{"as_of":"2026-08-17T15:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3f9854505ac6f4d52b845b7ca00cab57c8afb6dff6add8ad163962ac70371807","coverage":[{"denominator":67,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":67,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T16:44:21.175959Z","state":"measured"},{"denominator":67,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":67,"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/2508.21529/citation-record","integrity":"/paper/2508.21529/integrity","json":"/paper/2508.21529/citation-record.json","paper":"/paper/2508.21529"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T16:44:21.828096Z","title":"Artificial neural network approach for multi- phase segmentation of battery electrode nano-CT images,","venue":null,"work_id":"62147f7b-c125-4cf3-be98-0442581fc42b","year":2022},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:20.953703Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:9f6d0cb1d8f3bc117e4b7d96bc099a3fbcb88c93ef14bc467a9f04f461bbb7ff","observation_id":"6f9aa5e2-2195-4dec-bff7-b092b8c10fa3","resolution":{"observed_at":"2026-08-15T16:44:21.831392Z","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":"2026-08-15T16:44:21.809943Z","title":"Methods—Kintsugi Imaging of Battery Electrodes: Distinguishing Pores from the Carbon Binder Domain using Pt Deposition,","venue":null,"work_id":"5f62956f-47f3-4719-978d-ea6a8fc474e9","year":2022},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:20.960468Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:e174812c56e83904405fd2fe14d2c9047a298a3b5024ef7de9787724151500b1","observation_id":"ca8b3392-8ecd-4fb5-a686-6f1a078f3fdd","resolution":{"observed_at":"2026-08-15T16:44:21.813265Z","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":"2026-08-15T16:44:21.791503Z","title":"Au- tomated segmentation of large image datasets using artificial intelligence for microstructure characterisation and damage analysis,","venue":null,"work_id":"36309e15-574b-46bc-9951-0b0c942319df","year":2024},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:20.967492Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:9c9e52197453c3c5cd384ab850d1436366ccf5f64bfd09544c3c5aa29c32b001","observation_id":"fdbdd64f-d038-4958-a91c-51f45ee66500","resolution":{"observed_at":"2026-08-15T16:44:21.794862Z","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":"2026-08-15T16:44:21.782253Z","title":"Guiding the Design of Heteroge- neous Electrode Microstructures for Li-Ion Bat- teries: Microscopic Imaging, Predictive Model- ing, and Machine Learning,","venue":null,"work_id":"5853e064-3e91-4c99-991b-d772ef018e88","year":2021},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:20.970681Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:67e43d24220548df0c965c5c0aa286f972eb5872ea2ccac9cb751df4fe9bf089","observation_id":"883c3554-f173-44da-a3d6-85a27ca16439","resolution":{"observed_at":"2026-08-15T16:44:21.785612Z","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":"2026-08-15T16:44:21.772720Z","title":"Microstructure segmentation with deep learn- ing encoders pre-trained on a large microscopy dataset,","venue":null,"work_id":"1e0e52a7-796b-4a83-98de-cf1358ef642a","year":2022},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:20.974062Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:ec52ee3ab7d60367044c7378cd11a605b13b95cf663a13f701e00953f07216e7","observation_id":"e399500f-e483-435f-9935-42d2e9e1fc1b","resolution":{"observed_at":"2026-08-15T16:44:21.776335Z","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":"2026-08-15T16:44:21.818967Z","title":"Quantification and modeling of mechanical degradation in lithium-ion batteries based on nanoscale imaging,","venue":null,"work_id":"730f0bd2-cc69-42bb-b795-b510029e7602","year":2018},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:20.976962Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:23db495258c3c6b3a5266fa15e42146c435db9f3a1a051f8b7bc5bda09d232f3","observation_id":"40658e1e-bd00-46a4-be36-643ea8d97267","resolution":{"observed_at":"2026-08-15T16:44:21.822202Z","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":"2026-08-15T16:44:21.763516Z","title":"TauFactor: An open-source appli- 11 Docherty et al. Feature Upsampling & Micrograph Segmentation Preprint cation for calculating tortuosity factors from to- mographic data,","venue":null,"work_id":"d27bd770-8291-4169-bac3-aee7236c9245","year":2016},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:20.979865Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:8a5d569a2fac717b450b9b88a0c51bfec0c3afdb12c3d686e1a5dc79c943a54b","observation_id":"9cdfc7f8-9707-43fe-be1e-7cfe298a16d4","resolution":{"observed_at":"2026-08-15T16:44:21.766841Z","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":"2026-08-15T16:44:21.753782Z","title":"Taufactor 2: A gpu accelerated python tool for microstruc- tural analysis,","venue":null,"work_id":"62e110ff-4410-433b-a012-a32eded8187b","year":2023},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:20.982811Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:3ad586d3b8dcca5aab710e9dac3584b16201e668d166cef7e1f4133bd7051935","observation_id":"4fe8e8d9-6355-4c6d-b0e9-91543268f60d","resolution":{"observed_at":"2026-08-15T16:44:21.757470Z","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":"2026-08-15T16:44:21.743959Z","title":"X-ray