{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:UXIMGBVQW54ZNMRB35BNTHUPKL","short_pith_number":"pith:UXIMGBVQ","canonical_record":{"source":{"id":"2311.10472","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-11-17T11:56:53Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"c44821e4884589fede2427c3e944ac42b629d9c39b5242352d686933ad8c1f89","abstract_canon_sha256":"9d3cac577a538588eb30a57a7e98a7dbd4f49d67b9ba4700063e3befd8ad02d4"},"schema_version":"1.0"},"canonical_sha256":"a5d0c306b0b77996b221df42d99e8f52f5ef6db383b9ac7ad0465eeb17bf633a","source":{"kind":"arxiv","id":"2311.10472","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.10472","created_at":"2026-07-05T08:28:57Z"},{"alias_kind":"arxiv_version","alias_value":"2311.10472v1","created_at":"2026-07-05T08:28:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.10472","created_at":"2026-07-05T08:28:57Z"},{"alias_kind":"pith_short_12","alias_value":"UXIMGBVQW54Z","created_at":"2026-07-05T08:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"UXIMGBVQW54ZNMRB","created_at":"2026-07-05T08:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"UXIMGBVQ","created_at":"2026-07-05T08:28:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:UXIMGBVQW54ZNMRB35BNTHUPKL","target":"record","payload":{"canonical_record":{"source":{"id":"2311.10472","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-11-17T11:56:53Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"c44821e4884589fede2427c3e944ac42b629d9c39b5242352d686933ad8c1f89","abstract_canon_sha256":"9d3cac577a538588eb30a57a7e98a7dbd4f49d67b9ba4700063e3befd8ad02d4"},"schema_version":"1.0"},"canonical_sha256":"a5d0c306b0b77996b221df42d99e8f52f5ef6db383b9ac7ad0465eeb17bf633a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:28:57.801110Z","signature_b64":"Uf/jCmy1Ed/L9oumzoMloewiUplnr7GZwRCz8GHRURICbOPHAsfrmLv7ag2rhzf2tqAFtiyoGOBnUquExaK5Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5d0c306b0b77996b221df42d99e8f52f5ef6db383b9ac7ad0465eeb17bf633a","last_reissued_at":"2026-07-05T08:28:57.800610Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:28:57.800610Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.10472","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":"2026-07-05T08:28:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N2zIJl+OUBKFPy4RPwdkqEYu2ijAVfnBqBn+QIZFcnQgmz/jvCtf+3bybR7RM+DuPppynhHO9zNZiP+ZuABsBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T12:44:43.079434Z"},"content_sha256":"6012d4456ecfcfd2b2149e7e1785a3b7e41dfa89446d38ce10f30304c86220bc","schema_version":"1.0","event_id":"sha256:6012d4456ecfcfd2b2149e7e1785a3b7e41dfa89446d38ce10f30304c86220bc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:UXIMGBVQW54ZNMRB35BNTHUPKL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Aghiles Kebaili, J\\'er\\^ome Lapuyade-Lahorgue, Pierre Vera, Su Ruan","submitted_at":"2023-11-17T11:56:53Z","abstract_excerpt":"Despite the increasing use of deep learning in medical image segmentation, acquiring sufficient training data remains a challenge in the medical field. In response, data augmentation techniques have been proposed; however, the generation of diverse and realistic medical images and their corresponding masks remains a difficult task, especially when working with insufficient training sets. To address these limitations, we present an end-to-end architecture based on the Hamiltonian Variational Autoencoder (HVAE). This approach yields an improved posterior distribution approximation compared to tr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.10472","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/2311.10472/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":"2026-07-05T08:28:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RnobnDKteL/alVBxciqDq01d4pZ3qM/AwjdTIT0HpdujaKCn4uV3+fGHMgAkuyZm4cee9arddVxg1SM7xDGnCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T12:44:43.079804Z"},"content_sha256":"5398976095c1518de353ae06d617a92631f9853b178a87901416c6cba68845d8","schema_version":"1.0","event_id":"sha256:5398976095c1518de353ae06d617a92631f9853b178a87901416c6cba68845d8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UXIMGBVQW54ZNMRB35BNTHUPKL/bundle.json","state_url":"https://pith.science/pith/UXIMGBVQW54ZNMRB35BNTHUPKL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UXIMGBVQW54ZNMRB35BNTHUPKL/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":"2026-07-07T12:44:43Z","links":{"resolver":"https://pith.science/pith/UXIMGBVQW54ZNMRB35BNTHUPKL","bundle":"https://pith.science/pith/UXIMGBVQW54ZNMRB35BNTHUPKL/bundle.json","state":"https://pith.science/pith/UXIMGBVQW54ZNMRB35BNTHUPKL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UXIMGBVQW54ZNMRB35BNTHUPKL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:UXIMGBVQW54ZNMRB35BNTHUPKL","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":"9d3cac577a538588eb30a57a7e98a7dbd4f49d67b9ba4700063e3befd8ad02d4","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-11-17T11:56:53Z","title_canon_sha256":"c44821e4884589fede2427c3e944ac42b629d9c39b5242352d686933ad8c1f89"},"schema_version":"1.0","source":{"id":"2311.10472","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.10472","created_at":"2026-07-05T08:28:57Z"},{"alias_kind":"arxiv_version","alias_value":"2311.10472v1","created_at":"2026-07-05T08:28:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.10472","created_at":"2026-07-05T08:28:57Z"},{"alias_kind":"pith_short_12","alias_value":"UXIMGBVQW54Z","created_at":"2026-07-05T08:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"UXIMGBVQW54ZNMRB","created_at":"2026-07-05T08:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"UXIMGBVQ","created_at":"2026-07-05T08:28:57Z"}],"graph_snapshots":[{"event_id":"sha256:5398976095c1518de353ae06d617a92631f9853b178a87901416c6cba68845d8","target":"graph","created_at":"2026-07-05T08:28:57Z","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/2311.10472/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite the increasing use of deep learning in medical image segmentation, acquiring sufficient training data remains a challenge in the medical field. In response, data augmentation techniques have been proposed; however, the generation of diverse and realistic medical images and their corresponding masks remains a difficult task, especially when working with insufficient training sets. To address these limitations, we present an end-to-end architecture based on the Hamiltonian Variational Autoencoder (HVAE). This approach yields an improved posterior distribution approximation compared to tr","authors_text":"Aghiles Kebaili, J\\'er\\^ome Lapuyade-Lahorgue, Pierre Vera, Su Ruan","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-11-17T11:56:53Z","title":"End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.10472","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":"sha256:6012d4456ecfcfd2b2149e7e1785a3b7e41dfa89446d38ce10f30304c86220bc","target":"record","created_at":"2026-07-05T08:28:57Z","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":"9d3cac577a538588eb30a57a7e98a7dbd4f49d67b9ba4700063e3befd8ad02d4","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-11-17T11:56:53Z","title_canon_sha256":"c44821e4884589fede2427c3e944ac42b629d9c39b5242352d686933ad8c1f89"},"schema_version":"1.0","source":{"id":"2311.10472","kind":"arxiv","version":1}},"canonical_sha256":"a5d0c306b0b77996b221df42d99e8f52f5ef6db383b9ac7ad0465eeb17bf633a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a5d0c306b0b77996b221df42d99e8f52f5ef6db383b9ac7ad0465eeb17bf633a","first_computed_at":"2026-07-05T08:28:57.800610Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:28:57.800610Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Uf/jCmy1Ed/L9oumzoMloewiUplnr7GZwRCz8GHRURICbOPHAsfrmLv7ag2rhzf2tqAFtiyoGOBnUquExaK5Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T08:28:57.801110Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.10472","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6012d4456ecfcfd2b2149e7e1785a3b7e41dfa89446d38ce10f30304c86220bc","sha256:5398976095c1518de353ae06d617a92631f9853b178a87901416c6cba68845d8"],"state_sha256":"5e68b52edbff36b4eea40ed32f7605266a347dfcf983eda9bb0fc09be29ccc60"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+V81NHlyJqY2I8jVfURlguUUBUpAIVL04kY8208CGKjGzCkXd6tN/NwChUV3G/o2XesjJnTuPikAbaPC45mkBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T12:44:43.081932Z","bundle_sha256":"01cb9a283ad9fbe2cc6217835883afac9b7a18bc0126b29e3eb9ccb8e75d7b21"}}