{"as_of":"2026-08-11T15:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5b457d111b9cffd7d4fdb01ba502c79c4b2ef93c5fb009e312afeb11701ac554","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T21:20:46.503275Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.08116/citation-record","integrity":"/paper/2509.08116/integrity","json":"/paper/2509.08116/citation-record.json","paper":"/paper/2509.08116"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.392321Z","title":"Deep learning for healthcare applications based on physiological signals: A review,","venue":null,"work_id":"9085367b-3d8c-4155-936f-67d747d48cca","year":2018},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.274846Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:b870f6082905b8422b89a90202416a83542ef4cbe15fae0bcda389a8739cc581","observation_id":"46fa1a43-17f9-4953-9da1-7657e3a112d5","resolution":{"observed_at":"2026-08-04T21:20:47.396831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-11T10:12:11.384939Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-04T21:20:46.279827Z","title":"DINOv2: Learning robust visual features without supervision,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.279827Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:b4792e93636b2df1a737a6816daa096185e569c5d320ca51ff6cbc762602304a","observation_id":"18868781-093f-4806-b8ec-b558adfd1a18","resolution":{"observed_at":"2026-08-04T21:20:46.279827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.377225Z","title":"A simple framework for contrastive learning of visual representations,","venue":null,"work_id":"7e8f1a07-5a7f-492a-8171-a891078ba7b3","year":2020},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.284842Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:25f18258c0568208e38864318c119bff8e7d348fe705cd6d56ef170c6300fcc0","observation_id":"8e7279b6-7ee2-4bfa-977c-8703f0a2f3c8","resolution":{"observed_at":"2026-08-04T21:20:47.381874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.361464Z","title":"Bootstrap your own latent: A new approach to self-supervised learning,","venue":null,"work_id":"d4cf2532-3444-48c2-93b1-04a78e336aea","year":2020},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.289305Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:ffd69baf72a7aa83ef8d04582199019dbcce057b40eada502e855081c0762b00","observation_id":"a6511697-6fc0-4d6b-b5cf-1531d21c6df1","resolution":{"observed_at":"2026-08-04T21:20:47.366120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.294301Z","title":"Context autoencoder for self- supervised representation learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.294301Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:c6aef3666060462a74a3643c3e1945883fafd8031514b4f0e0659b4aa202dfd0","observation_id":"c118605b-0431-45ba-ad9b-212505390fd8","resolution":{"observed_at":"2026-08-04T21:20:46.294301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-04T21:20:46.299165Z","title":"Representation learning with contrastive predictive coding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.299165Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:d1cdfb5329860ada7f520f7e24f4d3ade5c503337febf36b47762c602100f040","observation_id":"0a0924e9-a3ac-41aa-b62a-eb777c4d39a1","resolution":{"observed_at":"2026-08-04T21:20:46.299165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.346126Z","title":"Masked autoencoders for point-cloud self-supervised learning,","venue":null,"work_id":"ff2836e9-f8a0-40f1-a72e-eaceffe1296a","year":2022},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.304466Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:a6d89d316dc62fabe5758aa2bf4c7a627897dc0295cd0f12e8cd5e00a7a8b772","observation_id":"48cd722e-a3ec-4074-85c9-f49e6ec5580c","resolution":{"observed_at":"2026-08-04T21:20:47.350701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.331991Z","title":"Self-supervised speech representation learning: A review,","venue":null,"work_id":"394a6330-aacd-406a-9436-406223cd5a70","year":2022},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.308731Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:61e2ef32f74e152225590c432722e7d6e116f9cd2c7d3f0c8a5e35e87120b166","observation_id":"438fe992-383e-471f-bcfa-07f7059241ff","resolution":{"observed_at":"2026-08-04T21:20:47.336248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.317088Z","title":"Which augmentation should