{"as_of":"2026-08-14T15:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ac4ead22021a4341f44924f4a92a9d70251b34d1520097e417bd8175e9a3577e","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T18:23:21.939923Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2502.00619/citation-record","integrity":"/paper/2502.00619/integrity","json":"/paper/2502.00619/citation-record.json","paper":"/paper/2502.00619"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:23:21.814910Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.814910Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:e96ca6f6ee8855cbaeaee4885b949641872a53b2d0f6058aea163dc3210a8632","observation_id":"fee70540-4927-4e61-9f54-a0464a0a9e8c","resolution":{"observed_at":"2026-08-09T18:23:21.814910Z","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":"2026-08-09T18:23:22.463566Z","title":null,"venue":null,"work_id":"50d02c63-c643-4f56-9c9e-f770a1060300","year":1995},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.819874Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:9513f48d2d355d26ea45664fa8942d2cca1cc4d03e7c32436ab6d610f0aa6919","observation_id":"5b0b4308-28b8-4e43-8c86-86f2f513703e","resolution":{"observed_at":"2026-08-09T18:23:22.468779Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T18:23:22.443650Z","title":null,"venue":null,"work_id":"18812d42-80a3-493b-bb6c-ebf23f1360af","year":2021},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.824001Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:2a98a1587994e6745eb441163b96189b2591a6529927af119071afc6441ec582","observation_id":"3fba6f3c-4e02-42d9-980e-8d855b3b73e6","resolution":{"observed_at":"2026-08-09T18:23:22.452198Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T18:23:22.418563Z","title":null,"venue":null,"work_id":"9b4359e2-bf5f-4e4c-8db6-94022e4cf7e3","year":2000},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.828282Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:7059af0fdf8aa6f72316d6f9e9d9710726db4b128d5c2f72df928b1215fd1076","observation_id":"83f8ecb7-c8dd-4556-a38a-869fd9ccebb5","resolution":{"observed_at":"2026-08-09T18:23:22.422503Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04306","last_updated":"2021-02-08T16:10:50Z","snapshot_observed_at":"2026-08-14T08:44:08.583921Z","submitted_at":"2021-02-08T16:10:50Z","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04306","snapshot_observed_at":"2026-08-09T18:23:21.831871Z","title":"L., and Zhou, Y","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.831871Z"},"links":{"cited_paper":"/paper/2102.04306","citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:ad8f74e681f6d260a4bf4f1e855a1158a0901ad438a7bd9e07d85b6593824092","observation_id":"a2a5c6af-08eb-4695-8be7-c74aa593945b","resolution":{"observed_at":"2026-08-09T18:23:21.831871Z","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":"2026-08-09T18:23:22.405990Z","title":"Low-rank mixture-of-experts for continual medical image segmentation","venue":null,"work_id":"f8f647e3-2751-48b8-8f48-801bc7d7cde4","year":2024},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.835848Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:06d3142fccd35db9edb8e6def52369e0e4256a15c7629d87401a6b355077f0b3","observation_id":"135d33f8-8cfb-4269-8972-10c34d14567c","resolution":{"observed_at":"2026-08-09T18:23:22.410245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:23:21.839488Z","title":"T., Rubanova, Y., Bettencourt, J., and Duvenaud, D","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.839488Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:e2f2e1461235c74e09bc0165c9bb2d79b45f759357a53a1934d78978f6d204fd","observation_id":"a4846e58-83fe-4ec2-9b89-55596bdcba94","resolution":{"observed_at":"2026-08-09T18:23:21.839488Z","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":"2026-08-09T18:23:21.843296Z","title":"S., Wicker, J., Sun, Q., and Lee, J","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.843296Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:ab058eeda0e4d266d6c34f599ba75f7695e4206c301483afe02e125ad4553bbd","observation_id":"4ce70830-514d-4089-a854-6de50a333b0b","resolution":{"observed_at":"2026-08-09T18:23:21.843296Z","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":"2026-08-09T18:23:22.387241Z","title":"S., Brox, T., and