{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:WUMPJ4B5LUOKY7DHE5DMKPYXKV","short_pith_number":"pith:WUMPJ4B5","canonical_record":{"source":{"id":"1502.04726","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-02-16T21:17:52Z","cross_cats_sorted":["cs.CV","math.OC"],"title_canon_sha256":"86584844916cf7be06ead050f670a73fb8554e7d93d872a5e774e8232393ab26","abstract_canon_sha256":"3441473f935afd44a1b13dd9b17a10cc6b3748609c7fdd3c1ee49a91bd3a102b"},"schema_version":"1.0"},"canonical_sha256":"b518f4f03d5d1cac7c672746c53f1755696cefc1d4466db828fa72beff6bbf70","source":{"kind":"arxiv","id":"1502.04726","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.04726","created_at":"2026-05-18T02:02:07Z"},{"alias_kind":"arxiv_version","alias_value":"1502.04726v1","created_at":"2026-05-18T02:02:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.04726","created_at":"2026-05-18T02:02:07Z"},{"alias_kind":"pith_short_12","alias_value":"WUMPJ4B5LUOK","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_16","alias_value":"WUMPJ4B5LUOKY7DH","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_8","alias_value":"WUMPJ4B5","created_at":"2026-05-18T12:29:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:WUMPJ4B5LUOKY7DHE5DMKPYXKV","target":"record","payload":{"canonical_record":{"source":{"id":"1502.04726","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-02-16T21:17:52Z","cross_cats_sorted":["cs.CV","math.OC"],"title_canon_sha256":"86584844916cf7be06ead050f670a73fb8554e7d93d872a5e774e8232393ab26","abstract_canon_sha256":"3441473f935afd44a1b13dd9b17a10cc6b3748609c7fdd3c1ee49a91bd3a102b"},"schema_version":"1.0"},"canonical_sha256":"b518f4f03d5d1cac7c672746c53f1755696cefc1d4466db828fa72beff6bbf70","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:02:07.917111Z","signature_b64":"uvg7CeQZB8cEEzWIsFCyKH1hv2gRhYB8OJ8MkWpZU/+iAZd+aE/x4viUwkf4Bz3XetZZnrb67HmvR1FhwmyjCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b518f4f03d5d1cac7c672746c53f1755696cefc1d4466db828fa72beff6bbf70","last_reissued_at":"2026-05-18T02:02:07.916092Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:02:07.916092Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1502.04726","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:02:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7+3G0lehWvvbkkSAtpY7sJ46qwE8MGKjNyc4zdBT7o8cqDKdp/+Oui2VCljhLO0SO611yXX+slDfotdQt9EMBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T01:10:45.129430Z"},"content_sha256":"5032cd50648c2586fed36ad41bd7782ecec07a97a1174f25b7a6deddcf7142d7","schema_version":"1.0","event_id":"sha256:5032cd50648c2586fed36ad41bd7782ecec07a97a1174f25b7a6deddcf7142d7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:WUMPJ4B5LUOKY7DHE5DMKPYXKV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ICR: Iterative Convex Refinement for Sparse Signal Recovery Using Spike and Slab Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","math.OC"],"primary_cat":"stat.ML","authors_text":"Hojjat S. Mousavi, Trac D. Tran, Vishal Monga","submitted_at":"2015-02-16T21:17:52Z","abstract_excerpt":"In this letter, we address sparse signal recovery using spike and slab priors. In particular, we focus on a Bayesian framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. The optimization resulting from spike and slab prior maximization is known to be a hard non-convex problem, and existing solutions involve simplifying assumptions and/or relaxations. We propose an approach called Iterative Convex Refinement (ICR) that aims to solve the aforementioned optimization problem directly allowing for greater generality in the sparse structure. Essentially, ICR "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.04726","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:02:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rl1jnU7/hc+5inJS2D5ZYY+YVB47ZUTKQV252f4Tp7GPyb+OHligI1iDD2EIG8QNZdBRd0drzx0pm5UZxC7pDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T01:10:45.130167Z"},"content_sha256":"9142450b3de7b3af7133231608c80802dd2115125cd736238922f7a7746a2756","schema_version":"1.0","event_id":"sha256:9142450b3de7b3af7133231608c80802dd2115125cd736238922f7a7746a2756"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WUMPJ4B5LUOKY7DHE5DMKPYXKV/bundle.json","state_url":"https://pith.science/pith/WUMPJ4B