{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:IPRLD5RM3JAA524W4GREZPXWOJ","short_pith_number":"pith:IPRLD5RM","canonical_record":{"source":{"id":"2406.16982","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-23T18:44:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"52f501ed2f096b2ada885fa9ff925633d235631115bd45db30e4d5710de40d1c","abstract_canon_sha256":"b95f1d75942c59770c97263e6a0d06a6de85d0417a48f416430362f864228de9"},"schema_version":"1.0"},"canonical_sha256":"43e2b1f62cda400eeb96e1a24cbef6727c05202f572373f7ae7977351222c048","source":{"kind":"arxiv","id":"2406.16982","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.16982","created_at":"2026-07-05T08:36:08Z"},{"alias_kind":"arxiv_version","alias_value":"2406.16982v1","created_at":"2026-07-05T08:36:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.16982","created_at":"2026-07-05T08:36:08Z"},{"alias_kind":"pith_short_12","alias_value":"IPRLD5RM3JAA","created_at":"2026-07-05T08:36:08Z"},{"alias_kind":"pith_short_16","alias_value":"IPRLD5RM3JAA524W","created_at":"2026-07-05T08:36:08Z"},{"alias_kind":"pith_short_8","alias_value":"IPRLD5RM","created_at":"2026-07-05T08:36:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:IPRLD5RM3JAA524W4GREZPXWOJ","target":"record","payload":{"canonical_record":{"source":{"id":"2406.16982","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-23T18:44:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"52f501ed2f096b2ada885fa9ff925633d235631115bd45db30e4d5710de40d1c","abstract_canon_sha256":"b95f1d75942c59770c97263e6a0d06a6de85d0417a48f416430362f864228de9"},"schema_version":"1.0"},"canonical_sha256":"43e2b1f62cda400eeb96e1a24cbef6727c05202f572373f7ae7977351222c048","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:36:08.549087Z","signature_b64":"Dug+sxFYudoO3n/DzAS/7zsbkRbw3hcTCOAg1fg+7+7q+YA8oW746/jMlAiTcmqPF3zWq8+QHcxlhI7NDPRBAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43e2b1f62cda400eeb96e1a24cbef6727c05202f572373f7ae7977351222c048","last_reissued_at":"2026-07-05T08:36:08.548699Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:36:08.548699Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.16982","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":"2026-07-05T08:36:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WJjpvtk4ef9z4A2pk8nzWlB7erWbL5etlRQ4hOSaOJeqq3bJuH357fz91vRWPN+kcGFDJ5RqZHGoQzWEMHYZBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:42:59.408901Z"},"content_sha256":"8b3f80a07586ce036412dc3cd1448815a4eb5ed83f2e03774d0fb61b05d79878","schema_version":"1.0","event_id":"sha256:8b3f80a07586ce036412dc3cd1448815a4eb5ed83f2e03774d0fb61b05d79878"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:IPRLD5RM3JAA524W4GREZPXWOJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Research on Disease Prediction Model Construction Based on Computer AI deep Learning Technology","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Lingxi Xiao, Muqing Li, Yang Lin, Yinqiu Feng, Zexi Chen, Ziyi Zhu","submitted_at":"2024-06-23T18:44:03Z","abstract_excerpt":"The prediction of disease risk factors can screen vulnerable groups for effective prevention and treatment, so as to reduce their morbidity and mortality. Machine learning has a great demand for high-quality labeling information, and labeling noise in medical big data poses a great challenge to efficient disease risk warning methods. Therefore, this project intends to study the robust learning algorithm and apply it to the early warning of infectious disease risk. A dynamic truncated loss model is proposed, which combines the traditional mutual entropy implicit weight feature with the mean var"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.16982","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/2406.16982/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":"2026-07-05T08:36:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DRobX3DRRvz99RMHTW89iWOjSUEBGef9a8+yHPeqJSK5NoZRgSK9MlCzDkqAhPgPuUTO7uDxMBSIoWoQvRqEBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:42:59.409279Z"},"content_sha256":"b9f98bbb25df8bbac182eb99605763eb65884dc53812ee35c889ba94bad06b7b","schema_version":"1.0","event_id":"sha256:b9f98bbb25df8bbac182eb99605763eb65884dc53812ee35c889ba94bad06b7b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IPRLD5RM3JAA524W4GREZPXWOJ/bundle.json","state_url":"https://pith.science/pith/IPRLD5RM3JAA524W4GREZPXWOJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IPRLD5RM3JAA524W4GREZPXWOJ/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":"2026-07-06T20:42:59Z","links":{"resolver":"https://pith.science/pith/IPRLD5RM3JAA524W4GREZPXWOJ","bundle":"https://pith.science/pith/IPRLD5RM3JAA524W4GREZPXWOJ/bundle.json","state":"https://pith.science/pith/IPRLD5RM3JAA524W4GREZPXWOJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IPRLD5RM3JAA524W4GREZPXWOJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:IPRLD5RM3JAA524W4GREZPXWOJ","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":"b95f1d75942c59770c97263e6a0d06a6de85d0417a48f416430362f864228de9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-23T18:44:03Z","title_canon_sha256":"52f501ed2f096b2ada885fa9ff925633d235631115bd45db30e4d5710de40d1c"},"schema_version":"1.0","source":{"id":"2406.16982","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.16982","created_at":"2026-07-05T08:36:08Z"},{"alias_kind":"arxiv_version","alias_value":"2406.16982v1","created_at":"2026-07-05T08:36:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.16982","created_at":"2026-07-05T08:36:08Z"},{"alias_kind":"pith_short_12","alias_value":"IPRLD5RM3JAA","created_at":"2026-07-05T08:36:08Z"},{"alias_kind":"pith_short_16","alias_value":"IPRLD5RM3JAA524W","created_at":"2026-07-05T08:36:08Z"},{"alias_kind":"pith_short_8","alias_value":"IPRLD5RM","created_at":"2026-07-05T08:36:08Z"}],"graph_snapshots":[{"event_id":"sha256:b9f98bbb25df8bbac182eb99605763eb65884dc53812ee35c889ba94bad06b7b","target":"graph","created_at":"2026-07-05T08:36:08Z","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/2406.16982/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The prediction of disease risk factors can screen vulnerable groups for effective prevention and treatment, so as to reduce their morbidity and mortality. Machine learning has a great demand for high-quality labeling information, and labeling noise in medical big data poses a great challenge to efficient disease risk warning methods. Therefore, this project intends to study the robust learning algorithm and apply it to the early warning of infectious disease risk. A dynamic truncated loss model is proposed, which combines the traditional mutual entropy implicit weight feature with the mean var","authors_text":"Lingxi Xiao, Muqing Li, Yang Lin, Yinqiu Feng, Zexi Chen, Ziyi Zhu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-23T18:44:03Z","title":"Research on Disease Prediction Model Construction Based on Computer AI deep Learning Technology"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.16982","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":"sha256:8b3f80a07586ce036412dc3cd1448815a4eb5ed83f2e03774d0fb61b05d79878","target":"record","created_at":"2026-07-05T08:36:08Z","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":"b95f1d75942c59770c97263e6a0d06a6de85d0417a48f416430362f864228de9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-23T18:44:03Z","title_canon_sha256":"52f501ed2f096b2ada885fa9ff925633d235631115bd45db30e4d5710de40d1c"},"schema_version":"1.0","source":{"id":"2406.16982","kind":"arxiv","version":1}},"canonical_sha256":"43e2b1f62cda400eeb96e1a24cbef6727c05202f572373f7ae7977351222c048","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"43e2b1f62cda400eeb96e1a24cbef6727c05202f572373f7ae7977351222c048","first_computed_at":"2026-07-05T08:36:08.548699Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:36:08.548699Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Dug+sxFYudoO3n/DzAS/7zsbkRbw3hcTCOAg1fg+7+7q+YA8oW746/jMlAiTcmqPF3zWq8+QHcxlhI7NDPRBAw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:36:08.549087Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.16982","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8b3f80a07586ce036412dc3cd1448815a4eb5ed83f2e03774d0fb61b05d79878","sha256:b9f98bbb25df8bbac182eb99605763eb65884dc53812ee35c889ba94bad06b7b"],"state_sha256":"a58f811c6d7450a5eec7b3d33e7e972ce0c52e02368f05cbe08d5390e2239789"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DrImNwwwqrp07ZAsMfQVCipsLWW3ecMbxxF90URQB0OJR+YlEewi03pap/nRzjofxgiq0ulAFQLWPDmOiN/WBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T20:42:59.411173Z","bundle_sha256":"47c2e548438e7e81542acec0139cad3f300cf12773c5585803333ef8f6403312"}}