Ronneberger, O","venue":null,"work_id":"ed327517-caf8-4fdf-8238-9669a858d82e","year":2016},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.846812Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:4b452943a674292497597368168c8d114250c9be01b38f362a4ac0ceb4777ea9","observation_id":"aa7186c8-25ee-4fd2-b670-8e41ee45425a","resolution":{"observed_at":"2026-08-09T18:23:22.391311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T18:23:22.376037Z","title":"C., Francis, B","venue":null,"work_id":"5b711a3b-093e-48e7-8c5f-9c9fb0667fd5","year":2013},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.850663Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:4d7beb84cca9ed188f178f1b46fb5df7038bc3ad8bc265a8a362c15fc6c47386","observation_id":"aefd50af-dcf3-4f6b-9a97-8d57b3a65be2","resolution":{"observed_at":"2026-08-09T18:23:22.379743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:23:21.853922Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.853922Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:e4c17448686d7d70c3eea721465f53be253d46c238c2a24a68a707fc6bf41ce5","observation_id":"d6dd4b04-c941-4ca7-9703-194d1702e9be","resolution":{"observed_at":"2026-08-09T18:23:21.853922Z","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":"2026-08-09T18:23:22.357990Z","title":"and Shen, Y","venue":null,"work_id":"7d0a460f-ad51-4f48-8825-e90b9417613b","year":2024},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.857557Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:04b9600d8104dde0a455735efafaaed698376fd472d8b911eea761de7ae8cdd4","observation_id":"8acc37a1-5dac-4f55-8ff9-33079ae613c4","resolution":{"observed_at":"2026-08-09T18:23:22.362124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.00983","last_updated":"2024-12-06T18:16:02Z","snapshot_observed_at":"2026-08-13T23:57:40.912195Z","submitted_at":"2024-07-01T05:47:58Z","title":"FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.00983","snapshot_observed_at":"2026-08-09T18:23:21.860879Z","title":"K., and Li, X","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.860879Z"},"links":{"cited_paper":"/paper/2407.00983","citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:6132a953ef85772301a6449abab997b1b521bfe8d8f29a68bf719f78125b010b","observation_id":"ae98679f-f7b0-453e-80dd-8a41005ee342","resolution":{"observed_at":"2026-08-09T18:23:21.860879Z","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":"2026-08-09T18:23:22.347295Z","title":"G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., et al","venue":null,"work_id":"777e8444-d10c-4734-8f7f-6b15def2f94a","year":2024},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.864541Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:5a43bd98e766f6d34bffa53c1d954b536b02268b19ae5706f26a21eb7a987442","observation_id":"8abe89cc-ff5b-4262-b93d-a359b81c7914","resolution":{"observed_at":"2026-08-09T18:23:22.351001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:23:21.867797Z","title":"Crafting papers on machine learning","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.867797Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:94b22ab6cc59356e095c3774d6cc322a86cdda01c45e608662066bb75a9d4fd5","observation_id":"c8986446-15ff-4e28-ae1a-5aa0efeb25f2","resolution":{"observed_at":"2026-08-09T18:23:21.867797Z","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":"2026-08-09T18:23:22.328569Z","title":"Fairdiff: Fair segmentation with point-image diffusion","venue":null,"work_id":"d881800b-d490-47fa-b96f-5b1f944c1f85","year":2024},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.871221Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:42ffa8906e50fa26db37477b99c2a03c58698c456ef8629c3c2c80e066ec73f1","observation_id":"b3a39421-c215-44e8-8a61-34d512e5f9ae","resolution":{"observed_at":"2026-08-09T18:23:22.332447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-09T20:34:52.923500Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-09T18:23:21.874463Z","title":"and Hutter, F","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.874463Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:d4b03ca9e66403d71d5b07e51ad99d91effcba1d567520c3ba3f713d4346f1ab","observation_id":"1bbd2978-732b-4c91-989a-782765f4f16c","resolution":{"observed_at":"2026-08-09T18:23:21.874463Z","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":"2026-08-09T18:23:22.316636Z","title":"Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations","venue":null,"work_id":"d9b44ab9-515a-4e81-ba65-363d976fa86b","year":2018},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.877913Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:db54ed824509f57798da86f75f4fef986881b76de2578f106f952df51ad1aea7","observation_id":"a48c2f01-3a42-4202-9d24-871920fafcac","resolution":{"observed_at":"2026-08-09T18:23:22.321159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:23:21.880944Z","title":"Learning adversarially fair and transferable