{"as_of":"2026-08-08T11:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3e23561dacf84d0f8e31fb818fdff9962b0913ced331388da14d26dd338026f2","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:37:04.893327Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":14,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2101.08030","last_updated":"2021-01-20T08:58:29Z","snapshot_observed_at":"2026-08-08T08:49:17.089830Z","submitted_at":"2021-01-20T08:58:29Z","title":"Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data","version":1},"cited_work":{"arxiv_id":"2101.08030","doi":"10.48550/arxiv.2101.08030","metadata_source":"arxiv_reference","pith_arxiv_id":"2101.08030","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Adversarial","venue":"arXiv (Cornell University)","work_id":"ccb74a3d-2bcd-462b-ad20-3eed718cebf5","year":2021},"citing_paper":{"arxiv_id":"2502.05564","last_updated":"2025-05-24T08:05:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-08T13:25:04Z","title":"TabICL: A Tabular Foundation Model for In-Context Learning on Large Data","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-05-20T13:35:02.018244Z"},"links":{"cited_paper":"/paper/2101.08030","citing_paper":"/paper/2502.05564"},"observation_digest":"sha256:31a675e5228fb4b0f13b25d3f38b860983fb69728a18ce957de9f05ee33dd0e7","observation_id":"fb8f0299-0ab6-4abf-838a-2813a788aa34","resolution":{"observed_at":"2026-05-20T13:35:02.210605Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2101.08030","last_updated":"2021-01-20T08:58:29Z","snapshot_observed_at":"2026-08-08T08:49:17.089830Z","submitted_at":"2021-01-20T08:58:29Z","title":"Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.08030","snapshot_observed_at":"2026-08-07T12:37:04.893327Z","title":"Adversarial attacks for tabular data: Application to fraud detection and imbalanced data","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.24267","last_updated":"2025-05-30T06:45:31Z","snapshot_observed_at":"2026-08-07T12:24:56.758348Z","submitted_at":"2025-05-30T06:45:31Z","title":"MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T12:37:04.893327Z"},"links":{"cited_paper":"/paper/2101.08030","citing_paper":"/paper/2505.24267"},"observation_digest":"sha256:b55f09f55a0aff322ba9cb524f2e04ea7fcf555b81f48d739cd5f3d43c40b21b","observation_id":"56346855-ecb4-4a9a-a2ad-ef942cfac08e","resolution":{"observed_at":"2026-08-07T12:37:04.893327Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.08030","last_updated":"2021-01-20T08:58:29Z","snapshot_observed_at":"2026-08-08T08:49:17.089830Z","submitted_at":"2021-01-20T08:58:29Z","title":"Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data","version":1},"cited_work":{"arxiv_id":"2101.08030","doi":"10.48550/arxiv.2101.08030","metadata_source":"arxiv_reference","pith_arxiv_id":"2101.08030","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Adversarial","venue":"arXiv (Cornell University)","work_id":"ccb74a3d-2bcd-462b-ad20-3eed718cebf5","year":2021},"citing_paper":{"arxiv_id":"2603.13970","last_updated":"2026-04-08T16:12:25Z","snapshot_observed_at":"2026-07-06T22:49:01.769027Z","submitted_at":"2026-03-14T14:53:50Z","title":"Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-15T11:21:09.307816Z"},"links":{"cited_paper":"/paper/2101.08030","citing_paper":"/paper/2603.13970"},"observation_digest":"sha256:43dce2e47b82b4a266d97ff400a9423d8e2d7b2535a7379e014070518115655d","observation_id":"f59bd82c-86a8-4d6e-98e8-ffb13362be79","resolution":{"observed_at":"2026-05-15T11:25:31.137128Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2101.08030","last_updated":"2021-01-20T08:58:29Z","snapshot_observed_at":"2026-08-08T08:49:17.089830Z","submitted_at":"2021-01-20T08:58:29Z","title":"Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data","version":1},"cited_work":{"arxiv_id":"2101.08030","doi":"10.48550/arxiv.2101.08030","metadata_source":"arxiv_reference","pith_arxiv_id":"2101.08030","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Adversarial","venue":"arXiv (Cornell University)","work_id":"ccb74a3d-2bcd-462b-ad20-3eed718cebf5","year":2021},"citing_paper":{"arxiv_id":"2605.30650","last_updated":"2026-05-28T23:10:04Z","snapshot_observed_at":"2026-07-06T23:39:53.132531Z","submitted_at":"2026-05-28T23:10:04Z","title":"When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-29T06:17:07.660975Z"},"links":{"cited_paper":"/paper/2101.08030","citing_paper":"/paper/2605.30650"},"observation_digest":"sha256:31cb7b9cf00c918d8ef23cb17b4ae2306d424c2a1dc12b2b23aa08813f799e4d","observation_id":"0435ace3-8467-411a-bea0-fca9c8e0feeb","resolution":{"observed_at":"2026-06-29T14:43:31.563202Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2101.08030/citation-record","integrity":"/paper/2101.08030/integrity","json":"/paper/2101.08030/citation-record.json","paper":"/paper/2101.08030"},"outbound":[],"paper":{"arxiv_id":"2101.08030","last_updated":"2021-01-20T08:58:29Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-08T08:49:17.089830Z","submitted_at":"2021-01-20T08:58:29Z","title":"Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2101.08030."}