{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:TYVWWZR3TOEPJ54MDFT3ZEOX6B","short_pith_number":"pith:TYVWWZR3","canonical_record":{"source":{"id":"2203.11178","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-21T17:56:12Z","cross_cats_sorted":["eess.SP","physics.med-ph"],"title_canon_sha256":"d97afbccddf9cedd523a178a1c58636fae4e3c95e632ff0a3f1e0da11cac27ae","abstract_canon_sha256":"94d7aa23ed53f8d4eb19ff63d56a69de2a5d98f32d46a9a74d3748fccdbf846c"},"schema_version":"1.0"},"canonical_sha256":"9e2b6b663b9b88f4f78c1967bc91d7f05de14c1fde720b62da6131c54dbbe5ce","source":{"kind":"arxiv","id":"2203.11178","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.11178","created_at":"2026-07-05T04:25:16Z"},{"alias_kind":"arxiv_version","alias_value":"2203.11178v3","created_at":"2026-07-05T04:25:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.11178","created_at":"2026-07-05T04:25:16Z"},{"alias_kind":"pith_short_12","alias_value":"TYVWWZR3TOEP","created_at":"2026-07-05T04:25:16Z"},{"alias_kind":"pith_short_16","alias_value":"TYVWWZR3TOEPJ54M","created_at":"2026-07-05T04:25:16Z"},{"alias_kind":"pith_short_8","alias_value":"TYVWWZR3","created_at":"2026-07-05T04:25:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:TYVWWZR3TOEPJ54MDFT3ZEOX6B","target":"record","payload":{"canonical_record":{"source":{"id":"2203.11178","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-21T17:56:12Z","cross_cats_sorted":["eess.SP","physics.med-ph"],"title_canon_sha256":"d97afbccddf9cedd523a178a1c58636fae4e3c95e632ff0a3f1e0da11cac27ae","abstract_canon_sha256":"94d7aa23ed53f8d4eb19ff63d56a69de2a5d98f32d46a9a74d3748fccdbf846c"},"schema_version":"1.0"},"canonical_sha256":"9e2b6b663b9b88f4f78c1967bc91d7f05de14c1fde720b62da6131c54dbbe5ce","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:25:16.498998Z","signature_b64":"CX0BxgvMadfrjMd7kWA+dLgBlb3fHQReetYknG+R+7j9q4Fkuv9BKK+cCO3xq6xCjJQ4RkQemkNukZlJVjI4Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e2b6b663b9b88f4f78c1967bc91d7f05de14c1fde720b62da6131c54dbbe5ce","last_reissued_at":"2026-07-05T04:25:16.498598Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:25:16.498598Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.11178","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:25:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qsZmice58/bv+Gc4dsJxfPZ3n0DgTr829J4qZ8lsbMo5cjUAUFvcV8Lg9IYtwVzp83P7PETm5hte3dHfWdOXCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T17:15:56.157995Z"},"content_sha256":"86335f4ce6820ce55979414ef287f79fbf6b47df80b9a70aba92b71e8a096421","schema_version":"1.0","event_id":"sha256:86335f4ce6820ce55979414ef287f79fbf6b47df80b9a70aba92b71e8a096421"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:TYVWWZR3TOEPJ54MDFT3ZEOX6B","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","physics.med-ph"],"primary_cat":"cs.LG","authors_text":"Congbo Cai, Kunyuan Guo, Qinqin Yang, Xiaobo Qu, Zi Wang","submitted_at":"2022-03-21T17:56:12Z","abstract_excerpt":"Deep learning has innovated the field of computational imaging. One of its bottlenecks is unavailable or insufficient training data. This article reviews an emerging paradigm, imaging physics-based data synthesis (IPADS), that can provide huge training data in biomedical magnetic resonance without or with few real data. Following the physical law of magnetic resonance, IPADS generates signals from differential equations or analytical solution models, making the learning more scalable, explainable, and better protecting privacy. Key components of IPADS learning, including signal generation mode"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.11178","kind":"arxiv","version":3},"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/2203.11178/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":"2026-07-05T04:25:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wj4Y1t67wBkhtV640PA/OxPoHhRua5wJGWdebNhPSxMhS5+13pBzeADl8QRYZuX/hKbs2WI5fDeEqBerBGwIBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T17:15:56.158384Z"},"content_sha256":"a3ae0737e9522b17b55bca9311eabe059c6415521755af250fe4bc7ae491deed","schema_version":"1.0","event_id":"sha256:a3ae0737e9522b17b55bca9311eabe059c6415521755af250fe4bc7ae491deed"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TYVWWZR3TOEPJ54MDFT3ZEOX6B/bundle.json","state_url":"https://pith.science/pith/TYVWWZR3TOEPJ54MDFT3ZEOX6