computed tomography,","venue":null,"work_id":"3f54a2f7-5f69-4b98-99c9-bdb1a73870a0","year":2021},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:20.985714Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:64517e538f272359ac9189556c5b707867d2d894ca2e938c59b58e9d00aaa4fb","observation_id":"4b330177-140a-4be6-8e4c-84d57bd016c7","resolution":{"observed_at":"2026-08-15T16:44:21.747176Z","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":"2026-08-15T16:44:21.734299Z","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation,","venue":null,"work_id":"0bb7c734-5f6b-4e8e-a3cc-5db7e6e0aed5","year":2015},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:20.988843Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:ca5172ff730e93eb0ce5907e7f5d60379fa138f40796a8d6890efc24d48703e1","observation_id":"cd046449-b5f7-46e1-ad2a-0c53cd605f32","resolution":{"observed_at":"2026-08-15T16:44:21.737607Z","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":"2026-08-15T16:44:21.724905Z","title":"Graph-constrained Contrastive Regularization for Semi-weakly V olumetric Seg- mentation,","venue":null,"work_id":"e0e16a4d-a9f4-4319-bdfb-4d4d9b56c4a3","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:20.991941Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:ebff40ced0987be5ee1f52d0a8cb0c481931d1349a9759d2ec51a28e166fd762","observation_id":"47bdd7a6-54b4-45af-8303-7d6363ef1f0f","resolution":{"observed_at":"2026-08-15T16:44:21.728300Z","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":"2304.02643","last_updated":"2023-04-05T17:59:46Z","snapshot_observed_at":"2026-08-08T05:14:59.435033Z","submitted_at":"2023-04-05T17:59:46Z","title":"Segment Anything","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.02643","snapshot_observed_at":"2026-08-15T16:44:20.995009Z","title":"Segment anything,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:20.995009Z"},"links":{"cited_paper":"/paper/2304.02643","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:f5f5e6cfc4878600e2f1fb3178cf005c8fffb545daa2ab69cad1042cb8e1d25c","observation_id":"68dc4126-e649-4c45-abbd-aafe8da04c1f","resolution":{"observed_at":"2026-08-15T16:44:20.995009Z","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":"2026-08-15T16:44:21.715437Z","title":"SAMBA: A Trainable Segmenta- tion Web-App with Smart Labelling,","venue":null,"work_id":"ee05aa85-3ef2-4be5-a9c7-d34bf8d7b52c","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:20.998929Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:2a8a9b9648ccd26290568600d4f7afa5cb4919ef46a223b89ed112e97b637838","observation_id":"b04b7ed9-1adc-4298-868b-ba72ca7b937e","resolution":{"observed_at":"2026-08-15T16:44:21.718652Z","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":"2026-08-15T16:44:21.696748Z","title":"Cellpose 2.0: how to train your own model,","venue":null,"work_id":"003c3084-8d8c-4d19-8b96-d69e01c5e6dc","year":2022},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.005221Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:acd029a22c11e3adb5d0b2d5f8a03bc8190d7f1ca0ca430d0a9765da78b6465a","observation_id":"6a08a985-60f6-464a-b874-042581660894","resolution":{"observed_at":"2026-08-15T16:44:21.700321Z","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":"2026-08-15T16:44:21.008189Z","title":"A Threshold Selection Method from Gray-Level Histograms,","venue":null,"work_id":null,"year":1979},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.008189Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:d569c460f58528db8753cc6a918c4c2cc593ea51fedffa6af0e14dbc9099a3fc","observation_id":"36b82d75-ad0f-4db3-8364-46fd17040bcb","resolution":{"observed_at":"2026-08-15T16:44:21.008189Z","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":"2026-08-15T16:44:21.682121Z","title":"Algorithm as 136: A k-means clustering algorithm,","venue":null,"work_id":"e9bf5964-c8ed-4a1d-9a85-852c0ccc023a","year":1979},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.011121Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:181e03a86720be94ef7bb1304e72b73fdcb7885ee352d3a0a84a644f8b1a7188","observation_id":"6554f6e1-db8b-4b88-b016-76b319bb5984","resolution":{"observed_at":"2026-08-15T16:44:21.685459Z","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":"2026-08-15T16:44:21.672763Z","title":"Topographic distance and watershed lines,","venue":null,"work_id":"12346842-45c1-4365-9322-ce49fb7b89d1","year":1994},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.014128Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:880345706457720008353d3e0168a9a4226b43539f20ef7d792e3089941705df","observation_id":"e2934b59-d9aa-432a-a648-06b0c23e450b","resolution":{"observed_at":"2026-08-15T16:44:21.676009Z","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":"2007.07350","last_updated":"2020-08-19T03:30:03Z","snapshot_observed_at":"2026-08-16T13:47:20.128723Z","submitted_at":"2020-07-14T20:55:10Z","title":"A Generalization of Otsu's Method and Minimum Error