5LUOKY7DHE5DMKPYXKV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WUMPJ4B5LUOKY7DHE5DMKPYXKV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-10T01:10:45Z","links":{"resolver":"https://pith.science/pith/WUMPJ4B5LUOKY7DHE5DMKPYXKV","bundle":"https://pith.science/pith/WUMPJ4B5LUOKY7DHE5DMKPYXKV/bundle.json","state":"https://pith.science/pith/WUMPJ4B5LUOKY7DHE5DMKPYXKV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WUMPJ4B5LUOKY7DHE5DMKPYXKV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:WUMPJ4B5LUOKY7DHE5DMKPYXKV","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":"3441473f935afd44a1b13dd9b17a10cc6b3748609c7fdd3c1ee49a91bd3a102b","cross_cats_sorted":["cs.CV","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-02-16T21:17:52Z","title_canon_sha256":"86584844916cf7be06ead050f670a73fb8554e7d93d872a5e774e8232393ab26"},"schema_version":"1.0","source":{"id":"1502.04726","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.04726","created_at":"2026-05-18T02:02:07Z"},{"alias_kind":"arxiv_version","alias_value":"1502.04726v1","created_at":"2026-05-18T02:02:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.04726","created_at":"2026-05-18T02:02:07Z"},{"alias_kind":"pith_short_12","alias_value":"WUMPJ4B5LUOK","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_16","alias_value":"WUMPJ4B5LUOKY7DH","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_8","alias_value":"WUMPJ4B5","created_at":"2026-05-18T12:29:47Z"}],"graph_snapshots":[{"event_id":"sha256:9142450b3de7b3af7133231608c80802dd2115125cd736238922f7a7746a2756","target":"graph","created_at":"2026-05-18T02:02:07Z","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":"In this letter, we address sparse signal recovery using spike and slab priors. In particular, we focus on a Bayesian framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. The optimization resulting from spike and slab prior maximization is known to be a hard non-convex problem, and existing solutions involve simplifying assumptions and/or relaxations. We propose an approach called Iterative Convex Refinement (ICR) that aims to solve the aforementioned optimization problem directly allowing for greater generality in the sparse structure. Essentially, ICR ","authors_text":"Hojjat S. Mousavi, Trac D. Tran, Vishal Monga","cross_cats":["cs.CV","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-02-16T21:17:52Z","title":"ICR: Iterative Convex Refinement for Sparse Signal Recovery Using Spike and Slab Priors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.04726","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":"sha256:5032cd50648c2586fed36ad41bd7782ecec07a97a1174f25b7a6deddcf7142d7","target":"record","created_at":"2026-05-18T02:02:07Z","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":"3441473f935afd44a1b13dd9b17a10cc6b3748609c7fdd3c1ee49a91bd3a102b","cross_cats_sorted":["cs.CV","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-02-16T21:17:52Z","title_canon_sha256":"86584844916cf7be06ead050f670a73fb8554e7d93d872a5e774e8232393ab26"},"schema_version":"1.0","source":{"id":"1502.04726","kind":"arxiv","version":1}},"canonical_sha256":"b518f4f03d5d1cac7c672746c53f1755696cefc1d4466db828fa72beff6bbf70","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b518f4f03d5d1cac7c672746c53f1755696cefc1d4466db828fa72beff6bbf70","first_computed_at":"2026-05-18T02:02:07.916092Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:02:07.916092Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uvg7CeQZB8cEEzWIsFCyKH1hv2gRhYB8OJ8MkWpZU/+iAZd+aE/x4viUwkf4Bz3XetZZnrb67HmvR1FhwmyjCg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:02:07.917111Z","signed_message":"canonical_sha256_bytes"},"source_id":"1502.04726","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5032cd50648c2586fed36ad41bd7782ecec07a97a1174f25b7a6deddcf7142d7","sha256:9142450b3de7b3af7133231608c80802dd2115125cd736238922f7a7746a2756"],"state_sha256":"b4fe4a172e28da189a7f0c050f8079e23c043470b636163e95265140ed38f304"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EYZL28TsrhMN0CqHhxIrVgkqAjZT62DGg7R+Rh7YwFnnoG0fPeot5cxfh+ZdP1TImrkJnXWNc8jzMqXbuC4ICA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T01:10:45.134108Z","bundle_sha256":"ca42a1c35b2d6dc96c21f3db2bceeb6758e88d33a191e35005bd4629ee185e78"}}