{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:URF73XYS3GF6SOYEEOLO6R472E","short_pith_number":"pith:URF73XYS","canonical_record":{"source":{"id":"1203.1548","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2012-03-07T17:31:27Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"13b8b2a14cb99921f21aad2920884b5723d707431415b2e6c421e3c29f798762","abstract_canon_sha256":"4e29d41b827d089bc52a640824c463c5179f9bc0d0781cc00db34fdaa5dcda84"},"schema_version":"1.0"},"canonical_sha256":"a44bfddf12d98be93b042396ef479fd1089a1dd8303da5bed771ee6f80b0448b","source":{"kind":"arxiv","id":"1203.1548","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1203.1548","created_at":"2026-05-18T02:21:17Z"},{"alias_kind":"arxiv_version","alias_value":"1203.1548v2","created_at":"2026-05-18T02:21:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1203.1548","created_at":"2026-05-18T02:21:17Z"},{"alias_kind":"pith_short_12","alias_value":"URF73XYS3GF6","created_at":"2026-05-18T12:27:23Z"},{"alias_kind":"pith_short_16","alias_value":"URF73XYS3GF6SOYE","created_at":"2026-05-18T12:27:23Z"},{"alias_kind":"pith_short_8","alias_value":"URF73XYS","created_at":"2026-05-18T12:27:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:URF73XYS3GF6SOYEEOLO6R472E","target":"record","payload":{"canonical_record":{"source":{"id":"1203.1548","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2012-03-07T17:31:27Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"13b8b2a14cb99921f21aad2920884b5723d707431415b2e6c421e3c29f798762","abstract_canon_sha256":"4e29d41b827d089bc52a640824c463c5179f9bc0d0781cc00db34fdaa5dcda84"},"schema_version":"1.0"},"canonical_sha256":"a44bfddf12d98be93b042396ef479fd1089a1dd8303da5bed771ee6f80b0448b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:21:17.215823Z","signature_b64":"D89gC/O4qJQGH1gnzxM5CBlHlcg4FkxQG5TGiycxOLASpGKbrSLdO9Bxmc/RFjZps2yujjFxBBkOei38uZIlCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a44bfddf12d98be93b042396ef479fd1089a1dd8303da5bed771ee6f80b0448b","last_reissued_at":"2026-05-18T02:21:17.215307Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:21:17.215307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1203.1548","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:21:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7JgMQ5BfMdrQHNU6LwWZlI9Hq757Wdd8XSApjxKk/pALUTN2wyytiZ5YJQeCAnboa8sM8jluw2lnKmZzHELUDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T05:46:27.653017Z"},"content_sha256":"c042a11ab052e9604064b0f4d2979c1a8dacc42c052cfb54e9b158115b69b69f","schema_version":"1.0","event_id":"sha256:c042a11ab052e9604064b0f4d2979c1a8dacc42c052cfb54e9b158115b69b69f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:URF73XYS3GF6SOYEEOLO6R472E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Retrieval of Sparse Solutions of Multiple-Measurement Vectors via Zero-point Attracting Projection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Hui Dai, Laming Chen, Wei Feng, Yang You, Yuantao Gu","submitted_at":"2012-03-07T17:31:27Z","abstract_excerpt":"A new sparse signal recovery algorithm for multiple-measurement vectors (MMV) problem is proposed in this paper. The sparse representation is iteratively drawn based on the idea of zero-point attracting projection (ZAP). In each iteration, the solution is first updated along the negative gradient direction of an approximate $\\ell_{2,0}$ norm to encourage sparsity, and then projected to the solution space to satisfy the under-determined equation. A variable step size scheme is adopted further to accelerate the convergence as well as to improve the recovery accuracy. Numerical simulations demons"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1203.1548","kind":"arxiv","version":2},"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":"2026-05-18T02:21:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HaBbK7/Y2tpTX2PbiMp0WIVQUFzFUxm1rM/OGv3LhV9EptLkfy8CmF34TIPsQM5xur7EwggNut5thWVvNR/DAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T05:46:27.653735Z"},"content_sha256":"4343cdd9e099446522c44be14313e00699e7a27c54f0d1adfd405693398b7059","schema_version":"1.0","event_id":"sha256:4343cdd9e099446522c44be14313e00699e7a27c54f0d1adfd405693398b7059"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/URF73XYS3GF6SOYEEOLO6R472E/bundle.json","state_url":"https://pith.science