{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:FE4PW6WQLEEDM4YPM2F7DQKCQQ","short_pith_number":"pith:FE4PW6WQ","canonical_record":{"source":{"id":"2210.02967","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-06T14:59:27Z","cross_cats_sorted":[],"title_canon_sha256":"0405a72f6f9ffb1e5cddc97831c59faa2fc435af69a9b3e06963c658f5498b2e","abstract_canon_sha256":"ef099e15181fecc23f4e23d37a953946d232f66269e76bb71925a26a17376ef2"},"schema_version":"1.0"},"canonical_sha256":"2938fb7ad0590836730f668bf1c14284378cccb5e7649c311b9559a607627cf4","source":{"kind":"arxiv","id":"2210.02967","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.02967","created_at":"2026-07-05T05:04:05Z"},{"alias_kind":"arxiv_version","alias_value":"2210.02967v1","created_at":"2026-07-05T05:04:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.02967","created_at":"2026-07-05T05:04:05Z"},{"alias_kind":"pith_short_12","alias_value":"FE4PW6WQLEED","created_at":"2026-07-05T05:04:05Z"},{"alias_kind":"pith_short_16","alias_value":"FE4PW6WQLEEDM4YP","created_at":"2026-07-05T05:04:05Z"},{"alias_kind":"pith_short_8","alias_value":"FE4PW6WQ","created_at":"2026-07-05T05:04:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:FE4PW6WQLEEDM4YPM2F7DQKCQQ","target":"record","payload":{"canonical_record":{"source":{"id":"2210.02967","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-06T14:59:27Z","cross_cats_sorted":[],"title_canon_sha256":"0405a72f6f9ffb1e5cddc97831c59faa2fc435af69a9b3e06963c658f5498b2e","abstract_canon_sha256":"ef099e15181fecc23f4e23d37a953946d232f66269e76bb71925a26a17376ef2"},"schema_version":"1.0"},"canonical_sha256":"2938fb7ad0590836730f668bf1c14284378cccb5e7649c311b9559a607627cf4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:04:05.847236Z","signature_b64":"uaYgWavcOiMlafr8K1+P0b52NNc8P4H/F0IrRgO78UKL3lgGXo+ffyBZmy6eGDQpkk8bbS2e4m1eTE3hLsqDAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2938fb7ad0590836730f668bf1c14284378cccb5e7649c311b9559a607627cf4","last_reissued_at":"2026-07-05T05:04:05.846844Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:04:05.846844Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.02967","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":"2026-07-05T05:04:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2u6IG/UIMw5Iy8h/Xj5Ew376om8F5qDqzCyoB5bbAxxLHfJN1v35gOlDra5qukJLyrgS9NIvrJfEz6suhiFkBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-16T08:28:28.306608Z"},"content_sha256":"6a20599122f42b8fdfef141ee5009593e41f6c149d94dcaaa2948bc14da9fb49","schema_version":"1.0","event_id":"sha256:6a20599122f42b8fdfef141ee5009593e41f6c149d94dcaaa2948bc14da9fb49"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:FE4PW6WQLEEDM4YPM2F7DQKCQQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Brian Zenger, John L. Sapp, Linwei Wang, Md Shakil Zaman, Rob S. MacLeod, Ryan Missel, Wilson W. Good, Xiajun Jiang, Zhiyuan Li","submitted_at":"2022-10-06T14:59:27Z","abstract_excerpt":"Clinical adoption of personalized virtual heart simulations faces challenges in model personalization and expensive computation. While an ideal solution is an efficient neural surrogate that at the same time is personalized to an individual subject, the state-of-the-art is either concerned with personalizing an expensive simulation model, or learning an efficient yet generic surrogate. This paper presents a completely new concept to achieve personalized neural surrogates in a single coherent framework of meta-learning (metaPNS). Instead of learning a single neural surrogate, we pursue the proc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.02967","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/2210.02967/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":"2026-07-05T05:04:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JsHPjx51AjiJGBvuaIMSaFE0MC8ftwO77pmmiBqMJVF6XGMu8TJPyQyqdLF91mS2V2vjv3rAqgW3u8qKRR2zAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-16T08:28:28.306981Z"},"content_sha256":"85fac69e0da2c0ff2ba35b6d38dc4165b23b7e913b13d8b18c183ef177ce7f2e","schema_version":"1.0","event_id":"sha256:85fac69e0da2c0ff2ba35b6d38dc4165b23b7e913b13d8b18c183ef177ce7f2e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FE4PW6WQLEEDM4YPM2F7DQKCQQ/bundle.json","state_url":"https://pith.science/pith/FE4PW6WQLEEDM4YPM2F7DQKCQQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FE4PW6WQLEEDM4YPM2F7DQKCQQ/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":"2026-07-16T08:28:28Z","links":{"resolver":"https://pith.science/pith/FE4PW6WQLEEDM4YPM2F7DQKCQQ","bundle":"https://pith.science/pith/FE4PW6WQLEEDM4YPM2F7DQKCQQ/bundle.json","state":"https://pith.science/pith/FE4PW6WQLEEDM4YPM2F7DQKCQQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FE4PW6WQLEEDM4YPM2F7DQKCQQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:FE4PW6WQLEEDM4YPM2F7DQKCQQ","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":"ef099e15181fecc23f4e23d37a953946d232f66269e76bb71925a26a17376ef2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-06T14:59:27Z","title_canon_sha256":"0405a72f6f9ffb1e5cddc97831c59faa2fc435af69a9b3e06963c658f5498b2e"},"schema_version":"1.0","source":{"id":"2210.02967","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.02967","created_at":"2026-07-05T05:04:05Z"},{"alias_kind":"arxiv_version","alias_value":"2210.02967v1","created_at":"2026-07-05T05:04:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.02967","created_at":"2026-07-05T05:04:05Z"},{"alias_kind":"pith_short_12","alias_value":"FE4PW6WQLEED","created_at":"2026-07-05T05:04:05Z"},{"alias_kind":"pith_short_16","alias_value":"FE4PW6WQLEEDM4YP","created_at":"2026-07-05T05:04:05Z"},{"alias_kind":"pith_short_8","alias_value":"FE4PW6WQ","created_at":"2026-07-05T05:04:05Z"}],"graph_snapshots":[{"event_id":"sha256:85fac69e0da2c0ff2ba35b6d38dc4165b23b7e913b13d8b18c183ef177ce7f2e","target":"graph","created_at":"2026-07-05T05:04:05Z","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/2210.02967/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Clinical adoption of personalized virtual heart simulations faces challenges in model personalization and expensive computation. While an ideal solution is an efficient neural surrogate that at the same time is personalized to an individual subject, the state-of-the-art is either concerned with personalizing an expensive simulation model, or learning an efficient yet generic surrogate. This paper presents a completely new concept to achieve personalized neural surrogates in a single coherent framework of meta-learning (metaPNS). Instead of learning a single neural surrogate, we pursue the proc","authors_text":"Brian Zenger, John L. Sapp, Linwei Wang, Md Shakil Zaman, Rob S. MacLeod, Ryan Missel, Wilson W. Good, Xiajun Jiang, Zhiyuan Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-06T14:59:27Z","title":"Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.02967","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":"sha256:6a20599122f42b8fdfef141ee5009593e41f6c149d94dcaaa2948bc14da9fb49","target":"record","created_at":"2026-07-05T05:04:05Z","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":"ef099e15181fecc23f4e23d37a953946d232f66269e76bb71925a26a17376ef2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-06T14:59:27Z","title_canon_sha256":"0405a72f6f9ffb1e5cddc97831c59faa2fc435af69a9b3e06963c658f5498b2e"},"schema_version":"1.0","source":{"id":"2210.02967","kind":"arxiv","version":1}},"canonical_sha256":"2938fb7ad0590836730f668bf1c14284378cccb5e7649c311b9559a607627cf4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2938fb7ad0590836730f668bf1c14284378cccb5e7649c311b9559a607627cf4","first_computed_at":"2026-07-05T05:04:05.846844Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:04:05.846844Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uaYgWavcOiMlafr8K1+P0b52NNc8P4H/F0IrRgO78UKL3lgGXo+ffyBZmy6eGDQpkk8bbS2e4m1eTE3hLsqDAw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:04:05.847236Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.02967","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6a20599122f42b8fdfef141ee5009593e41f6c149d94dcaaa2948bc14da9fb49","sha256:85fac69e0da2c0ff2ba35b6d38dc4165b23b7e913b13d8b18c183ef177ce7f2e"],"state_sha256":"2926cef34073edebe982182627ce9edeb8c1f37410292b3e6921e188af06b9d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BitqzG0OvZJ57axdTOW4Gq6gLikpvMaKNnnjfjiQtVjH43u5KRpcoMp9INvtw7rUEyZGbhKq9o11wQ76FPPJCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-16T08:28:28.309138Z","bundle_sha256":"bf8f12b0d0ddee72306873a48219c1cd2a7e21026b9fd160f5360ced115e5d9e"}}