{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:L3OUFUU2NAVERHWBTXZC6B6HKA","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":"99468167b3e0a9e04a80e7295c5834aa88bcb372510273d143226cde671295a6","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-04-20T03:58:44Z","title_canon_sha256":"60cbb1596fbb2ed0d3e7b58a1a25e1040e967bf6bb7c669a2eb82d15b1611baa"},"schema_version":"1.0","source":{"id":"1904.09398","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.09398","created_at":"2026-05-17T23:48:07Z"},{"alias_kind":"arxiv_version","alias_value":"1904.09398v1","created_at":"2026-05-17T23:48:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.09398","created_at":"2026-05-17T23:48:07Z"},{"alias_kind":"pith_short_12","alias_value":"L3OUFUU2NAVE","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"L3OUFUU2NAVERHWB","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"L3OUFUU2","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:c20d61302e685706e7f56848c6177c23ec43a2ce83b76bfc0de5f87434ecab39","target":"graph","created_at":"2026-05-17T23:48:07Z","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":"The orthogonal matching pursuit (OMP) algorithm is a commonly used algorithm for recovering $K$-sparse signals $\\x\\in \\mathbb{R}^{n}$ from linear model $\\y=\\A\\x$, where $\\A\\in \\mathbb{R}^{m\\times n}$ is a sensing matrix. A fundamental question in the performance analysis of OMP is the characterization of the probability that it can exactly recover $\\x$ for random matrix $\\A$. Although in many practical applications, in addition to the sparsity, $\\x$ usually also has some additional property (for example, the nonzero entries of $\\x$ independently and identically follow the Gaussian distribution","authors_text":"Jinming Wen, Wei Yu","cross_cats":["math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-04-20T03:58:44Z","title":"Exact Sparse Signal Recovery via Orthogonal Matching Pursuit with Prior Information"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.09398","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:eee855d234ed13a415dcdaff6b89ebf498001b0f329f308066634733544d13e6","target":"record","created_at":"2026-05-17T23:48:07Z","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":"99468167b3e0a9e04a80e7295c5834aa88bcb372510273d143226cde671295a6","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-04-20T03:58:44Z","title_canon_sha256":"60cbb1596fbb2ed0d3e7b58a1a25e1040e967bf6bb7c669a2eb82d15b1611baa"},"schema_version":"1.0","source":{"id":"1904.09398","kind":"arxiv","version":1}},"canonical_sha256":"5edd42d29a682a489ec19df22f07c750245936caedfda189754a47673fc792de","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5edd42d29a682a489ec19df22f07c750245936caedfda189754a47673fc792de","first_computed_at":"2026-05-17T23:48:07.199620Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:07.199620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IAC0nAlBkvvzY/1zH+9u+J9YmilZSkDbrxX7WTvKacjozIc1Gca5RRseNH0rtBCtAk7ou+MA3nSzdpDyoLXMBA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:07.200177Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.09398","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eee855d234ed13a415dcdaff6b89ebf498001b0f329f308066634733544d13e6","sha256:c20d61302e685706e7f56848c6177c23ec43a2ce83b76bfc0de5f87434ecab39"],"state_sha256":"e3b23cfa3631d91c3775550282683e02a918c08f624f0a06cbc16b768fd0d4f9"}