I use? An empirical investigation of augmentations for self-supervised phonocardiogram representation learning,","venue":null,"work_id":"6611dcbe-6317-41b4-b946-a031fe1f57a0","year":2023},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.312703Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:f3ddd425761da9a95e45388f5379402edb7323f562f757c9e97e48a7b1ee2b06","observation_id":"578f4927-2afc-45bb-9f51-db6541a51f14","resolution":{"observed_at":"2026-08-04T21:20:47.322293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41591-019-0359-9","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network,","venue":"Nature Medicine","work_id":"ebe1b509-d869-4519-88cd-bb42300e0596","year":2019},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.317303Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:e46d75d130b2d3dce69dada0a060d7376ec82fb009d805470633e1a536993180","observation_id":"b179acb5-7329-471c-bc8f-4df078249edc","resolution":{"observed_at":"2026-08-04T21:20:46.553581Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.301283Z","title":"AF classification from a short single-lead ECG recording: The PhysioNet/Computing in Cardiology Challenge 2017,","venue":null,"work_id":"fc722196-1d47-4b02-aeed-8c16c0056546","year":2017},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.321610Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:c7c9b1cc9e2751e4d7c7548c2915392fe81a006376eb1f5954958cd39f296389","observation_id":"371f590c-e5c5-4e06-bc4f-f034f5d27090","resolution":{"observed_at":"2026-08-04T21:20:47.306127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.285271Z","title":"Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals,","venue":null,"work_id":"43611e3d-e25a-4bfe-9425-93789c7151d6","year":2019},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.325806Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:72cf70f908b3f2caa6b1492f5419b99d6a5ec8d3c24f1577d98f9323e20cbdd5","observation_id":"83570c57-c3df-47a8-b4e8-e6970958f45c","resolution":{"observed_at":"2026-08-04T21:20:47.290599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.269361Z","title":"ECG segmentation using a deep learning model,","venue":null,"work_id":"9d654820-998e-4f5e-a75b-93afd761c46a","year":2022},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.329822Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:42d53e43292ce49d6288bad904bed6d885846df914d2e62b3dca59a698fa1c20","observation_id":"46eff3fa-7a17-4fb2-8d07-50635f868206","resolution":{"observed_at":"2026-08-04T21:20:47.274171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.252566Z","title":"Self-supervised representation learning from 12-lead ECG data,","venue":null,"work_id":"246a19c1-e4da-4fa0-bd4e-56a0f2fbbdfd","year":2022},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.334069Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:20bd1d83a5d7b34577532dcb13fdd6cdaf789ff1af74ca7763e1c86fb7a3b1ef","observation_id":"b517dce6-e924-44dc-93d8-a4fb150d5324","resolution":{"observed_at":"2026-08-04T21:20:47.257887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.236660Z","title":"3KG: Contrastive learning of 12-lead electrocardiograms using physiologically-inspired augmentations,","venue":null,"work_id":"fd38c410-5561-4f68-a88f-1554775d81a7","year":2021},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.338289Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:78ae335071f197c0e16b5e3d4fabfbdbc4dc6c55d2616dedf273b04c8f3dab6a","observation_id":"7a61cec1-c813-44c9-87b1-f8d03c974a31","resolution":{"observed_at":"2026-08-04T21:20:47.241224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.221993Z","title":"sCL-ST: Su- pervised contrastive learning with semantic transformations for multiple- lead ECG arrhythmia classification,","venue":null,"work_id":"aedad461-d990-423a-bdba-92f287a974ae","year":2023},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.342425Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:7c0c7fb2e6d3671a667751114f7f4b66bf154f736ba111ada0891af0dd633a65","observation_id":"8ece18d7-f90b-46c5-935b-7880747238db","resolution":{"observed_at":"2026-08-04T21:20:47.226498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.205671Z","title":"CLECG: A novel contrastive learning framework for electrocardiogram arrhythmia