{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:TEIOTJU4LXK7KTHFTKK7LKK4AM","short_pith_number":"pith:TEIOTJU4","schema_version":"1.0","canonical_sha256":"9910e9a69c5dd5f54ce59a95f5a95c030d557b34aa2c8dfd41d02d85e673bbd9","source":{"kind":"arxiv","id":"1011.3854","version":3},"attestation_state":"computed","paper":{"title":"A probabilistic and RIPless theory of compressed sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Emmanuel J. Candes, Yaniv Plan","submitted_at":"2010-11-16T23:16:43Z","abstract_excerpt":"This paper introduces a simple and very general theory of compressive sensing. In this theory, the sensing mechanism simply selects sensing vectors independently at random from a probability distribution F; it includes all models - e.g. Gaussian, frequency measurements - discussed in the literature, but also provides a framework for new measurement strategies as well. We prove that if the probability distribution F obeys a simple incoherence property and an isotropy property, one can faithfully recover approximately sparse signals from a minimal number of noisy measurements. The novelty is tha"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1011.3854","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2010-11-16T23:16:43Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"0e49a6327ccfa1ca8a316ecba5ea27f2998513b07b9e65f2bf7bf2c37f284095","abstract_canon_sha256":"b6afb6fd5bd9211041374bbfd4f1483537a7c1a0f1c92b07ad59fa9b0f6158c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:35:05.959468Z","signature_b64":"vCY7npuZjn/YwWUOGgdIafIMCidlW3d0hshoUIkx+AxlhocdMX0L1cvRCS9sriA5015J7wC30VQS45Cmk2fkAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9910e9a69c5dd5f54ce59a95f5a95c030d557b34aa2c8dfd41d02d85e673bbd9","last_reissued_at":"2026-05-18T04:35:05.958989Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:35:05.958989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A probabilistic and RIPless theory of compressed sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Emmanuel J. Candes, Yaniv Plan","submitted_at":"2010-11-16T23:16:43Z","abstract_excerpt":"This paper introduces a simple and very general theory of compressive sensing. In this theory, the sensing mechanism simply selects sensing vectors independently at random from a probability distribution F; it includes all models - e.g. Gaussian, frequency measurements - discussed in the literature, but also provides a framework for new measurement strategies as well. We prove that if the probability distribution F obeys a simple incoherence property and an isotropy property, one can faithfully recover approximately sparse signals from a minimal number of noisy measurements. The novelty is tha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1011.3854","kind":"arxiv","version":3},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1011.3854","created_at":"2026-05-18T04:35:05.959066+00:00"},{"alias_kind":"arxiv_version","alias_value":"1011.3854v3","created_at":"2026-05-18T04:35:05.959066+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1011.3854","created_at":"2026-05-18T04:35:05.959066+00:00"},{"alias_kind":"pith_short_12","alias_value":"TEIOTJU4LXK7","created_at":"2026-05-18T12:26:13.927090+00:00"},{"alias_kind":"pith_short_16","alias_value":"TEIOTJU4LXK7KTHF","created_at":"2026-05-18T12:26:13.927090+00:00"},{"alias_kind":"pith_short_8","alias_value":"TEIOTJU4","created_at":"2026-05-18T12:26:13.927090+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TEIOTJU4LXK7KTHFTKK7LKK4AM","json":"https://pith.science/pith/TEIOTJU4LXK7KTHFTKK7LKK4AM.json","graph_json":"https://pith.science/api/pith-number/TEIOTJU4LXK7KTHFTKK7LKK4AM/graph.json","events_json":"https://pith.science/api/pith-number/TEIOTJU4LXK7KTHFTKK7LKK4AM/events.json","paper":"https://pith.science/paper/TEIOTJU4"},"agent_actions":{"view_html":"https://pith.science/pith/TEIOTJU4LXK7KTHFTKK7LKK4AM","download_json":"https://pith.science/pith/TEIOTJU4LXK7KTHFTKK7LKK4AM.json","view_paper":"https://pith.science/paper/TEIOTJU4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1011.3854&json=true","fetch_graph":"https://pith.science/api/pith-number/TEIOTJU4LXK7KTHFTKK7LKK4AM/graph.json","fetch_events":"https://pith.science/api/pith-number/TEIOTJU4LXK7KTHFTKK7LKK4AM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TEIOTJU4LXK7KTHFTKK7LKK4AM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TEIOTJU4LXK7KTHFTKK7LKK4AM/action/storage_attestation","attest_author":"https://pith.science/pith/TEIOTJU4LXK7KTHFTKK7LKK4AM/action/author_attestation","sign_citation":"https://pith.science/pith/TEIOTJU4LXK7KTHFTKK7LKK4AM/action/citation_signature","submit_replication":"https://pith.science/pith/TEIOTJU4LXK7KTHFTKK7LKK4AM/action/replication_record"}},"created_at":"2026-05-18T04:35:05.959066+00:00","updated_at":"2026-05-18T04:35:05.959066+00:00"}