{"as_of":"2026-08-19T04:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b810eb45dd7ede23d3e89026c63cbbbdf6df84751ec2803e4ebaecf1b1bd3e3e","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":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":14,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T10:47:49.166908Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-23T20:43:25.495786Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":"1911.02855","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-07-22T00:22:10.391371Z","title":"ArXiv abs/1911.02855","venue":null,"work_id":"fae717af-50cf-4103-874e-9c37fe12e507","year":2019},"citing_paper":{"arxiv_id":"2409.11022","last_updated":"2026-05-15T09:10:20Z","snapshot_observed_at":"2026-08-18T10:39:47.709644Z","submitted_at":"2024-09-17T09:32:12Z","title":"DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition","version":7},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-23T20:40:09.530799Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2409.11022"},"observation_digest":"sha256:04b1c357ace2f10512ae612c1cb6be2ba6cd359155b3ac5aec8a31bf7b99ad2b","observation_id":"10e1a493-1701-46e0-bbcc-746d7d146bb3","resolution":{"observed_at":"2026-07-22T00:22:10.391371Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-08-12T15:48:47.598407Z","title":"Dice loss for data-imbalanced NLP tasks[J]","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2411.13917","last_updated":"2024-11-21T08:07:26Z","snapshot_observed_at":"2026-08-17T22:47:42.394884Z","submitted_at":"2024-11-21T08:07:26Z","title":"SpikEmo: Enhancing Emotion Recognition With Spiking Temporal Dynamics in Conversations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T15:48:47.598407Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2411.13917"},"observation_digest":"sha256:9087b673f08151f21cd031c4f1a2f068b7d838362ced5f160c6f107f31069116","observation_id":"594e79e0-6fb2-4a6b-a019-268ec9712253","resolution":{"observed_at":"2026-08-12T15:48:47.598407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-08-11T05:45:22.718923Z","title":"Dice loss for data-imbalanced nlp tasks,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2412.17247","last_updated":"2024-12-23T03:40:04Z","snapshot_observed_at":"2026-08-16T17:21:27.675806Z","submitted_at":"2024-12-23T03:40:04Z","title":"STeInFormer: Spatial-Temporal Interaction Transformer Architecture for Remote Sensing Change Detection","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T05:45:22.718923Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2412.17247"},"observation_digest":"sha256:443794bde57cd61376365ad11565d9ab9989a269878b0ffcda8c85ee7a0dc31a","observation_id":"71b6b376-5843-4946-bd56-3044fe9179de","resolution":{"observed_at":"2026-08-11T05:45:22.718923Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-08-09T16:38:50.840076Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.01108","last_updated":"2025-07-23T23:13:37Z","snapshot_observed_at":"2026-08-14T04:22:48.987105Z","submitted_at":"2025-02-03T06:56:40Z","title":"Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-09T16:38:50.840076Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2502.01108"},"observation_digest":"sha256:e0372ab0a3406cf769d281640f253319d13db8fe3e397663114b4116d641151c","observation_id":"5e7639f8-9e7c-4978-9acc-a03342ada439","resolution":{"observed_at":"2026-08-09T16:38:50.840076Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-08-16T10:47:49.166908Z","title":"Dice loss for data - imbalanced NLP tasks,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2504.17255","last_updated":"2025-04-24T05:19:47Z","snapshot_observed_at":"2026-08-18T20:00:30.713815Z","submitted_at":"2025-04-24T05:19:47Z","title":"3D Deep-learning-based Segmentation of Human Skin Sweat Glands and Their 3D Morphological Response to Temperature Variations","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T10:47:49.166908Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2504.17255"},"observation_digest":"sha256:4b48e9635e20f8ae288cb9c3fe313682b8f626efe070ab516ffa1b168f2b3236","observation_id":"5b8ed541-0a76-4004-b9b3-d5a752ddca8a","resolution":{"observed_at":"2026-08-16T10:47:49.166908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-08-07T14:24:41.915763Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.19128","last_updated":"2025-05-25T12:52:18Z","snapshot_observed_at":"2026-08-13T11:35:10.453630Z","submitted_at":"2025-05-25T12:52:18Z","title":"RetrieveAll: A Multilingual Named Entity Recognition Framework with Large Language Models","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T14:24:41.915763Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2505.19128"},"observation_digest":"sha256:bba0dd843223d8411b46e5f14fa3fc967a6a47b8242654ef188c455b1c712002","observation_id":"20bb6c7c-862e-4b0c-8e11-db2e2281f7ba","resolution":{"observed_at":"2026-08-07T14:24:41.915763Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-08-06T13:24:26.309876Z","title":"Dice loss for data-imbalanced nlp tasks","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2507.20740","last_updated":"2025-07-28T11:46:35Z","snapshot_observed_at":"2026-08-19T03:43:00.130113Z","submitted_at":"2025-07-28T11:46:35Z","title":"Implicit Counterfactual Learning for Audio-Visual Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T13:24:26.309876Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2507.20740"},"observation_digest":"sha256:150c6e0c791b608721db73b3cbbe25f0533450a3de2b19e5e00abe99029ffb97","observation_id":"4bda86bf-c11d-4172-9739-69e6e3a052d8","resolution":{"observed_at":"2026-08-06T13:24:26.309876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-08-05T20:20:12.530871Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.10681","last_updated":"2025-08-14T14:24:47Z","snapshot_observed_at":"2026-08-06T15:57:51.802224Z","submitted_at":"2025-08-14T14:24:47Z","title":"IADGPT: Unified LVLM for Few-Shot Industrial Anomaly Detection, Localization, and Reasoning via In-Context Learning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-05T20:20:12.530871Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2508.10681"},"observation_digest":"sha256:cdae78c8615b4565275e90f1a71c347d1adb07a1f424ad25a12515628f7a595b","observation_id":"15bfc27c-3775-459b-9f8e-1bbe73516059","resolution":{"observed_at":"2026-08-05T20:20:12.530871Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-08-05T19:01:53.034601Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.13584","last_updated":"2025-08-19T07:36:04Z","snapshot_observed_at":"2026-08-14T05:06:09.986400Z","submitted_at":"2025-08-19T07:36:04Z","title":"Temporal-Conditional Referring Video Object Segmentation with Noise-Free Text-to-Video Diffusion Model","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-05T19:01:53.034601Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2508.13584"},"observation_digest":"sha256:09359f286db2d08a634b844243dc11d238a5d11f2c02b844c52b4f0fad8063be","observation_id":"da649e16-38d3-4ac8-9dea-ba29c5f8d1a3","resolution":{"observed_at":"2026-08-05T19:01:53.034601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-08-03T17:28:41.373771Z","title":"Dice loss for data-imbalanced nlp tasks","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2512.09446","last_updated":"2026-07-02T13:02:35Z","snapshot_observed_at":"2026-08-12T07:45:24.503313Z","submitted_at":"2025-12-10T09:19:17Z","title":"Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T17:28:41.373771Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2512.09446"},"observation_digest":"sha256:ae0295637d0aecb2c9c130fd8d0dffcb91d8be1e6a26c69d5a477956542bb6a6","observation_id":"e0b1ab3b-ee41-4a7b-bd29-4505150c5d7b","resolution":{"observed_at":"2026-08-03T17:28:41.373771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-08-02T20:28:25.658936Z","title":"Dice loss for data-imbalanced nlp tasks,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2602.23204","last_updated":"2026-07-18T03:48:45Z","snapshot_observed_at":"2026-08-16T08:07:40.920602Z","submitted_at":"2026-02-26T16:53:36Z","title":"Motion-aware Event Suppression for Event Cameras","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-02T20:28:25.658936Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2602.23204"},"observation_digest":"sha256:a405ee9b9b9ecda178077285baf91aaa2de0c77714862aff5a6ca4c7a42ba651","observation_id":"7d1c3818-6927-48cb-880d-0f32bb0c16d5","resolution":{"observed_at":"2026-08-02T20:28:25.658936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":"1911.02855","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-07-22T00:22:10.391371Z","title":"ArXiv abs/1911.02855","venue":null,"work_id":"fae717af-50cf-4103-874e-9c37fe12e507","year":2019},"citing_paper":{"arxiv_id":"2604.04632","last_updated":"2026-04-06T12:30:46Z","snapshot_observed_at":"2026-07-06T22:53:33.177713Z","submitted_at":"2026-04-06T12:30:46Z","title":"InCTRLv2: Generalist Residual Models for Few-Shot Anomaly Detection and Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T20:26:21.587723Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2604.04632"},"observation_digest":"sha256:1c3d63efd58ef225c07dcba319c7cb0e85dfe77dbb96584c8107df7bd258d26c","observation_id":"344ecb0c-b952-43e1-be9a-ce03c0ae1b45","resolution":{"observed_at":"2026-07-22T00:22:10.391371Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-07-12T12:37:04.098637Z","title":"Dice loss for data-imbalanced nlp tasks.arXiv preprint arXiv:1911.02855, 2019","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2607.05416","last_updated":"2026-06-23T03:20:57Z","snapshot_observed_at":"2026-08-17T12:12:58.518995Z","submitted_at":"2026-06-23T03:20:57Z","title":"Text Distance from Nested and Hierarchical Repetitions: A Compression-Based Perspective","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-12T12:37:04.098637Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2607.05416"},"observation_digest":"sha256:1b3864178371495a99b16d1c3bf2509baf4f718204dab0fa6bfa114d30b8540c","observation_id":"ef68d755-5668-44f6-9f32-24be22e81930","resolution":{"observed_at":"2026-07-12T12:37:04.098637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02855","snapshot_observed_at":"2026-08-03T08:25:36.012333Z","title":"arXiv preprint arXiv:1911.02855 , year=","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2607.29370","last_updated":"2026-07-31T12:56:01Z","snapshot_observed_at":"2026-08-14T09:33:47.927367Z","submitted_at":"2026-07-31T12:56:01Z","title":"VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-03T08:25:36.012333Z"},"links":{"cited_paper":"/paper/1911.02855","citing_paper":"/paper/2607.29370"},"observation_digest":"sha256:6cac8b32178b11cc2cad50a09a6d7d1a30f498534fcff9b87dc7a57fc805a384","observation_id":"e35ed05b-238c-4e6b-84c8-628b93e02472","resolution":{"observed_at":"2026-08-03T08:25:36.012333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1911.02855/citation-record","integrity":"/paper/1911.02855/integrity","json":"/paper/1911.02855/citation-record.json","paper":"/paper/1911.02855"},"outbound":[],"paper":{"arxiv_id":"1911.02855","last_updated":"2026-07-29T07:55:45Z","latest_version":5,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-18T17:11:48.925187Z","submitted_at":"2019-11-07T11:14:05Z","title":"Dice Loss for Data-imbalanced NLP Tasks"},"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 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:1911.02855."}