVYAUQIOD6P74G2AG6AXF3X2Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Temporal Regularity in Video Sequences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amit K. Roy-Chowdhury, Jan Neumann, Jonghyun Choi, Larry S. Davis, Mahmudul Hasan","submitted_at":"2016-04-15T17:20:01Z","abstract_excerpt":"Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene. We approach this problem by learning a generative model for regular motion patterns, termed as regularity, using multiple sources with very limited supervision. Specifically, we propose two methods that are built upon the autoencoders for their ability to work with little to no supervision. We first leverage the conventional handcrafted spatio-temporal local features and learn a fully connected autoencoder on them. Second, we build"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.04574","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":"2026-05-18T01:17:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GORhcs/hE9SFSnVa7sLxLk6ofTqaF8v/KYzqNVv0+PbtPzNKLaBz3ye/+5zz7St1+9ZTeC4l71ycwinu6EGrBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T19:14:27.755007Z"},"content_sha256":"d9d53f39c301d8ef5cb5993bba4b89df82d7de6bde80958575df2b67a74bb447","schema_version":"1.0","event_id":"sha256:d9d53f39c301d8ef5cb5993bba4b89df82d7de6bde80958575df2b67a74bb447"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6QVYAUQIOD6P74G2AG6AXF3X2Y/bundle.json","state_url":"https://pith.science/pith/6QVYAUQIOD6P74G2AG6AXF3X2Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6QVYAUQIOD6P74G2AG6AXF3X2Y/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":"2026-06-01T19:14:27Z","links":{"resolver":"https://pith.science/pith/6QVYAUQIOD6P74G2AG6AXF3X2Y","bundle":"https://pith.science/pith/6QVYAUQIOD6P74G2AG6AXF3X2Y/bundle.json","state":"https://pith.science/pith/6QVYAUQIOD6P74G2AG6AXF3X2Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6QVYAUQIOD6P74G2AG6AXF3X2Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:6QVYAUQIOD6P74G2AG6AXF3X2Y","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":"d92db6db7fbea28634c26a2a5c455f0aadd91ce99e4b42dda963c9155fd95684","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-15T17:20:01Z","title_canon_sha256":"ab2bc8fb19578af14bebec53f2fdbad5149715d5ae2b0673240b621cdd62e2bd"},"schema_version":"1.0","source":{"id":"1604.04574","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.04574","created_at":"2026-05-18T01:17:01Z"},{"alias_kind":"arxiv_version","alias_value":"1604.04574v1","created_at":"2026-05-18T01:17:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.04574","created_at":"2026-05-18T01:17:01Z"},{"alias_kind":"pith_short_12","alias_value":"6QVYAUQIOD6P","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_16","alias_value":"6QVYAUQIOD6P74G2","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_8","alias_value":"6QVYAUQI","created_at":"2026-05-18T12:30:01Z"}],"graph_snapshots":[{"event_id":"sha256:d9d53f39c301d8ef5cb5993bba4b89df82d7de6bde80958575df2b67a74bb447","target":"graph","created_at":"2026-05-18T01:17:01Z","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":"Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene. We approach this problem by learning a generative model for regular motion patterns, termed as regularity, using multiple sources with very limited supervision. Specifically, we propose two methods that are built upon the autoencoders for their ability to work with little to no supervision. We first leverage the conventional handcrafted spatio-temporal local features and learn a fully connected autoencoder on them. Second, we build","authors_text":"Amit K. Roy-Chowdhury, Jan Neumann, Jonghyun Choi, Larry S. Davis, Mahmudul Hasan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-15T17:20:01Z","title":"Learning Temporal Regularity in Video Sequences"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.04574","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:e125497f666360c9958be6a06d5561f700eeb6de2f4bc64f8bc50803d87f2cf5","target":"record","created_at":"2026-05-18T01:17:01Z","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":"d92db6db7fbea28634c26a2a5c455f0aadd91ce99e4b42dda963c9155fd95684","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-15T17:20:01Z","title_canon_sha256":"ab2bc8fb19578af14bebec53f2fdbad5149715d5ae2b0673240b621cdd62e2bd"},"schema_version":"1.0","source":{"id":"1604.04574","kind":"arxiv","version":1}},"canonical_sha256":"f42b80520870fcfff0da01bc0b9777d63d7d8b0c256c5dfb34fbf180512a9b60","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f42b80520870fcfff0da01bc0b9777d63d7d8b0c256c5dfb34fbf180512a9b60","first_computed_at":"2026-05-18T01:17:01.810144Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:17:01.810144Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"52OvZnrrpnfL4N0cl/F9+2u/6CYWIG2Oksr21tWnr0dP0CsWvQilL0yWPYpGNWl/SWo+UUm+kwcTp3WBeMpuAA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:17:01.810835Z","signed_message":"canonical_sha256_bytes"},"source_id":"1604.04574","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e125497f666360c9958be6a06d5561f700eeb6de2f4bc64f8bc50803d87f2cf5","sha256:d9d53f39c301d8ef5cb5993bba4b89df82d7de6bde80958575df2b67a74bb447"],"state_sha256":"15134bc7190063f1820b16520d3c3454460c8fe4bf4aee9297ba9c81f1f93c00"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XY8HXih56SVWUOeGlxi1dMkj0R8sWH0ZHosAiwC4kj7xNl22KXPBjuq5bbBZEtjPqWzhW3s9Ey+6ZSrJmlHtCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T19:14:27.756961Z","bundle_sha256":"06bc4696295c86b592cf5d9d21149a6032934b5b7bfc3da8dab13fffcc4c30b9"}}