{"as_of":"2026-08-17T17:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ca2aecebefad2089374a260a4fe5f773be8dac6bf3f399829bd7f2667a87dc9f","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:34:37.447519Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2507.08205/citation-record","integrity":"/paper/2507.08205/integrity","json":"/paper/2507.08205/citation-record.json","paper":"/paper/2507.08205"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:34:45.972058Z","title":"Deep learning tech- niques for medical image segmentation: achievements and challenges,","venue":null,"work_id":"585af343-9cbe-4526-a23a-d7945de71264","year":2019},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:34.642907Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:d78a388d4a47e6fa74c276770d561cffacfd8f7c03c85eb5a4c64072f1b77eb9","observation_id":"f24dba22-5d17-42c3-a379-8e0bacaac569","resolution":{"observed_at":"2026-08-06T18:34:45.978825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.949068Z","title":"A review of deep-learning-based medical image segmentation methods,","venue":null,"work_id":"656a1b41-32cf-45d7-ba61-6797ba27945a","year":2021},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:34.704069Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:2775e6fca768711b47bf28ef647aaf305db3f37d0a2701f8fd99ea050f1579e7","observation_id":"1b34e72c-ca77-479a-865c-28fca436283d","resolution":{"observed_at":"2026-08-06T18:34:45.957453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:34.748306Z","title":"Medical image segmentation using deep learning: A survey,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:34.748306Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:036aaa24f2a11a7127bc12d77bc0b79ade107ea617f00e47f3cb92f5c5656fbc","observation_id":"e2a3595b-b48d-4eea-ae60-88c24f2f319d","resolution":{"observed_at":"2026-08-06T18:34:34.748306Z","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":"2026-08-06T18:34:45.912994Z","title":"Attention is all you need,","venue":null,"work_id":"873abe1e-75c2-46d9-bb15-479526629eed","year":2017},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:34.843711Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:7052cd4cc763e8dbfea7643d8f08d81c262c8e9d9ff3075cf933061e6ecd3335","observation_id":"ec386e2f-5f41-4f37-980e-daa343a2e80d","resolution":{"observed_at":"2026-08-06T18:34:45.924083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:34.908613Z","title":"An image is worth 16x16 words: Trans- formers for image recognition at scale,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:34.908613Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:177712e532219e0db4b212d8dc84d26af6f561315b510e2f94b4c7a7bcbc7f1c","observation_id":"1860535e-d8dd-46a1-94b3-cee59294c069","resolution":{"observed_at":"2026-08-06T18:34:34.908613Z","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":"2026-08-06T18:34:45.875346Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows,","venue":null,"work_id":"7fdcadc5-fd62-42d3-8c6e-60664d17abe6","year":2021},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:34.965320Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:2c398419e540b5d78c03de93ee8db552f3a917ceeeac02fb4bc9dcf48e6ea108","observation_id":"9bea73c4-7ee3-4bb5-93ee-0efcca34d6fc","resolution":{"observed_at":"2026-08-06T18:34:45.881472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.852269Z","title":"Transformers in medical image segmentation: A review,","venue":null,"work_id":"f7c2e0cd-56fa-40ab-b5b0-a113cd542c43","year":2023},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.012267Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:740d441fc46bbcd9113fceb9b26ae31c91cd286c070dfcb41b2eeea9aed0d42b","observation_id":"d5212363-aea6-451f-aa51-a5e2d8eafd36","resolution":{"observed_at":"2026-08-06T18:34:45.859626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.824714Z","title":"UNETR: Transformers for 3D medical image segmentation,","venue":null,"work_id":"bd517d91-9819-4fd3-a40c-afcea53dadd6","year":2022},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.049911Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:739fcffb5bb37b556d7d9a1db7649202b89300a5ec9cb9420ff3015a83b527fc","observation_id":"9663e41c-bf3e-49be-b697-72abbbbba100","resolution":{"observed_at":"2026-08-06T18:34:45.830420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.800111Z","title":"Swin-Unet: Unet-like pure transformer for medical image segmenta- tion,","venue":null,"work_id":"4adbba98-d8e3-41cb-b39e-5c8443a5b6bf","year":2022},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.105610Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:152085cb856fa710fcacede64a8ac188e3e8c5ba5e27f1181eb735c9d6a34a97","observation_id":"423db5c3-2019-48d8-8c21-dde382ebfc3d","resolution":{"observed_at":"2026-08-06T18:34:45.808309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.776324Z","title":"nn- Former: V olumetric medical image segmentation via a 3D transformer,","venue":null,"work_id":"c52f7624-8457-4e20-8a7d-f69e50c4250c","year":2023},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.132749Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:9453135a54b6ed55b32b6a9ab67615b5703f8e61573d24dde75b2e9b520194c9","observation_id":"f9e85d19-32d9-4432-a401-d6d67b6874ca","resolution":{"observed_at":"2026-08-06T18:34:45.783480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.743704Z","title":"UTNet: a hybrid transformer architecture for medical image segmentation,","venue":null,"work_id":"acda79c9-06d0-4907-b9bc-2d20b00288a0","year":2021},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.171613Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:99111a7d0b0f41a2e13dcf869076bf3b4c6a27eacb6af6d047604a1938b13bc7","observation_id":"7d58a364-ab73-4291-a006-3d7bbe10918e","resolution":{"observed_at":"2026-08-06T18:34:45.755785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.713751Z","title":"CoTr: Efficiently bridging CNN and transformer for 3D medical image segmentation,","venue":null,"work_id":"3473d95d-cb79-4217-a8d7-5507190cf427","year":2021},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.204554Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:34594dc9bd0cb89dbebe653fe13557af315d8d5118b2f7c64c9d6c8d67f14578","observation_id":"4d6aa52c-cecb-4278-8870-50b84bc9cf3c","resolution":{"observed_at":"2026-08-06T18:34:45.725246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.675347Z","title":"TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers,","venue":null,"work_id":"72d52fe2-81ce-44e0-b8c8-5c4fe4648f54","year":2024},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.245822Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:759bfe07dddfd57481f7d3f7f062904481c7698124893f3728d7d89fc89d8e74","observation_id":"a09d139f-a3b8-499b-a76c-b87920202194","resolution":{"observed_at":"2026-08-06T18:34:45.695447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.651458Z","title":"FNOSeg3D: Resolution-robust 3D image segmentation with Fourier neural operator,","venue":null,"work_id":"2ce3e9a6-f686-4d6e-8afa-4d070dd5da57","year":2023},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.289211Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:9dab768e1f61ab01ca88fc3641003ffd74f9acf43fed1f0667097814de1e9991","observation_id":"e975e474-8458-4d1b-82a6-6d53dd7fef69","resolution":{"observed_at":"2026-08-06T18:34:45.657525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.625393Z","title":"Fourier neural operator for parametric partial differential equations,","venue":null,"work_id":"1741998c-54c3-4fc0-8c98-fb3c6bce506c","year":2021},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.329721Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:7e872a560b4867c4cf970c8f6bccca33a2cf41a133a7090b99494e363fd95935","observation_id":"c00ddb7e-1eeb-4749-85a8-dd2e1349f103","resolution":{"observed_at":"2026-08-06T18:34:45.633275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.599221Z","title":"Self- normalizing neural networks,","venue":null,"work_id":"8d844b65-f186-41c0-b4c7-955dd1eccac0","year":2017},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.362862Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:f0d3a08ba16736148abaab4f9f3e5b17c932888acceacf93d2cc64e47631e9d8","observation_id":"1f8ac485-e205-454d-a7e1-6b8cf74a904b","resolution":{"observed_at":"2026-08-06T18:34:45.607619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.577131Z","title":"The multimodal brain tumor image segmentation benchmark (BRATS),","venue":null,"work_id":"43ad7b89-50f8-4f97-a0a3-b2227a0f201b","year":1993},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.400208Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:76d8d59440f1717f538ffb0be6de4670434c4a716b60c70c60d33553de69e2b1","observation_id":"e5044f40-5700-4bf9-b095-ca56e4661d21","resolution":{"observed_at":"2026-08-06T18:34:45.583749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.556668Z","title":"Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features,","venue":null,"work_id":"d4ff0ecc-4a8e-40b9-abcf-546ae297c7fc","year":2017},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.452644Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:f995aff64b0349c55f268fe3585561a596c8aa292fbf904895e09a70594e3a8c","observation_id":"fd10cd94-d44e-4f98-9f61-6730477aa801","resolution":{"observed_at":"2026-08-06T18:34:45.562643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.02314","last_updated":"2021-09-12T20:26:52Z","snapshot_observed_at":"2026-08-13T21:11:51.023488Z","submitted_at":"2021-07-05T23:12:06Z","title":"The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.02314","snapshot_observed_at":"2026-08-06T18:34:35.497192Z","title":"The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor seg- mentation and radiogenomic