{"as_of":"2026-08-07T16:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e2a1d10855ed5adbb4a879fea1f202cd364bd05e1f362c24282938615c743bfa","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":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":18,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:18:20.865380Z","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":"2026-07-02T15:37:05.977961Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-08-07T14:18:20.865380Z","title":"Eldele, M","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":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:20.865380Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2506.06310"},"observation_digest":"sha256:dc61f0db2fc4f0e5f1873b151ac08c61df144bf3149eebb3cc47fa60584e5197","observation_id":"18a88248-20fa-437c-9512-953b7c82e31a","resolution":{"observed_at":"2026-08-07T14:18:20.865380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-08-07T04:51:45.157478Z","title":"K., Li, X., and Guan, C","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.21803","last_updated":"2025-06-11T07:22:17Z","snapshot_observed_at":"2026-08-07T04:45:15.451739Z","submitted_at":"2025-06-11T07:22:17Z","title":"From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T04:51:45.157478Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2506.21803"},"observation_digest":"sha256:5bc9cda063d85e4fb832adb03c789885872669294c8ef2b31b253ca73c710235","observation_id":"735b43b3-058b-4ade-b9e5-549207f01e3f","resolution":{"observed_at":"2026-08-07T04:51:45.157478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-08-06T20:36:03.648166Z","title":"Time-series representation learning via temporal and contextual contrasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.02320","last_updated":"2025-07-03T05:12:06Z","snapshot_observed_at":"2026-08-06T20:29:56.198603Z","submitted_at":"2025-07-03T05:12:06Z","title":"Transformer-based EEG Decoding: A Survey","version":1},"reference_index":127,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:03.648166Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2507.02320"},"observation_digest":"sha256:7bd4d6bb0357222e3fff2c23981442a80d33b5d630da9cca9b3600f2f86f43ef","observation_id":"3a917491-56e3-4b76-a7b9-e58e7f6c1fc0","resolution":{"observed_at":"2026-08-06T20:36:03.648166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-08-06T19:52:19.759794Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.04600","last_updated":"2025-07-24T09:29:08Z","snapshot_observed_at":"2026-08-06T19:41:53.776821Z","submitted_at":"2025-07-07T01:35:55Z","title":"DisMS-TS: Eliminating Redundant Multi-Scale Features for Time Series Classification","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:52:19.759794Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2507.04600"},"observation_digest":"sha256:2cbde164c59e7f297b1c32a1ad1c56e120a7958531f8c20234d87c7ddd3b3083","observation_id":"3083d03f-163a-418e-8257-9b7e64a1c2cd","resolution":{"observed_at":"2026-08-06T19:52:19.759794Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-08-06T15:50:51.673284Z","title":"Eldele, M","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":9,"source":"pdf_text","source_observed_at":"2026-08-06T15:50:51.673284Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2507.14828"},"observation_digest":"sha256:40176b1119f0fac2900f20ceae38e967be890f42c50a4495f28c0148e37770c1","observation_id":"ff96397f-aa75-48a6-851f-63d5f037e16f","resolution":{"observed_at":"2026-08-06T15:50:51.673284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-08-06T13:00:57.513885Z","title":"Time-series representation learning via temporal and contextual contrasting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.21357","last_updated":"2025-07-28T21:56:17Z","snapshot_observed_at":"2026-08-06T13:00:52.787104Z","submitted_at":"2025-07-28T21:56:17Z","title":"A Contrastive Diffusion-based Network (CDNet) for Time Series Classification","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:57.513885Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2507.21357"},"observation_digest":"sha256:a37c1a19ec62a7310e06ed03c3958d735d2edef4e244dfaa311217d08d1e6504","observation_id":"e83fdb2e-1c4d-42b8-9284-35dcc758ed86","resolution":{"observed_at":"2026-08-06T13:00:57.513885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-08-05T23:23:38.737949Z","title":"Time-series representation learning via temporal and contextual contrasting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.05572","last_updated":"2025-08-07T17:05:43Z","snapshot_observed_at":"2026-08-05T23:23:32.675194Z","submitted_at":"2025-08-07T17:05:43Z","title":"Discrepancy-Aware Contrastive Adaptation in Medical Time Series