Thresholding","version":3},"cited_work":{"arxiv_id":"2007.07350","doi":null,"metadata_source":"pith","pith_arxiv_id":"2007.07350","snapshot_observed_at":"2026-08-15T16:44:21.396622Z","title":"A Generalization of Otsu's Method and Minimum Error Thresholding","venue":"cs.CV","work_id":"f7947195-3846-4fd6-b02e-f9ec3879f9f5","year":2020},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.017777Z"},"links":{"cited_paper":"/paper/2007.07350","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:1343e4d0d2b6faf059e464b8da4c9262b7cdacad9c04bc33725f481b65c48c36","observation_id":"fbd0381b-442e-48d9-ab2d-d38b255d416d","resolution":{"observed_at":"2026-08-15T16:44:21.400176Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-15T16:44:21.663219Z","title":"Resolving the Discrep- ancy in Tortuosity Factor Estimation for Li-Ion Battery Electrodes through Micro-Macro Mod- eling and Experiment,","venue":null,"work_id":"99f2d274-d136-4bb8-a93a-f8e0ecf93722","year":2018},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.021403Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:26ce5c11c42abbd9ed82d30b3d4303b3afed025274ce1c765ab62257bb5652e0","observation_id":"2d8f8bae-1e19-4420-9066-714b0dda8856","resolution":{"observed_at":"2026-08-15T16:44:21.666834Z","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":"2026-08-15T16:44:21.653592Z","title":"Trainable Weka Segmen- tation: a machine learning tool for microscopy pixel classification,","venue":null,"work_id":"5a184a47-3fe2-4b12-9263-83ad29d9a6ed","year":2017},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.024152Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:8d770080f01b218513eb903d95f6dee3290681d4e2e2cf2f3fc38250370df9c3","observation_id":"02fedf77-eb49-483b-9133-d455c79da541","resolution":{"observed_at":"2026-08-15T16:44:21.656909Z","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":"2026-08-15T16:44:21.643785Z","title":"ilastik: interactive machine learn- ing for (bio)image analysis,","venue":null,"work_id":"f58e623a-8908-4d3b-9cbb-ba1ac26654d6","year":2019},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.027618Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:2170a12a8afb0041766a2dc1c93107efb1e71dcfcd983715110b7ea767be0069","observation_id":"a293df3e-aeed-4932-a879-43fac1160774","resolution":{"observed_at":"2026-08-15T16:44:21.647178Z","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":"2410.19836","last_updated":"2025-08-06T10:05:27Z","snapshot_observed_at":"2026-08-16T13:47:19.828921Z","submitted_at":"2024-10-20T13:01:53Z","title":"Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.19836","snapshot_observed_at":"2026-08-15T16:44:21.030716Z","title":"Upsampling dinov2 features for un- supervised vision tasks and weakly super- vised materials segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.030716Z"},"links":{"cited_paper":"/paper/2410.19836","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:d1f8fffb0296f29f3301c0e8111edc4988a7094ceef70cb828915630858ddfaa","observation_id":"35967d0c-de02-40d2-9319-ae771e0f20a3","resolution":{"observed_at":"2026-08-15T16:44:21.030716Z","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":"2026-08-15T16:44:21.634354Z","title":"Auto-Context and Its Ap- plication to High-Level Vision Tasks and 3D Brain Image Segmentation,","venue":null,"work_id":"a1835764-1928-4959-a947-811931b05da5","year":2010},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.033937Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:3dad6c8c4e40a8c5ad064a0a3dedf15c3ccdb01fe19aa904ca2fc7951d730d93","observation_id":"f2ba7f75-74a0-4ff6-b132-39985a6ff97f","resolution":{"observed_at":"2026-08-15T16:44:21.637659Z","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":"2026-08-15T16:44:21.624269Z","title":"ExpertSegmentation: Segmentation for mi- croscopy with domain-informed targets via cus- tom loss,","venue":null,"work_id":"db72bd44-6a97-4351-83ae-71dcdb0fa4a9","year":2025},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.036934Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:dbd7b704312339937e7e0b4fec1f851d2b5fca09dee4ce04e61f32dfc5908987","observation_id":"45c8b5de-3f6d-4de2-a741-380a891bd929","resolution":{"observed_at":"2026-08-15T16:44:21.628227Z","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":"2104.14294","last_updated":"2021-05-24T17:49:18Z","snapshot_observed_at":"2026-08-04T11:32:10.695202Z","submitted_at":"2021-04-29T12:28:51Z","title":"Emerging Properties in Self-Supervised Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.14294","snapshot_observed_at":"2026-08-15T16:44:21.040155Z","title":"Emerg- ing Properties in Self-Supervised Vision Trans- formers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.040155Z"},"links":{"cited_paper":"/paper/2104.14294","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:41779449884d6faeafb76fbbc1bb247de1ca565186b2346728efb7eea3e7ff53","observation_id":"29b77fe9-18d2-432a-b706-b2f94eb68ccb","resolution":{"observed_at":"2026-08-15T16:44:21.040155Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-17T13:03:40.359628Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-15T16:44:21.043554Z","title":"DINOv2: Learn- ing Robust Visual