{"as_of":"2026-08-07T16:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d789fe8ad679063e682393e8f88a2656848463dac651fd524b2f9ae313d8d946","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:18:22.814651Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T16:47:10.219040Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.00750","last_updated":"2021-06-01T19:53:24Z","snapshot_observed_at":"2026-07-06T11:14:57.172488Z","submitted_at":"2021-06-01T19:53:24Z","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.00750","snapshot_observed_at":"2026-08-07T10:29:47.809996Z","title":"Tonekaboni, D","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05321","last_updated":"2025-06-05T17:57:11Z","snapshot_observed_at":"2026-08-07T10:19:06.341519Z","submitted_at":"2025-06-05T17:57:11Z","title":"LSM-2: Learning from Incomplete Wearable Sensor Data","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T10:29:47.809996Z"},"links":{"cited_paper":"/paper/2106.00750","citing_paper":"/paper/2506.05321"},"observation_digest":"sha256:2b3f7a173e9c5bfb773a2e976f03f3481ad6d0af7ed50e2c20a7e5b5b48a6dc1","observation_id":"29d35ee0-d90c-482c-98c6-6a1abf350598","resolution":{"observed_at":"2026-08-07T10:29:47.809996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00750","last_updated":"2021-06-01T19:53:24Z","snapshot_observed_at":"2026-07-06T11:14:57.172488Z","submitted_at":"2021-06-01T19:53:24Z","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.00750","snapshot_observed_at":"2026-08-07T14:18:22.814651Z","title":"Tonekaboni, D","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.06310","last_updated":"2025-05-26T05:04:03Z","snapshot_observed_at":"2026-08-07T14:10:01.475793Z","submitted_at":"2025-05-26T05:04:03Z","title":"Enhancing Contrastive Learning-based Electrocardiogram Pretrained Model with Patient Memory Queue","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:22.814651Z"},"links":{"cited_paper":"/paper/2106.00750","citing_paper":"/paper/2506.06310"},"observation_digest":"sha256:80f1a72a5954650861e15c592c0771d893e4151a4dda0b0e25d1c66036f87a74","observation_id":"6a58d2a3-a063-4da4-9560-c4266009c79b","resolution":{"observed_at":"2026-08-07T14:18:22.814651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00750","last_updated":"2021-06-01T19:53:24Z","snapshot_observed_at":"2026-07-06T11:14:57.172488Z","submitted_at":"2021-06-01T19:53:24Z","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.00750","snapshot_observed_at":"2026-08-06T16:42:42.295907Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.12774","last_updated":"2025-07-17T04:31:55Z","snapshot_observed_at":"2026-08-06T16:36:30.473430Z","submitted_at":"2025-07-17T04:31:55Z","title":"A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models","version":1},"reference_index":274,"source":"pdf_text","source_observed_at":"2026-08-06T16:42:42.295907Z"},"links":{"cited_paper":"/paper/2106.00750","citing_paper":"/paper/2507.12774"},"observation_digest":"sha256:a8e28852255a6fa2469355acae2705f9f8c76d9e89450bf123b1d9c3220b5acf","observation_id":"e3a8ea7c-5047-4e68-a789-a8a270f0b64e","resolution":{"observed_at":"2026-08-06T16:42:42.295907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00750","last_updated":"2021-06-01T19:53:24Z","snapshot_observed_at":"2026-07-06T11:14:57.172488Z","submitted_at":"2021-06-01T19:53:24Z","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.00750","snapshot_observed_at":"2026-08-06T15:50:51.741649Z","title":"Tonekaboni, D","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.14828","last_updated":"2025-07-20T05:39:25Z","snapshot_observed_at":"2026-08-06T15:44:07.108504Z","submitted_at":"2025-07-20T05:39:25Z","title":"eMargin: Revisiting Contrastive Learning with Margin-Based