{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:S5GYTEFUCLKOKBUDJLVENMDNQ7","short_pith_number":"pith:S5GYTEFU","canonical_record":{"source":{"id":"1406.4175","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2014-06-16T21:20:41Z","cross_cats_sorted":["math.IT","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"4f44104399cd0852cafffd92924561cc286a19bd418c3c00b0f58aaaf36cadf9","abstract_canon_sha256":"c961e6e5e7fb1eaa5f6a64e10c8447498aeb2df7944247aab856ac573527fa29"},"schema_version":"1.0"},"canonical_sha256":"974d8990b412d4e506834aea46b06d87e2c6498d72a49641d75418c35eaa9845","source":{"kind":"arxiv","id":"1406.4175","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1406.4175","created_at":"2026-05-18T01:17:01Z"},{"alias_kind":"arxiv_version","alias_value":"1406.4175v5","created_at":"2026-05-18T01:17:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.4175","created_at":"2026-05-18T01:17:01Z"},{"alias_kind":"pith_short_12","alias_value":"S5GYTEFUCLKO","created_at":"2026-05-18T12:28:49Z"},{"alias_kind":"pith_short_16","alias_value":"S5GYTEFUCLKOKBUD","created_at":"2026-05-18T12:28:49Z"},{"alias_kind":"pith_short_8","alias_value":"S5GYTEFU","created_at":"2026-05-18T12:28:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:S5GYTEFUCLKOKBUDJLVENMDNQ7","target":"record","payload":{"canonical_record":{"source":{"id":"1406.4175","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2014-06-16T21:20:41Z","cross_cats_sorted":["math.IT","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"4f44104399cd0852cafffd92924561cc286a19bd418c3c00b0f58aaaf36cadf9","abstract_canon_sha256":"c961e6e5e7fb1eaa5f6a64e10c8447498aeb2df7944247aab856ac573527fa29"},"schema_version":"1.0"},"canonical_sha256":"974d8990b412d4e506834aea46b06d87e2c6498d72a49641d75418c35eaa9845","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:17:01.320768Z","signature_b64":"oJzg+d/oQGNeu5ROiASjj9zcIWzpsLuKwQH128+dlEx66W1dzhgzhiu/eN1e7EmO78tX2NiFV/27EkTEjuQVBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"974d8990b412d4e506834aea46b06d87e2c6498d72a49641d75418c35eaa9845","last_reissued_at":"2026-05-18T01:17:01.319935Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:17:01.319935Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1406.4175","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:17:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ve6idPxCsNIGvni+heJCuRB1bRBftvDKWg0YilKEaHVSh/7VN89Fl/ZpxXUhwOMdaxMx/eAQJfVX6hVlZmvnAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T06:33:27.361827Z"},"content_sha256":"18dd3a14487e53d1dabba020ccdc4d90da20f39ec0407b51e4e7c7a10444a639","schema_version":"1.0","event_id":"sha256:18dd3a14487e53d1dabba020ccdc4d90da20f39ec0407b51e4e7c7a10444a639"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:S5GYTEFUCLKOKBUDJLVENMDNQ7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"From Denoising to Compressed Sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.IT","authors_text":"Arian Maleki, Christopher A. Metzler, Richard G. Baraniuk","submitted_at":"2014-06-16T21:20:41Z","abstract_excerpt":"A denoising algorithm seeks to remove noise, errors, or perturbations from a signal. Extensive research has been devoted to this arena over the last several decades, and as a result, today's denoisers can effectively remove large amounts of additive white Gaussian noise. A compressed sensing (CS) reconstruction algorithm seeks to recover a structured signal acquired using a small number of randomized measurements. Typical CS reconstruction algorithms can be cast as iteratively estimating a signal from a perturbed observation. This paper answers a natural question: How can one effectively emplo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.4175","kind":"arxiv","version":5},"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":"2026-05-18T01:17:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JavdLJzP53+6atr0wAMcZ8zsg7Q8MdLKw1gFea/toNWBa2aGlBo+0mMhaYutYpN5bfwiGmwL1SKKkcHmcGPhCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T06:33:27.362254Z"},"content_sha256":"a4e1f67672b8c1e1a391f7a54e75f1f26629cf9a6e10e00d34a04984ff402ea9","schema_version":"1.0","event_id":"sha256:a4e1f67672b8c1e1a391f7a54e75f1f26629cf9a6e10e00d34a04984ff402ea9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S5GYTEFUCLKOKBUDJLVENMDNQ7/bundle.json","state_url":"https://pith.science/pith/S5GYTEFUCLKOKBUDJLVENMDNQ7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S5GYTEFUCLKOKBUDJLVENMDNQ7/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":"2026-05-31T06:33:27Z","links":{"resolver":"https://pith.science/pith/S5GYTEFUCLKOKBUDJLVENMDNQ7","bundle":"https://pith.science/pith/S5GYTEFUCLKOKBUDJLVENMDNQ7/bundle.json","state":"https://pith.science/pith/S5GYTEFUCLKOKBUDJLVENMDNQ7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S5GYTEFUCLKOKBUDJLVENMDNQ7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:S5GYTEFUCLKOKBUDJLVENMDNQ7","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":"c961e6e5e7fb1eaa5f6a64e10c8447498aeb2df7944247aab856ac573527fa29","cross_cats_sorted":["math.IT","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2014-06-16T21:20:41Z","title_canon_sha256":"4f44104399cd0852cafffd92924561cc286a19bd418c3c00b0f58aaaf36cadf9"},"schema_version":"1.0","source":{"id":"1406.4175","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1406.4175","created_at":"2026-05-18T01:17:01Z"},{"alias_kind":"arxiv_version","alias_value":"1406.4175v5","created_at":"2026-05-18T01:17:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.4175","created_at":"2026-05-18T01:17:01Z"},{"alias_kind":"pith_short_12","alias_value":"S5GYTEFUCLKO","created_at":"2026-05-18T12:28:49Z"},{"alias_kind":"pith_short_16","alias_value":"S5GYTEFUCLKOKBUD","created_at":"2026-05-18T12:28:49Z"},{"alias_kind":"pith_short_8","alias_value":"S5GYTEFU","created_at":"2026-05-18T12:28:49Z"}],"graph_snapshots":[{"event_id":"sha256:a4e1f67672b8c1e1a391f7a54e75f1f26629cf9a6e10e00d34a04984ff402ea9","target":"graph","created_at":"2026-05-18T01:17:01Z","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 denoising algorithm seeks to remove noise, errors, or perturbations from a signal. Extensive research has been devoted to this arena over the last several decades, and as a result, today's denoisers can effectively remove large amounts of additive white Gaussian noise. A compressed sensing (CS) reconstruction algorithm seeks to recover a structured signal acquired using a small number of randomized measurements. Typical CS reconstruction algorithms can be cast as iteratively estimating a signal from a perturbed observation. This paper answers a natural question: How can one effectively emplo","authors_text":"Arian Maleki, Christopher A. Metzler, Richard G. Baraniuk","cross_cats":["math.IT","math.ST","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2014-06-16T21:20:41Z","title":"From Denoising to Compressed Sensing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.4175","kind":"arxiv","version":5},"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":"sha256:18dd3a14487e53d1dabba020ccdc4d90da20f39ec0407b51e4e7c7a10444a639","target":"record","created_at":"2026-05-18T01:17:01Z","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":"c961e6e5e7fb1eaa5f6a64e10c8447498aeb2df7944247aab856ac573527fa29","cross_cats_sorted":["math.IT","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2014-06-16T21:20:41Z","title_canon_sha256":"4f44104399cd0852cafffd92924561cc286a19bd418c3c00b0f58aaaf36cadf9"},"schema_version":"1.0","source":{"id":"1406.4175","kind":"arxiv","version":5}},"canonical_sha256":"974d8990b412d4e506834aea46b06d87e2c6498d72a49641d75418c35eaa9845","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"974d8990b412d4e506834aea46b06d87e2c6498d72a49641d75418c35eaa9845","first_computed_at":"2026-05-18T01:17:01.319935Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:17:01.319935Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oJzg+d/oQGNeu5ROiASjj9zcIWzpsLuKwQH128+dlEx66W1dzhgzhiu/eN1e7EmO78tX2NiFV/27EkTEjuQVBw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:17:01.320768Z","signed_message":"canonical_sha256_bytes"},"source_id":"1406.4175","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:18dd3a14487e53d1dabba020ccdc4d90da20f39ec0407b51e4e7c7a10444a639","sha256:a4e1f67672b8c1e1a391f7a54e75f1f26629cf9a6e10e00d34a04984ff402ea9"],"state_sha256":"1b99012399f3f2780d63a8fd04adf86293ad58bfa72caca41b4a881fcf40b9be"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jW45a1HasazcJYqgl5QEt0FU1bJHxwEfvGV5XTocF8R2jagAbf+lk1zFYPHyL3cvlZZm2zzLnHZBCuYzRUeNDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T06:33:27.365116Z","bundle_sha256":"b6f7517f9f356ce5d42616f089ea168c4a9befbbbb070b68a5864c058ab63894"}}