{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:OPM7XAYBE5LUINHF7CSZSUWZGF","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":"ccd19152de2b2816a1dddadff3175c888ec43c4855f5e64f87213d2ff8a5b930","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-06-10T01:51:51Z","title_canon_sha256":"c454fadc003dde3510be765ff9b53982fab2b47ddabc4e07bc8f17ca32b69377"},"schema_version":"1.0","source":{"id":"2606.11570","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.11570","created_at":"2026-06-11T01:09:56Z"},{"alias_kind":"arxiv_version","alias_value":"2606.11570v1","created_at":"2026-06-11T01:09:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11570","created_at":"2026-06-11T01:09:56Z"},{"alias_kind":"pith_short_12","alias_value":"OPM7XAYBE5LU","created_at":"2026-06-11T01:09:56Z"},{"alias_kind":"pith_short_16","alias_value":"OPM7XAYBE5LUINHF","created_at":"2026-06-11T01:09:56Z"},{"alias_kind":"pith_short_8","alias_value":"OPM7XAYB","created_at":"2026-06-11T01:09:56Z"}],"graph_snapshots":[{"event_id":"sha256:a641b5ad55681fe53a0270818199449a76adee40b4de8159a95ceaf3622db93e","target":"graph","created_at":"2026-06-11T01:09:56Z","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/2606.11570/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a spectral-based, unsupervised representation learning framework to derive low-dimensional embeddings for clinical concepts and patients in rare disease cohorts from electronic health records, where data are high-dimensional but sample sizes are limited. To overcome this challenge, we incorporate a knowledge matrix extracted from a broader population that shares a partially overlapping subspace with the rare-disease cohort. Our method departs from existing approaches by relaxing restrictive one-to-one signal-alignment assumptions between the latent data matrix and knowledge matrix, ","authors_text":"Feiqing Huang, Rong Ma, Tianxi Cai, Zongqi Xia","cross_cats":["cs.LG","stat.ME"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-06-10T01:51:51Z","title":"Enhancing Spectral Embedding through Robust and Flexible Knowledge Transfer in Electronic Health Records"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11570","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:d9adb4261e27efc8836688b8cd601ff591331b442e12bf267d151aae8b958af5","target":"record","created_at":"2026-06-11T01:09:56Z","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":"ccd19152de2b2816a1dddadff3175c888ec43c4855f5e64f87213d2ff8a5b930","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-06-10T01:51:51Z","title_canon_sha256":"c454fadc003dde3510be765ff9b53982fab2b47ddabc4e07bc8f17ca32b69377"},"schema_version":"1.0","source":{"id":"2606.11570","kind":"arxiv","version":1}},"canonical_sha256":"73d9fb830127574434e5f8a59952d93172c3b46285de4cb8ae3e98cef1d6a886","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73d9fb830127574434e5f8a59952d93172c3b46285de4cb8ae3e98cef1d6a886","first_computed_at":"2026-06-11T01:09:56.702504Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-11T01:09:56.702504Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Nu5HpzrGlOm4W/MBKckS18AbqCQMgPomGUUi4va2+5uUXA0jXsszY5E2A7XzFrUJQDg5C5z79izTvhgVH3xcBg==","signature_status":"signed_v1","signed_at":"2026-06-11T01:09:56.703388Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.11570","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d9adb4261e27efc8836688b8cd601ff591331b442e12bf267d151aae8b958af5","sha256:a641b5ad55681fe53a0270818199449a76adee40b4de8159a95ceaf3622db93e"],"state_sha256":"09e2dc9d939c9c4fae6744a7be879e165c092b3d2836ae91abc13bfb47bb8c50"}