{"as_of":"2026-08-16T11:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6602168210c57a7ec85ff337f5c2ffe781d71eda04479615ef103c8fbbd1a7f9","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:16:04.266112Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T22:22:01.730605Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-05T22:22:01.919275Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"cited_work":{"arxiv_id":"2505.01755","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.01755","snapshot_observed_at":"2026-08-05T22:22:01.919275Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","venue":"eess.IV","work_id":"9b935992-691d-432d-a0cd-a4bcaa53d4cb","year":2025},"citing_paper":{"arxiv_id":"2508.07140","last_updated":"2025-08-21T01:36:23Z","snapshot_observed_at":"2026-08-12T21:50:41.704947Z","submitted_at":"2025-08-10T02:00:45Z","title":"CMAMRNet: A Contextual Mask-Aware Network Enhancing Mural Restoration Through Comprehensive Mask Guidance","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T22:22:01.730605Z"},"links":{"cited_paper":"/paper/2505.01755","citing_paper":"/paper/2508.07140"},"observation_digest":"sha256:d26d5f4c7e5387b93573852fac5b2ed2bcb3f24639a61d337c85679e400dc0e2","observation_id":"5e56089d-2aa5-48ad-bf94-f3d4e36c1bc6","resolution":{"observed_at":"2026-08-05T22:22:01.923656Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.01755","snapshot_observed_at":"2026-07-11T16:30:23.146091Z","title":"arXiv preprint arXiv:2505.01755 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.04608","last_updated":"2026-07-06T02:28:30Z","snapshot_observed_at":"2026-08-02T15:59:40.053991Z","submitted_at":"2026-07-06T02:28:30Z","title":"Integrated Forward-Inverse Network for Lensless Image Reconstruction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-11T16:30:23.146091Z"},"links":{"cited_paper":"/paper/2505.01755","citing_paper":"/paper/2607.04608"},"observation_digest":"sha256:c98b0784b7038c1b234bea2d7f5eb5927a610c64e2e73c0770d06d8e7cfb9a51","observation_id":"3cbd5da9-5592-4c6c-a836-b06ba2104f5d","resolution":{"observed_at":"2026-07-11T16:30:23.146091Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2505.01755/citation-record","integrity":"/paper/2505.01755/integrity","json":"/paper/2505.01755/citation-record.json","paper":"/paper/2505.01755"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:16:05.306174Z","title":"Diffusercam: lensless single-exposure 3d imaging","venue":null,"work_id":"b84b7b96-18b2-4608-bbde-7be59b954c52","year":2017},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:03.996291Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:98fabdc9d9661675bfa199f2de887b4529b478abea7b605be30ce7cd613b9a60","observation_id":"da96689a-7b09-4890-9c66-f1d7ca01cfb1","resolution":{"observed_at":"2026-08-16T04:16:05.317227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:05.074525Z","title":"Cross-view geo-localization via learning correspondence semantic similarity knowledge","venue":null,"work_id":"4c1287d8-a3aa-4f53-b774-ad2530584314","year":2025},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.079355Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:d7f5841732d3b6b93192c16b4b706640c466adffb6206ee92c50bebdb6da5ebd","observation_id":"e65dba38-9923-4781-aa66-72e36ae180bd","resolution":{"observed_at":"2026-08-16T04:16:05.081085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.966920Z","title":"Iman: An adaptive network for robust npc mortality prediction with missing modalities","venue":null,"work_id":"42f9bb47-6406-4797-8bcf-5766457839cf","year":2024},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.104390Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:2ee978b109c87789d75e89507c3eff9220ec6a92d11e4cc9df659a9d3ee46f6b","observation_id":"3f6a88b6-ee5c-4a39-b474-777804b409ef","resolution":{"observed_at":"2026-08-16T04:16:04.976712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.931164Z","title":"Towards photorealistic reconstruction of highly multiplexed lensless images","venue":null,"work_id":"85a60668-5aa5-40fd-a614-e925b179b9f0","year":2019},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.110810Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:fe3cfd6aea6342b9594e153213204b99b77e40be0b4ff41b55a5c8a613389bc5","observation_id":"65307288-36a9-4a75-80f9-7406254be158","resolution":{"observed_at":"2026-08-16T04:16:04.941046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.873418Z","title":"Unrolled primal- dual networks for lensless cameras","venue":null,"work_id":"3fdb2b0f-d2ff-4a8b-97f9-cbfe2b9d4c54","year":2022},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.127980Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:5871bab7bbf527dd123fabde9ab8a5f73ed51ef168b69dc48715941b79a12296","observation_id":"d30de6f9-64b6-4982-bc47-c76d4a0c7bba","resolution":{"observed_at":"2026-08-16T04:16:04.881566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.793808Z","title":"Depth- aware