{"as_of":"2026-08-18T04:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6aa335b7291121a8e6c9787a42371c39700144ca7fc464187cecce779031a155","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T14:28:33.893625Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/1908.03129/citation-record","integrity":"/paper/1908.03129/integrity","json":"/paper/1908.03129/citation-record.json","paper":"/paper/1908.03129"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:28:34.937469Z","title":"The coming era of precision medicine for intensive care,","venue":null,"work_id":"70d11da7-bf6a-4325-b85a-d660999fada1","year":2017},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.589382Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:45a4e9f3a386006bf5ccfda96e0568a05746cece8565e879d93768b7da42a506","observation_id":"051df8f0-0e5a-4061-8118-34490dfc0396","resolution":{"observed_at":"2026-08-14T14:28:34.940433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.928864Z","title":"Continuous determination of optimal cerebral perfusion pressure in traumatic brain injury*,","venue":null,"work_id":"a1c5370c-2fc0-4903-8285-d184a9f46072","year":2012},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.592926Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:94607099460262c89c6a78c40ed6d69d84c543a596502c8d13a05d4a309bce94","observation_id":"a2e017eb-d7a3-47ef-bbbd-8038eb2f7d97","resolution":{"observed_at":"2026-08-14T14:28:34.932219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.919088Z","title":"Heart rate variability in critical care medicine: a systematic review,","venue":null,"work_id":"94f44c2a-4c28-4cdf-bfd5-c1cb78f2a085","year":2017},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.596495Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:beab68734518b316a05a2b63cae5f68b22ab41bea411ce59e30ff505de024213","observation_id":"721c919b-f6b7-47dc-9741-3f0000f6e1fa","resolution":{"observed_at":"2026-08-14T14:28:34.923251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.909944Z","title":"Multifractal analysis of hemodynamic behavior: intraoperative instability and its pharmacological manipulation,","venue":null,"work_id":"954bc37d-9e79-43ef-b646-29351666e4ca","year":2012},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.599927Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:7df03b4daf0c2cddb1f0559cfc2ae71c7345ba69ff60f307bbe57a1d8387d6e6","observation_id":"3240e1ef-c17f-4b55-8fa9-f30219db893d","resolution":{"observed_at":"2026-08-14T14:28:34.913239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.766916Z","title":"Early asymmetric Cardio-Cerebral causality and outcome after severe traumatic brain injury,","venue":null,"work_id":"58f79b34-8631-4d94-beec-605c1984f927","year":2017},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.603905Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:6a9857992c9d52d0d0980ece03e38e0de1e2552edbd3871592333a2de547b962","observation_id":"417b659e-0861-4197-896e-59ce4afd5848","resolution":{"observed_at":"2026-08-14T14:28:34.872918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.648371Z","title":"Feasibility of individualised severe traumatic brain injury management using an automated assessment of optimal cerebral perfusion pressure: the COGiTATE phase II study protocol,","venue":null,"work_id":"560af3ff-443c-41c1-a514-fca74a7bf19b","year":2019},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.606861Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:98870edbab34e6ef60f4d90613a2b5eb3d0c76b64ffe691976225b0ea764da5e","observation_id":"9f4387e2-5b41-4820-89f2-a79302eb51f3","resolution":{"observed_at":"2026-08-14T14:28:34.679026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.641102Z","title":"Reconstruction of missing physiological signals using artificial neural networks,","venue":null,"work_id":"0d3eefa0-fcb0-404b-b89a-16b2a65dd78a","year":2010},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.610135Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:9d5a128e19d9656f89b72be876e7a3beda5273bad57d6f056e610f1770a5966b","observation_id":"3ed01363-adfe-49b1-a195-426630bb9fe2","resolution":{"observed_at":"2026-08-14T14:28:34.643774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.633396Z","title":"Semi-supervised detection of intracranial pressure alarms using waveform dynamics,","venue":null,"work_id":"63062362-ec42-4c0d-b225-b8f7bb11d069","year":2013},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.614285Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:801c396f0b0b9986ae3dbb80c1c65b90eeb18356153452a55340fb30e10b86a2","observation_id":"d03ce3e5-605d-4e10-937a-77182425469c","resolution":{"observed_at":"2026-08-14T14:28:34.636375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.624775Z","title":"Alarms in the intensive care