classification,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.497192Z"},"links":{"cited_paper":"/paper/2107.02314","citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:79cd92a6eb212adc1c4bdd2cdbb1f8766343913f2ff22f852709aef030a4a9b0","observation_id":"9d41393c-724a-4b73-9729-aee5f53b984b","resolution":{"observed_at":"2026-08-06T18:34:35.497192Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01984","last_updated":"2023-07-05T02:00:14Z","snapshot_observed_at":"2026-08-16T15:18:42.910673Z","submitted_at":"2023-07-05T02:00:14Z","title":"The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01984","snapshot_observed_at":"2026-08-06T18:34:35.542587Z","title":"The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.542587Z"},"links":{"cited_paper":"/paper/2307.01984","citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:aa8a43d9cb172e28df401812e8791161009b8593edb3fdc86101d1cbd7225b38","observation_id":"ab379d83-9679-4011-a576-0f3525996ab4","resolution":{"observed_at":"2026-08-06T18:34:35.542587Z","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":"2026-08-06T18:34:45.537794Z","title":"DeepMitral: Fully automatic 3D echocardiography segmentation for patient specific mitral valve modelling,","venue":null,"work_id":"c2083570-2861-428c-a170-fd59cc9b39b4","year":2021},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.571988Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:cea78585b2c64d4be7015076c34281c5b8f739c4cd2fb6c77151fd3e5e13e3f8","observation_id":"f4c221db-87a9-4358-a08b-abf669347342","resolution":{"observed_at":"2026-08-06T18:34:45.543652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.516302Z","title":"U-Net: Convolutional net- works for biomedical image segmentation,","venue":null,"work_id":"7ea73574-268a-4a94-8b06-29dff96fa96d","year":2015},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.610011Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:34b8d2c4dc16793a8656ca860b1daa888cae1204c49ce8372a977cd83b9a8057","observation_id":"bfefd4fd-6e58-48d5-8238-b79fcfb971b0","resolution":{"observed_at":"2026-08-06T18:34:45.522669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.493886Z","title":"V-Net: Fully convolutional neural networks for volumetric medical image segmentation,","venue":null,"work_id":"da65052f-b12f-43b5-8e5f-d2fd5fef572b","year":2016},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.647878Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:5b40933b31f255d3403ebb457fb71a9faf833520e3e864ed5c80ed37ca5fd153","observation_id":"42804764-3357-416a-929c-a0da459cf026","resolution":{"observed_at":"2026-08-06T18:34:45.499721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.470604Z","title":"UNet++: A nested U-Net architecture for medical image segmentation,","venue":null,"work_id":"1ae980b2-42e1-4aee-88c1-504f8aee5187","year":2018},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.689654Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:3802c04e067d18649b0f14d7d9494d1a2fad4fe5260e392a661162b3e316cd16","observation_id":"765226b3-6db1-445e-8fc4-100b33555525","resolution":{"observed_at":"2026-08-06T18:34:45.477965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:35.727969Z","title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.727969Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:5adf5231d1aa5262013c785bedb04d33d2fe27dd0bbd37a68b6745dbd3d17b04","observation_id":"d0f16c6f-7583-49b9-963b-b6842b3be8b2","resolution":{"observed_at":"2026-08-06T18:34:35.727969Z","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":"2026-08-06T18:34:45.426683Z","title":"FDA: Fourier domain adaptation for semantic segmentation,","venue":null,"work_id":"652ed491-b001-4fa6-ad19-dd0fbfafb46f","year":2020},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.758018Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:f37f2490ecbad9df96032bdcaf93e7d3acff05fa5924c4bb278882c94c01be30","observation_id":"1d9ca776-454b-4289-b8d5-d71f09f92f74","resolution":{"observed_at":"2026-08-06T18:34:45.432634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.397450Z","title":"Global filter networks for image classification,","venue":null,"work_id":"5547ce89-5c0e-41f8-bb8d-e16fe0a79a74","year":2021},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.795754Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:47d19ec83c0cee23d6f70a3621cbea4a486830908199f37098fe9d22466bb0bf","observation_id":"32e61274-2eec-4323-a132-25dc43ec1b98","resolution":{"observed_at":"2026-08-06T18:34:45.403269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.371866Z","title":"FreMIM: Fourier transform meets masked image modeling for medical image segmentation,","venue":null,"work_id":"a5397e87-ca99-4586-98ae-cdb5e86a3a72","year":2024},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.861512Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:7e04bf9226a039d75b60b11b9f27d958631475953ccbdfeba79b17bf2a289faa","observation_id":"3a5c69ad-a324-4cff-8950-cebe0d8293f4","resolution":{"observed_at":"2026-08-06T18:34:45.377829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.353738Z","title":"Implicit