{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DT4PECDPY7IYFAQ6ECAKFZYE56","short_pith_number":"pith:DT4PECDP","schema_version":"1.0","canonical_sha256":"1cf8f2086fc7d182821e2080a2e704efa66a0a46c527d486d31cb24e2e5a3b0c","source":{"kind":"arxiv","id":"1812.07498","version":1},"attestation_state":"computed","paper":{"title":"Class Augmented Semi-Supervised Learning for Practical Clinical Analytics on Physiological Signals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","stat.ML"],"primary_cat":"physics.med-ph","authors_text":"Arijit Ukil, Arpan Pal, Chetanya Puri, Rituraj Singh, Soma Bandyopadhyay","submitted_at":"2018-11-29T09:12:54Z","abstract_excerpt":"Computational analysis on physiological signals would provide immense impact for enabling automated clinical analytics. However, the class imbalance issue where negative or minority class instances are rare in number impairs the robustness of the practical solution. The key idea of our approach is intelligent augmentation of minority class examples to construct smooth, unbiased decision boundary for robust semi-supervised learning. This solves the practical class imbalance problem in anomaly detection task for computational clinical analytics using physiological signals. We choose two critical"},"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":"1812.07498","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2018-11-29T09:12:54Z","cross_cats_sorted":["eess.SP","stat.ML"],"title_canon_sha256":"399608597acbd5d605c48dba12c17b66534b44a52359fbecc8dfbabbf56f9a8e","abstract_canon_sha256":"a8a21e7e0090b14c0ff0da36aa44f1e6d52fcef193d7577d1f81fd5463a05b95"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:00.333608Z","signature_b64":"ICnbgpIFgQbx5uKS+jRuHRMeKTTdBhcaZsGUDi6VRFDLk0zbEt+3Wh6WEuqaupqTPBxhC5XQLaIBTADU6yWDAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1cf8f2086fc7d182821e2080a2e704efa66a0a46c527d486d31cb24e2e5a3b0c","last_reissued_at":"2026-05-17T23:58:00.332979Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:00.332979Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Class Augmented Semi-Supervised Learning for Practical Clinical Analytics on Physiological Signals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","stat.ML"],"primary_cat":"physics.med-ph","authors_text":"Arijit Ukil, Arpan Pal, Chetanya Puri, Rituraj Singh, Soma Bandyopadhyay","submitted_at":"2018-11-29T09:12:54Z","abstract_excerpt":"Computational analysis on physiological signals would provide immense impact for enabling automated clinical analytics. However, the class imbalance issue where negative or minority class instances are rare in number impairs the robustness of the practical solution. The key idea of our approach is intelligent augmentation of minority class examples to construct smooth, unbiased decision boundary for robust semi-supervised learning. This solves the practical class imbalance problem in anomaly detection task for computational clinical analytics using physiological signals. We choose two critical"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07498","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":""},"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":"1812.07498","created_at":"2026-05-17T23:58:00.333085+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.07498v1","created_at":"2026-05-17T23:58:00.333085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07498","created_at":"2026-05-17T23:58:00.333085+00:00"},{"alias_kind":"pith_short_12","alias_value":"DT4PECDPY7IY","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"DT4PECDPY7IYFAQ6","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"DT4PECDP","created_at":"2026-05-18T12:32:19.392346+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/DT4PECDPY7IYFAQ6ECAKFZYE56","json":"https://pith.science/pith/DT4PECDPY7IYFAQ6ECAKFZYE56.json","graph_json":"https://pith.science/api/pith-number/DT4PECDPY7IYFAQ6ECAKFZYE56/graph.json","events_json":"https://pith.science/api/pith-number/DT4PECDPY7IYFAQ6ECAKFZYE56/events.json","paper":"https://pith.science/paper/DT4PECDP"},"agent_actions":{"view_html":"https://pith.science/pith/DT4PECDPY7IYFAQ6ECAKFZYE56","download_json":"https://pith.science/pith/DT4PECDPY7IYFAQ6ECAKFZYE56.json","view_paper":"https://pith.science/paper/DT4PECDP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.07498&json=true","fetch_graph":"https://pith.science/api/pith-number/DT4PECDPY7IYFAQ6ECAKFZYE56/graph.json","fetch_events":"https://pith.science/api/pith-number/DT4PECDPY7IYFAQ6ECAKFZYE56/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DT4PECDPY7IYFAQ6ECAKFZYE56/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DT4PECDPY7IYFAQ6ECAKFZYE56/action/storage_attestation","attest_author":"https://pith.science/pith/DT4PECDPY7IYFAQ6ECAKFZYE56/action/author_attestation","sign_citation":"https://pith.science/pith/DT4PECDPY7IYFAQ6ECAKFZYE56/action/citation_signature","submit_replication":"https://pith.science/pith/DT4PECDPY7IYFAQ6ECAKFZYE56/action/replication_record"}},"created_at":"2026-05-17T23:58:00.333085+00:00","updated_at":"2026-05-17T23:58:00.333085+00:00"}