{"as_of":"2026-08-11T11:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4833ce48212542c05bfbc5d5b3666dc0392255d8730644c29a5492f6137fd629","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T05:26:03.646783Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-05-18T14:46:29.325174Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1811.08295","last_updated":"2019-02-01T15:14:44Z","snapshot_observed_at":"2026-08-04T03:04:04.122073Z","submitted_at":"2018-11-20T14:54:24Z","title":"T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.08295","snapshot_observed_at":"2026-08-06T05:26:03.646783Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.02758","last_updated":"2025-08-03T17:07:08Z","snapshot_observed_at":"2026-08-07T12:37:52.841319Z","submitted_at":"2025-08-03T17:07:08Z","title":"CTBench: Cryptocurrency Time Series Generation Benchmark","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T05:26:03.646783Z"},"links":{"cited_paper":"/paper/1811.08295","citing_paper":"/paper/2508.02758"},"observation_digest":"sha256:3ed8a801751ac82a1a5d6bb4b2aa48e516eefa80bb9f090a1bb186c3afe0566e","observation_id":"8a58a823-918d-4ae5-9c43-4251c1b8f1ab","resolution":{"observed_at":"2026-08-06T05:26:03.646783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.08295","last_updated":"2019-02-01T15:14:44Z","snapshot_observed_at":"2026-08-04T03:04:04.122073Z","submitted_at":"2018-11-20T14:54:24Z","title":"T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling","version":2},"cited_work":{"arxiv_id":"1811.08295","doi":null,"metadata_source":"pith","pith_arxiv_id":"1811.08295","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling","venue":"cs.LG","work_id":"a9f4d5ec-b125-49d4-975f-967d7fdf6f01","year":2018},"citing_paper":{"arxiv_id":"2509.20846","last_updated":"2026-05-14T08:13:14Z","snapshot_observed_at":"2026-08-03T01:47:34.109311Z","submitted_at":"2025-09-25T07:34:46Z","title":"Causal Time Series Generation via Diffusion Models","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-05-18T14:45:29.515515Z"},"links":{"cited_paper":"/paper/1811.08295","citing_paper":"/paper/2509.20846"},"observation_digest":"sha256:68d3667c6d18935826dd8140c975181b755e7414450c6a8404a878f52b6127f7","observation_id":"b03c0cd6-9a62-49cc-989d-b1db55767258","resolution":{"observed_at":"2026-05-18T14:46:29.327090Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.08295","last_updated":"2019-02-01T15:14:44Z","snapshot_observed_at":"2026-08-04T03:04:04.122073Z","submitted_at":"2018-11-20T14:54:24Z","title":"T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.08295","snapshot_observed_at":"2026-08-03T09:34:54.286149Z","title":"T-CGAN: conditional generative adversarial network for data augmentation in noisy time series with irregular sampling.CoRR, abs/1811.08295, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2601.13534","last_updated":"2026-06-27T04:31:03Z","snapshot_observed_at":"2026-08-10T10:06:51.046415Z","submitted_at":"2026-01-20T02:45:03Z","title":"Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T09:34:54.286149Z"},"links":{"cited_paper":"/paper/1811.08295","citing_paper":"/paper/2601.13534"},"observation_digest":"sha256:46b1c0abad60be28239c72af30f3cc924136a553bf4a545162e16395d0367eb1","observation_id":"244e53dd-2008-481f-bb5c-850aea3b6e17","resolution":{"observed_at":"2026-08-03T09:34:54.286149Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1811.08295/citation-record","integrity":"/paper/1811.08295/integrity","json":"/paper/1811.08295/citation-record.json","paper":"/paper/1811.08295"},"outbound":[],"paper":{"arxiv_id":"1811.08295","last_updated":"2019-02-01T15:14:44Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T03:04:04.122073Z","submitted_at":"2018-11-20T14:54:24Z","title":"T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1811.08295."}