{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:EZ5TXNQQR3TS6UNFPHTFHY363A","short_pith_number":"pith:EZ5TXNQQ","canonical_record":{"source":{"id":"1910.09570","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2019-10-21T18:02:36Z","cross_cats_sorted":["cs.CV","eess.SP","stat.AP","stat.ML"],"title_canon_sha256":"11770e87c3108ffd62e3972d4e731ce102661bae221b83d1b2a0c6c0081511f1","abstract_canon_sha256":"97f3bee21c80e09529e5a6d8698a59ebe246647dfd5eef463e4936014cad162b"},"schema_version":"1.0"},"canonical_sha256":"267b3bb6108ee72f51a579e653e37ed81991a93ff5f4b626bffd38a1414f7ba5","source":{"kind":"arxiv","id":"1910.09570","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.09570","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"arxiv_version","alias_value":"1910.09570v1","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.09570","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"pith_short_12","alias_value":"EZ5TXNQQR3TS","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"pith_short_16","alias_value":"EZ5TXNQQR3TS6UNF","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"pith_short_8","alias_value":"EZ5TXNQQ","created_at":"2026-07-05T00:14:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:EZ5TXNQQR3TS6UNFPHTFHY363A","target":"record","payload":{"canonical_record":{"source":{"id":"1910.09570","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2019-10-21T18:02:36Z","cross_cats_sorted":["cs.CV","eess.SP","stat.AP","stat.ML"],"title_canon_sha256":"11770e87c3108ffd62e3972d4e731ce102661bae221b83d1b2a0c6c0081511f1","abstract_canon_sha256":"97f3bee21c80e09529e5a6d8698a59ebe246647dfd5eef463e4936014cad162b"},"schema_version":"1.0"},"canonical_sha256":"267b3bb6108ee72f51a579e653e37ed81991a93ff5f4b626bffd38a1414f7ba5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:14:00.483253Z","signature_b64":"ACVFw7VFxSIS5k9YK0VBG1iEK6+hdFirQC04mJtoiInyfVwyW8s2AYdURpQFJ7z99xdeJ4Nsw1BsFNXYZtLWCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"267b3bb6108ee72f51a579e653e37ed81991a93ff5f4b626bffd38a1414f7ba5","last_reissued_at":"2026-07-05T00:14:00.482751Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:14:00.482751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.09570","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:14:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VVCv/u6ILaXlSjvdmfPQRWgjWdJV/s52p1wRo+KrCyyv2+xq9uEb07OalFr1zojLuYmpPEcK8YxWaoHbVNdDBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T21:48:55.643676Z"},"content_sha256":"68b8a8575e145849a0f92a1dc61b7a36a55e4a1d0e5ba0b80e668ccd1aa8ef53","schema_version":"1.0","event_id":"sha256:68b8a8575e145849a0f92a1dc61b7a36a55e4a1d0e5ba0b80e668ccd1aa8ef53"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:EZ5TXNQQR3TS6UNFPHTFHY363A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","eess.SP","stat.AP","stat.ML"],"primary_cat":"q-bio.QM","authors_text":"Aaron Courville, Ahmad Chamseddine, Guillaume Androz, Joseph Paul Cohen, Pierre Fecteau, Shawn Tan, Yoshua Bengio","submitted_at":"2019-10-21T18:02:36Z","abstract_excerpt":"We release the largest public ECG dataset of continuous raw signals for representation learning containing 11 thousand patients and 2 billion labelled beats. Our goal is to enable semi-supervised ECG models to be made as well as to discover unknown subtypes of arrhythmia and anomalous ECG signal events. To this end, we propose an unsupervised representation learning task, evaluated in a semi-supervised fashion. We provide a set of baselines for different feature extractors that can be built upon. Additionally, we perform qualitative evaluations on results from PCA embeddings, where we identify"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.09570","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/1910.09570/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":"2026-07-05T00:14:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i5W7gNwtKThWu2fEQoSOSvKQKngxQ+Sc5Ui48FOJCILwKFY7iQVHFNGw33x2jKQOXZ00ieZy4gWnW2ySqiHuBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T21:48:55.644046Z"},"content_sha256":"0a00eeabc0ff83f11add52713c03460beb0d7d0606687c72e9e4ef3dd59837a7","schema_version":"1.0","event_id":"sha256:0a00eeabc0ff83f11add52713c03460beb0d7d0606687c72e9e4ef3dd59837a7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EZ5TXNQQR3TS6UNFPHTFHY363A/bundle.json","state_url":"https://pith.science/pith/EZ5TXNQQR3TS6UNFPHTFHY363A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EZ5TXNQQR3TS6UNFPHTFHY363A/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":"2026-07-06T21:48:55Z","links":{"resolver":"https://pith.science/pith/EZ5TXNQQR3TS6UNFPHTFHY363A","bundle":"https://pith.science/pith/EZ5TXNQQR3TS6UNFPHTFHY363A/bundle.json","state":"https://pith.science/pith/EZ5TXNQQR3TS6UNFPHTFHY363A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EZ5TXNQQR3TS6UNFPHTFHY363A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:EZ5TXNQQR3TS6UNFPHTFHY363A","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":"97f3bee21c80e09529e5a6d8698a59ebe246647dfd5eef463e4936014cad162b","cross_cats_sorted":["cs.CV","eess.SP","stat.AP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2019-10-21T18:02:36Z","title_canon_sha256":"11770e87c3108ffd62e3972d4e731ce102661bae221b83d1b2a0c6c0081511f1"},"schema_version":"1.0","source":{"id":"1910.09570","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.09570","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"arxiv_version","alias_value":"1910.09570v1","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.09570","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"pith_short_12","alias_value":"EZ5TXNQQR3TS","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"pith_short_16","alias_value":"EZ5TXNQQR3TS6UNF","created_at":"2026-07-05T00:14:00Z"},{"alias_kind":"pith_short_8","alias_value":"EZ5TXNQQ","created_at":"2026-07-05T00:14:00Z"}],"graph_snapshots":[{"event_id":"sha256:0a00eeabc0ff83f11add52713c03460beb0d7d0606687c72e9e4ef3dd59837a7","target":"graph","created_at":"2026-07-05T00:14:00Z","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/1910.09570/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We release the largest public ECG dataset of continuous raw signals for representation learning containing 11 thousand patients and 2 billion labelled beats. Our goal is to enable semi-supervised ECG models to be made as well as to discover unknown subtypes of arrhythmia and anomalous ECG signal events. To this end, we propose an unsupervised representation learning task, evaluated in a semi-supervised fashion. We provide a set of baselines for different feature extractors that can be built upon. Additionally, we perform qualitative evaluations on results from PCA embeddings, where we identify","authors_text":"Aaron Courville, Ahmad Chamseddine, Guillaume Androz, Joseph Paul Cohen, Pierre Fecteau, Shawn Tan, Yoshua Bengio","cross_cats":["cs.CV","eess.SP","stat.AP","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2019-10-21T18:02:36Z","title":"Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.09570","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":"sha256:68b8a8575e145849a0f92a1dc61b7a36a55e4a1d0e5ba0b80e668ccd1aa8ef53","target":"record","created_at":"2026-07-05T00:14:00Z","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":"97f3bee21c80e09529e5a6d8698a59ebe246647dfd5eef463e4936014cad162b","cross_cats_sorted":["cs.CV","eess.SP","stat.AP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2019-10-21T18:02:36Z","title_canon_sha256":"11770e87c3108ffd62e3972d4e731ce102661bae221b83d1b2a0c6c0081511f1"},"schema_version":"1.0","source":{"id":"1910.09570","kind":"arxiv","version":1}},"canonical_sha256":"267b3bb6108ee72f51a579e653e37ed81991a93ff5f4b626bffd38a1414f7ba5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"267b3bb6108ee72f51a579e653e37ed81991a93ff5f4b626bffd38a1414f7ba5","first_computed_at":"2026-07-05T00:14:00.482751Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:14:00.482751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ACVFw7VFxSIS5k9YK0VBG1iEK6+hdFirQC04mJtoiInyfVwyW8s2AYdURpQFJ7z99xdeJ4Nsw1BsFNXYZtLWCw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:14:00.483253Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.09570","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:68b8a8575e145849a0f92a1dc61b7a36a55e4a1d0e5ba0b80e668ccd1aa8ef53","sha256:0a00eeabc0ff83f11add52713c03460beb0d7d0606687c72e9e4ef3dd59837a7"],"state_sha256":"3c415d40729fccfb207085ba3dc03fb85f7b0a979f05d7c6548cc2932ac198ff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C2bRxoIF7LEBEfKUvyrgkpfqNap3pNSV5vB9qsTr5ziDwcIOCMQLd2rHajFNK7taFAVZTJgMDUbCNUSHqrtbAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T21:48:55.646024Z","bundle_sha256":"9d472e5b15f03349012c0d0fde5fc74bc95673c9c87c89c8995bac282c528d37"}}