{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:2GML32PYNJZTIBOLAVUYJI6CFP","short_pith_number":"pith:2GML32PY","schema_version":"1.0","canonical_sha256":"d198bde9f86a733405cb056984a3c22bdce4d365d84b616ac9e11fd2ec926aa1","source":{"kind":"arxiv","id":"1604.05640","version":2},"attestation_state":"computed","paper":{"title":"Achieving Super-Resolution in Multi-Rate Sampling Systems via Efficient Semidefinite Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"M. Ferreira Da Costa, W. Dai","submitted_at":"2016-04-19T16:27:05Z","abstract_excerpt":"Super-resolution theory aims to estimate the discrete components lying in a continuous space that constitute a sparse signal with optimal precision. This work investigates the potential of recent super-resolution techniques for spectral estimation in multi-rate sampling systems. It shows that, under the existence of a common supporting grid, and under a minimal separation constraint, the frequencies of a spectrally sparse signal can be exactly jointly recovered from the output of a semidefinite program (SDP). The algorithmic complexity of this approach is discussed, and an equivalent SDP of mi"},"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":"1604.05640","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2016-04-19T16:27:05Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"e6a5d63e219d6e1e52b74af2f9dc36f84cd92e2a57a7954c8761ff8c1f7ecd0b","abstract_canon_sha256":"3653b01951e0ef4c14d0687e3358b37ad19c495714b9bcefefa2ee1398764906"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:57:04.580130Z","signature_b64":"Qc9/TCex6r7gGlV+wk6Jkp/XOMGttsAR2ietTGqj0ejSMOhrjOsg8R2GzTl5a8PINrGrI0FwYTQOAofNuRfgAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d198bde9f86a733405cb056984a3c22bdce4d365d84b616ac9e11fd2ec926aa1","last_reissued_at":"2026-05-18T00:57:04.579538Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:57:04.579538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Achieving Super-Resolution in Multi-Rate Sampling Systems via Efficient Semidefinite Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"M. Ferreira Da Costa, W. Dai","submitted_at":"2016-04-19T16:27:05Z","abstract_excerpt":"Super-resolution theory aims to estimate the discrete components lying in a continuous space that constitute a sparse signal with optimal precision. This work investigates the potential of recent super-resolution techniques for spectral estimation in multi-rate sampling systems. It shows that, under the existence of a common supporting grid, and under a minimal separation constraint, the frequencies of a spectrally sparse signal can be exactly jointly recovered from the output of a semidefinite program (SDP). The algorithmic complexity of this approach is discussed, and an equivalent SDP of mi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.05640","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":"1604.05640","created_at":"2026-05-18T00:57:04.579622+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.05640v2","created_at":"2026-05-18T00:57:04.579622+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.05640","created_at":"2026-05-18T00:57:04.579622+00:00"},{"alias_kind":"pith_short_12","alias_value":"2GML32PYNJZT","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_16","alias_value":"2GML32PYNJZTIBOL","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_8","alias_value":"2GML32PY","created_at":"2026-05-18T12:29:55.572404+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/2GML32PYNJZTIBOLAVUYJI6CFP","json":"https://pith.science/pith/2GML32PYNJZTIBOLAVUYJI6CFP.json","graph_json":"https://pith.science/api/pith-number/2GML32PYNJZTIBOLAVUYJI6CFP/graph.json","events_json":"https://pith.science/api/pith-number/2GML32PYNJZTIBOLAVUYJI6CFP/events.json","paper":"https://pith.science/paper/2GML32PY"},"agent_actions":{"view_html":"https://pith.science/pith/2GML32PYNJZTIBOLAVUYJI6CFP","download_json":"https://pith.science/pith/2GML32PYNJZTIBOLAVUYJI6CFP.json","view_paper":"https://pith.science/paper/2GML32PY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.05640&json=true","fetch_graph":"https://pith.science/api/pith-number/2GML32PYNJZTIBOLAVUYJI6CFP/graph.json","fetch_events":"https://pith.science/api/pith-number/2GML32PYNJZTIBOLAVUYJI6CFP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2GML32PYNJZTIBOLAVUYJI6CFP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2GML32PYNJZTIBOLAVUYJI6CFP/action/storage_attestation","attest_author":"https://pith.science/pith/2GML32PYNJZTIBOLAVUYJI6CFP/action/author_attestation","sign_citation":"https://pith.science/pith/2GML32PYNJZTIBOLAVUYJI6CFP/action/citation_signature","submit_replication":"https://pith.science/pith/2GML32PYNJZTIBOLAVUYJI6CFP/action/replication_record"}},"created_at":"2026-05-18T00:57:04.579622+00:00","updated_at":"2026-05-18T00:57:04.579622+00:00"}