Analysis","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T23:23:38.737949Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2508.05572"},"observation_digest":"sha256:54d31aeff51425d59ff3de93b7ac0a51f9025e66a60736e8a7005f6c576a599d","observation_id":"9b15d9ec-0753-4b26-b72c-8c5e0d49b756","resolution":{"observed_at":"2026-08-05T23:23:38.737949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-08-06T10:15:32.897799Z","title":"K.; Li, X.; and Guan, C","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.08280","last_updated":"2025-08-01T05:27:44Z","snapshot_observed_at":"2026-08-06T10:15:26.196840Z","submitted_at":"2025-08-01T05:27:44Z","title":"MoSSDA: A Semi-Supervised Domain Adaptation Framework for Multivariate Time-Series Classification using Momentum Encoder","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T10:15:32.897799Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2508.08280"},"observation_digest":"sha256:2993f20ab7b19c5b19bb22d8a23069a5665a22abe1bd73e5e1837b989e143542","observation_id":"7aa472e5-f0e1-41aa-991b-c2ab893ded53","resolution":{"observed_at":"2026-08-06T10:15:32.897799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-08-05T05:29:53.509149Z","title":"Time-series representation learning via temporal and contextual contrasting.arXiv preprint arXiv:2106.14112, 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":10,"source":"pdf_text","source_observed_at":"2026-08-05T05:29:53.509149Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2509.05478"},"observation_digest":"sha256:f96ed4aa8452462fb2fa01bde4be33c7ba941709d57fc56588e8290913b6d657","observation_id":"6364eeb2-c9b4-4e37-be7a-963df7c04da1","resolution":{"observed_at":"2026-08-05T05:29:53.509149Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":"2106.14112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-07-02T15:37:05.977961Z","title":"Time-series representation learning via temporal and contextual contrasting.arXiv preprint arXiv:2106.14112","venue":null,"work_id":"f241f73f-2f85-447b-a69c-809e2724d9b1","year":2021},"citing_paper":{"arxiv_id":"2605.00130","last_updated":"2026-04-30T18:33:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-30T18:33:40Z","title":"Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-09T20:11:49.088371Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2605.00130"},"observation_digest":"sha256:25c3fee552131517be57a97b8c8a7e2b1afa3ab8aedb5ce3d301a4b4ed928a59","observation_id":"80e252cf-106a-4c7b-9df6-8384c2c398e9","resolution":{"observed_at":"2026-05-11T15:21:08.477117Z","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":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":"2106.14112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-07-02T15:37:05.977961Z","title":"Time-series representation learning via temporal and contextual contrasting.arXiv preprint arXiv:2106.14112","venue":null,"work_id":"f241f73f-2f85-447b-a69c-809e2724d9b1","year":2021},"citing_paper":{"arxiv_id":"2605.22043","last_updated":"2026-05-21T06:30:35Z","snapshot_observed_at":"2026-07-06T23:32:25.286443Z","submitted_at":"2026-05-21T06:30:35Z","title":"CASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-22T08:30:12.447896Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2605.22043"},"observation_digest":"sha256:cb0ce2c0e55544919d21a2a0c387b674cbd3302285bca9471affb3b6cb9dcbed","observation_id":"34107abc-b1f2-4f4f-9b1e-f6b6e5e51f5a","resolution":{"observed_at":"2026-05-22T08:31:16.763441Z","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":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":"2106.14112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-07-02T15:37:05.977961Z","title":"Time-series representation learning via temporal and contextual contrasting.arXiv preprint arXiv:2106.14112","venue":null,"work_id":"f241f73f-2f85-447b-a69c-809e2724d9b1","year":2021},"citing_paper":{"arxiv_id":"2605.22055","last_updated":"2026-05-21T06:45:50Z","snapshot_observed_at":"2026-07-06T23:32:25.286443Z","submitted_at":"2026-05-21T06:45:50Z","title":"Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-22T08:25:32.231942Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2605.22055"},"observation_digest":"sha256:b137a6d9c40c3ccaec3e2549fe967e52326e1ef6df8ca59d6bcaf9cea6fa6df2","observation_id":"6fbc6678-1f77-4d58-b3d9-c122218d6f1d","resolution":{"observed_at":"2026-05-22T08:26:16.647906Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":"2106.14112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-07-02T15:37:05.977961Z","title":"Time-series representation learning via temporal and contextual contrasting.arXiv preprint arXiv:2106.14112","venue":null,"work_id":"f241f73f-2f85-447b-a69c-809e2724d9b1","year":2021},"citing_paper":{"arxiv_id":"2605.22379","last_updated":"2026-05-21T12:09:42Z","snapshot_observed_at":"2026-07-06T23:32:44.699825Z","submitted_at":"2026-05-21T12:09:42Z","title":"Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-22T04:14:50.924786Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2605.22379"},"observation_digest":"sha256:5c25ba35c3f2dc00b45738d65b1cb8ede628e30450369f03dfd23b8875d70acc","observation_id":"e2bc9dfd-a10c-462b-b355-c9ae37ea4854","resolution":{"observed_at":"2026-05-22T04:16:02.684529Z","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":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":"2106.14112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-07-02T15:37:05.977961Z","title":"Time-series