{"as_of":"2026-08-17T12:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9992cd888ed0b128743bae72d138ef24efd1d85d489e9ed4b3100a5b85fca3eb","coverage":[{"denominator":57,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":57,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T18:50:39.878110Z","state":"measured"},{"denominator":57,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":57,"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/2506.18764/citation-record","integrity":"/paper/2506.18764/integrity","json":"/paper/2506.18764/citation-record.json","paper":"/paper/2506.18764"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1509.01570","last_updated":"2015-12-03T18:09:58Z","snapshot_observed_at":"2026-08-17T10:31:14.108427Z","submitted_at":"2015-09-04T19:28:50Z","title":"Real-time financial surveillance via quickest change-point detection methods","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.01570","snapshot_observed_at":"2026-08-15T18:50:39.628238Z","title":"Real-time financial surveillance via quickest change-point detection methods.arXiv:1509.01570, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.628238Z"},"links":{"cited_paper":"/paper/1509.01570","citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:86fe63f813081011278468143709abe03057428a93efe2ca2937927827da9efd","observation_id":"ecdabb92-d4a0-46c7-bb2e-f06d69aa36b8","resolution":{"observed_at":"2026-08-15T18:50:39.628238Z","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":"2026-08-15T18:50:40.760029Z","title":"Detection of uterine MMG contractions using a multiple change point estimator and the K-means cluster algorithm.IEEE Trans","venue":null,"work_id":"b36864cb-d410-4369-a4d9-37d7bce8fda2","year":2008},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.634488Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:e07607afeeed7abd747bd92cb8a5641efe6596c3c66075adbe9c4cda7e293b91","observation_id":"f4461b87-6263-4bc8-9fad-241825afa7e4","resolution":{"observed_at":"2026-08-15T18:50:40.765187Z","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":"2026-08-15T18:50:40.747790Z","title":"Online Bayesian change point detection algorithms for segmentation of epileptic activity","venue":null,"work_id":"82a51593-f316-4974-9a8b-35ae68dfdf37","year":2013},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.639317Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:377bcfe80840e67fba5016175f9083b2a31e7296bcff2ee07e34ea4d03ca5456","observation_id":"15082587-d265-4677-a210-a46e44137ec1","resolution":{"observed_at":"2026-08-15T18:50:40.752177Z","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":"2026-08-15T18:50:40.732271Z","title":"A review and comparison of changepoint detection techniques for climate data.J","venue":null,"work_id":"10129275-9b72-4606-957a-b2bc1e2ae240","year":2007},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.644506Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:473f0f1ce9198a665b13c10faecaeb080d71719abcf56c1524d081f52abad595","observation_id":"51000bad-0006-4b65-a08e-f75e3e200f04","resolution":{"observed_at":"2026-08-15T18:50:40.737861Z","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":"2026-08-15T18:50:40.717721Z","title":"Change-point detection of climate time series by nonparametric method","venue":null,"work_id":"3c71f3a7-aea5-4662-a769-e56cf0980b72","year":2010},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.649514Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:02450397b8dc88cdae99c5849c2f02e56d6bbeca38f129f60a304b4d7b90a8a6","observation_id":"ae6277b3-521a-4fa4-a871-0b2a16188247","resolution":{"observed_at":"2026-08-15T18:50:40.722653Z","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":"2310.17546","last_updated":"2024-07-30T15:44:31Z","snapshot_observed_at":"2026-08-16T20:05:26.616641Z","submitted_at":"2023-10-26T16:43:03Z","title":"A changepoint approach to modelling non-stationary soil moisture dynamics","version":2},"cited_work":{"arxiv_id":"2310.17546","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.17546","snapshot_observed_at":"2026-08-15T18:50:40.097538Z","title":"A changepoint approach to modelling non-stationary soil moisture