classification,","venue":null,"work_id":"8a95527d-4167-46b3-8147-be8f064d128c","year":1993},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.346575Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:16171f649db386873464e13045e835286765cf01feb59752d68e82cb18e178e2","observation_id":"fa7ae092-25f3-46a5-80db-876e2080e065","resolution":{"observed_at":"2026-08-04T21:20:47.211303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.189648Z","title":"CLOCS: Contrastive learning of cardiac signals across space, time and patients,","venue":null,"work_id":"33c8a1ab-8ed9-4fe2-adc3-7cfceecd9629","year":2021},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.350597Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:e4391c5cfc05b5028096965b65a60304343a8d620160aea59b74d87e1648ab95","observation_id":"3e5e9976-0070-4f0f-ada1-3d6704fdf213","resolution":{"observed_at":"2026-08-04T21:20:47.194656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.174256Z","title":"Contrast everything: A hierarchical contrastive framework for medical time-series,","venue":null,"work_id":"7d34ac84-8daa-4687-9d3f-65fe4e1f3918","year":2024},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.355020Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:b28f83fcbc53feecf65d3481163fb7ccbdea5616a3c1025673e4fe181ad84755","observation_id":"09c62203-9120-43a7-9397-cbd5f89ed736","resolution":{"observed_at":"2026-08-04T21:20:47.178737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.158525Z","title":"Lead-agnostic self-supervised learning for local and global representations of electrocardiogram,","venue":null,"work_id":"a72e5d35-e372-410b-b9b1-d91485d0ba1d","year":2022},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.359471Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:9d82971d09d3c0af7d45dadacdd7aa81018be74f01b54600b2c0ae4ae8ea351d","observation_id":"a6e33c10-be20-4d95-98a9-fd3ebeee85df","resolution":{"observed_at":"2026-08-04T21:20:47.163931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.143636Z","title":"Multi-channel masked autoen- coder and comprehensive evaluations for reconstructing 12-lead ECG from arbitrary single-lead ECG,","venue":null,"work_id":"a477bb68-7f87-4268-b0f3-447553b0b5df","year":2024},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.364319Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:3a6b5a616bb86ed5cd9f31305b810465829f598c1ba53450ddfb19a6b9c40ad6","observation_id":"041aee16-d919-4845-aa71-a088d28fe6cb","resolution":{"observed_at":"2026-08-04T21:20:47.148687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09450","last_updated":"2024-03-19T16:17:00Z","snapshot_observed_at":"2026-08-10T00:26:58.645276Z","submitted_at":"2024-02-02T10:04:13Z","title":"Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09450","snapshot_observed_at":"2026-08-04T21:20:46.368208Z","title":"Guiding masked representation learning to capture spatio-temporal relationships of electrocardiogram,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.368208Z"},"links":{"cited_paper":"/paper/2402.09450","citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:365c010c9d0bc2f40c5ba5f8b2669a8a0fb41b9a475eb5d195060dd408feb315","observation_id":"2b721905-3d5a-4c22-9c82-205d030ca766","resolution":{"observed_at":"2026-08-04T21:20:46.368208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.32744","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.669976Z","title":"Learn- ing representations for multi-lead electrocardiograms from morphol- ogy–rhythm contrast,","venue":null,"work_id":"75f09732-45e2-43bf-b39b-763ee80f2a4b","year":2025},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.372601Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:6a4072ae42254e69557a2d58d3049c78f64108a1fd320d066f1e07d0afa46e8b","observation_id":"d45a03ae-3fa0-4ac2-ba58-adce7eb49dd3","resolution":{"observed_at":"2026-08-04T21:20:46.678374Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.128385Z","title":"Self-supervised inter–intra period-aware ECG representation learning for detecting atrial