{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:LVRMDGDYISYA4FQKECVDUMQTPY","short_pith_number":"pith:LVRMDGDY","canonical_record":{"source":{"id":"2110.09680","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2021-10-19T01:14:08Z","cross_cats_sorted":["cs.LG","stat.AP"],"title_canon_sha256":"837d350357a74fd1cd5066a85bad4433643ed61aa01f162b28369add1878446a","abstract_canon_sha256":"26b89caf0b43fc6e17bf6709faf24adff9329ecdfabc2d3477aa34550f1cf4ce"},"schema_version":"1.0"},"canonical_sha256":"5d62c1987844b00e160a20aa3a32137e2c877aa027af0e9c601f8edf0b2c8f49","source":{"kind":"arxiv","id":"2110.09680","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.09680","created_at":"2026-07-05T08:03:37Z"},{"alias_kind":"arxiv_version","alias_value":"2110.09680v3","created_at":"2026-07-05T08:03:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.09680","created_at":"2026-07-05T08:03:37Z"},{"alias_kind":"pith_short_12","alias_value":"LVRMDGDYISYA","created_at":"2026-07-05T08:03:37Z"},{"alias_kind":"pith_short_16","alias_value":"LVRMDGDYISYA4FQK","created_at":"2026-07-05T08:03:37Z"},{"alias_kind":"pith_short_8","alias_value":"LVRMDGDY","created_at":"2026-07-05T08:03:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:LVRMDGDYISYA4FQKECVDUMQTPY","target":"record","payload":{"canonical_record":{"source":{"id":"2110.09680","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2021-10-19T01:14:08Z","cross_cats_sorted":["cs.LG","stat.AP"],"title_canon_sha256":"837d350357a74fd1cd5066a85bad4433643ed61aa01f162b28369add1878446a","abstract_canon_sha256":"26b89caf0b43fc6e17bf6709faf24adff9329ecdfabc2d3477aa34550f1cf4ce"},"schema_version":"1.0"},"canonical_sha256":"5d62c1987844b00e160a20aa3a32137e2c877aa027af0e9c601f8edf0b2c8f49","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:03:37.766665Z","signature_b64":"LBr7sR8ks444ooTuxB5Vhc5tHQwYwIklmiCz90O+/tqBXJoJMLoLm05kldwOcCzD37NaLXXMXoj380mcCJmeAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d62c1987844b00e160a20aa3a32137e2c877aa027af0e9c601f8edf0b2c8f49","last_reissued_at":"2026-07-05T08:03:37.766155Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:03:37.766155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.09680","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:03:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4dxROcPbUiPSs/MPhmkSKCmkJb1IrlK8dwk4AnLJzISRHtwfWo7XLndLh6EQQD2Pjz0JM2MSwxxNHncpZz41Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:26:47.049255Z"},"content_sha256":"c152a637db6a526c20ee0feebf2ac2c5621cded156171e123af97c625999f581","schema_version":"1.0","event_id":"sha256:c152a637db6a526c20ee0feebf2ac2c5621cded156171e123af97c625999f581"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:LVRMDGDYISYA4FQKECVDUMQTPY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multilevel Stochastic Optimization for Imputation in Massive Medical Data Records","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.AP"],"primary_cat":"stat.ML","authors_text":"Julio Enrique Castrillon-Candas, Mark Kon, Snezana Milanovic, Wenrui Li, Xiaoyu Wang, Yuetian Sun","submitted_at":"2021-10-19T01:14:08Z","abstract_excerpt":"It has long been a recognized problem that many datasets contain significant levels of missing numerical data. A potentially critical predicate for application of machine learning methods to datasets involves addressing this problem. However, this is a challenging task. In this paper, we apply a recently developed multi-level stochastic optimization approach to the problem of imputation in massive medical records. The approach is based on computational applied mathematics techniques and is highly accurate. In particular, for the Best Linear Unbiased Predictor (BLUP) this multi-level formulatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.09680","kind":"arxiv","version":3},"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/2110.09680/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":"2026-07-05T08:03:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iQkTGUUGrAqjWfUaxdOM1o/Cg7Y35exlpzf02waWY7BoAzzdIy1ABvhbrL/dDRJ4kHm8wqGWuLUV0lf0YioZBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:26:47.049664Z"},"content_sha256":"f65f213dba9a2c35f049c507e0a5285f4c4cf10857aa902835c011f9f378c8d3","schema_version":"1.0","event_id":"sha256:f65f213dba9a2c35f049c507e0a5285f4c4cf10857aa902835c011f9f378c8d3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LVRMDGDYISYA4FQKECVDUMQTPY/bundle.json","state_url":"https://pith.science/pith