{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:KY4O5W4DAZXX6ENRCEWOUVY22H","short_pith_number":"pith:KY4O5W4D","schema_version":"1.0","canonical_sha256":"5638eedb83066f7f11b1112cea571ad1e08eb5857b3877cb9ff177b918000bce","source":{"kind":"arxiv","id":"1412.7646","version":2},"attestation_state":"computed","paper":{"title":"Sub-linear Time Support Recovery for Compressed Sensing using Sparse-Graph Codes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Dong Yin, Kannan Ramchandran, Ramtin Pedarsani, Sameer Pawar, Xiao Li","submitted_at":"2014-12-24T11:55:14Z","abstract_excerpt":"We study the support recovery problem for compressed sensing, where the goal is to reconstruct the a high-dimensional $K$-sparse signal $\\mathbf{x}\\in\\mathbb{R}^N$, from low-dimensional linear measurements with and without noise. Our key contribution is a new compressed sensing framework through a new family of carefully designed sparse measurement matrices associated with minimal measurement costs and a low-complexity recovery algorithm. The measurement matrix in our framework is designed based on the well-crafted sparsification through capacity-approaching sparse-graph codes, where the spars"},"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":"1412.7646","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2014-12-24T11:55:14Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"6b7f98064950511230a64f62f2bcc2d91a29f0ff57c98f9080132256ce91eff1","abstract_canon_sha256":"f8c8cc5d2b031b924504f3dfd07e8a5189189a93807ae69017acdbaf7e991302"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:40.641264Z","signature_b64":"ddmPG9zUNZS6esWun359HeTbxqfgYq0rur3GNAwweFi4ZOCw/fTYvMA9gE79YAn8YkLr2JxnW40PdwrlEvMWDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5638eedb83066f7f11b1112cea571ad1e08eb5857b3877cb9ff177b918000bce","last_reissued_at":"2026-05-18T00:22:40.640711Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:40.640711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sub-linear Time Support Recovery for Compressed Sensing using Sparse-Graph Codes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Dong Yin, Kannan Ramchandran, Ramtin Pedarsani, Sameer Pawar, Xiao Li","submitted_at":"2014-12-24T11:55:14Z","abstract_excerpt":"We study the support recovery problem for compressed sensing, where the goal is to reconstruct the a high-dimensional $K$-sparse signal $\\mathbf{x}\\in\\mathbb{R}^N$, from low-dimensional linear measurements with and without noise. Our key contribution is a new compressed sensing framework through a new family of carefully designed sparse measurement matrices associated with minimal measurement costs and a low-complexity recovery algorithm. The measurement matrix in our framework is designed based on the well-crafted sparsification through capacity-approaching sparse-graph codes, where the spars"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.7646","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1412.7646","created_at":"2026-05-18T00:22:40.640788+00:00"},{"alias_kind":"arxiv_version","alias_value":"1412.7646v2","created_at":"2026-05-18T00:22:40.640788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.7646","created_at":"2026-05-18T00:22:40.640788+00:00"},{"alias_kind":"pith_short_12","alias_value":"KY4O5W4DAZXX","created_at":"2026-05-18T12:28:35.611951+00:00"},{"alias_kind":"pith_short_16","alias_value":"KY4O5W4DAZXX6ENR","created_at":"2026-05-18T12:28:35.611951+00:00"},{"alias_kind":"pith_short_8","alias_value":"KY4O5W4D","created_at":"2026-05-18T12:28:35.611951+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/KY4O5W4DAZXX6ENRCEWOUVY22H","json":"https://pith.science/pith/KY4O5W4DAZXX6ENRCEWOUVY22H.json","graph_json":"https://pith.science/api/pith-number/KY4O5W4DAZXX6ENRCEWOUVY22H/graph.json","events_json":"https://pith.science/api/pith-number/KY4O5W4DAZXX6ENRCEWOUVY22H/events.json","paper":"https://pith.science/paper/KY4O5W4D"},"agent_actions":{"view_html":"https://pith.science/pith/KY4O5W4DAZXX6ENRCEWOUVY22H","download_json":"https://pith.science/pith/KY4O5W4DAZXX6ENRCEWOUVY22H.json","view_paper":"https://pith.science/paper/KY4O5W4D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1412.7646&json=true","fetch_graph":"https://pith.science/api/pith-number/KY4O5W4DAZXX6ENRCEWOUVY22H/graph.json","fetch_events":"https://pith.science/api/pith-number/KY4O5W4DAZXX6ENRCEWOUVY22H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KY4O5W4DAZXX6ENRCEWOUVY22H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KY4O5W4DAZXX6ENRCEWOUVY22H/action/storage_attestation","attest_author":"https://pith.science/pith/KY4O5W4DAZXX6ENRCEWOUVY22H/action/author_attestation","sign_citation":"https://pith.science/pith/KY4O5W4DAZXX6ENRCEWOUVY22H/action/citation_signature","submit_replication":"https://pith.science/pith/KY4O5W4DAZXX6ENRCEWOUVY22H/action/replication_record"}},"created_at":"2026-05-18T00:22:40.640788+00:00","updated_at":"2026-05-18T00:22:40.640788+00:00"}