B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TYVWWZR3TOEPJ54MDFT3ZEOX6B/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":"2026-07-08T17:15:56Z","links":{"resolver":"https://pith.science/pith/TYVWWZR3TOEPJ54MDFT3ZEOX6B","bundle":"https://pith.science/pith/TYVWWZR3TOEPJ54MDFT3ZEOX6B/bundle.json","state":"https://pith.science/pith/TYVWWZR3TOEPJ54MDFT3ZEOX6B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TYVWWZR3TOEPJ54MDFT3ZEOX6B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:TYVWWZR3TOEPJ54MDFT3ZEOX6B","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":"94d7aa23ed53f8d4eb19ff63d56a69de2a5d98f32d46a9a74d3748fccdbf846c","cross_cats_sorted":["eess.SP","physics.med-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-21T17:56:12Z","title_canon_sha256":"d97afbccddf9cedd523a178a1c58636fae4e3c95e632ff0a3f1e0da11cac27ae"},"schema_version":"1.0","source":{"id":"2203.11178","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.11178","created_at":"2026-07-05T04:25:16Z"},{"alias_kind":"arxiv_version","alias_value":"2203.11178v3","created_at":"2026-07-05T04:25:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.11178","created_at":"2026-07-05T04:25:16Z"},{"alias_kind":"pith_short_12","alias_value":"TYVWWZR3TOEP","created_at":"2026-07-05T04:25:16Z"},{"alias_kind":"pith_short_16","alias_value":"TYVWWZR3TOEPJ54M","created_at":"2026-07-05T04:25:16Z"},{"alias_kind":"pith_short_8","alias_value":"TYVWWZR3","created_at":"2026-07-05T04:25:16Z"}],"graph_snapshots":[{"event_id":"sha256:a3ae0737e9522b17b55bca9311eabe059c6415521755af250fe4bc7ae491deed","target":"graph","created_at":"2026-07-05T04:25:16Z","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/2203.11178/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning has innovated the field of computational imaging. One of its bottlenecks is unavailable or insufficient training data. This article reviews an emerging paradigm, imaging physics-based data synthesis (IPADS), that can provide huge training data in biomedical magnetic resonance without or with few real data. Following the physical law of magnetic resonance, IPADS generates signals from differential equations or analytical solution models, making the learning more scalable, explainable, and better protecting privacy. Key components of IPADS learning, including signal generation mode","authors_text":"Congbo Cai, Kunyuan Guo, Qinqin Yang, Xiaobo Qu, Zi Wang","cross_cats":["eess.SP","physics.med-ph"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-21T17:56:12Z","title":"Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.11178","kind":"arxiv","version":3},"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":"sha256:86335f4ce6820ce55979414ef287f79fbf6b47df80b9a70aba92b71e8a096421","target":"record","created_at":"2026-07-05T04:25:16Z","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":"94d7aa23ed53f8d4eb19ff63d56a69de2a5d98f32d46a9a74d3748fccdbf846c","cross_cats_sorted":["eess.SP","physics.med-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-21T17:56:12Z","title_canon_sha256":"d97afbccddf9cedd523a178a1c58636fae4e3c95e632ff0a3f1e0da11cac27ae"},"schema_version":"1.0","source":{"id":"2203.11178","kind":"arxiv","version":3}},"canonical_sha256":"9e2b6b663b9b88f4f78c1967bc91d7f05de14c1fde720b62da6131c54dbbe5ce","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9e2b6b663b9b88f4f78c1967bc91d7f05de14c1fde720b62da6131c54dbbe5ce","first_computed_at":"2026-07-05T04:25:16.498598Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:25:16.498598Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CX0BxgvMadfrjMd7kWA+dLgBlb3fHQReetYknG+R+7j9q4Fkuv9BKK+cCO3xq6xCjJQ4RkQemkNukZlJVjI4Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:25:16.498998Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.11178","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:86335f4ce6820ce55979414ef287f79fbf6b47df80b9a70aba92b71e8a096421","sha256:a3ae0737e9522b17b55bca9311eabe059c6415521755af250fe4bc7ae491deed"],"state_sha256":"79adff5e588e742b0cfbbee133afc54f84f20e8dff3b3346bf1c19c5ca4a7e8c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OCMOMQCI3Maz+rH80hnB2d6xHkOxIdVF1qCWLKpVS/IdtAPDC6qdXispVq7yGajssKyEnUIXJtNnkUTHHKQwDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T17:15:56.160655Z","bundle_sha256":"d5e591d506b0cfd01f37166b6b749f04e90d0fbafab01c1dd7d19f1743e2dc04"}}