representations","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.880944Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:33149d192ca1a25196c1f11499c71c9bcd8b011e879ae9e1e568c194cb19335e","observation_id":"33c878af-d5c0-4a6a-bb7a-94b6c705a174","resolution":{"observed_at":"2026-08-09T18:23:21.880944Z","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":"2026-08-09T18:23:22.290554Z","title":"K., Cho, Y., Lee, I","venue":null,"work_id":"5c74d59f-65b5-4e27-a5f0-76252f555ab7","year":2024},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.884359Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:e377e47475c88134c71c1fe57c15f9f81167fa078199c312798b3aa859f3a844","observation_id":"2e4a3999-6740-4a68-835d-bb36f5c85d52","resolution":{"observed_at":"2026-08-09T18:23:22.296657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:23:21.888075Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.888075Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:e317ecd04331fe430620c0f3707a766d65597dd85ad2a84a80414636a1cbcf53","observation_id":"8488df3c-1eac-45a8-b3c2-a98467bbca54","resolution":{"observed_at":"2026-08-09T18:23:21.888075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10191","last_updated":"2024-03-19T14:09:31Z","snapshot_observed_at":"2026-08-14T06:22:14.613938Z","submitted_at":"2024-01-18T18:25:29Z","title":"Divide and not forget: Ensemble of selectively trained experts in Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10191","snapshot_observed_at":"2026-08-09T18:23:21.891173Z","title":"Divide and not forget: Ensemble of selectively trained experts in continual learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.891173Z"},"links":{"cited_paper":"/paper/2401.10191","citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:6145481630c8d3d3b31d0e896ba03fd5222e914a65188d9de77fdb8c7ca8fe0d","observation_id":"a2560474-ff1d-4539-b0ed-0f5dd7076423","resolution":{"observed_at":"2026-08-09T18:23:21.891173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.08731","last_updated":"2020-04-02T05:40:29Z","snapshot_observed_at":"2026-08-14T08:06:00.989531Z","submitted_at":"2019-11-20T06:43:41Z","title":"Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.08731","snapshot_observed_at":"2026-08-09T18:23:21.894847Z","title":"W., Hashimoto, T","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.894847Z"},"links":{"cited_paper":"/paper/1911.08731","citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:0c7f41c9f0abb562a2b3c7ee2bcc4f5c766028acb984c886a01ee1a0d4474912","observation_id":"e221a7a2-aa81-4902-8571-212ee1678750","resolution":{"observed_at":"2026-08-09T18:23:21.894847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1701.06538","last_updated":"2017-01-23T18:10:00Z","snapshot_observed_at":"2026-08-13T11:35:07.866136Z","submitted_at":"2017-01-23T18:10:00Z","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.06538","snapshot_observed_at":"2026-08-09T18:23:21.898392Z","title":"Outrageously large neural networks: The sparsely-gated mixture-of-experts layer","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.898392Z"},"links":{"cited_paper":"/paper/1701.06538","citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:577a4e315b18bdbd55633bad07993f3bc2517ed343659cf0b91ae920cafa6456","observation_id":"ca5834b1-f8a6-4975-a247-d19134946bde","resolution":{"observed_at":"2026-08-09T18:23:21.898392Z","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":"2026-08-09T18:23:22.262761Z","title":"Layer-parallel training of residual networks with auxiliary variable networks","venue":null,"work_id":"10918fa9-eebe-4d45-96c0-492084e52f28","year":2024},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.902090Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:73987934de2af6cffcb84cccefc4f24db202778c1823791f16aa1a8836590056","observation_id":"bf85e0db-32d1-47e2-8571-d62ff6a02a9d","resolution":{"observed_at":"2026-08-09T18:23:22.267998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T18:23:22.247725Z","title":"A., Branche, A","venue":null,"work_id":"5b9eba41-2934-4376-9d55-3458129bff75","year":2023},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.905300Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:511e0866d572b8646717424025ef38a793eb11d9d367524e2763ad1e93b47a74","observation_id":"14528ea3-8552-4f36-a7fa-e9f8e59a9947","resolution":{"observed_at":"2026-08-09T18:23:22.252468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T18:23:22.230771Z","title":"Fairseg: A large-scale medical image segmentation dataset for fairness learning using segment anything model with fair error-bound scaling","venue":null,"work_id":"20881f25-ceda-4d82-a6ca-35bdfc853e08","year":2024},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.908365Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:847bae3b099a3b8e2874c54f30a1461d79e64f0fd80703331d94900c7e9c9b06","observation_id":"2c159290-94ff-4d82-81e4-925806600fbb","resolution":{"observed_at":"2026-08-09T18:23:22.236653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:23:21.912418Z","title":"M., Huang, H., Khan, M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.912418Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:dfc4816ef0b5b896c06c9d18e89106275c9b8e254c0559ac824f73db6a1ac5d4","observation_id":"fa992d86-70d9-4a76-8a4b-ec326afc7f0a","resolution":{"observed_at":"2026-08-09T18:23:21.912418Z","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":"2026-08-09T18:23:21.915614Z","title":"The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.915614Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:704aae8c799a93df4be901dd99cfc85acd116819cef898fb2fb5cd528d43c721","observation_id":"c17d0c8c-43b1-4686-b7e4-6c92b139b581","resolution":{"observed_at":"2026-08-09T18:23:21.915614Z","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":"2026-08-09T18:23:21.919238Z","title":"N., Kaiser, ., and Polosukhin, I","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.919238Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:6605d55439039015e27a8cd70fd92ad8951419db71ab78450076d06e2949f382","observation_id":"32f0708e-89e0-4963-8b6d-0f66fc467f25","resolution":{"observed_at":"2026-08-09T18:23:21.919238Z","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":"2026-08-09T18:23:22.196502Z","title":"Sam-med3d-moe: Towards a non-forgetting segment anything model via mixture of experts for 3d medical image segmentation","venue":null,"work_id":"0aa449e8-6253-47a5-89f9-331838a30e2d","year":2024},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.922783Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:df7b54845bc0842b0da42f2230d3be45ccef1c4e9c0e8ecb67dc2c2568fd39c1","observation_id":"98190359-5476-4448-9a97-4595aa02d265","resolution":{"observed_at":"2026-08-09T18:23:22.200820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T18:23:21.926107Z","title":"A proposal on machine learning via dynamical systems","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.926107Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:18e84d2edf8c799fee8ebb602e2a493f07ce4283cebcd361c4c4a90eec6fded4","observation_id":"cc40f252-dd09-48ce-8b9f-71a72f4dfe84","resolution":{"observed_at":"2026-08-09T18:23:21.926107Z","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":"2026-08-09T18:23:22.176825Z","title":"Mode switching control design with initial value compensation and its application to head positioning control on magnetic disk drives","venue":null,"work_id":"1e9efd5b-1d84-40bb-b233-8c586180fe88","year":1996},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.929444Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:8a54656df3d4d20c581ee26ed6a3aa0b09d767e0eeed825a6dbc93b23b44504b","observation_id":"2d0a22ec-99d5-48cc-9041-85d63eb654e1","resolution":{"observed_at":"2026-08-09T18:23:22.180976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T18:23:22.164813Z","title":"Boosting continual learning of vision-language models via mixture-of-experts adapters","venue":null,"work_id":"17ad4a92-3e85-4d48-827a-1d5797822a6d","year":2024},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.932963Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:0e9b909c4b87edba1c678d827726b0e96f8b8c17997ffe8dc8c0f0c2ccc9abda","observation_id":"22f1dd84-bc9d-4f05-b43c-21bc42a1da5e","resolution":{"observed_at":"2026-08-09T18:23:22.169120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T18:23:22.151561Z","title":"Sliding mode switching control of manipulators based on disturbance observer","venue":null,"work_id":"a5edcc82-f1e5-4601-b6a5-045f88173b7b","year":2017},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.936224Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:bd30cf858edea0073e749a7080919e862ba63c80fee907e702e94104d87ccd14","observation_id":"818dcbd1-24d7-49a9-9e13-47285cef1652","resolution":{"observed_at":"2026-08-09T18:23:22.155986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T18:23:22.134073Z","title":"D., Visentin, M., Qiao, M., Gu, R., Ouyang, C., Liu, Y., Matthews, P","venue":null,"work_id":"cd97e7ad-3fa6-4ac8-872d-2682eb566273","year":2024},"citing_paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-09T18:23:21.939923Z"},"links":{"citing_paper":"/paper/2502.00619"},"observation_digest":"sha256:97db52911e560d3284a8e128fd9bfd3d0b34e284490a20e3ce9e007801e552e2","observation_id":"cf9640e8-6d95-448a-a320-e1fc23726681","resolution":{"observed_at":"2026-08-09T18:23:22.141161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.00619","last_updated":"2025-05-27T20:28:19Z","latest_version":2,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-13T20:44:55.928394Z","submitted_at":"2025-02-02T01:10:31Z","title":"Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":20,"verified_exact":0,"verified_fuzzy":16},"total_outbound_references":36},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2502.00619."}