Separation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T15:50:51.741649Z"},"links":{"cited_paper":"/paper/2106.00750","citing_paper":"/paper/2507.14828"},"observation_digest":"sha256:d9bab9a123a4d103c685671d6de332c1005b67c0d15cf43d062e7ad6fc41b91e","observation_id":"f774c58b-7bb9-4893-8d09-35862cb5d4cc","resolution":{"observed_at":"2026-08-06T15:50:51.741649Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00750","last_updated":"2021-06-01T19:53:24Z","snapshot_observed_at":"2026-07-06T11:14:57.172488Z","submitted_at":"2021-06-01T19:53:24Z","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.00750","snapshot_observed_at":"2026-08-05T05:29:53.582320Z","title":"Unsupervised representation learning for time series with temporal neighborhood coding.arXiv preprint arXiv:2106.00750, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.05478","last_updated":"2025-09-05T20:10:09Z","snapshot_observed_at":"2026-08-05T05:29:52.286297Z","submitted_at":"2025-09-05T20:10:09Z","title":"PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T05:29:53.582320Z"},"links":{"cited_paper":"/paper/2106.00750","citing_paper":"/paper/2509.05478"},"observation_digest":"sha256:71694a3cd0aa1a8de92661f3a657162c9924ccffb4609b25b2a71304421577fb","observation_id":"c541daf5-8783-47d5-acac-0a4a2a54df0b","resolution":{"observed_at":"2026-08-05T05:29:53.582320Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00750","last_updated":"2021-06-01T19:53:24Z","snapshot_observed_at":"2026-07-06T11:14:57.172488Z","submitted_at":"2021-06-01T19:53:24Z","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.00750","snapshot_observed_at":"2026-08-03T19:34:05.637686Z","title":"Unsupervised representation learning for time series with temporal neighborhood coding.arXiv preprint arXiv:2106.00750,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.00239","last_updated":"2026-06-07T22:52:48Z","snapshot_observed_at":"2026-08-03T19:34:03.096776Z","submitted_at":"2025-11-28T22:53:31Z","title":"Self-Supervised Dynamical System Representations for Physiological Time-Series","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T19:34:05.637686Z"},"links":{"cited_paper":"/paper/2106.00750","citing_paper":"/paper/2512.00239"},"observation_digest":"sha256:04394069182a8c19ffd4c4f6264c361412e70e5d8c62881158881e1701b07bea","observation_id":"9780c82f-8bac-4e4b-9a8f-6ad2a947bbd7","resolution":{"observed_at":"2026-08-03T19:34:05.637686Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00750","last_updated":"2021-06-01T19:53:24Z","snapshot_observed_at":"2026-07-06T11:14:57.172488Z","submitted_at":"2021-06-01T19:53:24Z","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding","version":1},"cited_work":{"arxiv_id":"2106.00750","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.00750","snapshot_observed_at":"2026-07-02T16:47:10.219040Z","title":"Unsupervised representation learning for time series with temporal neighborhood coding.arXiv preprint arXiv:2106.00750","venue":null,"work_id":"7f400dc2-fb87-4fd3-9be8-10b453823246","year":2021},"citing_paper":{"arxiv_id":"2512.16001","last_updated":"2026-04-22T18:05:52Z","snapshot_observed_at":"2026-07-06T22:39:23.633419Z","submitted_at":"2025-12-17T22:10:39Z","title":"Concurrence: A dependence criterion for time series, applied to biological data","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-16T21:06:39.731162Z"},"links":{"cited_paper":"/paper/2106.00750","citing_paper":"/paper/2512.16001"},"observation_digest":"sha256:8d2c6e4d307b640d78a2c6e12e5945f45a7ddcf9d2987681cbda99092c0f2161","observation_id":"731e70ad-dda0-43c3-989c-efa395966178","resolution":{"observed_at":"2026-05-16T21:08:32.597358Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00750","last_updated":"2021-06-01T19:53:24Z","snapshot_observed_at":"2026-07-06T11:14:57.172488Z","submitted_at":"2021-06-01T19:53:24Z","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding","version":1},"cited_work":{"arxiv_id":"2106.00750","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.00750","snapshot_observed_at":"2026-07-02T16:47:10.219040Z","title":"Unsupervised