{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:FOFRDOV4MMO4VIKQPUWELUEK22","short_pith_number":"pith:FOFRDOV4","canonical_record":{"source":{"id":"1808.04495","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-14T00:18:33Z","cross_cats_sorted":[],"title_canon_sha256":"b3a808d4ab632f2c341cfa6806b61dd5830612fde1528de7c2d96ec622a6317d","abstract_canon_sha256":"d709254707d3c936bc767d9d594a8a6b1525ba42b99301431334eefe851d495e"},"schema_version":"1.0"},"canonical_sha256":"2b8b11babc631dcaa1507d2c45d08ad6bf60f6b2721299aaed8aa1fd728af1a4","source":{"kind":"arxiv","id":"1808.04495","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.04495","created_at":"2026-05-17T23:54:48Z"},{"alias_kind":"arxiv_version","alias_value":"1808.04495v1","created_at":"2026-05-17T23:54:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04495","created_at":"2026-05-17T23:54:48Z"},{"alias_kind":"pith_short_12","alias_value":"FOFRDOV4MMO4","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"FOFRDOV4MMO4VIKQ","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"FOFRDOV4","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:FOFRDOV4MMO4VIKQPUWELUEK22","target":"record","payload":{"canonical_record":{"source":{"id":"1808.04495","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-14T00:18:33Z","cross_cats_sorted":[],"title_canon_sha256":"b3a808d4ab632f2c341cfa6806b61dd5830612fde1528de7c2d96ec622a6317d","abstract_canon_sha256":"d709254707d3c936bc767d9d594a8a6b1525ba42b99301431334eefe851d495e"},"schema_version":"1.0"},"canonical_sha256":"2b8b11babc631dcaa1507d2c45d08ad6bf60f6b2721299aaed8aa1fd728af1a4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:48.262778Z","signature_b64":"/Gj+Vmsh6qnWqkJjqOAEMhsY4xMTQ95hQEUFMLBtJor4l779l/rsfzQMogEzb9VuEZchPBabEb8Dp7Hcy0McAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b8b11babc631dcaa1507d2c45d08ad6bf60f6b2721299aaed8aa1fd728af1a4","last_reissued_at":"2026-05-17T23:54:48.262158Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:48.262158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.04495","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":"2026-05-17T23:54:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YZLBJtq9gf2QZddR/0ZaO/j1UHBgbZ9ZDV7ZUIzglwv9W7rme0D8p/KYfbSdo9d8uJaJFPSVBNpy3JAqXQdmAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T12:08:07.797335Z"},"content_sha256":"83fe2dc435f227d0c78c66288bfaa6ef2a318ff587b63f7482456090e1cf589d","schema_version":"1.0","event_id":"sha256:83fe2dc435f227d0c78c66288bfaa6ef2a318ff587b63f7482456090e1cf589d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:FOFRDOV4MMO4VIKQPUWELUEK22","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ben Wang, Chuck Zhang, Geet Lahoti, Jialei Chen, Kan Wang, Mani A Vannan, Yujia Xie, Zhen Qian, Zih Huei Wang","submitted_at":"2018-08-14T00:18:33Z","abstract_excerpt":"Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions. Very often, however, researchers find the historical records of emerging surgical techniques, such as the transcatheter aortic valve replacement (TAVR), are highly scarce in quantity. In this paper, we address this challenge by proposing novel generative invertible networks (GIN) to select features and generate high-quality virtual patients that may potentially serve as an additional data source for machine learning. Combining a convolutional neural network (CNN) and g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04495","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":"2026-05-17T23:54:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"syMaTienG2Y4Vjam9jbUNeBl2KRqL1aVlmiuGb1ggETMjTo/lcr3jHHnvPXBrUDRYHk4GTI4nZiv8TXWdKQoAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T12:08:07.797679Z"},"content_sha256":"ed517ca1e0f2f438cddd0a6b1bcc7fd175d48752f06c8dc867722bf3fc59d2f1","schema_version":"1.0","event_id":"sha256:ed517ca1e0f2f438cddd0a6b1bcc7fd175d48752f06c8dc867722bf3fc59d2f1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FOFRDOV4MMO4VIKQPUWELUEK22/bundle.json","state_url":"https://pith.science/pith/FOFRDOV4MMO4VIKQPUWELUEK22/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FOFRDOV4MMO4VIKQPUWELUEK22/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":"2026-05-30T12:08:07Z","links":{"resolver":"https://pith.science/pith/FOFRDOV4MMO4VIKQPUWELUEK22","bundle":"https://pith.science/pith/FOFRDOV4MMO4VIKQPUWELUEK22/bundle.json","state":"https://pith.science/pith/FOFRDOV4MMO4VIKQPUWELUEK22/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FOFRDOV4MMO4VIKQPUWELUEK22/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:FOFRDOV4MMO4VIKQPUWELUEK22","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":"d709254707d3c936bc767d9d594a8a6b1525ba42b99301431334eefe851d495e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-14T00:18:33Z","title_canon_sha256":"b3a808d4ab632f2c341cfa6806b61dd5830612fde1528de7c2d96ec622a6317d"},"schema_version":"1.0","source":{"id":"1808.04495","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.04495","created_at":"2026-05-17T23:54:48Z"},{"alias_kind":"arxiv_version","alias_value":"1808.04495v1","created_at":"2026-05-17T23:54:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04495","created_at":"2026-05-17T23:54:48Z"},{"alias_kind":"pith_short_12","alias_value":"FOFRDOV4MMO4","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"FOFRDOV4MMO4VIKQ","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"FOFRDOV4","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:ed517ca1e0f2f438cddd0a6b1bcc7fd175d48752f06c8dc867722bf3fc59d2f1","target":"graph","created_at":"2026-05-17T23:54:48Z","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":"Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions. Very often, however, researchers find the historical records of emerging surgical techniques, such as the transcatheter aortic valve replacement (TAVR), are highly scarce in quantity. In this paper, we address this challenge by proposing novel generative invertible networks (GIN) to select features and generate high-quality virtual patients that may potentially serve as an additional data source for machine learning. Combining a convolutional neural network (CNN) and g","authors_text":"Ben Wang, Chuck Zhang, Geet Lahoti, Jialei Chen, Kan Wang, Mani A Vannan, Yujia Xie, Zhen Qian, Zih Huei Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-14T00:18:33Z","title":"Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04495","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":"sha256:83fe2dc435f227d0c78c66288bfaa6ef2a318ff587b63f7482456090e1cf589d","target":"record","created_at":"2026-05-17T23:54:48Z","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":"d709254707d3c936bc767d9d594a8a6b1525ba42b99301431334eefe851d495e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-14T00:18:33Z","title_canon_sha256":"b3a808d4ab632f2c341cfa6806b61dd5830612fde1528de7c2d96ec622a6317d"},"schema_version":"1.0","source":{"id":"1808.04495","kind":"arxiv","version":1}},"canonical_sha256":"2b8b11babc631dcaa1507d2c45d08ad6bf60f6b2721299aaed8aa1fd728af1a4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2b8b11babc631dcaa1507d2c45d08ad6bf60f6b2721299aaed8aa1fd728af1a4","first_computed_at":"2026-05-17T23:54:48.262158Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:54:48.262158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/Gj+Vmsh6qnWqkJjqOAEMhsY4xMTQ95hQEUFMLBtJor4l779l/rsfzQMogEzb9VuEZchPBabEb8Dp7Hcy0McAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:54:48.262778Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.04495","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:83fe2dc435f227d0c78c66288bfaa6ef2a318ff587b63f7482456090e1cf589d","sha256:ed517ca1e0f2f438cddd0a6b1bcc7fd175d48752f06c8dc867722bf3fc59d2f1"],"state_sha256":"95c80973a4efe6704a5f47bde06b93f5c93050b949d2193e6a337c67f855d9d2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZVVeeBAWUV6GqwZzYZ89g//8SR3Z0A0z+pmRNDSXGin9lhFg4LL3u2DZ7twWPLiKXaHU0c7264BmMysbTcaRBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T12:08:07.799531Z","bundle_sha256":"b16f2f89af14d7c2d4b509764eb45adc159f043802042f8bedef6e9e8bf9192f"}}