unit: how can the number of false alarms be reduced?,","venue":null,"work_id":"9f2ffd5c-68c1-44f1-bc73-608518053a8e","year":2001},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.616966Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:921abe06017138204717aeef893171e2ed9dd1341f6c7bd1ed583d15cfeef027","observation_id":"8293d0b6-47eb-4f06-b417-cb7142bc4a2a","resolution":{"observed_at":"2026-08-14T14:28:34.627982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.616080Z","title":"A signal abnormality index for arterial blood pressure waveforms,","venue":null,"work_id":"4c0edcb7-03fc-492d-8f53-a05ef5381b06","year":2006},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.619605Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:a07f832b5d3eb64daafe5e0bb712a3fccb280fdb07e26fe0ceb6b25a3d355ae5","observation_id":"60cf6ca2-f272-4a4b-aacd-7035c7eb5bad","resolution":{"observed_at":"2026-08-14T14:28:34.619310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.608195Z","title":"An active learning framework for enhancing identification of non-artifactual intracranial pressure waveforms,","venue":null,"work_id":"b90008fb-37d2-4c9e-9c6d-b454bd921600","year":2019},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.622676Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:74da3a24de64c8d64fc61bc9d9715c2fd83673a835d1a0650d70b05a45285adf","observation_id":"b9cb3705-747d-46e8-ab02-dbb4b6abcbf9","resolution":{"observed_at":"2026-08-14T14:28:34.611229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.590271Z","title":"Anomaly detection: A survey,","venue":null,"work_id":"89a08162-c1e8-4ad7-b8f9-94546a0c44eb","year":2009},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.625643Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:376ec7ed9ded2a1ef3c1e606724fbcb3c2795958fdf6c15fa9d5d583dd89e39d","observation_id":"2070e8fc-3f9f-42a1-946f-609a709a6e50","resolution":{"observed_at":"2026-08-14T14:28:34.603216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.04599","last_updated":"2017-08-03T13:29:46Z","snapshot_observed_at":"2026-08-15T12:29:38.248604Z","submitted_at":"2017-06-14T17:33:50Z","title":"On Calibration of Modern Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.04599","snapshot_observed_at":"2026-08-14T14:28:33.628600Z","title":"On Calibration of Modern Neural Networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.628600Z"},"links":{"cited_paper":"/paper/1706.04599","citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:be0f9bffdaa652a88fb8a1c431f65a84a4b3c389256cc2605819afecc4ff2b29","observation_id":"a0783483-979a-44f5-8081-2262c07a17f9","resolution":{"observed_at":"2026-08-14T14:28:33.628600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.5663","last_updated":"2014-03-22T17:12:07Z","snapshot_observed_at":"2026-08-15T22:21:57.868869Z","submitted_at":"2013-12-19T17:46:46Z","title":"k-Sparse Autoencoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.5663","snapshot_observed_at":"2026-08-14T14:28:33.632238Z","title":"k-sparse autoencoders,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.632238Z"},"links":{"cited_paper":"/paper/1312.5663","citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:794281821923c184a6d67f9a71bb0b7552d4d4390b4ab8ac0884a98e44494583","observation_id":"747129bc-b1ac-4d6f-b500-31afd518f875","resolution":{"observed_at":"2026-08-14T14:28:33.632238Z","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":"2026-08-14T14:28:34.440108Z","title":"Extracting and composing robust features with denoising autoencoders,","venue":null,"work_id":"1cb56d52-b180-4488-aa67-5a396aa264ac","year":2008},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.635424Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:f7f7afc1a8c67b6aa1ae2bb4477809ef4c7ba52c6d1116a5913cddda07ed4375","observation_id":"e86c4699-41dd-4a88-8901-0c0a422cd509","resolution":{"observed_at":"2026-08-14T14:28:34.511175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.398200Z","title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,","venue":null,"work_id":"7002aaa3-6841-436f-b09f-34b679116a5c","year":2010},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.637824Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:c1e7898676bcf2240b5f8a6d953f1ad0dac9ba855256166e41d1ccd8e758ebc2","observation_id":"c7bbc009-5e4f-4f34-ab9c-0a67e1eee692","resolution":{"observed_at":"2026-08-14T14:28:34.401995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.389856Z","title":"Contractive auto-encoders: Explicit invariance during feature