test-time training for zero-shot video object seg- mentation","venue":null,"work_id":"f8470d43-eac0-4b32-9cef-233651af095d","year":2024},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.144945Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:7b9c199de7a3f5f976fa47cd767600520ef7af1fd48e062afc1bcb09a42ecb14","observation_id":"ae934e0e-59df-47b8-a925-fea644f4e4c8","resolution":{"observed_at":"2026-08-16T04:16:04.803038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.744938Z","title":"Devi- gnet: High-resolution vignetting removal via a dual aggre- gated fusion transformer with adaptive channel expansion","venue":null,"work_id":"c3e704ae-16ba-41d6-a7ee-ec7cf1207c47","year":2024},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.153164Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:b4d91ba6da3896a3cbd5a91e60aa17c8d77ffb3db1a097c82d50eb524f469329","observation_id":"ac43dbe0-4c99-4a05-83f9-a0c74444606f","resolution":{"observed_at":"2026-08-16T04:16:04.753329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.705192Z","title":"Learned reconstructions for practical mask-based lensless imaging","venue":null,"work_id":"dd8539d8-2ee9-4128-a85a-0d898a83ec1d","year":2019},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.159821Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:6a3a2c4abf94dd20268677c3d4f07b80188aedf34b4481c2640cf34ba9cb4e37","observation_id":"b187c886-d60f-4a12-8df6-1e570f2445ff","resolution":{"observed_at":"2026-08-16T04:16:04.713211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.666887Z","title":"Introductory lectures on convex programming volume i: Basic course","venue":null,"work_id":"9d594517-f841-496d-bd6d-95982407c48a","year":1998},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.167510Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:cda83290714624b89b2d1af410383b2d2346f956181e136c87083029a96c20fd","observation_id":"a758ab9c-3a43-4e22-9c43-f82a00407fa3","resolution":{"observed_at":"2026-08-16T04:16:04.674694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.605492Z","title":"Robust lensless image reconstruction via psf estimation","venue":null,"work_id":"dcd76949-129e-4f40-a0e4-cb1b08232330","year":2021},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.187396Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:d1ddeec2949222e0064c3679a8e771b123874eb057b9abda006ca19551d4ae6b","observation_id":"d93933a7-aa48-4a35-8a97-4aa0ca7066b4","resolution":{"observed_at":"2026-08-16T04:16:04.614258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.473980Z","title":"Extrapolation, interpola- tion, and smoothing of stationary time series: with engi- neering applications","venue":null,"work_id":"1cdf7351-5e2a-4a6f-b679-6d4f55a8f06a","year":1949},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.214725Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:9dd03dd67c642f5ee9655191594c56a944dd271c40bda2cb4d9f71a5206f1035","observation_id":"9578e2be-1cbd-49d1-a60f-db25c68e1b43","resolution":{"observed_at":"2026-08-16T04:16:04.480952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.05578","last_updated":"2024-10-08T00:26:29Z","snapshot_observed_at":"2026-08-12T22:28:40.400948Z","submitted_at":"2024-10-08T00:26:29Z","title":"Swift Sampler: Efficient Learning of Sampler by 10 Parameters","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.05578","snapshot_observed_at":"2026-08-16T04:16:04.237008Z","title":"Swift sampler: Efficient learning of sampler by 10 param- eters","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.237008Z"},"links":{"cited_paper":"/paper/2410.05578","citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:6a3f39df2de670cc235dc8d725da6669654d2dcc90da7c38c773b498e61f7792","observation_id":"59fe1d40-38d9-48d9-b6bb-d880cd4e9740","resolution":{"observed_at":"2026-08-16T04:16:04.237008Z","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":"2026-08-16T04:16:04.425246Z","title":"Robust reconstruction with deep learning to handle model mismatch in lensless imaging","venue":null,"work_id":"70c41063-f0bc-466a-93fa-673aa7e58ee4","year":2021},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.245692Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:148ac3b18a93e1790ebbb26b3443e00625c39c464c5bf3fac1ca6fb8e8c1efdf","observation_id":"07a264ee-81f1-4a4b-8be6-101b9f7a39f6","resolution":{"observed_at":"2026-08-16T04:16:04.431528Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.392862Z","title":"Smaformer: Synergistic multi- attention transformer for medical image