fibrillation,","venue":null,"work_id":"f349a470-a37a-4d9a-be4a-ea1a8a1bdcda","year":2025},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.376694Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:f6589e2d71134bbef47502bfaa5c33550a4003c8e6ac6dbf5d23effd17496eaa","observation_id":"bbc1a8cf-20b2-4f47-a175-5b97e875837f","resolution":{"observed_at":"2026-08-04T21:20:47.133318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.114008Z","title":null,"venue":null,"work_id":"80c9c769-cde3-48af-bf57-74359d91ad02","year":2006},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.380982Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:165cd39b4c954e0549b64598f7762c2c1cab21329c86d43ebd5dc00dc270bc6b","observation_id":"58ba29f0-8456-4e1a-aec1-f01d4c209fc3","resolution":{"observed_at":"2026-08-04T21:20:47.118401Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.098807Z","title":"Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network,","venue":null,"work_id":"debb2d97-3eb8-455f-b2b4-39e967e16db1","year":2018},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.385062Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:c7969563df9a532a185f5a7221cb30e658cc6b6b7e84e7e4628437c75147fad8","observation_id":"0694d574-07c3-4609-96d0-7b098e54da25","resolution":{"observed_at":"2026-08-04T21:20:47.103711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.085068Z","title":"Surawicz and T","venue":null,"work_id":"2a14dbeb-5fc3-4c5d-8c56-e028863c23e0","year":2008},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.389101Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:1ae4d9ac57ab2f83f2812ca99697a2049217821f8af981734e8e9229aab9df1c","observation_id":"8e237e44-b4bb-4655-928a-0ba414fafbc2","resolution":{"observed_at":"2026-08-04T21:20:47.089491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.071073Z","title":"Support vector machine- based expert system for reliable heartbeat recognition,","venue":null,"work_id":"d6519894-4a00-4de6-b4c7-bdbe374bf63f","year":2004},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.393382Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:49a842cfbb103b3c961b4444e6a32cadee89d2ee86b27083891dab0c086850fc","observation_id":"e88ad938-4657-4f12-b8ec-fc3fe4e90c6e","resolution":{"observed_at":"2026-08-04T21:20:47.075928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.056499Z","title":"Automated patient-specific clas- sification of premature ventricular contractions,","venue":null,"work_id":"9c56db70-9520-418a-91c7-d18651248087","year":2008},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.397777Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:9b89ce1cbbd2981ffef907da51446640a079e6aa26205ac28423bbe38ae3126b","observation_id":"94eb1a68-a28e-4cfd-a9fb-56270ac08413","resolution":{"observed_at":"2026-08-04T21:20:47.061169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.042291Z","title":"ECG feature extraction and classification using wavelet transform and support vector machines,","venue":null,"work_id":"9eaab271-877e-4b83-b4e2-c142ba79cc80","year":2005},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.401812Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:a0b01606442e5eece7921603d510882572ece2f58ffe91ac7fd0c9960d837fc8","observation_id":"8db51bc0-92f7-41d2-acab-d08288c2974b","resolution":{"observed_at":"2026-08-04T21:20:47.046770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.026585Z","title":"Patient-specific ECG classification by deeper CNN from generic to dedicated,","venue":null,"work_id":"96f99fe1-304e-4bf8-a190-d1b908003cf6","year":2018},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.405868Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:c6c5f2d11cd535e0863bcdb0acff9070efb54f752b2ad6ead7beeafdabecc543","observation_id":"e0f40be0-fe39-4703-89a5-c49c1b27bfcd","resolution":{"observed_at":"2026-08-04T21:20:47.031274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:47.011128Z","title":"A novel imbalanced-dataset mitigation and ECG classification model based on combined 1D CBAM autoen- coder and lightweight CNN,","venue":null,"work_id":"bbcd22d9-3eb3-48bd-83de-12e670de4cc3","year":2024},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.409861Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:5249a7e1ff90ccf83a944c6f99d53e6ca7daeb353467130e940fc715a3f7905b","observation_id":"1191d62a-bb6c-485e-9936-aabc01a778ee","resolution":{"observed_at":"2026-08-04T21:20:47.016116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.996435Z","title":"A deep learning model for the classification of atrial fibrillation in critically ill patients,","venue":null,"work_id":"a412f151-bfd0-462c-a084-9aed6cb252e3","year":2023},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.413647Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:275faadf7d45532c9ede86dc94a13699048df6b05b1507d4c21c92618cdf9e17","observation_id":"ad8f7ad4-ceb3-41ea-a702-ccdc20deba95","resolution":{"observed_at":"2026-08-04T21:20:47.001163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.981431Z","title":"Classification