/LVRMDGDYISYA4FQKECVDUMQTPY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LVRMDGDYISYA4FQKECVDUMQTPY/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":"2026-07-06T18:26:47Z","links":{"resolver":"https://pith.science/pith/LVRMDGDYISYA4FQKECVDUMQTPY","bundle":"https://pith.science/pith/LVRMDGDYISYA4FQKECVDUMQTPY/bundle.json","state":"https://pith.science/pith/LVRMDGDYISYA4FQKECVDUMQTPY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LVRMDGDYISYA4FQKECVDUMQTPY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:LVRMDGDYISYA4FQKECVDUMQTPY","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":"26b89caf0b43fc6e17bf6709faf24adff9329ecdfabc2d3477aa34550f1cf4ce","cross_cats_sorted":["cs.LG","stat.AP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2021-10-19T01:14:08Z","title_canon_sha256":"837d350357a74fd1cd5066a85bad4433643ed61aa01f162b28369add1878446a"},"schema_version":"1.0","source":{"id":"2110.09680","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.09680","created_at":"2026-07-05T08:03:37Z"},{"alias_kind":"arxiv_version","alias_value":"2110.09680v3","created_at":"2026-07-05T08:03:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.09680","created_at":"2026-07-05T08:03:37Z"},{"alias_kind":"pith_short_12","alias_value":"LVRMDGDYISYA","created_at":"2026-07-05T08:03:37Z"},{"alias_kind":"pith_short_16","alias_value":"LVRMDGDYISYA4FQK","created_at":"2026-07-05T08:03:37Z"},{"alias_kind":"pith_short_8","alias_value":"LVRMDGDY","created_at":"2026-07-05T08:03:37Z"}],"graph_snapshots":[{"event_id":"sha256:f65f213dba9a2c35f049c507e0a5285f4c4cf10857aa902835c011f9f378c8d3","target":"graph","created_at":"2026-07-05T08:03:37Z","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/2110.09680/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"It has long been a recognized problem that many datasets contain significant levels of missing numerical data. A potentially critical predicate for application of machine learning methods to datasets involves addressing this problem. However, this is a challenging task. In this paper, we apply a recently developed multi-level stochastic optimization approach to the problem of imputation in massive medical records. The approach is based on computational applied mathematics techniques and is highly accurate. In particular, for the Best Linear Unbiased Predictor (BLUP) this multi-level formulatio","authors_text":"Julio Enrique Castrillon-Candas, Mark Kon, Snezana Milanovic, Wenrui Li, Xiaoyu Wang, Yuetian Sun","cross_cats":["cs.LG","stat.AP"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2021-10-19T01:14:08Z","title":"Multilevel Stochastic Optimization for Imputation in Massive Medical Data Records"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.09680","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:c152a637db6a526c20ee0feebf2ac2c5621cded156171e123af97c625999f581","target":"record","created_at":"2026-07-05T08:03:37Z","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":"26b89caf0b43fc6e17bf6709faf24adff9329ecdfabc2d3477aa34550f1cf4ce","cross_cats_sorted":["cs.LG","stat.AP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2021-10-19T01:14:08Z","title_canon_sha256":"837d350357a74fd1cd5066a85bad4433643ed61aa01f162b28369add1878446a"},"schema_version":"1.0","source":{"id":"2110.09680","kind":"arxiv","version":3}},"canonical_sha256":"5d62c1987844b00e160a20aa3a32137e2c877aa027af0e9c601f8edf0b2c8f49","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5d62c1987844b00e160a20aa3a32137e2c877aa027af0e9c601f8edf0b2c8f49","first_computed_at":"2026-07-05T08:03:37.766155Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:03:37.766155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LBr7sR8ks444ooTuxB5Vhc5tHQwYwIklmiCz90O+/tqBXJoJMLoLm05kldwOcCzD37NaLXXMXoj380mcCJmeAA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:03:37.766665Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.09680","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c152a637db6a526c20ee0feebf2ac2c5621cded156171e123af97c625999f581","sha256:f65f213dba9a2c35f049c507e0a5285f4c4cf10857aa902835c011f9f378c8d3"],"state_sha256":"1bbeee551949883808fdc53baa456e89bbf4d399d1e6a7f52d2e38ecf3f91b92"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9b5CZw+4SA74+nn/ghyUtpLD7WgucVLkLCN+n0aI79DF21YWs6CpshOOpzysww94aHs3kV9US8y1+YwcnIlnBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T18:26:47.051555Z","bundle_sha256":"6b9b7ce3341f9a510cb86b1769c333fedc02c9ea93a90de2de897e2190073ef1"}}