{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:WP35JII34AK6CF4MLMNULUGHSF","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":"c56dafba8b840f010814dd847618134fc4819bbd28060039149388bd8bec602c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2014-12-29T20:56:36Z","title_canon_sha256":"395e65e7eb2e37cddcae25b971d7776906da202fc09e213f7c7450e9d0198d9f"},"schema_version":"1.0","source":{"id":"1412.8464","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1412.8464","created_at":"2026-05-18T01:35:32Z"},{"alias_kind":"arxiv_version","alias_value":"1412.8464v2","created_at":"2026-05-18T01:35:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.8464","created_at":"2026-05-18T01:35:32Z"},{"alias_kind":"pith_short_12","alias_value":"WP35JII34AK6","created_at":"2026-05-18T12:28:54Z"},{"alias_kind":"pith_short_16","alias_value":"WP35JII34AK6CF4M","created_at":"2026-05-18T12:28:54Z"},{"alias_kind":"pith_short_8","alias_value":"WP35JII3","created_at":"2026-05-18T12:28:54Z"}],"graph_snapshots":[{"event_id":"sha256:976762c16663d06b4cc78dba49b1677692ff90fc50fbfa83d87127963bce225d","target":"graph","created_at":"2026-05-18T01:35:32Z","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"},"paper":{"abstract_excerpt":"We propose a globally convergent alternating minimization (AM) algorithm for image reconstruction in transmission tomography, which extends automatic relevance determination (ARD) to Poisson noise models with Beer's law. The algorithm promotes solutions that are sparse in the pixel/voxel-differences domain by introducing additional latent variables, one for each pixel/voxel, and then learning these variables from the data using a hierarchical Bayesian model. Importantly, the proposed AM algorithm is free of any tuning parameters with image quality comparable to standard penalized likelihood me","authors_text":"David G. Politte, David J. Brady, Joseph A. O'Sullivan, Lawrence Carin, Shaobo Han, Soysal Degirmenci, Yan Kaganovsky","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2014-12-29T20:56:36Z","title":"Alternating Minimization Algorithm with Automatic Relevance Determination for Transmission Tomography under Poisson Noise"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.8464","kind":"arxiv","version":2},"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":"sha256:4a00dd61dbd3ea4bb8a72222c4281374a6550652c15e0f616d5b7c0ba3ad067e","target":"record","created_at":"2026-05-18T01:35:32Z","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":"c56dafba8b840f010814dd847618134fc4819bbd28060039149388bd8bec602c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2014-12-29T20:56:36Z","title_canon_sha256":"395e65e7eb2e37cddcae25b971d7776906da202fc09e213f7c7450e9d0198d9f"},"schema_version":"1.0","source":{"id":"1412.8464","kind":"arxiv","version":2}},"canonical_sha256":"b3f7d4a11be015e1178c5b1b45d0c7914b6b1a8c51ef5c197b20d77ad2a31dc0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b3f7d4a11be015e1178c5b1b45d0c7914b6b1a8c51ef5c197b20d77ad2a31dc0","first_computed_at":"2026-05-18T01:35:32.552937Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:35:32.552937Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CagzV78EvorV46jOjCej4ymDNNjM8YSmaWnBnI1gC0wWGut2nWWIUwmMUJ+HjVwrxzMkKKmZi3qRKJUc79vyCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:35:32.553569Z","signed_message":"canonical_sha256_bytes"},"source_id":"1412.8464","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4a00dd61dbd3ea4bb8a72222c4281374a6550652c15e0f616d5b7c0ba3ad067e","sha256:976762c16663d06b4cc78dba49b1677692ff90fc50fbfa83d87127963bce225d"],"state_sha256":"3c1b63d518bc49eb5a09f1c3d8312cbddd3e538df1aca42c61d425e165e76a47"}