neural representation in medical imaging: A comparative survey,","venue":null,"work_id":"0b34f901-8ecd-4e32-9bc2-459206c69999","year":2023},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.923892Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:fcb3573610bfab3951ff565f9f8e39991c7d8665885c8667f5fac114b4c56adf","observation_id":"8afa3ef5-af51-4077-83c2-e533b6c8e7a2","resolution":{"observed_at":"2026-08-06T18:34:45.359186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.332199Z","title":"Implicit neural representations for medical imaging segmentation,","venue":null,"work_id":"355e6f9a-7c32-4455-a159-640ba7766db0","year":2022},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:35.964367Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:9ff3f9d429b2d8420ac7f553c3aa2e419f69e21c52cf35ed3233861ec7b7ca3e","observation_id":"68def06f-821d-4b9f-a416-2deba22af8d3","resolution":{"observed_at":"2026-08-06T18:34:45.340549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.212131Z","title":"Implicit anatomical rendering for medical image segmentation with stochastic experts,","venue":null,"work_id":"26c95533-eb31-4d62-805c-9a219b357918","year":2023},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:36.024043Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:d734f9ecccc70bf45413c9672fe64c6a825a9172f6038c13c673faa8e60d0de0","observation_id":"d705ba10-8295-4743-a3ae-8f6ded436727","resolution":{"observed_at":"2026-08-06T18:34:45.297145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:45.055182Z","title":"NISF: Neural implicit segmentation functions,","venue":null,"work_id":"edef212b-1ecd-470f-b339-ae8ef71c9ddc","year":2023},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:36.104485Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:2144e6a9d1733c8725a8770873b76b55839dcf72d4fd9c94009915f9b072cfd4","observation_id":"032ee923-a84d-466e-8837-50d03339d525","resolution":{"observed_at":"2026-08-06T18:34:45.163876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:44.432574Z","title":"Aubert and P","venue":null,"work_id":"5d7f96ba-5778-433a-8aa1-7fc1466fb49e","year":2006},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:36.205383Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:59c5f7eadc6fa1a675caf932ab1e05cbe14e5f0ed51df549f935f03380a0995e","observation_id":"a49b2001-677c-461e-bd45-e99fc8c6c374","resolution":{"observed_at":"2026-08-06T18:34:44.940353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:39.882956Z","title":"Sapiro, Geometric partial differential equations and image analysis","venue":null,"work_id":"ea713e9c-07d2-4661-bdca-e368b8bce595","year":2006},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:36.285698Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:17f936a4114f437adfb7c705eb529de44ce871212ff32a9721277035a6c63cb1","observation_id":"008f42c8-330f-4420-a502-bab9208280d0","resolution":{"observed_at":"2026-08-06T18:34:40.370992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.03485","last_updated":"2020-03-07T01:56:20Z","snapshot_observed_at":"2026-08-15T17:57:12.049079Z","submitted_at":"2020-03-07T01:56:20Z","title":"Neural Operator: Graph Kernel Network for Partial Differential Equations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.03485","snapshot_observed_at":"2026-08-06T18:34:36.387291Z","title":"Neural operator: Graph kernel network for partial differential equations,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:36.387291Z"},"links":{"cited_paper":"/paper/2003.03485","citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:02b83b4b7dc2e2cbe777f243f373feefce022c9d5a68f9dccbd30fe4b3073ca5","observation_id":"043ce268-2a4d-482e-a241-f6e72fda6a6a","resolution":{"observed_at":"2026-08-06T18:34:36.387291Z","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":"2026-08-06T18:34:39.330714Z","title":"A more symmetrical Fourier analysis applied to transmission problems,","venue":null,"work_id":"d0cc1006-97d5-484a-b7f6-264f40a3510e","year":1942},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:36.490942Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:18c213e5666b53f789bfd9615edd4cffa46ad1689ec6eb7ea4224dbc0d6d69b9","observation_id":"64f61b50-4f68-4037-9870-2b17370f9908","resolution":{"observed_at":"2026-08-06T18:34:39.485199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:39.221582Z","title":"Discrete Hartley transform,","venue":null,"work_id":"621901f2-2d37-407b-a657-1109d1542a2b","year":1983},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:36.583751Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:d0299366d55bb4afca1b96d89f9d486a840742577540a03c4a40ee678eb090b9","observation_id":"fd675ae9-d8be-457e-8e72-b763f563898c","resolution":{"observed_at":"2026-08-06T18:34:39.258833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:39.088497Z","title":"The