extraction,","venue":null,"work_id":"ececfdd8-56b5-43b4-b525-f8db5235b206","year":2011},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.640243Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:f3f6032dca8fa14baf5deb328644a1ac19296a5f87f56d0b125be8ba4df3cf54","observation_id":"ef739eff-fd90-4022-b07e-dd9f1063420f","resolution":{"observed_at":"2026-08-14T14:28:34.392695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.02691","last_updated":"2019-12-11T17:33:13Z","snapshot_observed_at":"2026-08-14T16:19:43.723457Z","submitted_at":"2019-06-06T16:35:38Z","title":"An Introduction to Variational Autoencoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.02691","snapshot_observed_at":"2026-08-14T14:28:33.642632Z","title":"An introduction to variational autoencoders,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.642632Z"},"links":{"cited_paper":"/paper/1906.02691","citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:2d6b3ea27122b7fc0ea91256773df6bf02fcac9f8ff46ea3cb5d59b62524e2a4","observation_id":"7dd54483-5f96-4c80-b20c-c8139edee306","resolution":{"observed_at":"2026-08-14T14:28:33.642632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-08-14T23:50:45.029465Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-14T14:28:33.645241Z","title":"Auto-Encoding Variational Bayes,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.645241Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:4260ae3e3b3bf20eefe8ae0c7cb1101730aaac7f1bdf519c8c70f7af5fc96e37","observation_id":"8a7d5e1f-1028-4431-a3a4-b2db75bf3886","resolution":{"observed_at":"2026-08-14T14:28:33.645241Z","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":"2026-08-14T14:28:34.380128Z","title":"Variational autoencoder based anomaly detection usingreconstruction probability,","venue":null,"work_id":"320ec3f2-24c4-4770-818e-380ee3cffcde","year":2015},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.648008Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:3ea03e634d78083de3b199ea1b80be6bd3f2980628cb9168f8a90c3592497720","observation_id":"d068efee-1f41-44b7-a5ba-c5d83287f398","resolution":{"observed_at":"2026-08-14T14:28:34.384610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.05908","last_updated":"2021-01-03T16:56:46Z","snapshot_observed_at":"2026-08-14T21:52:07.048587Z","submitted_at":"2016-06-19T21:02:30Z","title":"Tutorial on Variational Autoencoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.05908","snapshot_observed_at":"2026-08-14T14:28:33.650808Z","title":"Tutorial on variational autoencoders,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.650808Z"},"links":{"cited_paper":"/paper/1606.05908","citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:ee651478da1e005ae5e877e946dc62fdba41920d31efc5fedbc076bd0dd975c6","observation_id":"b4613411-36ec-4fe7-b7e0-eec9308e9764","resolution":{"observed_at":"2026-08-14T14:28:33.650808Z","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":"2026-08-14T14:28:34.280533Z","title":null,"venue":null,"work_id":"35a70648-b53a-45f3-97c9-94a8d2a7c3e4","year":2019},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.653346Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:937dff87342c6a2d9cc8576c05c774e0fb3b2f824810d5d6d3e6ac489de8914a","observation_id":"487c7aaa-f480-4b8b-a37c-0a4956e2dacb","resolution":{"observed_at":"2026-08-14T14:28:34.374786Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.166055Z","title":"Principal components analysis of images via back propagation,","venue":null,"work_id":"658d7b94-4934-47e3-b660-239efa9100eb","year":1988},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.694308Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:0a028c9c1eee2cf7a0e3fb69c28365cbd7cfad8b5c647f24238f432d870211cf","observation_id":"2ecdd634-66cb-498f-9fbd-e86176832a3f","resolution":{"observed_at":"2026-08-14T14:28:34.252674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.139123Z","title":"Chollet and Others, “Keras,” 2015","venue":null,"work_id":"283150bf-de3a-40fc-8c6d-634252329dd1","year":2015},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.747081Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:4af533095e4e04bea960f40f07a135bb82f62a1389ff1b1f42c75106f2d8512d","observation_id":"fb56d46f-2a3f-4af7-b456-2325efa1296b","resolution":{"observed_at":"2026-08-14T14:28:34.142159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1603.04467","last_updated":"2016-03-16T16:57:12Z","snapshot_observed_at":"2026-08-14T22:06:04.800050Z","submitted_at":"2016-03-14T20:50:20Z","title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.04467","snapshot_observed_at":"2026-08-14T14:28:33.805725Z","title":"TensorFlow: Large-Scale machine learning on heterogeneous distributed systems,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.805725Z"},"links":{"cited_paper":"/paper/1603.04467","citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:181b7e903af96d4ed25e2eb2d9e010e5641281bdc6f7fa54408854709fe95ab5","observation_id":"6537afee-56c1-4028-bca4-cabc89af3566","resolution":{"observed_at":"2026-08-14T14:28:33.805725Z","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":"2026-08-14T14:28:34.130399Z","title":"Chollet, Deep