dynamics","venue":"stat.AP","work_id":"e119d2b1-c996-45df-bfdc-b7bb1e1c435f","year":2023},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.654946Z"},"links":{"cited_paper":"/paper/2310.17546","citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:80b38dcc9f15c67bdb393daf53b11fe3fefa13ef72f34fa28834cc415d552206","observation_id":"98281808-b70e-400a-b5a6-c2b8ff748baa","resolution":{"observed_at":"2026-08-15T18:50:40.101950Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2403.03388","last_updated":"2024-03-06T00:49:17Z","snapshot_observed_at":"2026-08-16T23:59:38.662300Z","submitted_at":"2024-03-06T00:49:17Z","title":"Is a Recent Surge in Global Warming Detectable?","version":1},"cited_work":{"arxiv_id":"2403.03388","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.03388","snapshot_observed_at":"2026-08-15T18:50:40.078489Z","title":"Is a Recent Surge in Global Warming Detectable?","venue":"stat.AP","work_id":"31486cef-a2e8-4a40-88d9-b4a207e9d776","year":2024},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.660850Z"},"links":{"cited_paper":"/paper/2403.03388","citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:166c1418e0350dc1b9ccad6a1fee52ab5a0f09f85beffb5fcd2666647ea2bf85","observation_id":"681a06c8-ef92-476b-8c1a-f35d5f850351","resolution":{"observed_at":"2026-08-15T18:50:40.083471Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-15T18:50:40.704638Z","title":"Audio segmentation for speech recognition using segment features","venue":null,"work_id":"e6a4ead4-6356-4622-9926-316755fb7aab","year":2009},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.665747Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:ab996c0e214cdd11befa42fed4186736cdfd3b9373fe12650121c8e9fd4ad59f","observation_id":"a1e8bf4b-a670-454c-9b50-653798da1f37","resolution":{"observed_at":"2026-08-15T18:50:40.709010Z","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":"2026-08-15T18:50:40.684661Z","title":"Speaker change point detection using deep neural nets","venue":null,"work_id":"282a4b0f-c0c6-40d9-a5a9-c0fd2a1b9bf1","year":2015},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.670678Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:7a9027d2775240508c544d9dfbd2b8d28cac3fd2cab2d0658f94ee6c3e7f903f","observation_id":"1b36040d-f751-492b-8814-f1155966aed6","resolution":{"observed_at":"2026-08-15T18:50:40.690839Z","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":"2026-08-15T18:50:40.671907Z","title":"Statistically significant detection of linguistic change","venue":null,"work_id":"6b20ba68-1a3b-4148-9248-cc72e751ec83","year":2015},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.675267Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:b7b14d981b0f2a41306dd0927516a0730c11ceb0d777d8cd3bc925ff89a8d9e0","observation_id":"94f316e6-969b-49f0-8360-4a85a9625cff","resolution":{"observed_at":"2026-08-15T18:50:40.676243Z","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":"2026-08-15T18:50:40.656378Z","title":"A survey of methods for time series change point detection.Knowl","venue":null,"work_id":"eddd3f12-9e18-4b06-960d-775489bbc222","year":2017},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.679491Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:1288be7f8800ecb52845d1b8dc78edb846e6951f655b5ac4fae871dc82bf055c","observation_id":"db8a56ff-b206-4945-9220-9ae426611014","resolution":{"observed_at":"2026-08-15T18:50:40.660990Z","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":"2003.06222","last_updated":"2022-02-12T15:03:25Z","snapshot_observed_at":"2026-07-06T09:04:29.300484Z","submitted_at":"2020-03-13T12:23:41Z","title":"An Evaluation of Change Point Detection Algorithms","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.06222","snapshot_observed_at":"2026-08-15T18:50:39.684014Z","title":"An evaluation of change point detection algorithms.arXiv:2003.06222, 2020","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.684014Z"},"links":{"cited_paper":"/paper/2003.06222","citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:f4c6175b4f6684fa585fabf3424de35adb4ac64103f3c1d1ea4dbd3a194dea30","observation_id":"81077a12-a2a4-491e-9034-6de8683c5eac","resolution":{"observed_at":"2026-08-15T18:50:39.684014Z","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":"2026-08-15T18:50:40.643296Z","title":"Selective review of offline change point detection methods.Signal Process., 167:107299, 