{"as_of":"2026-08-18T10:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:62f64945a784bd79a41e234a9d6eadfe6d6c60825ac7dc9e85e2cff7272ef62a","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T05:53:18.716985Z","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":"2026-08-14T05:53:18.787594Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1805.04170","last_updated":"2018-05-10T20:38:56Z","snapshot_observed_at":"2026-08-17T20:28:30.660673Z","submitted_at":"2018-05-10T20:38:56Z","title":"Unifying Data, Model and Hybrid Parallelism in Deep Learning via Tensor Tiling","version":1},"cited_work":{"arxiv_id":"1805.04170","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.04170","snapshot_observed_at":"2026-08-14T05:53:18.787594Z","title":"Unifying Data, Model and Hybrid Parallelism in Deep Learning via Tensor Tiling","venue":"cs.DC","work_id":"6161dbcf-75a5-414c-9650-b066c0ee77fe","year":2018},"citing_paper":{"arxiv_id":"1909.00562","last_updated":"2019-09-09T06:12:02Z","snapshot_observed_at":"2026-08-17T01:30:45.703153Z","submitted_at":"2019-09-02T06:41:34Z","title":"Hybrid Data-Model Parallel Training for Sequence-to-Sequence Recurrent Neural Network Machine Translation","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-14T05:53:18.716985Z"},"links":{"cited_paper":"/paper/1805.04170","citing_paper":"/paper/1909.00562"},"observation_digest":"sha256:9f0f683fb25db7d2532cb01034621151ab77cc18a33a18d1856b6957639e7d25","observation_id":"b520807d-98d4-4625-8517-427fcd95a6f8","resolution":{"observed_at":"2026-08-14T05:53:18.796890Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1805.04170/citation-record","integrity":"/paper/1805.04170/integrity","json":"/paper/1805.04170/citation-record.json","paper":"/paper/1805.04170"},"outbound":[],"paper":{"arxiv_id":"1805.04170","last_updated":"2018-05-10T20:38:56Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-17T20:28:30.660673Z","submitted_at":"2018-05-10T20:38:56Z","title":"Unifying Data, Model and Hybrid Parallelism in Deep Learning via Tensor Tiling"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:1805.04170."}