{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:EC7WNCMKCIUKC2LIJ6WUSA7JH2","short_pith_number":"pith:EC7WNCMK","canonical_record":{"source":{"id":"2505.10856","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-16T04:50:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"dc0da26400669bdf815ff3d2e581ea6dedf88c4b6f4e998563bcd0f2710d0fcc","abstract_canon_sha256":"f7f5022e18807b27a35351ab88ba36e07cb0e88e5b5228ea5cfc0c2535ce784b"},"schema_version":"1.0"},"canonical_sha256":"20bf66898a1228a169684fad4903e93ebea9ed4b0752e95f2b48aa8ee2bdde2b","source":{"kind":"arxiv","id":"2505.10856","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.10856","created_at":"2026-07-05T11:04:07Z"},{"alias_kind":"arxiv_version","alias_value":"2505.10856v1","created_at":"2026-07-05T11:04:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10856","created_at":"2026-07-05T11:04:07Z"},{"alias_kind":"pith_short_12","alias_value":"EC7WNCMKCIUK","created_at":"2026-07-05T11:04:07Z"},{"alias_kind":"pith_short_16","alias_value":"EC7WNCMKCIUKC2LI","created_at":"2026-07-05T11:04:07Z"},{"alias_kind":"pith_short_8","alias_value":"EC7WNCMK","created_at":"2026-07-05T11:04:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:EC7WNCMKCIUKC2LIJ6WUSA7JH2","target":"record","payload":{"canonical_record":{"source":{"id":"2505.10856","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-16T04:50:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"dc0da26400669bdf815ff3d2e581ea6dedf88c4b6f4e998563bcd0f2710d0fcc","abstract_canon_sha256":"f7f5022e18807b27a35351ab88ba36e07cb0e88e5b5228ea5cfc0c2535ce784b"},"schema_version":"1.0"},"canonical_sha256":"20bf66898a1228a169684fad4903e93ebea9ed4b0752e95f2b48aa8ee2bdde2b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:07.172841Z","signature_b64":"A7uK1/zOx4vNuB2S7sAfJhsm6B3WVrLKwoPI3zHtbqLzQ/WnbzKaZlirdj4GOJIwY6/f5LdQYZYJ6q3wf+mbBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"20bf66898a1228a169684fad4903e93ebea9ed4b0752e95f2b48aa8ee2bdde2b","last_reissued_at":"2026-07-05T11:04:07.172348Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:07.172348Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.10856","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":"2026-07-05T11:04:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vAQjx1kfBzgyYV+uPgpTq5vCCtRtqEgB2WQSxFgT7upPVGZb8RwIvqc9ODczmuOtArXu22KTN1QnV6S45ZF9Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T23:32:40.629279Z"},"content_sha256":"a26d9c4c0448d27dc68f365d50e0084623ccc0e81507e33172926b40b1c2399d","schema_version":"1.0","event_id":"sha256:a26d9c4c0448d27dc68f365d50e0084623ccc0e81507e33172926b40b1c2399d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:EC7WNCMKCIUKC2LIJ6WUSA7JH2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Haishuai Wang, Hongwei Wang, Jiajun Bu, Jialong Guo, Ke Liu, Mengxuan Li","submitted_at":"2025-05-16T04:50:15Z","abstract_excerpt":"Healthcare data frequently contain a substantial proportion of missing values, necessitating effective time series imputation to support downstream disease diagnosis tasks. However, existing imputation methods focus on discrete data points and are unable to effectively model sparse data, resulting in particularly poor performance for imputing substantial missing values. In this paper, we propose a novel approach, ImputeINR, for time series imputation by employing implicit neural representations (INR) to learn continuous functions for time series. ImputeINR leverages the merits of INR in that t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10856","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/2505.10856/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":"2026-07-05T11:04:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F5eukG6NGEs9ii8lamdongY+j9EKC02rPUuQ7/krQ7EwHsMFShiQUfIPkSLxUDkKG8clPJEL4n6DpPPOJ5mkBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T23:32:40.629905Z"},"content_sha256":"3097eb03b8863f781daa15f876e26e68d8d3dc72342406bb041eec347c1f4370","schema_version":"1.0","event_id":"sha256:3097eb03b8863f781daa15f876e26e68d8d3dc72342406bb041eec347c1f4370"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EC7WNCMKCIUKC2LIJ6WUSA7JH2/bundle.json","state_url":"https://pith.science/pith/EC7WNCMKCIUKC2LIJ6WUSA7JH2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EC7WNCMKCIUKC2LIJ6WUSA7JH2/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":"2026-08-16T23:32:40Z","links":{"resolver":"https://pith.science/pith/EC7WNCMKCIUKC2LIJ6WUSA7JH2","bundle":"https://pith.science/pith/EC7WNCMKCIUKC2LIJ6WUSA7JH2/bundle.json","state":"https://pith.science/pith/EC7WNCMKCIUKC2LIJ6WUSA7JH2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EC7WNCMKCIUKC2LIJ6WUSA7JH2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:EC7WNCMKCIUKC2LIJ6WUSA7JH2","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":"f7f5022e18807b27a35351ab88ba36e07cb0e88e5b5228ea5cfc0c2535ce784b","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-16T04:50:15Z","title_canon_sha256":"dc0da26400669bdf815ff3d2e581ea6dedf88c4b6f4e998563bcd0f2710d0fcc"},"schema_version":"1.0","source":{"id":"2505.10856","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.10856","created_at":"2026-07-05T11:04:07Z"},{"alias_kind":"arxiv_version","alias_value":"2505.10856v1","created_at":"2026-07-05T11:04:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10856","created_at":"2026-07-05T11:04:07Z"},{"alias_kind":"pith_short_12","alias_value":"EC7WNCMKCIUK","created_at":"2026-07-05T11:04:07Z"},{"alias_kind":"pith_short_16","alias_value":"EC7WNCMKCIUKC2LI","created_at":"2026-07-05T11:04:07Z"},{"alias_kind":"pith_short_8","alias_value":"EC7WNCMK","created_at":"2026-07-05T11:04:07Z"}],"graph_snapshots":[{"event_id":"sha256:3097eb03b8863f781daa15f876e26e68d8d3dc72342406bb041eec347c1f4370","target":"graph","created_at":"2026-07-05T11:04:07Z","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/2505.10856/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Healthcare data frequently contain a substantial proportion of missing values, necessitating effective time series imputation to support downstream disease diagnosis tasks. However, existing imputation methods focus on discrete data points and are unable to effectively model sparse data, resulting in particularly poor performance for imputing substantial missing values. In this paper, we propose a novel approach, ImputeINR, for time series imputation by employing implicit neural representations (INR) to learn continuous functions for time series. ImputeINR leverages the merits of INR in that t","authors_text":"Haishuai Wang, Hongwei Wang, Jiajun Bu, Jialong Guo, Ke Liu, Mengxuan Li","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-16T04:50:15Z","title":"ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10856","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:a26d9c4c0448d27dc68f365d50e0084623ccc0e81507e33172926b40b1c2399d","target":"record","created_at":"2026-07-05T11:04:07Z","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":"f7f5022e18807b27a35351ab88ba36e07cb0e88e5b5228ea5cfc0c2535ce784b","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-16T04:50:15Z","title_canon_sha256":"dc0da26400669bdf815ff3d2e581ea6dedf88c4b6f4e998563bcd0f2710d0fcc"},"schema_version":"1.0","source":{"id":"2505.10856","kind":"arxiv","version":1}},"canonical_sha256":"20bf66898a1228a169684fad4903e93ebea9ed4b0752e95f2b48aa8ee2bdde2b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"20bf66898a1228a169684fad4903e93ebea9ed4b0752e95f2b48aa8ee2bdde2b","first_computed_at":"2026-07-05T11:04:07.172348Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:04:07.172348Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"A7uK1/zOx4vNuB2S7sAfJhsm6B3WVrLKwoPI3zHtbqLzQ/WnbzKaZlirdj4GOJIwY6/f5LdQYZYJ6q3wf+mbBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:04:07.172841Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.10856","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a26d9c4c0448d27dc68f365d50e0084623ccc0e81507e33172926b40b1c2399d","sha256:3097eb03b8863f781daa15f876e26e68d8d3dc72342406bb041eec347c1f4370"],"state_sha256":"412d155deaa0fd34602a244f6ee71403f4560ab183896aae6db60797be95d48c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Eahkg6dhFccfhwgrnwaUS3dIzpv0ZcquTxXf2neq7+yu6vHh0nr1SqHCVmZL4CYIOy1gcwrVJZoJ1JVC05J8Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T23:32:40.634972Z","bundle_sha256":"fb9f8c02b2f3b8a53dbcb665ce5bb64f2ad3dc96fbfa145e02367ea7c5f16ee3"}}