{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:4M6H5CTSP6P4ZFKAFWNIKHM6R2","short_pith_number":"pith:4M6H5CTS","canonical_record":{"source":{"id":"2309.09987","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-14T19:29:14Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"e51450926877b42c62a067ffb999d9f5a0da38a3d3d4f49f91ccb7f41e18e08f","abstract_canon_sha256":"7f9574faf00b22fcbdfe448c438272fbdb369763c37452aaa0c347e46663be78"},"schema_version":"1.0"},"canonical_sha256":"e33c7e8a727f9fcc95402d9a851d9e8e9933566f80420f07d0f71ce38d124a4a","source":{"kind":"arxiv","id":"2309.09987","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.09987","created_at":"2026-07-05T06:52:08Z"},{"alias_kind":"arxiv_version","alias_value":"2309.09987v1","created_at":"2026-07-05T06:52:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.09987","created_at":"2026-07-05T06:52:08Z"},{"alias_kind":"pith_short_12","alias_value":"4M6H5CTSP6P4","created_at":"2026-07-05T06:52:08Z"},{"alias_kind":"pith_short_16","alias_value":"4M6H5CTSP6P4ZFKA","created_at":"2026-07-05T06:52:08Z"},{"alias_kind":"pith_short_8","alias_value":"4M6H5CTS","created_at":"2026-07-05T06:52:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:4M6H5CTSP6P4ZFKAFWNIKHM6R2","target":"record","payload":{"canonical_record":{"source":{"id":"2309.09987","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-14T19:29:14Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"e51450926877b42c62a067ffb999d9f5a0da38a3d3d4f49f91ccb7f41e18e08f","abstract_canon_sha256":"7f9574faf00b22fcbdfe448c438272fbdb369763c37452aaa0c347e46663be78"},"schema_version":"1.0"},"canonical_sha256":"e33c7e8a727f9fcc95402d9a851d9e8e9933566f80420f07d0f71ce38d124a4a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:52:08.800695Z","signature_b64":"XYZVaMm3mbgFMiGQucjlvn6vd2BmmoXZAf6Iu7Z9JLrpOEbzmPmtVtfqMy8AFZzAe17Z+w+QJ5WI2h/VuzVcBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e33c7e8a727f9fcc95402d9a851d9e8e9933566f80420f07d0f71ce38d124a4a","last_reissued_at":"2026-07-05T06:52:08.800191Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:52:08.800191Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.09987","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":"2026-07-05T06:52:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lQIo8JQmxvH7tKKGuiZoOxoCxQPoJCtd3EthLxqZcp2pJvOic6h2j+oITVSZpXiZ2C4lQ0r1jtd6riNFUSdeDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T23:43:41.311213Z"},"content_sha256":"e69a3233844a958264cb0cbe6980d24ef15a2a2df8367b38a8a9cb0fd549c890","schema_version":"1.0","event_id":"sha256:e69a3233844a958264cb0cbe6980d24ef15a2a2df8367b38a8a9cb0fd549c890"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:4M6H5CTSP6P4ZFKAFWNIKHM6R2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TCGF: A unified tensorized consensus graph framework for multi-view representation learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Liang Wang, Qiang Liu, Shu Wu, Wei Wei, Xiangzhu Meng","submitted_at":"2023-09-14T19:29:14Z","abstract_excerpt":"Multi-view learning techniques have recently gained significant attention in the machine learning domain for their ability to leverage consistency and complementary information across multiple views. However, there remains a lack of sufficient research on generalized multi-view frameworks that unify existing works into a scalable and robust learning framework, as most current works focus on specific styles of multi-view models. Additionally, most multi-view learning works rely heavily on specific-scale scenarios and fail to effectively comprehend multiple scales holistically. These limitations"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.09987","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2309.09987/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T06:52:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"odctpHfHZo3lLlEpqRrxpzgUgfOUEmgSZ7kQ00pu1BAssQz6PgChA2VMIDJTgnSR3OhkgIRpKHobO13PX1VgAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T23:43:41.311739Z"},"content_sha256":"74e653ba690ff29b5ec2c0405961e3eb5a4594f2e516e45af155e194480be906","schema_version":"1.0","event_id":"sha256:74e653ba690ff29b5ec2c0405961e3eb5a4594f2e516e45af155e194480be906"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4M6H5CTSP6P4ZFKAFWNIKHM6R2/bundle.json","state_url":"https://