Features without Supervision,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.043554Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:b58bdb2bcd3eb03e66553695c1f675dd8781704ebe35c4862154dc1a685f188d","observation_id":"df1c7a7d-3e65-4ccd-a6d6-0b331d550845","resolution":{"observed_at":"2026-08-15T16:44:21.043554Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.08243","last_updated":"2023-04-13T17:59:37Z","snapshot_observed_at":"2026-08-16T16:01:20.302377Z","submitted_at":"2023-01-19T18:59:01Z","title":"Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.08243","snapshot_observed_at":"2026-08-15T16:44:21.046796Z","title":"Self-supervised learning from images with a joint-embedding predictive architecture,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.046796Z"},"links":{"cited_paper":"/paper/2301.08243","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:4c4e4882fceaecef842c42ecd57b179d0d9b34ed1ac228d43d527e6010fa79a4","observation_id":"936e2cc1-5e40-4f34-adc5-e7037454092d","resolution":{"observed_at":"2026-08-15T16:44:21.046796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.05814","last_updated":"2022-10-15T21:18:49Z","snapshot_observed_at":"2026-08-17T08:19:54.748810Z","submitted_at":"2021-12-10T20:15:03Z","title":"Deep ViT Features as Dense Visual Descriptors","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.05814","snapshot_observed_at":"2026-08-15T16:44:21.050130Z","title":"Deep ViT Features as Dense Visual Descrip- tors,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.050130Z"},"links":{"cited_paper":"/paper/2112.05814","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:bd37587a90ea78272aeb8239881446482c1bd7ff7b04e906ee7cce076789d97e","observation_id":"f36cbf6a-aa6b-4343-89c2-ea0305796b62","resolution":{"observed_at":"2026-08-15T16:44:21.050130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.10516","last_updated":"2024-04-01T20:57:45Z","snapshot_observed_at":"2026-08-16T14:09:21.603187Z","submitted_at":"2024-03-15T17:57:06Z","title":"FeatUp: A Model-Agnostic Framework for Features at Any Resolution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.10516","snapshot_observed_at":"2026-08-15T16:44:21.053563Z","title":"FeatUp: A Model-Agnostic Framework for Features at Any Resolution,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.053563Z"},"links":{"cited_paper":"/paper/2403.10516","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:e97745e949d72f94184f3cc0e99147574acb3f7dcc2f60eb559ac8ce92d87575","observation_id":"a2560142-688b-46ed-a4bd-665f742adb0c","resolution":{"observed_at":"2026-08-15T16:44:21.053563Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14625","last_updated":"2024-10-29T01:50:33Z","snapshot_observed_at":"2026-08-16T14:07:34.404734Z","submitted_at":"2024-03-21T17:59:55Z","title":"LiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT Descriptors","version":2},"cited_work":{"arxiv_id":"2403.14625","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.14625","snapshot_observed_at":"2026-08-15T16:44:21.333933Z","title":"LiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT Descriptors","venue":"cs.CV","work_id":"7ab3be64-0e42-4770-a69c-8ee8da67d5d0","year":2024},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.056858Z"},"links":{"cited_paper":"/paper/2403.14625","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:55ffe224c63a3f379f143180feddededccc0fb9bbd1218781d02ea7fa65b4979","observation_id":"47bc0ff4-3f84-4d32-b951-95a106cb7692","resolution":{"observed_at":"2026-08-15T16:44:21.337939Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2504.14032","last_updated":"2025-04-18T18:46:08Z","snapshot_observed_at":"2026-08-16T13:47:45.619265Z","submitted_at":"2025-04-18T18:46:08Z","title":"LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models","version":1},"cited_work":{"arxiv_id":"2504.14032","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.14032","snapshot_observed_at":"2026-08-15T16:44:21.321080Z","title":"LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models","venue":"cs.CV","work_id":"6ad7cbee-9d89-4494-a148-f8cba714f04e","year":2025},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.060148Z"},"links":{"cited_paper":"/paper/2504.14032","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:1c5db2f46a2a3316cf4b99f11570a229d9a4d4ed113fb62e960cbb3c96c7e6c2","observation_id":"2a3c4294-09b6-4365-b009-02afd379bed9","resolution":{"observed_at":"2026-08-15T16:44:21.324779Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-15T16:44:21.614060Z","title":"haesleinhuepf/napari-accelerated-pixel-and- object- classification: 0.14.1,","venue":null,"work_id":"4b53a6b3-721a-40f6-86ad-a0a00b322fe2","year":2023},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.063714Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:fbe4866cac88275d9bda4b60da67c16fa58c3b9315ef26484d2df2841d5c5429","observation_id":"8c5080cc-41e5-4e57-8248-6895150a1bf8","resolution":{"observed_at":"2026-08-15T16:44:21.618249Z","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":"10.1021/acs.cgd.9b01515","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T16:44:21.201034Z","title":"Measuring the Particle Packing of