{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:E3UPGFWLO2VE75ZO7GEZI6HMJH","short_pith_number":"pith:E3UPGFWL","schema_version":"1.0","canonical_sha256":"26e8f316cb76aa4ff72ef9899478ec49ca14ab7498fdc9f0321a3a58036cb72a","source":{"kind":"arxiv","id":"2606.10410","version":1},"attestation_state":"computed","paper":{"title":"A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","q-bio.QM"],"primary_cat":"cs.LG","authors_text":"Davood Fattahi, Runze Yan, Saurabh Kataria, Xiao Hu, Zhaoliang Chen","submitted_at":"2026-06-09T04:35:16Z","abstract_excerpt":"Objective: Accurate classification of physiological signals in real-world deployments is challenged by sensor noise, motion artifacts, and distribution shifts between training and deployment data. Inference-time augmentation (ITA), which applies augmentations during inference rather than retraining, offers a simple, model-agnostic mechanism to improve robustness. However, ITA application to physiological signals has remained narrow in scope, relying on limited augmentation methods with fixed, unoptimized parameters. This work proposes a unified ITA framework to address that gap.\n  Approach: Th"},"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":"2606.10410","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-09T04:35:16Z","cross_cats_sorted":["eess.SP","q-bio.QM"],"title_canon_sha256":"7e89c4e7901bedb74f64c6baad9c0b1d14823843dead7d0ced5b4a6dbdb29043","abstract_canon_sha256":"b24fa0842695479fa60e9bdfe2d5ac69eafb95bb3cd409e6711259b18da63114"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T01:10:17.483935Z","signature_b64":"I6tMFBpdZsKCCsRuMmNTIFhvHmDvt7wjBU7sPSwlhM08vMvRrjUdjbhXPBS41RjqT4UUSGPHUimsrDBPMy/8Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26e8f316cb76aa4ff72ef9899478ec49ca14ab7498fdc9f0321a3a58036cb72a","last_reissued_at":"2026-06-10T01:10:17.483014Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T01:10:17.483014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","q-bio.QM"],"primary_cat":"cs.LG","authors_text":"Davood Fattahi, Runze Yan, Saurabh Kataria, Xiao Hu, Zhaoliang Chen","submitted_at":"2026-06-09T04:35:16Z","abstract_excerpt":"Objective: Accurate classification of physiological signals in real-world deployments is challenged by sensor noise, motion artifacts, and distribution shifts between training and deployment data. Inference-time augmentation (ITA), which applies augmentations during inference rather than retraining, offers a simple, model-agnostic mechanism to improve robustness. However, ITA application to physiological signals has remained narrow in scope, relying on limited augmentation methods with fixed, unoptimized parameters. This work proposes a unified ITA framework to address that gap.\n  Approach: Th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10410","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/2606.10410/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.10410","created_at":"2026-06-10T01:10:17.483151+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.10410v1","created_at":"2026-06-10T01:10:17.483151+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.10410","created_at":"2026-06-10T01:10:17.483151+00:00"},{"alias_kind":"pith_short_12","alias_value":"E3UPGFWLO2VE","created_at":"2026-06-10T01:10:17.483151+00:00"},{"alias_kind":"pith_short_16","alias_value":"E3UPGFWLO2VE75ZO","created_at":"2026-06-10T01:10:17.483151+00:00"},{"alias_kind":"pith_short_8","alias_value":"E3UPGFWL","created_at":"2026-06-10T01:10:17.483151+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/E3UPGFWLO2VE75ZO7GEZI6HMJH","json":"https://pith.science/pith/E3UPGFWLO2VE75ZO7GEZI6HMJH.json","graph_json":"https://pith.science/api/pith-number/E3UPGFWLO2VE75ZO7GEZI6HMJH/graph.json","events_json":"https://pith.science/api/pith-number/E3UPGFWLO2VE75ZO7GEZI6HMJH/events.json","paper":"https://pith.science/paper/E3UPGFWL"},"agent_actions":{"view_html":"https://pith.science/pith/E3UPGFWLO2VE75ZO7GEZI6HMJH","download_json":"https://pith.science/pith/E3UPGFWLO2VE75ZO7GEZI6HMJH.json","view_paper":"https://pith.science/paper/E3UPGFWL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.10410&json=true","fetch_graph":"https://pith.science/api/pith-number/E3UPGFWLO2VE75ZO7GEZI6HMJH/graph.json","fetch_events":"https://pith.science/api/pith-number/E3UPGFWLO2VE75ZO7GEZI6HMJH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E3UPGFWLO2VE75ZO7GEZI6HMJH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E3UPGFWLO2VE75ZO7GEZI6HMJH/action/storage_attestation","attest_author":"https://pith.science/pith/E3UPGFWLO2VE75ZO7GEZI6HMJH/action/author_attestation","sign_citation":"https://pith.science/pith/E3UPGFWLO2VE75ZO7GEZI6HMJH/action/citation_signature","submit_replication":"https://pith.science/pith/E3UPGFWLO2VE75ZO7GEZI6HMJH/action/replication_record"}},"created_at":"2026-06-10T01:10:17.483151+00:00","updated_at":"2026-06-10T01:10:17.483151+00:00"}