of ECG arrhythmia using recurrent neural networks,","venue":null,"work_id":"a4405c67-311d-4d92-91e6-dc01db176fef","year":2018},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.417961Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:638c9727c0be7324f4b90ad106b9a72faec2456e50014a3a5ff1c91ea6034c65","observation_id":"88f5d924-4418-462e-9355-9dcf4bdf6dc6","resolution":{"observed_at":"2026-08-04T21:20:46.985814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.966687Z","title":"Deep learning-based classification of ECG signals using RNN and LSTM mechanism,","venue":null,"work_id":"74465bcb-fb85-4603-bf3a-6803a3dd057c","year":2024},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.421928Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:11bd21e02db2e21961c4771cdc403d4283b292af8c601ea9edf87daa63d12a6a","observation_id":"72339b50-3dc2-48c8-a2e6-10afca09e436","resolution":{"observed_at":"2026-08-04T21:20:46.971239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.951992Z","title":"Generative adversarial network with transformer generator for boosting ECG classification,","venue":null,"work_id":"cb7395c6-fa54-43f2-a47a-04ea38abf55c","year":2023},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.425684Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:eede0d421c67a1710441d98771e1265d1cdc4fb6bb3bbbf1db004766aacdb92c","observation_id":"b41eeeb4-f866-42d1-aef0-610c6d242244","resolution":{"observed_at":"2026-08-04T21:20:46.956991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.936965Z","title":"ECGTransForm: Empowering adap- tive ECG arrhythmia classification framework with bidirectional trans- former,","venue":null,"work_id":"690f69ae-6a31-4f2f-8a12-4b9598270e9f","year":2024},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.429569Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:692d87a2b701f5d1fd9d01b9fb73bbc24d65c01d669033606afa3ed3b6270576","observation_id":"421cff4b-6876-46d7-93ab-0d362da51a45","resolution":{"observed_at":"2026-08-04T21:20:46.941551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.05178","last_updated":"2025-05-30T15:29:06Z","snapshot_observed_at":"2026-08-10T13:11:55.709646Z","submitted_at":"2024-08-09T17:06:49Z","title":"ECG-FM: An Open Electrocardiogram Foundation Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.05178","snapshot_observed_at":"2026-08-04T21:20:46.433562Z","title":"ECG-FM: An open electrocar- diogram foundation model,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.433562Z"},"links":{"cited_paper":"/paper/2408.05178","citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:c67360a69e509dcfdf0a3ee36907c76db491595db1a1238e64fa650167644b0c","observation_id":"761b7113-5ad7-472b-9dd1-f714d2ca2016","resolution":{"observed_at":"2026-08-04T21:20:46.433562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.922523Z","title":"DinoSR: Self- distillation and online clustering for self-supervised speech representa- tion learning,","venue":null,"work_id":"0d3f4a34-12b5-454d-a3f5-3a871f051a5e","year":2023},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.437897Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:9a7d3b90d22c66b2f71a22da294c97cb6801a3b4bc3a0dd4bfaaf193344b61e6","observation_id":"414ffc12-40cb-4329-9945-524260b7fc4b","resolution":{"observed_at":"2026-08-04T21:20:46.926975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.907654Z","title":"Adversarial spatiotemporal contrastive learning for electrocardiogram signals,","venue":null,"work_id":"ae3e8105-78e8-4123-81fb-030b9be77cf8","year":2024},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.441807Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:d83b94f4861140baa6fed4ebebff380e0f072fee0f6255b6f87e2a7f3df69a34","observation_id":"3cd8dd80-be24-46c0-a9b2-c104a42c7e50","resolution":{"observed_at":"2026-08-04T21:20:46.912075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.893407Z","title":"Boosting contrastive self-supervised learning with false-negative can- cellation,","venue":null,"work_id":"5bec6046-5df1-4c85-99e0-361089dfb80d","year":2022},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.445628Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:22f1ee7eb5a53a9bfce180d451d2c0f8bf0514811d27230621a3703d2d839ec6","observation_id":"ad0a6fe4-de3a-4357-b40a-ba2eccde9541","resolution":{"observed_at":"2026-08-04T21:20:46.897909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.878143Z","title":"MaeFE: Masked autoencoders family of electrocardiogram for self-supervised pre-training and transfer learning,","venue":null,"work_id":"7a98d28f-7b28-4245-86b1-dc8388007741","year":2023},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.449480Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:59da80b64b809850359c94a27c0a895dd573a0f621311fa5df4d4d74960347cb","observation_id":"8ba960d9-bef8-473d-a465-ee63d680d0c3","resolution":{"observed_at":"2026-08-04T21:20:46.882611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.07136","last_updated":"2024-04-23T01:39:28Z","snapshot_observed_at":"2026-08-06T12:01:38.918550Z","submitted_at":"2023-08-31T09:21:23Z","title":"Masked Transformer for Electrocardiogram Classification","version":3},"cited_work":{"arxiv_id":"2309.07136","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.07136","snapshot_observed_at":"2026-08-04T21:20:46.630577Z","title":"Masked Transformer for Electrocardiogram Classification","venue":"eess.SP","work_id":"8fe03bfe-6ade-4af7-854d-09e13147d2ff","year":2023},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.453361Z"},"links":{"cited_paper":"/paper/2309.07136","citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:357926db64664c8079992e081529064feaa78a67a51f22db96e49905d4038cc8","observation_id":"0975a4c9-a973-4885-8d74-921c9b1f4e12","resolution":{"observed_at":"2026-08-04T21:20:46.636057Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00519","last_updated":"2024-10-25T20:56:20Z","snapshot_observed_at":"2026-08-10T19:00:56.904754Z","submitted_at":"2023-11-01T13:44:45Z","title":"REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.00519","snapshot_observed_at":"2026-08-04T21:20:46.457732Z","title":"RE- BAR: Retrieval-based reconstruction for time-series contrastive learn- ing,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.457732Z"},"links":{"cited_paper":"/paper/2311.00519","citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:f4c3f86352821e88c499c804f228c495ac3f94798a9f1c05d2000b815e780e59","observation_id":"4b2afd83-58bb-44b2-a7bc-af684c98a132","resolution":{"observed_at":"2026-08-04T21:20:46.457732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02131","last_updated":"2025-05-07T16:04:16Z","snapshot_observed_at":"2026-08-09T03:40:41.867080Z","submitted_at":"2024-10-03T01:24:09Z","title":"Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02131","snapshot_observed_at":"2026-08-04T21:20:46.462344Z","title":"C-MELT: Contrastive enhanced masked auto-encoders for ECG-language pre-training,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.462344Z"},"links":{"cited_paper":"/paper/2410.02131","citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:48d46b4f1f8a72a56dd5c2993087c79ced1256b256629d645e7d5bb4e3465897","observation_id":"d0cd86f7-c0f2-40b9-a5d5-35bf02a28d4c","resolution":{"observed_at":"2026-08-04T21:20:46.462344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.466807Z","title":"NeuroKit2: A python toolbox for neurophysiological signal processing,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.466807Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:05e493066f96ffca1f69deb68b51a33311edb51f25b03ca310cc79eb9b498225","observation_id":"137f9f64-f62f-4360-af53-86d212375b7d","resolution":{"observed_at":"2026-08-04T21:20:46.466807Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.862683Z","title":"A real-time QRS detection algorithm,","venue":null,"work_id":"c2ee5688-deb2-4eb8-abd7-456e13fa9317","year":1985},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.470724Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:3101910678b2530dff1de5377f1c075443a593c07eed9aec60803101873380fc","observation_id":"408e5744-78cf-4e3d-bc15-dc5fce459074","resolution":{"observed_at":"2026-08-04T21:20:46.867343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11477","last_updated":"2020-10-22T06:09:10Z","snapshot_observed_at":"2026-08-07T05:13:16.889179Z","submitted_at":"2020-06-20T02:35:02Z","title":"wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.11477","snapshot_observed_at":"2026-08-04T21:20:46.474596Z","title":"wav2vec 2.0: A framework for self-supervised learning of speech