2020","venue":null,"work_id":"d411f169-0bd2-4307-bb3f-a8c4ff1361ea","year":2020},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.687831Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:882a7953696a27cd02d6d2f999ea92f6ad451a7b4e19706b1e4887adab95a51c","observation_id":"bf30ff32-fd60-4385-9b11-597eccd98ccd","resolution":{"observed_at":"2026-08-15T18:50:40.647555Z","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":"2026-08-15T18:50:40.629980Z","title":"High-dimensional changepoint detection via a geometrically inspired mapping.Stat","venue":null,"work_id":"65cb6bb5-9f32-438c-a449-45f562362bbe","year":2020},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.691832Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:44e6de1a7e5347c6f689fbe304b0ab846cbea0513c46802d6dbc6dca301bf9eb","observation_id":"ee39089c-638a-426c-800f-7f65b765315f","resolution":{"observed_at":"2026-08-15T18:50:40.634259Z","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":"2105.08976","last_updated":"2026-07-27T03:58:26Z","snapshot_observed_at":"2026-08-16T18:23:44.317087Z","submitted_at":"2021-05-19T08:13:51Z","title":"High-dimensional Change-point Detection Using Generalized Homogeneity Metrics","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.08976","snapshot_observed_at":"2026-08-15T18:50:39.695759Z","title":"High-dimensional change-point detection using generalized homogeneity metrics.arXiv:2105.08976, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.695759Z"},"links":{"cited_paper":"/paper/2105.08976","citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:63307b7c5c2de504de0744ca1f2cacadb98e6dfbc97b7bec2471c904fc5c3f01","observation_id":"1479b076-3b96-4f0f-9456-4cc99fb7a696","resolution":{"observed_at":"2026-08-15T18:50:39.695759Z","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":"2026-08-15T18:50:40.618271Z","title":"Anomaly detection using the Kullback- Leibler divergence metric","venue":null,"work_id":"eb627a3b-9537-453f-a1c1-e32e1dccd516","year":2008},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.700463Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:bd7406967b9676f7178488328a3b5a5155121bf0c859a8fdc46d554aeab124ba","observation_id":"21da5cac-9a40-4171-8c21-1666e46aa0d1","resolution":{"observed_at":"2026-08-15T18:50:40.622598Z","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":"2026-08-15T18:50:40.605366Z","title":"Tracking changes using Kullback-Leibler divergence for the continual learning","venue":null,"work_id":"10a22bf9-4ed7-49a9-8719-24453646f023","year":2022},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.705070Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:ce9491673f341076241862223fc33928f408a525146b995f17dc8956c309950e","observation_id":"531f3244-3ced-4cbd-b47d-cb5b3293ee17","resolution":{"observed_at":"2026-08-15T18:50:40.609424Z","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":"2026-08-15T18:50:40.588405Z","title":"Automatic segmentation, classification and clustering of broadcast news audio","venue":null,"work_id":"3b544c42-29dc-4fb5-8f8b-9f58fa3990cb","year":1997},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.709576Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:39c17c334186e877a55b80c0269ed5aeb893f456dcae8f9bc62f4b5bc18faee3","observation_id":"942a9693-ad5e-4177-a0f0-45f4480d511f","resolution":{"observed_at":"2026-08-15T18:50:40.593814Z","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":"2026-08-15T18:50:40.572878Z","title":"Multispectral change detection using multivariate Kullback-Leibler distance.ISPRS J","venue":null,"work_id":"18315c62-5058-4e61-afdd-d3c6ce069d0a","year":2019},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.713851Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:d45c7b7b4ae6944c7381f15f8db5faa3989ac747854c208ad6ce836643652e7a","observation_id":"12dacb58-2bfd-4a20-897b-d8ab9c4adb7a","resolution":{"observed_at":"2026-08-15T18:50:40.577227Z","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":"2026-08-15T18:50:40.556740Z","title":"Direct importance estimation for covariate shift adaptation.Ann","venue":null,"work_id":"e516d424-0351-4e91-aef4-f03e1a6a89ff","year":2008},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.718834Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:19dcd5ff1661a68c396b9c416b63481568b843e98ae0ca7e35747eee2fe9fe44","observation_id":"a29eb00b-315b-4f1d-bb34-77391c45a901","resolution":{"observed_at":"2026-08-15T18:50:40.562286Z","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":"2026-08-15T18:50:40.541677Z","title":"Change-point detection in time-series data by direct density-ratio