segmentation","venue":null,"work_id":"c6fcfd76-ad0c-4e76-8d1a-48abb675ed4e","year":2024},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.252448Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:c1f0bf8896eaabba31e8305a0654dece7637d3b8348d8c5203f43d588fd46089","observation_id":"7ba97297-fb34-42eb-8145-0098baaa1400","resolution":{"observed_at":"2026-08-16T04:16:04.406510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.364201Z","title":"Docdeshadower: Frequency-aware transformer for document shadow removal","venue":null,"work_id":"9c66eec2-8d68-4fb4-b9db-e6aca5b88b47","year":2024},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.266112Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:c94450de8ecd8cfc08b7783515878cbddf8fe9324c669ed24754bdb6338de8cc","observation_id":"6a46ffd5-db07-4b43-9137-d940db1b7438","resolution":{"observed_at":"2026-08-16T04:16:04.373604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.448241Z","title":"Tcia: A transformer-cnn model with illumination adaptation for enhancing cell im- age saliency and contrast","venue":null,"work_id":"f28d63d9-8210-4cb6-a22a-c5b33a15447b","year":2025},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":1949,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.222600Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:74b6faea67000e42f7b694aadba90a71a562f33f98d3380470f7c64cc122276d","observation_id":"fa2b5329-ad46-4869-8788-1c938be6ebc4","resolution":{"observed_at":"2026-08-16T04:16:04.456478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.640678Z","title":"Harmonicnerf: Geometry- informed synthetic view augmentation for 3d scene re- construction in driving scenarios","venue":null,"work_id":"ebb2cecf-8ce3-4cfe-a7a6-d8f467d79cf5","year":2024},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":1998,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.177436Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:20c76048334d5f6779b92752cd16b404c6485f8bb8198315b9b591875af325be","observation_id":"4def5827-c23c-4b21-8547-625c3e54a79d","resolution":{"observed_at":"2026-08-16T04:16:04.648318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.539666Z","title":"An admm algorithm for a class of total variation regularized estimation problems","venue":null,"work_id":"e9f47d75-906f-4804-85f3-4ba8bf5b089d","year":2012},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2001,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.199034Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:22f083ee1bb028519cc29b3bfc3fc91c942a5e200119b5a71a05bcf761250dfd","observation_id":"f6520dd8-ef8a-4afa-bf66-f8f06f4b8a1f","resolution":{"observed_at":"2026-08-16T04:16:04.549805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:05.004657Z","title":"Dual-hybrid at- tention network for specular highlight removal","venue":null,"work_id":"e2e05b43-e86e-452e-90c6-5e05a9d966fa","year":2024},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2005,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.092160Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:905baef2b67c404a9d3574bf8b9901aac76ca85984d95ad4f8318c22865041ae","observation_id":"14a5efd2-af45-471b-b950-f219c31d864e","resolution":{"observed_at":"2026-08-16T04:16:05.016071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:05.213718Z","title":"Phlatcam: Designed phase-mask based thin lensless cam- era","venue":null,"work_id":"8fc4a3e4-4bca-48f8-9a7e-2c1e0ba88d46","year":2020},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.029895Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:5e75b3a3ed4f6fffb6e2faa95e266ffff4a628666086b630cf14378d74fe004a","observation_id":"4bd50484-d0d2-4f5f-8052-4b2046da5058","resolution":{"observed_at":"2026-08-16T04:16:05.224327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:05.150002Z","title":"Phocolens: Pho- torealistic and consistent reconstruction in lensless imag- ing","venue":null,"work_id":"4f23ba5b-c53f-4fdf-92c7-54f5590d0509","year":2024},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.051449Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:2bd97eeca4950e95960fb129388211414f6abb0b17fefe1cf409fcc32e893bc6","observation_id":"b1ac4228-7267-427b-9c81-405110ea1086","resolution":{"observed_at":"2026-08-16T04:16:05.161401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.500112Z","title":"Global convergence of admm in nonconvex nonsmooth optimization","venue":null,"work_id":"f4834701-e6f9-4f97-90be-64ec9a9fe221","year":2019},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.205820Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:2dd1384782b659a752fc45519c4f15b9acd9be43d4701d018f18a2560c7aace1","observation_id":"6cef2141-0923-456d-82ec-94f47c82c5dd","resolution":{"observed_at":"2026-08-16T04:16:04.510760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:05.245786Z","title":"Flatcam: Thin, lensless cameras using coded aperture and