{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:QRGA6BNUSW6B7YOTVYJHJ5GWPT","short_pith_number":"pith:QRGA6BNU","canonical_record":{"source":{"id":"1710.10062","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-10-27T10:32:23Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"a3223a469ab3f883d77c245ee3e3043222e4b77d383d137e265d3e4d8a58c727","abstract_canon_sha256":"235ebb3007e2bf8465e127e02b6ed26b0bbb4f19f1214e6072527b5796d1561f"},"schema_version":"1.0"},"canonical_sha256":"844c0f05b495bc1fe1d3ae1274f4d67cd5ad30ee7e028296ca70f0e31a0e78d7","source":{"kind":"arxiv","id":"1710.10062","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.10062","created_at":"2026-05-18T00:09:00Z"},{"alias_kind":"arxiv_version","alias_value":"1710.10062v1","created_at":"2026-05-18T00:09:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10062","created_at":"2026-05-18T00:09:00Z"},{"alias_kind":"pith_short_12","alias_value":"QRGA6BNUSW6B","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QRGA6BNUSW6B7YOT","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QRGA6BNU","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:QRGA6BNUSW6B7YOTVYJHJ5GWPT","target":"record","payload":{"canonical_record":{"source":{"id":"1710.10062","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-10-27T10:32:23Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"a3223a469ab3f883d77c245ee3e3043222e4b77d383d137e265d3e4d8a58c727","abstract_canon_sha256":"235ebb3007e2bf8465e127e02b6ed26b0bbb4f19f1214e6072527b5796d1561f"},"schema_version":"1.0"},"canonical_sha256":"844c0f05b495bc1fe1d3ae1274f4d67cd5ad30ee7e028296ca70f0e31a0e78d7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:00.388678Z","signature_b64":"y12/7AhkmHtjJdAY63XfKgW+WvzIzMNpfkCREOGTpua1QqSV5q76PXjNQdIy9zVbOp5qbJYJ2juu+iAgFOVbAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"844c0f05b495bc1fe1d3ae1274f4d67cd5ad30ee7e028296ca70f0e31a0e78d7","last_reissued_at":"2026-05-18T00:09:00.387977Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:00.387977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.10062","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":"2026-05-18T00:09:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"khpscM4ZUjfQ/eoLNvgL188OTtFxvdJ2MKNGgmXo2bMGzDgoPHdAsatY5c75tUbAljowzcVq0H9medOBa97OAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T04:07:03.436530Z"},"content_sha256":"ac8209008eafee7848257ea2ba6b06db39ac47d95ca41de5b171417b2573f926","schema_version":"1.0","event_id":"sha256:ac8209008eafee7848257ea2ba6b06db39ac47d95ca41de5b171417b2573f926"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:QRGA6BNUSW6B7YOTVYJHJ5GWPT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recovery of Structured Signals with Prior Information via Maximizing Correlation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Wei Cui, Xu Zhang, Yulong Liu","submitted_at":"2017-10-27T10:32:23Z","abstract_excerpt":"This paper considers the problem of recovering a structured signal from a relatively small number of noisy measurements with the aid of a similar signal which is known beforehand. We propose a new approach to integrate prior information into the standard recovery procedure by maximizing the correlation between the prior knowledge and the desired signal. We then establish performance guarantees (in terms of the number of measurements) for the proposed method under sub-Gaussian measurements. Specific structured signals including sparse vectors, block-sparse vectors, and low-rank matrices are als"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10062","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":"2026-05-18T00:09:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d5jzplfv3Lk2FWb/4yZ2EWEmtb9QZ1BAEZBIr83o+P+2Y6F68vg4AI/gD5fLYZLy/qOmQUiaO9lNHNlm4YqCAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T04:07:03.436870Z"},"content_sha256":"17f565e465355fd89335a517d7749b7d664092e9646d0f009eaefe0a25532c7a","schema_version":"1.0","event_id":"sha256:17f565e465355fd89335a517d7749b7d664092e9646d0f009eaefe0a25532c7a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QRGA6BNUSW6B7YOTVYJHJ5GWPT/bundle.json","state_url":"https://pith.science/pith