estimation","venue":null,"work_id":"7d82bab2-ea87-4a7f-b10e-1982e670887d","year":2009},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.722871Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:3267c22e12cbc7dc392a112a62570a7069444e610c1948426c9d93afb0189088","observation_id":"eb22a22e-0407-4917-992c-5cac826a1734","resolution":{"observed_at":"2026-08-15T18:50:40.546428Z","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":"2026-08-15T18:50:40.527903Z","title":"Change-point detection in time-series data by relative density-ratio estimation.Neural Netw., 43:72–83, 2013","venue":null,"work_id":"adaf498c-f5ba-4974-a0ff-108295192a9f","year":2013},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.726889Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:02df469d527d6452f910f331e888985e10a16e3d2d0fb215544a8b0acb3244d5","observation_id":"28bbfbb3-9b4a-4747-b1db-de6659d44bda","resolution":{"observed_at":"2026-08-15T18:50:40.532233Z","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":"2026-08-15T18:50:40.515204Z","title":"Efficient multistream classification using direct density ratio estimation","venue":null,"work_id":"c0d9495f-970a-43a5-a5a8-40f7c2f1df2c","year":2017},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.730890Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:8679904a8e09f200bdb88cc75b21cf39a195a1e87af9faf6fb8f6d0793341e29","observation_id":"b0f73df0-e2fd-443c-bad1-6f2e1daa284b","resolution":{"observed_at":"2026-08-15T18:50:40.519693Z","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":"2026-08-15T18:50:40.500762Z","title":"Learning phase transitions by confusion.Nature Phys., 13(5):435–439, 2017","venue":null,"work_id":"02232442-2e5e-4482-a0ed-c2bfb544b135","year":2017},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.734975Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:ac52927e5f6dec18644018e14e470158f10a51ee5bc7ce1826b670ed47750862","observation_id":"8b6db7bc-a37d-47ed-aff9-437b781a897e","resolution":{"observed_at":"2026-08-15T18:50:40.505667Z","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":"2026-08-15T18:50:40.487581Z","title":"Replacing neural networks by optimal analytical predictors for the detection of phase transitions.Phys","venue":null,"work_id":"c55314f0-b532-41c4-a14d-aa94f718e620","year":2022},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.738987Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:3447a82aba7b35aaec61a5a66f25dedf646ccf07d8eb73f049ef89514e784474","observation_id":"eba01a32-6da3-4e19-86dc-a5c3cd65effe","resolution":{"observed_at":"2026-08-15T18:50:40.491879Z","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":"2026-08-15T18:50:40.473836Z","title":"Mapping out phase diagrams with generative classifiers.Phys","venue":null,"work_id":"328f4349-5be5-47dc-a8c4-f19cef255b40","year":2024},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.743205Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:ea8d30ed96eaa7c85b4a1823a20f15a65b8d61bb82435d794492be4d3e9da822","observation_id":"1fa35a30-2760-495c-84a9-8a48a5f27f26","resolution":{"observed_at":"2026-08-15T18:50:40.478525Z","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":"2311.09128","last_updated":"2023-11-15T17:17:49Z","snapshot_observed_at":"2026-08-16T14:43:00.484265Z","submitted_at":"2023-11-15T17:17:49Z","title":"Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.09128","snapshot_observed_at":"2026-08-15T18:50:39.747137Z","title":"Fast detection of phase transitions with multi-task learning-by-confusion.arXiv:2311.09128, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.747137Z"},"links":{"cited_paper":"/paper/2311.09128","citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:12b6a642e90c5601f0ed0d8068c8edebeacc1de53858478f1a6c03183001f445","observation_id":"8d98629c-467e-4f14-a8d2-52fd70854c0d","resolution":{"observed_at":"2026-08-15T18:50:39.747137Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.10710","last_updated":"2023-11-17T18:59:35Z","snapshot_observed_at":"2026-08-16T20:04:41.171807Z","submitted_at":"2023-11-17T18:59:35Z","title":"Machine learning phase transitions: Connections to the Fisher information","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.10710","snapshot_observed_at":"2026-08-15T18:50:39.751886Z","title":"Machine learning phase transitions: Connections to the Fisher