l-Glutamic Acid Crystals through X-ray Computed To- mography for Understanding Powder Flow and Consolidation Behavior,","venue":null,"work_id":"c1dde2d5-a5f1-4b8b-908e-066d0f9375cb","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.067815Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:fc24d805dec9dd50b67395c34a7b462227db384f7a613ce24addb381c2da0410","observation_id":"0b8e73c4-e1ef-4526-af85-da652c0d21e7","resolution":{"observed_at":"2026-08-15T16:44:21.206123Z","resolver_source":"doi","status":"verified_exact"},"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":"2026-08-15T16:44:21.604261Z","title":"Attention is all you need,","venue":null,"work_id":"c80f809e-922b-4c42-896d-9dbc29d29821","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.071697Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:de27751f5bd0c9a142c92b87ec959f029f72d8a44d3b4a5b661f556ff0389788","observation_id":"5f70754a-3077-423b-af23-0de2163635f3","resolution":{"observed_at":"2026-08-15T16:44:21.607710Z","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":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-16T09:25:53.087782Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-15T16:44:21.078038Z","title":"An Image is Worth 16x16 Words: Transformers for Im- age Recognition at Scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.078038Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:03978091dd8d0b0fc11de04975460daee302e8c7d87963fb1dc02a9e5946d972","observation_id":"88992bbb-001b-4266-9c23-da7669f0214f","resolution":{"observed_at":"2026-08-15T16:44:21.078038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10739","last_updated":"2020-06-18T17:59:11Z","snapshot_observed_at":"2026-07-06T09:30:32.320227Z","submitted_at":"2020-06-18T17:59:11Z","title":"Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.10739","snapshot_observed_at":"2026-08-15T16:44:21.081309Z","title":"Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Do- mains,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.081309Z"},"links":{"cited_paper":"/paper/2006.10739","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:3b67382cbc648b5ffc6a601be02bd25a4c42353ab4f2b793f2c2ffb192590ab5","observation_id":"27eb93fe-cb7e-4b88-bd28-9b8fb2d5418d","resolution":{"observed_at":"2026-08-15T16:44:21.081309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10891","last_updated":"2024-04-07T06:52:21Z","snapshot_observed_at":"2026-08-16T14:26:04.438068Z","submitted_at":"2024-01-19T18:59:52Z","title":"Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10891","snapshot_observed_at":"2026-08-15T16:44:21.085210Z","title":"Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.085210Z"},"links":{"cited_paper":"/paper/2401.10891","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:cb620ea657f83ed565a75fdc2ee7b5065260b00460155649fc6e0b59e93ddc9f","observation_id":"8e7c7494-16c5-4190-ac1d-e8f5f586a914","resolution":{"observed_at":"2026-08-15T16:44:21.085210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.12210","last_updated":"2023-06-28T14:15:22Z","snapshot_observed_at":"2026-08-16T15:38:07.925154Z","submitted_at":"2023-04-24T15:49:53Z","title":"A Cookbook of Self-Supervised Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.12210","snapshot_observed_at":"2026-08-15T16:44:21.088434Z","title":"A cookbook of self-supervised learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.088434Z"},"links":{"cited_paper":"/paper/2304.12210","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:9ff6668c7afb00cb7cd9d79e9efa9b13a352ead290b4d2f49024faa6459b6bce","observation_id":"180efaeb-337a-412d-8ea7-9b329e2106e6","resolution":{"observed_at":"2026-08-15T16:44:21.088434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.06377","last_updated":"2021-12-19T19:23:25Z","snapshot_observed_at":"2026-08-15T19:53:40.614050Z","submitted_at":"2021-11-11T18:46:40Z","title":"Masked Autoencoders Are Scalable Vision Learners","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.06377","snapshot_observed_at":"2026-08-15T16:44:21.091781Z","title":"Masked Autoencoders Are Scalable Vision Learners,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.091781Z"},"links":{"cited_paper":"/paper/2111.06377","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:ed599fb6a303aad28d6d67a6a6673c3ac97d51863db888bf4ab0ca8d6c705e89","observation_id":"f0626955-3d19-4c45-a8f4-74a58113199f","resolution":{"observed_at":"2026-08-15T16:44:21.091781Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.08934","last_updated":"2020-08-03T22:17:31Z","snapshot_observed_at":"2026-08-07T21:12:33.939201Z","submitted_at":"2020-03-19T17:57:23Z","title":"NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.08934","snapshot_observed_at":"2026-08-15T16:44:21.095280Z","title":"NeRF: Representing Scenes as Neural Radi- ance Fields for View Synthesis,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.095280Z"},"links":{"cited_paper":"/paper/2003.08934","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:bc2ed77b23552db7ea04c0ee731de383e1e9e00f978c8c145a8920de92a0e60f","observation_id":"500841d0-ef03-4e70-ac23-3d91c3397bbd","resolution":{"observed_at":"2026-08-15T16:44:21.095280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.16588","last_updated":"2024-04-12T09:38:33Z","snapshot_observed_at":"2026-08-07T09:40:32.614733Z","submitted_at":"2023-09-28T16:45:46Z","title":"Vision