importance of phase in signals,","venue":null,"work_id":"ac3fa328-6776-40e4-888d-3f50a42db9f5","year":1981},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:36.674645Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:e342ac8b65d4004318622853ba06bbffcec9c850b20598a48e5d3095767f41e7","observation_id":"b0707556-e928-44d6-9763-50411ffbac00","resolution":{"observed_at":"2026-08-06T18:34:39.143078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:38.900518Z","title":"3D segmentation with fully trainable Gabor kernels and Pearson’s correlation coefficient,","venue":null,"work_id":"316c7e86-4326-4fb9-8c75-94d26300c5b0","year":2022},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:36.746856Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:cb9cc092cace3a4de60a587c0d79a29e06c01902159c3ff96d5c17b131612d0d","observation_id":"ffc18dd8-de76-4c97-b5e8-8e56dffc5eda","resolution":{"observed_at":"2026-08-06T18:34:38.952314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-06T18:34:36.841973Z","title":"Adam: A method for stochastic optimiza- tion,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:36.841973Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:68ac0bd162642b17384b73799495add2adabfdaf465a1a789a484107fe819eb8","observation_id":"529473cb-324b-4987-a645-8cb3f45c6bcf","resolution":{"observed_at":"2026-08-06T18:34:36.841973Z","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":"2026-08-06T18:34:38.768999Z","title":"SGDR: Stochastic gradient descent with warm restarts,","venue":null,"work_id":"b7601aa1-c310-4ca2-aeab-9438a4392bb9","year":2017},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:36.906996Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:f4438a20866b11356cb543f34423488633d9fe4c57b1ed84aa3f70d3352e1d38","observation_id":"3e344fd2-7ac8-4ef9-9ca2-aa6f5b5691bd","resolution":{"observed_at":"2026-08-06T18:34:38.836733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:38.663496Z","title":"BraTS 2023 Challenge,","venue":null,"work_id":"4a253858-f0af-4920-982b-66d05e343fd3","year":2023},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:37.006774Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:a8d9eaa9bd4c9b487796397b5cfdac7acd8646aa78515310d69a98a8a7d7aeff","observation_id":"24de7945-11e4-4a56-8ac9-ae98b35049be","resolution":{"observed_at":"2026-08-06T18:34:38.717279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:38.517396Z","title":"The 2023 Kidney and Kidney Tumor Segmentation Challenge,","venue":null,"work_id":"a93f80a6-ff58-443f-84b9-9731fbf7d19e","year":2023},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:37.094089Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:d7fcf3e959cdfc2cb925c1c2f4bf0cfd9c297e74ab99f4188f2428970d04f4e0","observation_id":"bff25a08-624f-4445-bf83-dfb13c76c7b0","resolution":{"observed_at":"2026-08-06T18:34:38.595786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:38.262537Z","title":"MVSEG2023,","venue":null,"work_id":"3c44499c-42d7-4b18-ad6e-06a3e1330df2","year":2023},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:37.182788Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:4b9c2a3553877f2d8b69891ee1593e8b4b6c2e9a821a2d3f5ff65048f917970c","observation_id":"e5fe692a-5149-4fb5-8cae-897fc117f7fd","resolution":{"observed_at":"2026-08-06T18:34:38.382574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:38.009155Z","title":"3D segmentation with exponential logarithmic loss for highly unbalanced object sizes,","venue":null,"work_id":"23db0881-1501-4ed0-9a7a-47cb5dab32df","year":2018},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:37.296461Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:669a06abf5298b2cbded271ad1d991c1e7a5d8e5b4b55efcaad130b22253524b","observation_id":"dddc4c52-92a2-4f77-8266-aed10b714aa7","resolution":{"observed_at":"2026-08-06T18:34:38.139619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T18:34:37.755386Z","title":"Variability in image quality and radiation dose within and across 97 medical facilities,","venue":null,"work_id":"afbf1c06-03bf-497a-adad-a44d2387c11b","year":2021},"citing_paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T18:34:37.447519Z"},"links":{"citing_paper":"/paper/2507.08205"},"observation_digest":"sha256:c0ed41d4ad8f9dc0d7a58a77c1b947e06b86db83a53f7256e2609ec0d8b1d2d0","observation_id":"6105f3ef-b6a8-4f28-8bf5-37fcbeb64c9e","resolution":{"observed_at":"2026-08-06T18:34:37.861143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.08205","last_updated":"2025-07-10T22:33:19Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T12:34:17.780892Z","submitted_at":"2025-07-10T22:33:19Z","title":"HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":39},"total_outbound_references":46},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2507.08205."}