{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:HDB2DVO6BYDH6QYCE2UYHMKJXM","short_pith_number":"pith:HDB2DVO6","schema_version":"1.0","canonical_sha256":"38c3a1d5de0e067f430226a983b149bb3111665f836a5a5c6fcf7db9d2ef4a7d","source":{"kind":"arxiv","id":"1505.06270","version":1},"attestation_state":"computed","paper":{"title":"Two-Dimensional Pattern-Coupled Sparse Bayesian Learning via Generalized Approximate Message Passing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Hongbin Li, Jun Fang, Lizao Zhang","submitted_at":"2015-05-23T03:34:14Z","abstract_excerpt":"We consider the problem of recovering two-dimensional (2-D) block-sparse signals with \\emph{unknown} cluster patterns. Two-dimensional block-sparse patterns arise naturally in many practical applications such as foreground detection and inverse synthetic aperture radar imaging. To exploit the block-sparse structure, we introduce a 2-D pattern-coupled hierarchical Gaussian prior model to characterize the statistical pattern dependencies among neighboring coefficients. Unlike the conventional hierarchical Gaussian prior model where each coefficient is associated independently with a unique hyper"},"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":"1505.06270","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-05-23T03:34:14Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"379ead000bca3a775d7b4f558e85b0467602b789a9b6c3b438776fee92859869","abstract_canon_sha256":"0867b751b115c79564af1044af4e700b78c7ab023e65705ad68fcf6734763060"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:14:04.274354Z","signature_b64":"N4FMJt245kH3Y+WAM4n7Bj2CrSQRkmzWU+TP2G5mOQqsSkZ0txoETtz3UQf2g8hulnsj7QwP+bfRKzKgc65GCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38c3a1d5de0e067f430226a983b149bb3111665f836a5a5c6fcf7db9d2ef4a7d","last_reissued_at":"2026-05-18T01:14:04.273657Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:14:04.273657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Two-Dimensional Pattern-Coupled Sparse Bayesian Learning via Generalized Approximate Message Passing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Hongbin Li, Jun Fang, Lizao Zhang","submitted_at":"2015-05-23T03:34:14Z","abstract_excerpt":"We consider the problem of recovering two-dimensional (2-D) block-sparse signals with \\emph{unknown} cluster patterns. Two-dimensional block-sparse patterns arise naturally in many practical applications such as foreground detection and inverse synthetic aperture radar imaging. To exploit the block-sparse structure, we introduce a 2-D pattern-coupled hierarchical Gaussian prior model to characterize the statistical pattern dependencies among neighboring coefficients. Unlike the conventional hierarchical Gaussian prior model where each coefficient is associated independently with a unique hyper"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.06270","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1505.06270","created_at":"2026-05-18T01:14:04.273769+00:00"},{"alias_kind":"arxiv_version","alias_value":"1505.06270v1","created_at":"2026-05-18T01:14:04.273769+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.06270","created_at":"2026-05-18T01:14:04.273769+00:00"},{"alias_kind":"pith_short_12","alias_value":"HDB2DVO6BYDH","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_16","alias_value":"HDB2DVO6BYDH6QYC","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_8","alias_value":"HDB2DVO6","created_at":"2026-05-18T12:29:22.688609+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HDB2DVO6BYDH6QYCE2UYHMKJXM","json":"https://pith.science/pith/HDB2DVO6BYDH6QYCE2UYHMKJXM.json","graph_json":"https://pith.science/api/pith-number/HDB2DVO6BYDH6QYCE2UYHMKJXM/graph.json","events_json":"https://pith.science/api/pith-number/HDB2DVO6BYDH6QYCE2UYHMKJXM/events.json","paper":"https://pith.science/paper/HDB2DVO6"},"agent_actions":{"view_html":"https://pith.science/pith/HDB2DVO6BYDH6QYCE2UYHMKJXM","download_json":"https://pith.science/pith/HDB2DVO6BYDH6QYCE2UYHMKJXM.json","view_paper":"https://pith.science/paper/HDB2DVO6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1505.06270&json=true","fetch_graph":"https://pith.science/api/pith-number/HDB2DVO6BYDH6QYCE2UYHMKJXM/graph.json","fetch_events":"https://pith.science/api/pith-number/HDB2DVO6BYDH6QYCE2UYHMKJXM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HDB2DVO6BYDH6QYCE2UYHMKJXM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HDB2DVO6BYDH6QYCE2UYHMKJXM/action/storage_attestation","attest_author":"https://pith.science/pith/HDB2DVO6BYDH6QYCE2UYHMKJXM/action/author_attestation","sign_citation":"https://pith.science/pith/HDB2DVO6BYDH6QYCE2UYHMKJXM/action/citation_signature","submit_replication":"https://pith.science/pith/HDB2DVO6BYDH6QYCE2UYHMKJXM/action/replication_record"}},"created_at":"2026-05-18T01:14:04.273769+00:00","updated_at":"2026-05-18T01:14:04.273769+00:00"}