Transformers Need Registers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.16588","snapshot_observed_at":"2026-08-15T16:44:21.098377Z","title":"Vision Transformers Need Registers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.098377Z"},"links":{"cited_paper":"/paper/2309.16588","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:0dccb0a1faa6942f29b681df51eb825f218f6641b7161f64c07f2f2d238550da","observation_id":"69d77d56-ca0f-431e-a0a2-5ca76c10276c","resolution":{"observed_at":"2026-08-15T16:44:21.098377Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.20229","last_updated":"2024-07-29T17:59:21Z","snapshot_observed_at":"2026-08-16T13:30:07.812960Z","submitted_at":"2024-07-29T17:59:21Z","title":"Improving 2D Feature Representations by 3D-Aware Fine-Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.20229","snapshot_observed_at":"2026-08-15T16:44:21.101657Z","title":"Improving 2d feature represen- tations by 3d-aware fine-tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.101657Z"},"links":{"cited_paper":"/paper/2407.20229","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:8fd031fc168f4c958355587f7eb23565adecf7b2cdb0f7d8e461fa172db1240c","observation_id":"ea6e3987-fe35-454d-938c-2548287394ab","resolution":{"observed_at":"2026-08-15T16:44:21.101657Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1409.0575","last_updated":"2015-01-30T01:23:59Z","snapshot_observed_at":"2026-08-14T23:21:09.382166Z","submitted_at":"2014-09-01T22:29:38Z","title":"ImageNet Large Scale Visual Recognition Challenge","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.0575","snapshot_observed_at":"2026-08-15T16:44:21.104872Z","title":"Imagenet large scale visual recognition challenge,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.104872Z"},"links":{"cited_paper":"/paper/1409.0575","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:febd5d2699cf687efb8597d6a146cb25cfc507e979e6badbe79247b2ba7fafef","observation_id":"375dab69-7dfd-405d-a7b3-107ff6ae4099","resolution":{"observed_at":"2026-08-15T16:44:21.104872Z","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":"2026-08-15T16:44:21.589369Z","title":"Random Forests,","venue":null,"work_id":"b8fe17d1-5780-478d-be22-c0c0462b51d5","year":2001},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.108211Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:cc1c4fccc2a786bcccd183a18e6995bdd37d3ce16cd9f6b86c85964728ccda59","observation_id":"61d12f48-0591-44cc-ae62-bc5b25d28069","resolution":{"observed_at":"2026-08-15T16:44:21.592564Z","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":"2026-08-15T16:44:21.579669Z","title":"Xgboost: A scal- able tree boosting system,","venue":null,"work_id":"ed5badb8-5476-4693-9e69-cb865b3aba55","year":2016},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.111138Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:3fa9719d666227d05768386f237c364590a1633310c566ca4e9d32d17b4f7554","observation_id":"c73a77a1-ab28-49a3-86d2-af4101d13367","resolution":{"observed_at":"2026-08-15T16:44:21.583080Z","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":"2026-08-15T16:44:21.569902Z","title":"Biphase cathode sem","venue":null,"work_id":"76b23c9d-4fcf-47de-ab3b-e912c29fbe5c","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.113946Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:68159dc7d1675e4a51da7a29bea88bf66fdcb6ce76cb2680144818ed42e27369","observation_id":"0c526eae-d0c7-4e45-89cb-983319381801","resolution":{"observed_at":"2026-08-15T16:44:21.573085Z","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":"2026-08-15T16:44:21.559623Z","title":"Electron micrographs","venue":null,"work_id":"dcba101b-c922-4257-ae73-ac3686e38460","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.117081Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:f8b9001aeac18822df5ef33bcadfdc3f970280d665d9c47aa859dd02a0c8e229","observation_id":"8fbff0a5-6eeb-48e8-b7a8-5fe7232b2e05","resolution":{"observed_at":"2026-08-15T16:44:21.563124Z","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":"2026-08-15T16:44:21.549386Z","title":"Semi-automatic deter- mination of cell surface areas used in systems biology.,","venue":null,"work_id":"a5ff525c-c8b3-402c-bc2a-51451980e4c6","year":2013},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.120255Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:3d82a0d6142973981fc07e463c39030a1bfe588d006df2962ec272109b7fcde6","observation_id":"2d899abd-8a4c-4176-90db-cfd532b0622c","resolution":{"observed_at":"2026-08-15T16:44:21.553104Z","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":"2026-08-15T16:44:21.539037Z","title":null,"venue":null,"work_id":"17e17c32-dae0-4408-b060-2d90d9dd3074","year":2021},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.123674Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:357fab9c4f5c4a402ec53d1aab1f7db4f3a352109cee0d844e2a5cc25c46708a","observation_id":"7f71eabf-5174-42c5-89d9-4f4e0354caa7","resolution":{"observed_at":"2026-08-15T16:44:21.542768Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1512.03385","last_updated":"2015-12-10T19:51:55Z","snapshot_observed_at":"2026-07-06T04:39:28.429064Z","submitted_at":"2015-12-10T19:51:55Z","title":"Deep