{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ONP7ZOYQNEO4UJLW6A5VA23XXW","short_pith_number":"pith:ONP7ZOYQ","canonical_record":{"source":{"id":"2312.03796","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-06T14:33:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"12f5df084c455cab3b47afb36d2101a0f9fe99f34394a12e44b0fcc65245ebeb","abstract_canon_sha256":"9affe09b437e71045f88dc7e49000a7bb70c6d0666b0ce1262038efbb372fc13"},"schema_version":"1.0"},"canonical_sha256":"735ffcbb10691dca2576f03b506b77bdb757f9c8bf819517d719b4df0e803350","source":{"kind":"arxiv","id":"2312.03796","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.03796","created_at":"2026-07-05T07:21:15Z"},{"alias_kind":"arxiv_version","alias_value":"2312.03796v1","created_at":"2026-07-05T07:21:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.03796","created_at":"2026-07-05T07:21:15Z"},{"alias_kind":"pith_short_12","alias_value":"ONP7ZOYQNEO4","created_at":"2026-07-05T07:21:15Z"},{"alias_kind":"pith_short_16","alias_value":"ONP7ZOYQNEO4UJLW","created_at":"2026-07-05T07:21:15Z"},{"alias_kind":"pith_short_8","alias_value":"ONP7ZOYQ","created_at":"2026-07-05T07:21:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ONP7ZOYQNEO4UJLW6A5VA23XXW","target":"record","payload":{"canonical_record":{"source":{"id":"2312.03796","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-06T14:33:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"12f5df084c455cab3b47afb36d2101a0f9fe99f34394a12e44b0fcc65245ebeb","abstract_canon_sha256":"9affe09b437e71045f88dc7e49000a7bb70c6d0666b0ce1262038efbb372fc13"},"schema_version":"1.0"},"canonical_sha256":"735ffcbb10691dca2576f03b506b77bdb757f9c8bf819517d719b4df0e803350","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:21:15.160648Z","signature_b64":"QEHc0l3ydfr081XwcmfXrqtQi8WEXoNV3mDPpCe1leJgLGej/eoA4WmKDOxfGfKvYg6FKYteHcY8TduwrbehAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"735ffcbb10691dca2576f03b506b77bdb757f9c8bf819517d719b4df0e803350","last_reissued_at":"2026-07-05T07:21:15.160147Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:21:15.160147Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.03796","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":"2026-07-05T07:21:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hi9UsJmyW3xh/h0HCxe5e1IFqia0M57Zb0IEkb6zwMXDf+eAuulmNEIiOoZn1fCdYOAxkw1VrscjCZ+Nwa2zCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T08:55:49.405136Z"},"content_sha256":"8dcc226d4b1a1af431a0b26fbbe7bde9a434e9c7787849dd3fad6a31379e8d19","schema_version":"1.0","event_id":"sha256:8dcc226d4b1a1af431a0b26fbbe7bde9a434e9c7787849dd3fad6a31379e8d19"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ONP7ZOYQNEO4UJLW6A5VA23XXW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Guoxing Wang, Hao Wu, Hongbo Guo, Xinzi Xu","submitted_at":"2023-12-06T14:33:50Z","abstract_excerpt":"Multi-modal biomedical time series (MBTS) data offers a holistic view of the physiological state, holding significant importance in various bio-medical applications. Owing to inherent noise and distribution gaps across different modalities, MBTS can be complex to model. Various deep learning models have been developed to learn representations of MBTS but still fall short in robustness due to the ignorance of modal-to-modal variations. This paper presents a multi-scale and multi-modal biomedical time series representation learning (MBSL) network with contrastive learning to migrate these variat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.03796","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/2312.03796/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":"2026-07-05T07:21:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cpL91B/j5m2x32Gs9KxtWOXdWU3S+cA3byj2BP3GlFlXBhgdJRG+QiFuHVpQ5vfWMuKw67+n8f6qGT4hj5hIDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T08:55:49.406993Z"},"content_sha256":"83ab4e76925d2f42e75164f24059178572a37078f4e8a35f7a99466b959b9ed0","schema_version":"1.0","event_id":"sha256:83ab4e76925d2f42e75164f24059178572a37078f4e8a35f7a99466b959b9ed0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ONP7ZOYQNEO4UJLW6A5VA23XXW/bundle.json","state_url":"https://pith.science/pith/ONP7ZOYQNEO4UJLW6A5VA23XXW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ONP7ZOYQNEO4UJLW6A5VA23XXW/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":"2026-08-17T08:55:49Z","links":{"resolver":"https://pith.science/pith/ONP7ZOYQNEO4UJLW6A5VA23XXW","bundle":"https://pith.science/pith/ONP7ZOYQNEO4UJLW6A5VA23XXW/bundle.json","state":"https://pith.science/pith/ONP7ZOYQNEO4UJLW6A5VA23XXW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ONP7ZOYQNEO4UJLW6A5VA23XXW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ONP7ZOYQNEO4UJLW6A5VA23XXW","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":"9affe09b437e71045f88dc7e49000a7bb70c6d0666b0ce1262038efbb372fc13","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-06T14:33:50Z","title_canon_sha256":"12f5df084c455cab3b47afb36d2101a0f9fe99f34394a12e44b0fcc65245ebeb"},"schema_version":"1.0","source":{"id":"2312.03796","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.03796","created_at":"2026-07-05T07:21:15Z"},{"alias_kind":"arxiv_version","alias_value":"2312.03796v1","created_at":"2026-07-05T07:21:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.03796","created_at":"2026-07-05T07:21:15Z"},{"alias_kind":"pith_short_12","alias_value":"ONP7ZOYQNEO4","created_at":"2026-07-05T07:21:15Z"},{"alias_kind":"pith_short_16","alias_value":"ONP7ZOYQNEO4UJLW","created_at":"2026-07-05T07:21:15Z"},{"alias_kind":"pith_short_8","alias_value":"ONP7ZOYQ","created_at":"2026-07-05T07:21:15Z"}],"graph_snapshots":[{"event_id":"sha256:83ab4e76925d2f42e75164f24059178572a37078f4e8a35f7a99466b959b9ed0","target":"graph","created_at":"2026-07-05T07:21:15Z","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/2312.03796/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-modal biomedical time series (MBTS) data offers a holistic view of the physiological state, holding significant importance in various bio-medical applications. Owing to inherent noise and distribution gaps across different modalities, MBTS can be complex to model. Various deep learning models have been developed to learn representations of MBTS but still fall short in robustness due to the ignorance of modal-to-modal variations. This paper presents a multi-scale and multi-modal biomedical time series representation learning (MBSL) network with contrastive learning to migrate these variat","authors_text":"Guoxing Wang, Hao Wu, Hongbo Guo, Xinzi Xu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-06T14:33:50Z","title":"Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.03796","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":"sha256:8dcc226d4b1a1af431a0b26fbbe7bde9a434e9c7787849dd3fad6a31379e8d19","target":"record","created_at":"2026-07-05T07:21:15Z","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":"9affe09b437e71045f88dc7e49000a7bb70c6d0666b0ce1262038efbb372fc13","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-06T14:33:50Z","title_canon_sha256":"12f5df084c455cab3b47afb36d2101a0f9fe99f34394a12e44b0fcc65245ebeb"},"schema_version":"1.0","source":{"id":"2312.03796","kind":"arxiv","version":1}},"canonical_sha256":"735ffcbb10691dca2576f03b506b77bdb757f9c8bf819517d719b4df0e803350","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"735ffcbb10691dca2576f03b506b77bdb757f9c8bf819517d719b4df0e803350","first_computed_at":"2026-07-05T07:21:15.160147Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:21:15.160147Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QEHc0l3ydfr081XwcmfXrqtQi8WEXoNV3mDPpCe1leJgLGej/eoA4WmKDOxfGfKvYg6FKYteHcY8TduwrbehAg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:21:15.160648Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.03796","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8dcc226d4b1a1af431a0b26fbbe7bde9a434e9c7787849dd3fad6a31379e8d19","sha256:83ab4e76925d2f42e75164f24059178572a37078f4e8a35f7a99466b959b9ed0"],"state_sha256":"d11a68a5cbbbf21f63415b983c3fd697abc145892e9f4f43947ebac41549c34d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"43FR9gHgyOLSpPxJy3YUb6F5j53UFfS08Ss0lfL32XkJfduZ6kPiyBp1zF1I/u6aKXKM9oVZtCV4OT8okoweAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T08:55:49.416823Z","bundle_sha256":"3bc21c22ec354f88029369f23498aa38014b9fa5323a3d87b4a22c9789c6b143"}}