information.arXiv:2311.10710, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.751886Z"},"links":{"cited_paper":"/paper/2311.10710","citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:b1c332dbd8fd9c69f6b61c4f7881c75cbce2006ccfeb56dbd2179e5c19a68814","observation_id":"7b13673d-0ac7-447b-8eb3-bf65dac6a6bd","resolution":{"observed_at":"2026-08-15T18:50:39.751886Z","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":"2026-08-15T18:50:40.449510Z","title":"Quantum chi-squared tomography and mutual infor- mation testing.Quantum, 8:1381, 2024","venue":null,"work_id":"f420dae5-b1de-4a59-9374-06b83f4cb692","year":2024},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.757273Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:84102a7c49a22029eb5c26f6acfc1225ab7e4201377b90fbd25f5f603a7d3d46","observation_id":"e57c75a7-2c70-402b-aa44-d796bbcf818c","resolution":{"observed_at":"2026-08-15T18:50:40.456697Z","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":"2026-08-15T18:50:40.434102Z","title":"Neural changepoint detection in news data: A learning-by-confusion approach for identifying shifts in public dis- course","venue":null,"work_id":"ef8b75b6-09f7-4a46-8a7a-aa0420813aa4","year":2025},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.761747Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:d57cc37010c593b055e4d8e072815c5d5b8de4d9c781ce279fe977c67aaba678","observation_id":"878ba1a2-8586-404f-b790-122ab864b007","resolution":{"observed_at":"2026-08-15T18:50:40.439681Z","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":"2026-08-15T18:50:40.419929Z","title":"DistilRoBERTa Base Sentence Transformer, 2022","venue":null,"work_id":"b42ebfd4-161e-42f3-8a13-9177cca6ba3c","year":2022},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.765725Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:d921383d75a3f9f75a12a8358f7f733ace0f6a75b4b639c299e219e097c50358","observation_id":"68ea3daf-7368-4408-bd59-7ef0bf213685","resolution":{"observed_at":"2026-08-15T18:50:40.424697Z","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":"2026-08-15T18:50:40.403030Z","title":"Term-weighting approaches in automatic text retrieval","venue":null,"work_id":"3e0c893f-3de7-436d-bd06-5057eb521906","year":1988},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.769760Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:8aab60657d5e8d78277b57a1b272a416e889511cabdd6b7bea9f431a7c4d3760","observation_id":"acee0169-1c60-423e-9e56-8348f61369f0","resolution":{"observed_at":"2026-08-15T18:50:40.408027Z","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":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-15T18:50:39.774491Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.774491Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:e8960fbe27dc91099aacf2cf3e429d452a2358a5c8333112d75b6055ec1d817f","observation_id":"e451c6dc-a0b0-44e2-bd55-2b8c3c1d7a6d","resolution":{"observed_at":"2026-08-15T18:50:39.774491Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:50:39.778908Z","title":"Optuna: A next-generation hyperparameter optimization framework","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.778908Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:a9b48f649defabeca7fa1b0f17c0de022c6190629691e417f1013469d91a0a1f","observation_id":"e1540282-df79-4b53-8289-de6f679d28dc","resolution":{"observed_at":"2026-08-15T18:50:39.778908Z","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":"2026-08-15T18:50:40.380436Z","title":"Latent Dirichlet allocation.J","venue":null,"work_id":"27a947a6-4f4b-41b6-953e-71c75155f2b4","year":2003},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.783167Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:dcb677d13081e6fec2510014cd18ecaa55e59203cb18131685fca729c54f2db1","observation_id":"f2515a72-5409-488d-a06d-634a691dbd75","resolution":{"observed_at":"2026-08-15T18:50:40.385723Z","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":"2026-08-15T18:50:40.367406Z","title":"Change point detection in time series data using autoencoders with a time-invariant representation.IEEE Trans","venue":null,"work_id":"18f9eeb3-232f-4ed1-9cac-e679456e1c86","year":2021},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.788062Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:0855f3cf9ab571443f83e7fccc358a593e95ab5eb36f3f964da78ad54ae2c9c6","observation_id":"ff385502-c749-4e6c-8a2a-934209979442","resolution":{"observed_at":"2026-08-15T18:50:40.372793Z","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":"2026-08-15T18:50:40.354771Z","title":"Time