/pith/URF73XYS3GF6SOYEEOLO6R472E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/URF73XYS3GF6SOYEEOLO6R472E/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":"2026-06-05T05:46:27Z","links":{"resolver":"https://pith.science/pith/URF73XYS3GF6SOYEEOLO6R472E","bundle":"https://pith.science/pith/URF73XYS3GF6SOYEEOLO6R472E/bundle.json","state":"https://pith.science/pith/URF73XYS3GF6SOYEEOLO6R472E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/URF73XYS3GF6SOYEEOLO6R472E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:URF73XYS3GF6SOYEEOLO6R472E","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":"4e29d41b827d089bc52a640824c463c5179f9bc0d0781cc00db34fdaa5dcda84","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2012-03-07T17:31:27Z","title_canon_sha256":"13b8b2a14cb99921f21aad2920884b5723d707431415b2e6c421e3c29f798762"},"schema_version":"1.0","source":{"id":"1203.1548","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1203.1548","created_at":"2026-05-18T02:21:17Z"},{"alias_kind":"arxiv_version","alias_value":"1203.1548v2","created_at":"2026-05-18T02:21:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1203.1548","created_at":"2026-05-18T02:21:17Z"},{"alias_kind":"pith_short_12","alias_value":"URF73XYS3GF6","created_at":"2026-05-18T12:27:23Z"},{"alias_kind":"pith_short_16","alias_value":"URF73XYS3GF6SOYE","created_at":"2026-05-18T12:27:23Z"},{"alias_kind":"pith_short_8","alias_value":"URF73XYS","created_at":"2026-05-18T12:27:23Z"}],"graph_snapshots":[{"event_id":"sha256:4343cdd9e099446522c44be14313e00699e7a27c54f0d1adfd405693398b7059","target":"graph","created_at":"2026-05-18T02:21:17Z","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":"A new sparse signal recovery algorithm for multiple-measurement vectors (MMV) problem is proposed in this paper. The sparse representation is iteratively drawn based on the idea of zero-point attracting projection (ZAP). In each iteration, the solution is first updated along the negative gradient direction of an approximate $\\ell_{2,0}$ norm to encourage sparsity, and then projected to the solution space to satisfy the under-determined equation. A variable step size scheme is adopted further to accelerate the convergence as well as to improve the recovery accuracy. Numerical simulations demons","authors_text":"Hui Dai, Laming Chen, Wei Feng, Yang You, Yuantao Gu","cross_cats":["math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2012-03-07T17:31:27Z","title":"Retrieval of Sparse Solutions of Multiple-Measurement Vectors via Zero-point Attracting Projection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1203.1548","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:c042a11ab052e9604064b0f4d2979c1a8dacc42c052cfb54e9b158115b69b69f","target":"record","created_at":"2026-05-18T02:21:17Z","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":"4e29d41b827d089bc52a640824c463c5179f9bc0d0781cc00db34fdaa5dcda84","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2012-03-07T17:31:27Z","title_canon_sha256":"13b8b2a14cb99921f21aad2920884b5723d707431415b2e6c421e3c29f798762"},"schema_version":"1.0","source":{"id":"1203.1548","kind":"arxiv","version":2}},"canonical_sha256":"a44bfddf12d98be93b042396ef479fd1089a1dd8303da5bed771ee6f80b0448b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a44bfddf12d98be93b042396ef479fd1089a1dd8303da5bed771ee6f80b0448b","first_computed_at":"2026-05-18T02:21:17.215307Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:21:17.215307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"D89gC/O4qJQGH1gnzxM5CBlHlcg4FkxQG5TGiycxOLASpGKbrSLdO9Bxmc/RFjZps2yujjFxBBkOei38uZIlCg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:21:17.215823Z","signed_message":"canonical_sha256_bytes"},"source_id":"1203.1548","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c042a11ab052e9604064b0f4d2979c1a8dacc42c052cfb54e9b158115b69b69f","sha256:4343cdd9e099446522c44be14313e00699e7a27c54f0d1adfd405693398b7059"],"state_sha256":"15b601245744e7fa1352250013ff64a71be27ed977b42832a598fba90ac4f5db"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VjJVIk2/6uBBsgb4o+CjTv/rggihmXvHzKdR7lzHtZc8/V9pqu+eGkqXG4Gz9kWLQo1JFL76+LGtnm5y4xEMBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T05:46:27.657425Z","bundle_sha256":"3bf7573ffa1baa3e1cf6c3e08f35ea07b0c6c1b259afd3b882401ddc8bce6146"}}