Learning with Python","venue":null,"work_id":"1a0f9287-fdae-40e5-997b-304fe61e6a9a","year":2017},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.842354Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:d8415c094812d2c486879b72e34811e082268411a4c9b6c24a38c7f8ec6cfca8","observation_id":"da171a41-69ee-46b6-aaab-85e0bda53361","resolution":{"observed_at":"2026-08-14T14:28:34.133749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1602.02282","last_updated":"2016-05-27T09:05:10Z","snapshot_observed_at":"2026-08-14T22:11:24.496372Z","submitted_at":"2016-02-06T17:32:48Z","title":"Ladder Variational Autoencoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.02282","snapshot_observed_at":"2026-08-14T14:28:33.877572Z","title":"Ladder variational autoencoders,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.877572Z"},"links":{"cited_paper":"/paper/1602.02282","citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:1e7c61b7d86baaa6b2193be7478bc1e9a0cd065c93fa3f0f4089416f8960b960","observation_id":"49c7bcb5-0e66-4fc0-aa15-e808ad43c687","resolution":{"observed_at":"2026-08-14T14:28:33.877572Z","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":"2026-08-14T14:28:34.121134Z","title":"Density estimation: Variational autoencoders","venue":null,"work_id":"c3bd7c54-b6ff-4616-a65a-965c7640481d","year":2018},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.880094Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:1a46e9ebe0c699d38a48c2b6a76f96a00a572530268b26274b1c7c2ce5ce8841","observation_id":"5e5590de-a880-45ed-8e4f-9c2377a3bab3","resolution":{"observed_at":"2026-08-14T14:28:34.124625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.111987Z","title":"ELBO surgery: yet another way to carve up the variational evidence lower bound,","venue":null,"work_id":"fa91ddfd-1c9d-406f-a6d7-48a1176ec9e3","year":2016},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.882644Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:59a2effd0bd665a514dfe3d90b660b1b18e619ffb1491f6e8dd41df328157650","observation_id":"3ac75fc4-7395-4863-8e34-e7ca604e019a","resolution":{"observed_at":"2026-08-14T14:28:34.115166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.102954Z","title":"Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury,","venue":null,"work_id":"698d2365-a1c5-4858-ba0e-a0f4708109e3","year":2019},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.885349Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:533047a77a72b198dafe7e5618217d1e8d34c70fbf34520595646f1511097cc3","observation_id":"eb8679ff-99dd-4d2a-966c-cb36412eba6d","resolution":{"observed_at":"2026-08-14T14:28:34.106309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.093092Z","title":"b-V AE: Learning basic visual concepts with a constrained variational framework,","venue":null,"work_id":"183504b5-548f-498c-a9fe-dac673ac0341","year":2017},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.888312Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:1ffe20302352b6255f706955dab011a1c50599f04807c076b506b9f6b503480a","observation_id":"ffd51ad6-be0f-497a-b2e0-342fa8466d76","resolution":{"observed_at":"2026-08-14T14:28:34.095945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:34.083443Z","title":"Attenuation in invasive blood pressure measurement systems,","venue":null,"work_id":"8018c24a-d643-445a-92f9-0dceddc09405","year":2006},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.890909Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:1485712e4f098c2cce1f8d3d3ef986ef1b285cfb80956e84985046dcc86fefc4","observation_id":"c032ecab-8e64-4a1a-a75b-a8402a2d9239","resolution":{"observed_at":"2026-08-14T14:28:34.086713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-14T14:28:33.963885Z","title":"Tolerance regions for a multivariate normal population,","venue":null,"work_id":"decdbe7e-f4b1-49eb-88c6-e85bcd5373b2","year":1964},"citing_paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning","version":4},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T14:28:33.893625Z"},"links":{"citing_paper":"/paper/1908.03129"},"observation_digest":"sha256:97cbd8cda50abf99b1885950e064b84943090c5467615aa71b23219e5701115a","observation_id":"dbac611a-5cf9-4ed2-af34-8fb786a0abf6","resolution":{"observed_at":"2026-08-14T14:28:34.051010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.03129","last_updated":"2020-01-05T18:16:39Z","latest_version":4,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-17T21:45:59.544310Z","submitted_at":"2019-08-08T15:41:04Z","title":"DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":25},"total_outbound_references":33},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:1908.03129."}