{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RXNCRNWRJV4WXWWUCX2RLJAPV4","short_pith_number":"pith:RXNCRNWR","schema_version":"1.0","canonical_sha256":"8dda28b6d14d796bdad415f515a40faf3b578c7a72f4157749cf32b23479d772","source":{"kind":"arxiv","id":"2605.18008","version":1},"attestation_state":"computed","paper":{"title":"Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ciaran Bench, Mohammad Moulaeifard, Nils Strodthoff, Philip J. Aston","submitted_at":"2026-05-18T08:03:04Z","abstract_excerpt":"Uncertainty quantification (UQ) is critical for safety-critical domains like healthcare, yet it is rarely evaluated under realistic out-of-distribution (OOD) conditions. Here, we assessed predictive performance and uncertainty reliability for deep learning-based blood pressure (BP) estimation from photoplethysmography (PPG) signals under both in-distribution (ID) and OOD settings. Using an XResNet1D-50 trained on PulseDB and tested on four external datasets, we compared deep ensembles (DE) and Monte Carlo dropout (MCD) with Gaussian negative log-likelihood (GNLL) and mean squared error (MSE) l"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.18008","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-18T08:03:04Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ab9ac81fd75dc0fa7d27b206e52c79c5cd33d4b52bb4d1f7f08aac851a12d802","abstract_canon_sha256":"e15c194b51e935499751f51ea76519fe0dfe051f47f736e377f1f861a051d24d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:05:10.962797Z","signature_b64":"5Qu4O9xbRpuJYUY9QXU+Xovo8TFm9CxvizebqQ5qEAXCLhcXq0/ZD3yu6ljpZQtdaRequ4IrKjzGNVsffE1/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8dda28b6d14d796bdad415f515a40faf3b578c7a72f4157749cf32b23479d772","last_reissued_at":"2026-05-20T00:05:10.961961Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:05:10.961961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ciaran Bench, Mohammad Moulaeifard, Nils Strodthoff, Philip J. Aston","submitted_at":"2026-05-18T08:03:04Z","abstract_excerpt":"Uncertainty quantification (UQ) is critical for safety-critical domains like healthcare, yet it is rarely evaluated under realistic out-of-distribution (OOD) conditions. Here, we assessed predictive performance and uncertainty reliability for deep learning-based blood pressure (BP) estimation from photoplethysmography (PPG) signals under both in-distribution (ID) and OOD settings. Using an XResNet1D-50 trained on PulseDB and tested on four external datasets, we compared deep ensembles (DE) and Monte Carlo dropout (MCD) with Gaussian negative log-likelihood (GNLL) and mean squared error (MSE) l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18008","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/2605.18008/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T23:33:35.533206Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"dbf786dce953d4f4ac89c4e2516cc020b97a0798cc71dcc4e369befbcd7bc4d4"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.18008","created_at":"2026-05-20T00:05:10.962086+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.18008v1","created_at":"2026-05-20T00:05:10.962086+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18008","created_at":"2026-05-20T00:05:10.962086+00:00"},{"alias_kind":"pith_short_12","alias_value":"RXNCRNWRJV4W","created_at":"2026-05-20T00:05:10.962086+00:00"},{"alias_kind":"pith_short_16","alias_value":"RXNCRNWRJV4WXWWU","created_at":"2026-05-20T00:05:10.962086+00:00"},{"alias_kind":"pith_short_8","alias_value":"RXNCRNWR","created_at":"2026-05-20T00:05:10.962086+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RXNCRNWRJV4WXWWUCX2RLJAPV4","json":"https://pith.science/pith/RXNCRNWRJV4WXWWUCX2RLJAPV4.json","graph_json":"https://pith.science/api/pith-number/RXNCRNWRJV4WXWWUCX2RLJAPV4/graph.json","events_json":"https://pith.science/api/pith-number/RXNCRNWRJV4WXWWUCX2RLJAPV4/events.json","paper":"https://pith.science/paper/RXNCRNWR"},"agent_actions":{"view_html":"https://pith.science/pith/RXNCRNWRJV4WXWWUCX2RLJAPV4","download_json":"https://pith.science/pith/RXNCRNWRJV4WXWWUCX2RLJAPV4.json","view_paper":"https://pith.science/paper/RXNCRNWR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.18008&json=true","fetch_graph":"https://pith.science/api/pith-number/RXNCRNWRJV4WXWWUCX2RLJAPV4/graph.json","fetch_events":"https://pith.science/api/pith-number/RXNCRNWRJV4WXWWUCX2RLJAPV4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RXNCRNWRJV4WXWWUCX2RLJAPV4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RXNCRNWRJV4WXWWUCX2RLJAPV4/action/storage_attestation","attest_author":"https://pith.science/pith/RXNCRNWRJV4WXWWUCX2RLJAPV4/action/author_attestation","sign_citation":"https://pith.science/pith/RXNCRNWRJV4WXWWUCX2RLJAPV4/action/citation_signature","submit_replication":"https://pith.science/pith/RXNCRNWRJV4WXWWUCX2RLJAPV4/action/replication_record"}},"created_at":"2026-05-20T00:05:10.962086+00:00","updated_at":"2026-05-20T00:05:10.962086+00:00"}