representation learning via temporal and contextual contrasting.arXiv preprint arXiv:2106.14112","venue":null,"work_id":"f241f73f-2f85-447b-a69c-809e2724d9b1","year":2021},"citing_paper":{"arxiv_id":"2605.31249","last_updated":"2026-05-29T12:48:12Z","snapshot_observed_at":"2026-08-01T10:14:02.823731Z","submitted_at":"2026-05-29T12:48:12Z","title":"Learning Cardiac Latent Representations in Vectorcardiogram Space","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T23:37:15.894948Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2605.31249"},"observation_digest":"sha256:32750016725f33b7f3ffcc490241de4504faa30b27ae0895e85c7de1973f5ae5","observation_id":"267467c5-60b0-4540-b7d7-7f9d527766c2","resolution":{"observed_at":"2026-06-28T23:42:49.672603Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":"2106.14112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-07-02T15:37:05.977961Z","title":"Time-series representation learning via temporal and contextual contrasting.arXiv preprint arXiv:2106.14112","venue":null,"work_id":"f241f73f-2f85-447b-a69c-809e2724d9b1","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":7,"source":"pdf_text","source_observed_at":"2026-07-02T15:35:25.976071Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2607.00956"},"observation_digest":"sha256:6800683f431fd2a10bc382e59a6deb2f8e9c51230854155b29f22decca01eebb","observation_id":"ec6a81d8-e9b2-456f-b5f9-603470c3761b","resolution":{"observed_at":"2026-07-02T15:37:05.835208Z","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":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":"2106.14112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-07-02T15:37:05.977961Z","title":"Time-series representation learning via temporal and contextual contrasting.arXiv preprint arXiv:2106.14112","venue":null,"work_id":"f241f73f-2f85-447b-a69c-809e2724d9b1","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":10,"source":"pdf_text","source_observed_at":"2026-07-02T15:32:42.523362Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2607.00958"},"observation_digest":"sha256:3e53da70a157ae3abd025eb2a96aed41d17c180943fa77bd37b9693bb21cf696","observation_id":"5962929b-e3d2-4044-9580-de134db72b41","resolution":{"observed_at":"2026-07-02T15:37:05.979468Z","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":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-07-12T01:22:51.284207Z","title":"arXiv preprint arXiv:2106.14112 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.03585","last_updated":"2026-07-03T20:02:28Z","snapshot_observed_at":"2026-08-02T18:24:57.296412Z","submitted_at":"2026-07-03T20:02:28Z","title":"Modular Foundation Models for Time-Series Perception in Digital Twins","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-07-12T01:22:51.284207Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2607.03585"},"observation_digest":"sha256:3b59ff7da5b96960795aedc035598afc477dc5d91907377096d6c7ad80fbeb95","observation_id":"15025d7b-89af-46c1-b74b-e5e26c86e434","resolution":{"observed_at":"2026-07-12T01:22:51.284207Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14112","last_updated":"2021-06-26T23:56:31Z","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14112","snapshot_observed_at":"2026-07-11T21:25:04.813061Z","title":"Time-series representation learning via temporal and contextual contrasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.04139","last_updated":"2026-07-05T06:44:17Z","snapshot_observed_at":"2026-07-11T21:25:04.401281Z","submitted_at":"2026-07-05T06:44:17Z","title":"Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-11T21:25:04.813061Z"},"links":{"cited_paper":"/paper/2106.14112","citing_paper":"/paper/2607.04139"},"observation_digest":"sha256:48ef0cffc0f332baea60419b2f65bd1859a42c3fc40164e802ab01de0213e2ce","observation_id":"3c1b026e-a943-4932-b5d5-7f165061b713","resolution":{"observed_at":"2026-07-11T21:25:04.813061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2106.14112/citation-record","integrity":"/paper/2106.14112/integrity","json":"/paper/2106.14112/citation-record.json","paper":"/paper/2106.14112"},"outbound":[],"paper":{"arxiv_id":"2106.14112","last_updated":"2021-06-26T23:56:31Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T11:23:26.988614Z","submitted_at":"2021-06-26T23:56:31Z","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting"},"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 18 inbound Pith citation observations for arXiv:2106.14112."}