{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LWYZ5K5QM34SX342LLD33BAHWO","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"cffaab2250f91bce353e846bd7b2dadbe821f9eccc145e546dcd281b3249bab8","cross_cats_sorted":["cs.NA","math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2025-01-16T21:20:41Z","title_canon_sha256":"b0821d1036c6214b00a6c22bfb6b60acc3d9669c6fb2082da14cbb4709b52cc0"},"schema_version":"1.0","source":{"id":"2501.09845","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.09845","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"arxiv_version","alias_value":"2501.09845v1","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09845","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"pith_short_12","alias_value":"LWYZ5K5QM34S","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"pith_short_16","alias_value":"LWYZ5K5QM34SX342","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"pith_short_8","alias_value":"LWYZ5K5Q","created_at":"2026-07-05T10:02:12Z"}],"graph_snapshots":[{"event_id":"sha256:947be4b4b4c82245d3f22e63e3d220754b94fbcc02cb4a4ddd9354bc3c3c60fe","target":"graph","created_at":"2026-07-05T10:02:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.09845/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This study presents the development of a spatially adaptive weighting strategy for Total Variation regularization, aimed at addressing under-determined linear inverse problems. The method leverages the rapid computation of an accurate approximation of the true image (or its gradient magnitude) through a neural network. Our approach operates without requiring prior knowledge of the noise intensity in the data and avoids the iterative recomputation of weights. Additionally, the paper includes a theoretical analysis of the proposed method, establishing its validity as a regularization approach. T","authors_text":"Andrea Sebastiani, Davide Evangelista, Elena Loli Piccolomini, Elena Morotti","cross_cats":["cs.NA","math.OC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2025-01-16T21:20:41Z","title":"Adaptive Weighted Total Variation boosted by learning techniques in few-view tomographic imaging"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09845","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:89478db0d4bc7e27564e709d3d1378c561c437322ec94e718beacb78b24f8753","target":"record","created_at":"2026-07-05T10:02:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"cffaab2250f91bce353e846bd7b2dadbe821f9eccc145e546dcd281b3249bab8","cross_cats_sorted":["cs.NA","math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2025-01-16T21:20:41Z","title_canon_sha256":"b0821d1036c6214b00a6c22bfb6b60acc3d9669c6fb2082da14cbb4709b52cc0"},"schema_version":"1.0","source":{"id":"2501.09845","kind":"arxiv","version":1}},"canonical_sha256":"5db19eabb066f92bef9a5ac7bd8407b3a237518d2d408835e8fb0d4f65e86afc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5db19eabb066f92bef9a5ac7bd8407b3a237518d2d408835e8fb0d4f65e86afc","first_computed_at":"2026-07-05T10:02:12.221893Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:02:12.221893Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"602uCm+ip5TLs9ZDXmd3TU735U/5Tt8QBjDm/kog/awBCRKas7KOHJVTsH2/c+hqQT349DO1p4oF0r49w+SrBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:02:12.222285Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.09845","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:89478db0d4bc7e27564e709d3d1378c561c437322ec94e718beacb78b24f8753","sha256:947be4b4b4c82245d3f22e63e3d220754b94fbcc02cb4a4ddd9354bc3c3c60fe"],"state_sha256":"0b0c2b235600295e4afb12eb499896e5ee028929ef4ccbeb2f5d2c8b5ec8fc4e"}