representations,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.474596Z"},"links":{"cited_paper":"/paper/2006.11477","citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:606543edda91fc3d234d50bba8ef9ba21d78f386ef319d8e2cda4654dbd48a10","observation_id":"b3f898ce-78a2-4c97-8ba0-8d481e8ea208","resolution":{"observed_at":"2026-08-04T21:20:46.474596Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.847786Z","title":"PhysioBank, Phys- ioToolkit and PhysioNet: Components of a new research resource for complex physiologic signals,","venue":null,"work_id":"b837a0e7-672a-498d-bb22-ee7f8e99c13f","year":2000},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.478830Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:d7f3769d895c6a64590e302431642f1c1466ceb8b7f67fdca4035176e890aca8","observation_id":"27c4b378-2999-4073-b84a-b92fb0cb75d8","resolution":{"observed_at":"2026-08-04T21:20:46.852528Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.830498Z","title":"MIMIC-IV-ECG: Diagnostic electrocardiogram matched subset (version 1.0),","venue":null,"work_id":"bf4e13b9-267f-4c70-b9ed-55e97c489e2d","year":2023},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.483154Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:da24c18775b68a8a6d98ec3bbabbffd5e88dbf0b6f456c95e19bebf0d1c2b6b4","observation_id":"3df8350a-3656-4c48-8650-7656e4d1160c","resolution":{"observed_at":"2026-08-04T21:20:46.836295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.813960Z","title":"Will two do? Varying dimensions in electrocardiography: The PhysioNet/Computing in Cardi- ology Challenge 2021,","venue":null,"work_id":"a490b0f8-8def-4d38-afb1-93a723310ad0","year":2021},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.487160Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:898c79971a74fd7316e65e87bd1e1ae2e52eceb716f84eada538a779741cc127","observation_id":"407f0a4d-b7e4-4e6b-a2ac-e5687f94af10","resolution":{"observed_at":"2026-08-04T21:20:46.818799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.795548Z","title":"Classification of 12- lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020,","venue":null,"work_id":"ecf2de83-4c1c-4eb6-826e-c37fa8b49f3b","year":2020},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.491119Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:50eef72ca0aa073a01eb94e1e76c44645bc0de774de9a652be4c9e2d76fffa59","observation_id":"f7e3679c-0b25-4841-a7ae-fd54f1917e0d","resolution":{"observed_at":"2026-08-04T21:20:46.801481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.777845Z","title":"fairseq: A fast, extensible toolkit for sequence modeling,","venue":null,"work_id":"86d401d3-8d43-42db-ac73-f80a4e46745c","year":2019},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.495318Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:32fa2c59770f315a68646122b4bb17e990f65f13b545bccf2b616a8349e8b0d3","observation_id":"d9b00b61-05de-4417-bc32-56043a9c9a44","resolution":{"observed_at":"2026-08-04T21:20:46.783275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.760345Z","title":"fairseq-signals: Self-supervised learning framework for biosig- nals (ECG, PPG),","venue":null,"work_id":"5294c217-1358-454c-b382-f6d152460be6","year":2025},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.499268Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:3177661b6e007b50b2fdbb6e2d2a5532182e38cedc080a0da5237672ee987dad","observation_id":"1e1205c1-165a-453e-bfa1-2ef4513f330a","resolution":{"observed_at":"2026-08-04T21:20:46.766153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:20:46.742711Z","title":"What makes for good views for contrastive learning?","venue":null,"work_id":"75b77e3f-46db-465f-a0b7-23416a20109a","year":2020},"citing_paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-04T21:20:46.503275Z"},"links":{"citing_paper":"/paper/2509.08116"},"observation_digest":"sha256:5f3f8ac6c51f3d30b813d14a2a1d20a665efbdfcf5a5851b63e841e93e89e825","observation_id":"6edd39e0-37c1-4331-8bf6-08d5db0d3f7b","resolution":{"observed_at":"2026-08-04T21:20:46.748124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.08116","last_updated":"2025-09-09T19:44:50Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T04:34:50.615383Z","submitted_at":"2025-09-09T19:44:50Z","title":"Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":9,"verified_exact":2,"verified_fuzzy":42},"total_outbound_references":55},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2509.08116."}