series change point detec- tion with self-supervised contrastive predictive coding","venue":null,"work_id":"26ce7956-1a45-450d-8b50-70c6ebb2ca00","year":2021},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.792605Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:a153a67db9ea87a7f861158c209639cf76a71f67a62aa58835ecbc105184b764","observation_id":"b25ce6c7-b7c3-4aff-ac20-54899c7c4d80","resolution":{"observed_at":"2026-08-15T18:50:40.359068Z","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":"1905.06913","last_updated":"2019-05-16T17:17:55Z","snapshot_observed_at":"2026-08-14T16:31:04.472500Z","submitted_at":"2019-05-16T17:17:55Z","title":"Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series","version":1},"cited_work":{"arxiv_id":"1905.06913","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.06913","snapshot_observed_at":"2026-08-15T18:50:39.974607Z","title":"Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series","venue":"cs.LG","work_id":"6944b037-05bb-4348-a512-a0e1a8af4a49","year":2019},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.796710Z"},"links":{"cited_paper":"/paper/1905.06913","citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:6795a214974450adfcf1b920d79dffa4986ead8e4de46c1a288e6cedf2f6cc15","observation_id":"331ae54a-48db-4529-9bc3-8ced25d9d2ec","resolution":{"observed_at":"2026-08-15T18:50:39.980129Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-15T18:50:40.342126Z","title":"Zero-shot speaker change point detection using large language models","venue":null,"work_id":"7b0e2499-71b2-480d-9142-ae72c2f2ed91","year":2023},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.801837Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:409cb0491302b594482ef14a7caf0ee5a4ae384c7443400f238f29b92f4f621a","observation_id":"4dbd88ec-e9d3-4ac9-8f95-4adfe8d1b0e6","resolution":{"observed_at":"2026-08-15T18:50:40.346481Z","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":"1905.09876","last_updated":"2019-05-23T19:04:56Z","snapshot_observed_at":"2026-08-14T16:27:34.539749Z","submitted_at":"2019-05-23T19:04:56Z","title":"Deep density ratio estimation for change point detection","version":1},"cited_work":{"arxiv_id":"1905.09876","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.09876","snapshot_observed_at":"2026-08-15T18:50:39.954163Z","title":"Deep density ratio estimation for change point detection","venue":"cs.LG","work_id":"c319c613-624a-42b8-9e67-a5cdba1871c6","year":2019},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.806054Z"},"links":{"cited_paper":"/paper/1905.09876","citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:6cde616ac84da73946f6cd4b82542f5bcd10b80d5e937b25cc57bfbc6d2e21bc","observation_id":"52f04713-e70d-4a22-9fd9-1bdc8d726b40","resolution":{"observed_at":"2026-08-15T18:50:39.959616Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"1911.00405","last_updated":"2019-11-05T15:13:22Z","snapshot_observed_at":"2026-08-17T08:04:45.074184Z","submitted_at":"2019-11-01T14:31:59Z","title":"Training Neural Networks for Likelihood/Density Ratio Estimation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.00405","snapshot_observed_at":"2026-08-15T18:50:39.810440Z","title":"Training neural networks for likelihood/density ratio estimation.arXiv:1911.00405, 2019","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.810440Z"},"links":{"cited_paper":"/paper/1911.00405","citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:c27be919f4a795a8bfb1b4089db901c9c31a3ecb79e2c03ebbc51bdcae6a2edc","observation_id":"ed33979b-702c-466e-8d3c-0d39687701c5","resolution":{"observed_at":"2026-08-15T18:50:39.810440Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.05276","last_updated":"2021-03-09T07:56:36Z","snapshot_observed_at":"2026-08-16T18:40:16.568980Z","submitted_at":"2021-03-09T07:56:36Z","title":"Continual Density Ratio Estimation in an Online Setting","version":1},"cited_work":{"arxiv_id":"2103.05276","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.05276","snapshot_observed_at":"2026-08-15T18:50:39.916202Z","title":"Continual Density Ratio Estimation in an Online