/QRGA6BNUSW6B7YOTVYJHJ5GWPT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QRGA6BNUSW6B7YOTVYJHJ5GWPT/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":"2026-06-02T04:07:03Z","links":{"resolver":"https://pith.science/pith/QRGA6BNUSW6B7YOTVYJHJ5GWPT","bundle":"https://pith.science/pith/QRGA6BNUSW6B7YOTVYJHJ5GWPT/bundle.json","state":"https://pith.science/pith/QRGA6BNUSW6B7YOTVYJHJ5GWPT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QRGA6BNUSW6B7YOTVYJHJ5GWPT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:QRGA6BNUSW6B7YOTVYJHJ5GWPT","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":"235ebb3007e2bf8465e127e02b6ed26b0bbb4f19f1214e6072527b5796d1561f","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-10-27T10:32:23Z","title_canon_sha256":"a3223a469ab3f883d77c245ee3e3043222e4b77d383d137e265d3e4d8a58c727"},"schema_version":"1.0","source":{"id":"1710.10062","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.10062","created_at":"2026-05-18T00:09:00Z"},{"alias_kind":"arxiv_version","alias_value":"1710.10062v1","created_at":"2026-05-18T00:09:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10062","created_at":"2026-05-18T00:09:00Z"},{"alias_kind":"pith_short_12","alias_value":"QRGA6BNUSW6B","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QRGA6BNUSW6B7YOT","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QRGA6BNU","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:17f565e465355fd89335a517d7749b7d664092e9646d0f009eaefe0a25532c7a","target":"graph","created_at":"2026-05-18T00:09:00Z","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":"This paper considers the problem of recovering a structured signal from a relatively small number of noisy measurements with the aid of a similar signal which is known beforehand. We propose a new approach to integrate prior information into the standard recovery procedure by maximizing the correlation between the prior knowledge and the desired signal. We then establish performance guarantees (in terms of the number of measurements) for the proposed method under sub-Gaussian measurements. Specific structured signals including sparse vectors, block-sparse vectors, and low-rank matrices are als","authors_text":"Wei Cui, Xu Zhang, Yulong Liu","cross_cats":["math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-10-27T10:32:23Z","title":"Recovery of Structured Signals with Prior Information via Maximizing Correlation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10062","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:ac8209008eafee7848257ea2ba6b06db39ac47d95ca41de5b171417b2573f926","target":"record","created_at":"2026-05-18T00:09:00Z","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":"235ebb3007e2bf8465e127e02b6ed26b0bbb4f19f1214e6072527b5796d1561f","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-10-27T10:32:23Z","title_canon_sha256":"a3223a469ab3f883d77c245ee3e3043222e4b77d383d137e265d3e4d8a58c727"},"schema_version":"1.0","source":{"id":"1710.10062","kind":"arxiv","version":1}},"canonical_sha256":"844c0f05b495bc1fe1d3ae1274f4d67cd5ad30ee7e028296ca70f0e31a0e78d7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"844c0f05b495bc1fe1d3ae1274f4d67cd5ad30ee7e028296ca70f0e31a0e78d7","first_computed_at":"2026-05-18T00:09:00.387977Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:09:00.387977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"y12/7AhkmHtjJdAY63XfKgW+WvzIzMNpfkCREOGTpua1QqSV5q76PXjNQdIy9zVbOp5qbJYJ2juu+iAgFOVbAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:09:00.388678Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.10062","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ac8209008eafee7848257ea2ba6b06db39ac47d95ca41de5b171417b2573f926","sha256:17f565e465355fd89335a517d7749b7d664092e9646d0f009eaefe0a25532c7a"],"state_sha256":"4423738b3305bba83313e34634dfceaa82962c995e682421d214414844c4af8c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nAMQRtWoSurqv8npGJ70+N8DHP7pD0S0D0bi9an3KnYnF61i6So7vLpi5jVT9VzfyoZi/X+3e+gJJNkmh+bcDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T04:07:03.438749Z","bundle_sha256":"f88f4f00a36fcb882a8bec0459dda47c019f49b554981d007fa6837a94e29c60"}}