{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:2GJASTHAGSSDAUSHDH5VY5EPK7","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":"44c2761077eca900df9ef78e92b6da3fdd733e0611de46162d5cf7812da2f2af","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-21T11:42:20Z","title_canon_sha256":"18abb967b08bae5ff1b75de8188f6f1e9d8bce88eb91519215cef407a34047d7"},"schema_version":"1.0","source":{"id":"2405.13089","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.13089","created_at":"2026-07-05T08:30:33Z"},{"alias_kind":"arxiv_version","alias_value":"2405.13089v3","created_at":"2026-07-05T08:30:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.13089","created_at":"2026-07-05T08:30:33Z"},{"alias_kind":"pith_short_12","alias_value":"2GJASTHAGSSD","created_at":"2026-07-05T08:30:33Z"},{"alias_kind":"pith_short_16","alias_value":"2GJASTHAGSSDAUSH","created_at":"2026-07-05T08:30:33Z"},{"alias_kind":"pith_short_8","alias_value":"2GJASTHA","created_at":"2026-07-05T08:30:33Z"}],"graph_snapshots":[{"event_id":"sha256:0f8799fa325b2095a403e3ce1338324f0796629c5180502cce9365864a1181c6","target":"graph","created_at":"2026-07-05T08:30:33Z","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/2405.13089/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In many practical real-world applications, data missing is a very common phenomenon, making the development of data-driven artificial intelligence theory and technology increasingly difficult. Data completion is an important method for missing data preprocessing. Most existing miss-ing data completion models directly use the known information in the missing data set but ignore the impact of the data label information contained in the data set on the missing data completion model. To this end, this paper proposes a missing data completion model SEGAN based on semi-supervised learning, which mai","authors_text":"Jianfeng Zhang, Peijian Cao, Peiran Liu, Peng Lu, Weifeng Wu, Xianfei Qiu, Xiaohua Pan, Yangyang Wu, Zhen Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-21T11:42:20Z","title":"SEGAN: semi-supervised learning approach for missing data imputation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.13089","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:bcb1393a867f9d0ad8b360355153226875e076a11d3ab6efde766f25ea4cf87b","target":"record","created_at":"2026-07-05T08:30:33Z","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":"44c2761077eca900df9ef78e92b6da3fdd733e0611de46162d5cf7812da2f2af","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-21T11:42:20Z","title_canon_sha256":"18abb967b08bae5ff1b75de8188f6f1e9d8bce88eb91519215cef407a34047d7"},"schema_version":"1.0","source":{"id":"2405.13089","kind":"arxiv","version":3}},"canonical_sha256":"d192094ce034a430524719fb5c748f57cbedccc3a3c19e44e41a5820ba16d7dc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d192094ce034a430524719fb5c748f57cbedccc3a3c19e44e41a5820ba16d7dc","first_computed_at":"2026-07-05T08:30:33.634994Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:30:33.634994Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fe3b83/PAhw6o6Y6NQ/hJjxaCIXkCOK/ZQBnd2h9cxNNwnY+VrqT+tPZx0CZ1hTAaBDQW7KGBSmbeimoXZsdAA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:30:33.635519Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.13089","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bcb1393a867f9d0ad8b360355153226875e076a11d3ab6efde766f25ea4cf87b","sha256:0f8799fa325b2095a403e3ce1338324f0796629c5180502cce9365864a1181c6"],"state_sha256":"c2961b908d9eb3840b05f27fb2272930c68e31e8301c6fcee91745b2d741e68a"}