computation","venue":null,"work_id":"c7e59fe9-f647-464c-ac04-cafdd0193e5d","year":2016},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.020138Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:8ca998ceff5f004ff0d86a8f7868806aa5860221787bd1184c84699189d22c14","observation_id":"f4e9c313-0095-4931-b955-17794b3687e5","resolution":{"observed_at":"2026-08-16T04:16:05.253012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:05.268973Z","title":"Flatcam: Replacing lenses with masks and computation","venue":null,"work_id":"6d5ebf5d-12b4-41fe-bbe6-e1cf619c6868","year":2015},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.012235Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:7f1de701ba277065cea5ece5c427ba7b18736d13e41fea0b7ac5b0efa937267f","observation_id":"26c9df25-c63b-4cba-a492-02036da24982","resolution":{"observed_at":"2026-08-16T04:16:05.283220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.900947Z","title":"Flatnet: Towards photorealistic scene reconstruc- tion from lensless measurements","venue":null,"work_id":"4f191857-6452-4a9e-a039-0a781fa90b21","year":2020},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.121000Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:1ea1ce710f7fba87b6bb783a52f4d120f5f433f3cab76c53ecdd5574d0c0c23c","observation_id":"fb53c1ab-69be-49ce-a8f3-fadbb5f7f113","resolution":{"observed_at":"2026-08-16T04:16:04.908235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:05.182365Z","title":"Distributed op- timization and statistical learning via the alternating direc- tion method of multipliers","venue":null,"work_id":"54f05b0b-ef85-43bf-bf29-eca55eed161e","year":2011},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.038672Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:0afc66ea042273f91b441e06166a37db94a5d934e3796b3c4444871401a0567e","observation_id":"ea16b84d-d23e-4d61-be28-5d6d3e3eae2c","resolution":{"observed_at":"2026-08-16T04:16:05.190467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.568443Z","title":"Thin observation module by bound op- tics (tombo): concept and experimental verification","venue":null,"work_id":"b1724c2b-8ece-446d-b2a6-00da1774afa8","year":2001},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.192206Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:c6d2dca19e647b046dfe9b6be15fe5154a339e4bb766ff27ed05c6a9374b62b2","observation_id":"f40831e7-eea6-4179-9ec0-a3299a84dc80","resolution":{"observed_at":"2026-08-16T04:16:04.582684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:04.837549Z","title":"Accelerated proximal gradient methods for nonconvex programming","venue":null,"work_id":"147aa418-977a-40de-b178-8cbf9917797b","year":2015},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.137627Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:b0a9a77d7edef4c60ce51e4f1c5002610299606c7bafa851fc5d9698a23d65c2","observation_id":"853eacad-c60e-454e-83df-b4b5d7ba59b3","resolution":{"observed_at":"2026-08-16T04:16:04.848510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:05.096458Z","title":"Medprompt: Cross-modal prompting for multi-task medical image translation","venue":null,"work_id":"d3bb3132-d0a2-4616-aafa-0a04ad8d72d0","year":2024},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.069402Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:34f80045247f1de68b315170de50c5ae3f5c8ebcb1cbe96466953b2c70ce0882","observation_id":"64dc603c-0a4d-4bed-8070-b59d85e3aca1","resolution":{"observed_at":"2026-08-16T04:16:05.104434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:05.124048Z","title":"Brain diffuser: An end-to-end brain image to brain network pipeline","venue":null,"work_id":"4302861f-f7ca-4d40-9f43-89cfd502effd","year":2023},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.062394Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:f99c45a5788fe244ab33b4c3a0d747b19af862c87d9437d59ce6e656a1fa3ee7","observation_id":"6a920fd8-094e-4a3c-b06f-4c79e754c3b6","resolution":{"observed_at":"2026-08-16T04:16:05.133022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T04:16:05.054571Z","title":"Introduction to Fourier optics","venue":null,"work_id":"1fac8486-0512-46b5-b662-19bc6c396eba","year":2005},"citing_paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-16T04:16:04.084938Z"},"links":{"citing_paper":"/paper/2505.01755"},"observation_digest":"sha256:8690d5f31c3e84c775464604195c0d4f43ddaf101c306a327981994be0b5a746","observation_id":"6b6c00b8-0662-487b-b5bf-9f9e0fc34e99","resolution":{"observed_at":"2026-08-16T04:16:05.059988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.01755","last_updated":"2025-05-03T09:11:52Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-16T04:09:04.991693Z","submitted_at":"2025-05-03T09:11:52Z","title":"LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":0,"verified_fuzzy":30},"total_outbound_references":31},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 2 inbound Pith citation observations for arXiv:2505.01755."}