Residual Learning for Image Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03385","snapshot_observed_at":"2026-08-15T16:44:21.126745Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.126745Z"},"links":{"cited_paper":"/paper/1512.03385","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:413c8890ae1f6bd413958ae0c40b3e57a00c8a71de504f6fd0d85f789ccc4cdf","observation_id":"e233ce07-9a31-4a9a-87b9-2454608533ec","resolution":{"observed_at":"2026-08-15T16:44:21.126745Z","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":"2026-08-15T16:44:21.800891Z","title":"Utilizing active learning to accel- erate segmentation of microstructures with tiny annotation budgets,","venue":null,"work_id":"586ee2f8-177a-4875-9635-aab87668058d","year":2024},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.130109Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:f2c85a86b3ffae59b49d41bb74a6ec1ec8d4481eff8a97f8f5e49b03a9fc4e1f","observation_id":"ade0db72-fab0-4679-8e94-cf9aa4052f8b","resolution":{"observed_at":"2026-08-15T16:44:21.804218Z","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":"2026-08-15T16:44:21.529184Z","title":"Array programming with NumPy,","venue":null,"work_id":"33b8a48c-6d4c-433c-9a89-e98bffe7205b","year":2020},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.132864Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:ced5ccc0aced53d8daa47efe39fa5e00899aa20acc471d70a97680f22ce4f06a","observation_id":"84478421-e4ac-4f81-826e-1b92a42eaa38","resolution":{"observed_at":"2026-08-15T16:44:21.532428Z","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":"2026-08-15T16:44:21.519694Z","title":"scikit-image: image processing in python,","venue":null,"work_id":"ec9e95ff-fe3d-4579-bd02-5f2e3dca54b2","year":2014},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.135826Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:73bcbfb19041d99029eef008e8cdeeb5675145d52814c09be378cc955845a622","observation_id":"e2b3963a-1d58-4c7f-a8c2-b6586f00363f","resolution":{"observed_at":"2026-08-15T16:44:21.523092Z","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":"2026-08-15T16:44:21.509949Z","title":"Heterogeneity of the Dominant Causes of Performance Loss in End-of-Life Cathodes and Their Consequences for Direct Recycling,","venue":null,"work_id":"967bd59d-7df6-454e-ae78-0843dcef7a0b","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.139620Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:509d83551b8c2dd9953c3032debd3e15ad7c5074551ba0ec694a604dde3d3291","observation_id":"c59c741d-834f-4b0a-8e89-2c7e1e0c0592","resolution":{"observed_at":"2026-08-15T16:44:21.513638Z","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":"2505.02075","last_updated":"2025-05-04T11:59:26Z","snapshot_observed_at":"2026-08-16T13:45:42.131752Z","submitted_at":"2025-05-04T11:59:26Z","title":"Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation","version":1},"cited_work":{"arxiv_id":"2505.02075","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.02075","snapshot_observed_at":"2026-08-15T16:44:21.225823Z","title":"Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation","venue":"cs.CV","work_id":"135b4589-5895-42af-b288-f67fb6826e47","year":2025},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.142769Z"},"links":{"cited_paper":"/paper/2505.02075","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:ec0dcff8b185a8c3d6fb83f071af67399fe31ae86a68f2e7aef4791f48b65bae","observation_id":"f8737f15-daf1-4c69-95c4-d278f4caf19b","resolution":{"observed_at":"2026-08-15T16:44:21.229535Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-15T16:44:21.499879Z","title":"Sim ´eoni, H","venue":null,"work_id":"481de882-227e-4b5a-a019-35bd70ad61b5","year":2025},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.146074Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:fdef339779b50e24c5bada07928a630c393acce9cf3bfcb23328cef5aba9cb18","observation_id":"5f61dd21-ccfe-4e2d-9846-7c23211ad388","resolution":{"observed_at":"2026-08-15T16:44:21.503943Z","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":"2026-08-15T16:44:21.488940Z","title":"Doitpoms micrograph library,","venue":null,"work_id":"0f2a0c0f-d461-4e20-8d61-2aabcaa73814","year":2000},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.149219Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:2b923310353a85e5a0fd49cdb524d110a881b3e8aa27dac6a48812a7a884cd71","observation_id":"f7175fa1-b073-4ae6-a774-f65dd2ad400a","resolution":{"observed_at":"2026-08-15T16:44:21.493406Z","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":"2026-08-15T16:44:21.479548Z","title":"Micrograph 394,","venue":null,"work_id":"4b575449-3d1f-4133-8023-4e7dae5055f7","year":2002},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.152692Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:5100879573aacc919764e5ab93f076c42e5592ef390df55fe7c64d09898a76f6","observation_id":"5abc565c-7bac-4e2d-ba08-35983e53a506","resolution":{"observed_at":"2026-08-15T16:44:21.482924Z","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":"2026-08-15T16:44:21.468021Z","title":"Mosilib: Innovative anode materials for more powerful and sustainable batter- ies","venue":null,"work_id":"f2b24407-c4d8-4fd0-b47e-acbfd099f1fa","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.155773Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:ae03cbfc6864b46f334a76a95ffb6a4234dce69f9836dee8e00ec03c077cadb7","observation_id":"2b1d0e68-33a1-4ea3-8277-d530a6c1e751","resolution":{"observed_at":"2026-08-15T16:44:21.472799Z","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":"2401.02957","last_updated":"2024-07-22T09:07:27Z","snapshot_observed_at":"2026-08-16T14:29:32.915735Z","submitted_at":"2024-01-05T18:59:52Z","title":"Denoising Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.02957","snapshot_observed_at":"2026-08-15T16:44:21.158717Z","title":"Denoising vision transformers,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.158717Z"},"links":{"cited_paper":"/paper/2401.02957","citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:7093b96664bdc5000db8c864c0bcd8ccfdfadcaa02cc697fdaea1645d1dd1374","observation_id":"db952d83-25e4-4407-a61f-19927d03e5f1","resolution":{"observed_at":"2026-08-15T16:44:21.158717Z","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":"2026-08-15T16:44:21.457506Z","title":null,"venue":null,"work_id":"f108807d-8a36-450d-8223-e7919c343c3a","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.162095Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:af44301aa133a2256ee196cd0cce8b2f1f3a61d88f7d0df6adc6af6b554d5bd5","observation_id":"4d120755-886e-4781-8650-7422d5e988c4","resolution":{"observed_at":"2026-08-15T16:44:21.461258Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-15T16:44:21.446897Z","title":null,"venue":null,"work_id":"09626528-d3ca-4cba-b2b5-87284ce54cf1","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.165622Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:d431cc30c2cd83e8c610b5ec839c87fd3d7ebab28cb4312af5728422f0340d2a","observation_id":"71feb969-b4f4-4be7-9363-1a0739ebd20a","resolution":{"observed_at":"2026-08-15T16:44:21.449937Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-15T16:44:21.437515Z","title":null,"venue":null,"work_id":"e82879db-ca15-4294-9a3f-e4cf258a27eb","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.169690Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:d1a6b411913db93547258e68240b3b986c5fae0922588cda357020b3bb69a7c6","observation_id":"7db148db-fbef-4439-b2ed-3c43efdd4c3d","resolution":{"observed_at":"2026-08-15T16:44:21.440714Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-15T16:44:21.427324Z","title":null,"venue":null,"work_id":"f7bd3ccc-ce05-4b51-b162-72c6ea490be9","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.172894Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:55fcad0a527ae007d97f37e2a26e95aa8f31b8e6295ae3c6150019028d35fd38","observation_id":"3201cb7e-2516-4836-aaa3-31003d1bfb28","resolution":{"observed_at":"2026-08-15T16:44:21.430686Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-15T16:44:21.416550Z","title":"S2 Hyperparameters The training hyperparameters for our upsampler is detailed in Table S1","venue":null,"work_id":"4737cd3c-5c92-4c14-a209-4cffb11312e1","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.175959Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:9b29c9b9002f99767875ae18f021b1635ef23cbd49b4d878df8b5ea769338a47","observation_id":"81a227f3-0d9f-479f-bc66-070523db3e36","resolution":{"observed_at":"2026-08-15T16:44:21.421059Z","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":"2026-08-15T16:44:21.074737Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.074737Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:1c13f374bacffe69702c0192129754400d1191e5f2236130ba74206a2f004ccd","observation_id":"f1e33fb7-488a-4040-89b7-eabbdb1e8c5b","resolution":{"observed_at":"2026-08-15T16:44:21.074737Z","resolver_source":null,"status":"parse_uncertain"},"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":"2026-08-15T16:44:21.706452Z","title":null,"venue":null,"work_id":"6590508b-0f06-4968-966e-829af943cdd2","year":null},"citing_paper":{"arxiv_id":"2508.21529","last_updated":"2025-08-29T11:37:43Z","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-15T16:44:21.001952Z"},"links":{"citing_paper":"/paper/2508.21529"},"observation_digest":"sha256:9c377e443a2b78dbd09fc7bd27c537641729e614df80d235e3e0e7579657e496","observation_id":"a944201d-1bcb-4c87-9fb1-659faf240da4","resolution":{"observed_at":"2026-08-15T16:44:21.709514Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2508.21529","last_updated":"2025-08-29T11:37:43Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T15:51:22.354420Z","submitted_at":"2025-08-29T11:37:43Z","title":"Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation"},"reference_resolution":{"displayed":67,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":25,"verified_exact":5,"verified_fuzzy":36},"total_outbound_references":67},"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 67 of 67 outbound references and 0 inbound Pith citation observations for arXiv:2508.21529."}