{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:FO4GGCKKS4EFE2KRONN46FFQHF","short_pith_number":"pith:FO4GGCKK","schema_version":"1.0","canonical_sha256":"2bb863094a9708526951735bcf14b03959fe82d18c3875e7cc5d273f06b33d61","source":{"kind":"arxiv","id":"1903.00767","version":1},"attestation_state":"computed","paper":{"title":"Large Scale 2D Spectral Compressed Sensing in Continuous Domain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Jian-Feng Cai, Weiyu Xu, Yang Yang","submitted_at":"2019-03-02T21:26:06Z","abstract_excerpt":"We consider the problem of spectral compressed sensing in continuous domain, which aims to recover a 2-dimensional spectrally sparse signal from partially observed time samples. The signal is assumed to be a superposition of s complex sinusoids. We propose a semidefinite program for the 2D signal recovery problem. Our model is able to handle large scale 2D signals of size 500*500, whereas traditional approaches only handle signals of size around 20*20."},"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":"1903.00767","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-03-02T21:26:06Z","cross_cats_sorted":[],"title_canon_sha256":"4b3beaebc47630c2f080b223ed4e9a7c522dac7681a33ba6aef1aa13c1a5274b","abstract_canon_sha256":"1e0c46c98245f784a174152f032766926e2e98cb9ac9333b6c0da52bc87b84a6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:13.342340Z","signature_b64":"i2mFKTcojyIv57yuxAoxRmqL/k3+NZgTfn/9PKHOAGWPCbCSw6m0DQfoZVszjUf1QHjDc/A2eiqq3QHZX0cyBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2bb863094a9708526951735bcf14b03959fe82d18c3875e7cc5d273f06b33d61","last_reissued_at":"2026-05-17T23:52:13.341611Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:13.341611Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large Scale 2D Spectral Compressed Sensing in Continuous Domain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Jian-Feng Cai, Weiyu Xu, Yang Yang","submitted_at":"2019-03-02T21:26:06Z","abstract_excerpt":"We consider the problem of spectral compressed sensing in continuous domain, which aims to recover a 2-dimensional spectrally sparse signal from partially observed time samples. The signal is assumed to be a superposition of s complex sinusoids. We propose a semidefinite program for the 2D signal recovery problem. Our model is able to handle large scale 2D signals of size 500*500, whereas traditional approaches only handle signals of size around 20*20."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00767","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1903.00767","created_at":"2026-05-17T23:52:13.341706+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.00767v1","created_at":"2026-05-17T23:52:13.341706+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00767","created_at":"2026-05-17T23:52:13.341706+00:00"},{"alias_kind":"pith_short_12","alias_value":"FO4GGCKKS4EF","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"FO4GGCKKS4EFE2KR","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"FO4GGCKK","created_at":"2026-05-18T12:33:15.570797+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/FO4GGCKKS4EFE2KRONN46FFQHF","json":"https://pith.science/pith/FO4GGCKKS4EFE2KRONN46FFQHF.json","graph_json":"https://pith.science/api/pith-number/FO4GGCKKS4EFE2KRONN46FFQHF/graph.json","events_json":"https://pith.science/api/pith-number/FO4GGCKKS4EFE2KRONN46FFQHF/events.json","paper":"https://pith.science/paper/FO4GGCKK"},"agent_actions":{"view_html":"https://pith.science/pith/FO4GGCKKS4EFE2KRONN46FFQHF","download_json":"https://pith.science/pith/FO4GGCKKS4EFE2KRONN46FFQHF.json","view_paper":"https://pith.science/paper/FO4GGCKK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.00767&json=true","fetch_graph":"https://pith.science/api/pith-number/FO4GGCKKS4EFE2KRONN46FFQHF/graph.json","fetch_events":"https://pith.science/api/pith-number/FO4GGCKKS4EFE2KRONN46FFQHF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FO4GGCKKS4EFE2KRONN46FFQHF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FO4GGCKKS4EFE2KRONN46FFQHF/action/storage_attestation","attest_author":"https://pith.science/pith/FO4GGCKKS4EFE2KRONN46FFQHF/action/author_attestation","sign_citation":"https://pith.science/pith/FO4GGCKKS4EFE2KRONN46FFQHF/action/citation_signature","submit_replication":"https://pith.science/pith/FO4GGCKKS4EFE2KRONN46FFQHF/action/replication_record"}},"created_at":"2026-05-17T23:52:13.341706+00:00","updated_at":"2026-05-17T23:52:13.341706+00:00"}