representation learning for time series with temporal neighborhood coding.arXiv preprint arXiv:2106.00750","venue":null,"work_id":"7f400dc2-fb87-4fd3-9be8-10b453823246","year":2021},"citing_paper":{"arxiv_id":"2606.07365","last_updated":"2026-06-05T15:08:50Z","snapshot_observed_at":"2026-08-06T17:14:03.171539Z","submitted_at":"2026-06-05T15:08:50Z","title":"A robust PPG foundation model using multimodal physiological supervision","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-27T22:20:51.569973Z"},"links":{"cited_paper":"/paper/2106.00750","citing_paper":"/paper/2606.07365"},"observation_digest":"sha256:82b7b607ad755be1523b7c43f75392389431b7389f05fff95b18b4f2eeb20103","observation_id":"a85f7723-413d-420f-9951-9acf105bccc5","resolution":{"observed_at":"2026-07-02T16:47:10.220589Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00750","last_updated":"2021-06-01T19:53:24Z","snapshot_observed_at":"2026-07-06T11:14:57.172488Z","submitted_at":"2021-06-01T19:53:24Z","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding","version":1},"cited_work":{"arxiv_id":"2106.00750","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.00750","snapshot_observed_at":"2026-07-02T16:47:10.219040Z","title":"Unsupervised representation learning for time series with temporal neighborhood coding.arXiv preprint arXiv:2106.00750","venue":null,"work_id":"7f400dc2-fb87-4fd3-9be8-10b453823246","year":2021},"citing_paper":{"arxiv_id":"2607.00956","last_updated":"2026-07-01T13:54:20Z","snapshot_observed_at":"2026-07-07T00:06:35.457630Z","submitted_at":"2026-07-01T13:54:20Z","title":"Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-02T15:35:25.976071Z"},"links":{"cited_paper":"/paper/2106.00750","citing_paper":"/paper/2607.00956"},"observation_digest":"sha256:697abbc55ed0bc20eac39c846b66930f756954da229d333fe2869273553772a7","observation_id":"ba74c5ce-2bd7-4e14-a92a-2e9a70e6c36d","resolution":{"observed_at":"2026-07-02T15:37:05.848501Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00750","last_updated":"2021-06-01T19:53:24Z","snapshot_observed_at":"2026-07-06T11:14:57.172488Z","submitted_at":"2021-06-01T19:53:24Z","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding","version":1},"cited_work":{"arxiv_id":"2106.00750","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.00750","snapshot_observed_at":"2026-07-02T16:47:10.219040Z","title":"Unsupervised representation learning for time series with temporal neighborhood coding.arXiv preprint arXiv:2106.00750","venue":null,"work_id":"7f400dc2-fb87-4fd3-9be8-10b453823246","year":2021},"citing_paper":{"arxiv_id":"2607.00958","last_updated":"2026-07-01T13:56:21Z","snapshot_observed_at":"2026-07-07T00:06:35.457630Z","submitted_at":"2026-07-01T13:56:21Z","title":"LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-02T15:32:42.523362Z"},"links":{"cited_paper":"/paper/2106.00750","citing_paper":"/paper/2607.00958"},"observation_digest":"sha256:db16b620202e161622c01dad32c2de0da52554775ae6b67db72d2bad996b3c60","observation_id":"9950da09-4c72-4479-a7cc-c30036edab9c","resolution":{"observed_at":"2026-07-02T15:37:06.018822Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2106.00750/citation-record","integrity":"/paper/2106.00750/integrity","json":"/paper/2106.00750/citation-record.json","paper":"/paper/2106.00750"},"outbound":[],"paper":{"arxiv_id":"2106.00750","last_updated":"2021-06-01T19:53:24Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T11:14:57.172488Z","submitted_at":"2021-06-01T19:53:24Z","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2106.00750."}