{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GG3CGTEOC6RT6EFYXFJ2QVQPXQ","short_pith_number":"pith:GG3CGTEO","schema_version":"1.0","canonical_sha256":"31b6234c8e17a33f10b8b953a8560fbc204f5a8bed2d9cabdfaa156a5e1338d2","source":{"kind":"arxiv","id":"2401.00816","version":3},"attestation_state":"computed","paper":{"title":"Glimpse: Generalized Locality for Scalable and Robust CT","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"AmirEhsan Khorashadizadeh, Ivan Dokmani\\'c, Tianlin Liu, Valentin Debarnot","submitted_at":"2024-01-01T17:15:42Z","abstract_excerpt":"Deep learning has become the state-of-the-art approach to medical tomographic imaging. A common approach is to feed the result of a simple inversion, for example the backprojection, to a multiscale convolutional neural network (CNN) which computes the final reconstruction. Despite good results on in-distribution test data, this often results in overfitting certain large-scale structures and poor generalization on out-of-distribution (OOD) samples. Moreover, the memory and computational complexity of multiscale CNNs scale unfavorably with image resolution, making them impractical for applicatio"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2401.00816","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-01-01T17:15:42Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"3a2e56c160e27620b667ab4863309973aabad05fd94121ea7a2c013af1426ab4","abstract_canon_sha256":"30e4956725e23b69d12c3f80f74318b1a931dd84b78c3760a7ffdb576e824b20"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:20:02.150048Z","signature_b64":"0lb19Yg6puPgK6aAqT2LpIc/twHIpno549/+uSdQ2VVwV3DPt0EdxSHWK/oJtw3i6KFSJKG1xVuNK2kkX/dDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31b6234c8e17a33f10b8b953a8560fbc204f5a8bed2d9cabdfaa156a5e1338d2","last_reissued_at":"2026-07-05T11:20:02.149579Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:20:02.149579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Glimpse: Generalized Locality for Scalable and Robust CT","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"AmirEhsan Khorashadizadeh, Ivan Dokmani\\'c, Tianlin Liu, Valentin Debarnot","submitted_at":"2024-01-01T17:15:42Z","abstract_excerpt":"Deep learning has become the state-of-the-art approach to medical tomographic imaging. A common approach is to feed the result of a simple inversion, for example the backprojection, to a multiscale convolutional neural network (CNN) which computes the final reconstruction. Despite good results on in-distribution test data, this often results in overfitting certain large-scale structures and poor generalization on out-of-distribution (OOD) samples. Moreover, the memory and computational complexity of multiscale CNNs scale unfavorably with image resolution, making them impractical for applicatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.00816","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.00816/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2401.00816","created_at":"2026-07-05T11:20:02.149641+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.00816v3","created_at":"2026-07-05T11:20:02.149641+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.00816","created_at":"2026-07-05T11:20:02.149641+00:00"},{"alias_kind":"pith_short_12","alias_value":"GG3CGTEOC6RT","created_at":"2026-07-05T11:20:02.149641+00:00"},{"alias_kind":"pith_short_16","alias_value":"GG3CGTEOC6RT6EFY","created_at":"2026-07-05T11:20:02.149641+00:00"},{"alias_kind":"pith_short_8","alias_value":"GG3CGTEO","created_at":"2026-07-05T11:20:02.149641+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.08350","citing_title":"Benchmarking learned algorithms for computed tomography image reconstruction tasks","ref_index":98,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GG3CGTEOC6RT6EFYXFJ2QVQPXQ","json":"https://pith.science/pith/GG3CGTEOC6RT6EFYXFJ2QVQPXQ.json","graph_json":"https://pith.science/api/pith-number/GG3CGTEOC6RT6EFYXFJ2QVQPXQ/graph.json","events_json":"https://pith.science/api/pith-number/GG3CGTEOC6RT6EFYXFJ2QVQPXQ/events.json","paper":"https://pith.science/paper/GG3CGTEO"},"agent_actions":{"view_html":"https://pith.science/pith/GG3CGTEOC6RT6EFYXFJ2QVQPXQ","download_json":"https://pith.science/pith/GG3CGTEOC6RT6EFYXFJ2QVQPXQ.json","view_paper":"https://pith.science/paper/GG3CGTEO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.00816&json=true","fetch_graph":"https://pith.science/api/pith-number/GG3CGTEOC6RT6EFYXFJ2QVQPXQ/graph.json","fetch_events":"https://pith.science/api/pith-number/GG3CGTEOC6RT6EFYXFJ2QVQPXQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GG3CGTEOC6RT6EFYXFJ2QVQPXQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GG3CGTEOC6RT6EFYXFJ2QVQPXQ/action/storage_attestation","attest_author":"https://pith.science/pith/GG3CGTEOC6RT6EFYXFJ2QVQPXQ/action/author_attestation","sign_citation":"https://pith.science/pith/GG3CGTEOC6RT6EFYXFJ2QVQPXQ/action/citation_signature","submit_replication":"https://pith.science/pith/GG3CGTEOC6RT6EFYXFJ2QVQPXQ/action/replication_record"}},"created_at":"2026-07-05T11:20:02.149641+00:00","updated_at":"2026-07-05T11:20:02.149641+00:00"}