pith.science/pith/4M6H5CTSP6P4ZFKAFWNIKHM6R2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4M6H5CTSP6P4ZFKAFWNIKHM6R2/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":"2026-08-10T23:43:41Z","links":{"resolver":"https://pith.science/pith/4M6H5CTSP6P4ZFKAFWNIKHM6R2","bundle":"https://pith.science/pith/4M6H5CTSP6P4ZFKAFWNIKHM6R2/bundle.json","state":"https://pith.science/pith/4M6H5CTSP6P4ZFKAFWNIKHM6R2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4M6H5CTSP6P4ZFKAFWNIKHM6R2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:4M6H5CTSP6P4ZFKAFWNIKHM6R2","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":"7f9574faf00b22fcbdfe448c438272fbdb369763c37452aaa0c347e46663be78","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-14T19:29:14Z","title_canon_sha256":"e51450926877b42c62a067ffb999d9f5a0da38a3d3d4f49f91ccb7f41e18e08f"},"schema_version":"1.0","source":{"id":"2309.09987","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.09987","created_at":"2026-07-05T06:52:08Z"},{"alias_kind":"arxiv_version","alias_value":"2309.09987v1","created_at":"2026-07-05T06:52:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.09987","created_at":"2026-07-05T06:52:08Z"},{"alias_kind":"pith_short_12","alias_value":"4M6H5CTSP6P4","created_at":"2026-07-05T06:52:08Z"},{"alias_kind":"pith_short_16","alias_value":"4M6H5CTSP6P4ZFKA","created_at":"2026-07-05T06:52:08Z"},{"alias_kind":"pith_short_8","alias_value":"4M6H5CTS","created_at":"2026-07-05T06:52:08Z"}],"graph_snapshots":[{"event_id":"sha256:74e653ba690ff29b5ec2c0405961e3eb5a4594f2e516e45af155e194480be906","target":"graph","created_at":"2026-07-05T06:52:08Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2309.09987/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-view learning techniques have recently gained significant attention in the machine learning domain for their ability to leverage consistency and complementary information across multiple views. However, there remains a lack of sufficient research on generalized multi-view frameworks that unify existing works into a scalable and robust learning framework, as most current works focus on specific styles of multi-view models. Additionally, most multi-view learning works rely heavily on specific-scale scenarios and fail to effectively comprehend multiple scales holistically. These limitations","authors_text":"Liang Wang, Qiang Liu, Shu Wu, Wei Wei, Xiangzhu Meng","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-14T19:29:14Z","title":"TCGF: A unified tensorized consensus graph framework for multi-view representation learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.09987","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:e69a3233844a958264cb0cbe6980d24ef15a2a2df8367b38a8a9cb0fd549c890","target":"record","created_at":"2026-07-05T06:52:08Z","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":"7f9574faf00b22fcbdfe448c438272fbdb369763c37452aaa0c347e46663be78","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-14T19:29:14Z","title_canon_sha256":"e51450926877b42c62a067ffb999d9f5a0da38a3d3d4f49f91ccb7f41e18e08f"},"schema_version":"1.0","source":{"id":"2309.09987","kind":"arxiv","version":1}},"canonical_sha256":"e33c7e8a727f9fcc95402d9a851d9e8e9933566f80420f07d0f71ce38d124a4a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e33c7e8a727f9fcc95402d9a851d9e8e9933566f80420f07d0f71ce38d124a4a","first_computed_at":"2026-07-05T06:52:08.800191Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:52:08.800191Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XYZVaMm3mbgFMiGQucjlvn6vd2BmmoXZAf6Iu7Z9JLrpOEbzmPmtVtfqMy8AFZzAe17Z+w+QJ5WI2h/VuzVcBw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:52:08.800695Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.09987","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e69a3233844a958264cb0cbe6980d24ef15a2a2df8367b38a8a9cb0fd549c890","sha256:74e653ba690ff29b5ec2c0405961e3eb5a4594f2e516e45af155e194480be906"],"state_sha256":"46afce9e87745026ff34fccd86dcc10c8313192ec3deda8e7f3b50d1bff54c43"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h5q3b52X0A9qEtWuX0JkO8Qj8yeRO1BCrUqOIDt8QJNt+xBDwrIt13RJvQQbwqP1SwZWynr8QNZJKc8aGGpOCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T23:43:41.317062Z","bundle_sha256":"3d9909afdb3183d7c3a000ba3200d6fc5e9d95afa45ff03de4001ade2fd379b4"}}