Setting","venue":"stat.ML","work_id":"acc97d68-42c0-4f62-a22d-c7a4bf29d2bf","year":2021},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.814969Z"},"links":{"cited_paper":"/paper/2103.05276","citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:ee59a8fa121816670eca7f6cbd585659337d5ceb5f0d859d196e323a40cd106b","observation_id":"2c2185dd-edee-4d54-b658-bd4cfadd1783","resolution":{"observed_at":"2026-08-15T18:50:39.922805Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-15T18:50:40.328145Z","title":"Inferences for case-control and semiparametric two-sample density ratio models","venue":null,"work_id":"3b91e8b6-d935-49f0-a814-5b12f58d3503","year":1998},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.819190Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:7012fa1a2112aab809690ba7da9199ddd4c4def06b06cab90114a497e2c136da","observation_id":"ba9b533b-c2c5-405f-b48d-24133e8e7d10","resolution":{"observed_at":"2026-08-15T18:50:40.332778Z","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":"2026-08-15T18:50:40.314114Z","title":"Semiparametric density estimation under a two-sample density ratio model.Bernoulli, 10(4):583–604, 2004","venue":null,"work_id":"b39278e9-05e2-4d90-8850-b9b76a506443","year":2004},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.823288Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:26d6c020e60d7d76818544a7cbc572d4ff17ed85d6c2c437a5d58171f8f8abe7","observation_id":"cc003b36-b1b9-4358-bde2-f2a251c0f1c2","resolution":{"observed_at":"2026-08-15T18:50:40.318576Z","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":"2026-08-15T18:50:40.300332Z","title":"Discriminative learning under covariate shift.J","venue":null,"work_id":"86a4a7a2-f9d1-42e1-9cf1-f8bcfccae430","year":2009},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.827455Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:bcce922ee8addfedfbf19b9b3543e3b5fc249fe55a746b04151462f536c326cc","observation_id":"a014e138-756e-4455-910a-115ded66d630","resolution":{"observed_at":"2026-08-15T18:50:40.304783Z","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":"2026-08-15T18:50:40.288052Z","title":"Cambridge University Press, 2012","venue":null,"work_id":"9f11f9af-ac45-4d36-8cfc-cabf3d3747ca","year":2012},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.831171Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:1995d412187aed2ae0f2575d6d1eed2c90f08d73564625777639e4de51695ffb","observation_id":"d8e8600e-a832-4c5b-aed3-0156cd929d4d","resolution":{"observed_at":"2026-08-15T18:50:40.291973Z","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":"2026-08-15T18:50:40.273701Z","title":"Linking losses for density ratio and class-probability estimation","venue":null,"work_id":"65c634c6-0820-43f2-949d-c5ae6c5577b6","year":2016},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.835140Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:d84d6e232083b8f85f32e0d318573ba0004572f6d8c10c521dcbe456d60e76e4","observation_id":"1351f7ae-3663-4b86-9235-afe22b21866c","resolution":{"observed_at":"2026-08-15T18:50:40.278802Z","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":"2026-08-15T18:50:40.259522Z","title":"Breaking the curse of horizon: Infinite-horizon off-policy estimation.Adv","venue":null,"work_id":"cb988687-7edf-4c08-b43b-2838bb9e6158","year":2018},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.839218Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:80827840c39d8caaa88cac7098d083cc87049eb33bdaef230766a96f3b97b6f8","observation_id":"fca0d306-a79d-435f-9bb1-7814b24fa058","resolution":{"observed_at":"2026-08-15T18:50:40.264296Z","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":"2026-08-15T18:50:40.244678Z","title":"Gutmann and Aapo Hyvärinen","venue":null,"work_id":"b91511fa-a66f-44f1-bae6-38a827774ed7","year":2012},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.843043Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:4eca97daf95b6b4e1a570d1accbe0fb30c79850159bd0f5a2ac73fb13ac63e7d","observation_id":"7baed615-7d18-4d72-91c5-ccae257e439f","resolution":{"observed_at":"2026-08-15T18:50:40.249713Z","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":"2026-08-15T18:50:40.230688Z","title":"f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization","venue":null,"work_id":"296abddc-503d-4291-9940-b06a54070fda","year":2016},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.847495Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:5e5b148f259424460f7d18566ea5da42302afae66cbd70acaa3c8654bd982cb9","observation_id":"78eff131-ad98-4909-bca1-c3adfae1f788","resolution":{"observed_at":"2026-08-15T18:50:40.235574Z","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":"2026-08-15T18:50:40.216576Z","title":"Unsupervised change analysis using supervised learning","venue":null,"work_id":"32e684f8-5e55-49e9-9b9b-4eeebeace167","year":2008},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.851416Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:431cca8c9466e99812247990fa1b2ccf76fee1a400d3589a7b02d1e76a21ec4d","observation_id":"18d2458c-21a5-430b-8da6-96ade825d5da","resolution":{"observed_at":"2026-08-15T18:50:40.220711Z","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":"2026-08-15T18:50:40.198922Z","title":"Change point detection with neural online density-ratio estimator","venue":null,"work_id":"b9615434-57a6-42f4-8321-2f3e09c5199b","year":2023},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.855907Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:bcd023f037ba9a01fcbdb5f2ee72fe611930ad1bea708d16a8235bc8a306ed41","observation_id":"787c1b60-a7eb-4943-b1ec-9a2bf2a8127f","resolution":{"observed_at":"2026-08-15T18:50:40.205150Z","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":"2026-08-15T18:50:40.183129Z","title":"Leveraging change point detection to discover natural experiments in data.EPJ Data Sci., 11(1):49, 2022","venue":null,"work_id":"0c5f0fd6-5b91-4e72-b93c-aa19a5e14962","year":2022},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.861239Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:155ecb03bbdde2b5091489dbcfb7e250d0c6518cac792b1b0594081965fa2234","observation_id":"ef31ce3f-535a-461b-9bd1-4076247f19af","resolution":{"observed_at":"2026-08-15T18:50:40.187910Z","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":"2026-08-15T18:50:40.166525Z","title":"Discovering collective narratives shifts in online discussions","venue":null,"work_id":"d3d69996-586c-464f-91a3-679561f26a58","year":2024},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.865795Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:28c85fa27f392553db9c3192d370f5fa4545bbba704c919a3b55048e0d5fe4da","observation_id":"a25cdc21-ee5a-41b2-afb6-ae430cb5ccc6","resolution":{"observed_at":"2026-08-15T18:50:40.171577Z","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":"2026-08-15T18:50:40.152404Z","title":null,"venue":null,"work_id":"37c6911a-22a2-427c-8238-09c491c5ab2d","year":2020},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.869934Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:1b22ab14181165cfb48c8a715f28df6b0a24f355afea9d5ac765f8f8614c5de1","observation_id":"02df97eb-8bf7-4c6a-af67-f3f3a99503c1","resolution":{"observed_at":"2026-08-15T18:50:40.156989Z","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":"2026-08-15T18:50:40.138608Z","title":"Featurized density ratio estimation","venue":null,"work_id":"eeebe5e8-9382-4026-83f2-7eae17d65a50","year":2021},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.874072Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:1284ac3feb08c95bc572b7ebf5952716aec1bf9843450c92960e46a873037929","observation_id":"b866d6ba-103b-449c-bc69-aa54c0ba35a0","resolution":{"observed_at":"2026-08-15T18:50:40.143025Z","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":"2026-08-15T18:50:40.125033Z","title":"A comparison of single and multiple changepoint techniques for time series data.Comput","venue":null,"work_id":"5d90e791-aef2-4cfe-b232-08158f5ff2dd","year":2022},"citing_paper":{"arxiv_id":"2506.18764","last_updated":"2025-06-23T15:33:30Z","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:39.878110Z"},"links":{"citing_paper":"/paper/2506.18764"},"observation_digest":"sha256:d60a4ca32383b190ec8cd6056171177251f6fad6d2ec94236ca4f9038ec48dde","observation_id":"a7b793b4-2987-42ec-ac6b-2c3d9001851c","resolution":{"observed_at":"2026-08-15T18:50:40.129086Z","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":"2506.18764","last_updated":"2025-06-23T15:33:30Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T20:04:53.512388Z","submitted_at":"2025-06-23T15:33:30Z","title":"Neural Total Variation Distance Estimators for Changepoint Detection in News Data"},"reference_resolution":{"displayed":57,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":5,"verified_fuzzy":43},"total_outbound_references":57},"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 17 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2506.18764."}