{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:TCL4PPHJOWAAQDEIUUMT36PJAG","short_pith_number":"pith:TCL4PPHJ","schema_version":"1.0","canonical_sha256":"9897c7bce97580080c88a5193df9e901af9f8e9853f7b41fd4f07cf6047b3213","source":{"kind":"arxiv","id":"1604.02100","version":2},"attestation_state":"computed","paper":{"title":"Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.NA","math.IT","math.NA","math.SP","physics.med-ph"],"primary_cat":"stat.ML","authors_text":"Di Guo, Hengfa Lu, Jian-Feng Cai, Jiaxi Ying, Jihui Wu, Qingtao Wei, Xiaobo Qu, Zhong Chen","submitted_at":"2016-04-06T12:51:07Z","abstract_excerpt":"Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging. For fast data acquisition or other inevitable reasons, however, only a small amount of samples may be acquired and thus how to recover the full signal becomes an active research topic. But existing approaches can not efficiently recover $N$-dimensional exponential signals with $N\\geq 3$. In this paper, we study the problem of recovering N-dimensional (particularly $N\\geq 3$) exponential signals from partial observations, and formulate this problem as a low-rank t"},"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":"1604.02100","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-04-06T12:51:07Z","cross_cats_sorted":["cs.IT","cs.NA","math.IT","math.NA","math.SP","physics.med-ph"],"title_canon_sha256":"04b0319abf114576853a86fe2a256f803ff9a903a2fe2511fa3d0c7fd375a437","abstract_canon_sha256":"1760e64873954850d1331d48db6739a94d742710f4dbb445560b91d926de5318"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T17:10:09.947928Z","signature_b64":"P6CdMIX9YsUlcxW/PzoVBT+SVSLuYbdYU0xuXM3bPay/fpRFDB9VzL/LtnJaQSS9GsctN9kRRKSsgQbgh/r2BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9897c7bce97580080c88a5193df9e901af9f8e9853f7b41fd4f07cf6047b3213","last_reissued_at":"2026-06-04T17:10:09.947370Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T17:10:09.947370Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.NA","math.IT","math.NA","math.SP","physics.med-ph"],"primary_cat":"stat.ML","authors_text":"Di Guo, Hengfa Lu, Jian-Feng Cai, Jiaxi Ying, Jihui Wu, Qingtao Wei, Xiaobo Qu, Zhong Chen","submitted_at":"2016-04-06T12:51:07Z","abstract_excerpt":"Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging. For fast data acquisition or other inevitable reasons, however, only a small amount of samples may be acquired and thus how to recover the full signal becomes an active research topic. But existing approaches can not efficiently recover $N$-dimensional exponential signals with $N\\geq 3$. In this paper, we study the problem of recovering N-dimensional (particularly $N\\geq 3$) exponential signals from partial observations, and formulate this problem as a low-rank t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.02100","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1604.02100/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"1604.02100","created_at":"2026-06-04T17:10:09.947437+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.02100v2","created_at":"2026-06-04T17:10:09.947437+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.02100","created_at":"2026-06-04T17:10:09.947437+00:00"},{"alias_kind":"pith_short_12","alias_value":"TCL4PPHJOWAA","created_at":"2026-06-04T17:10:09.947437+00:00"},{"alias_kind":"pith_short_16","alias_value":"TCL4PPHJOWAAQDEI","created_at":"2026-06-04T17:10:09.947437+00:00"},{"alias_kind":"pith_short_8","alias_value":"TCL4PPHJ","created_at":"2026-06-04T17:10:09.947437+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/TCL4PPHJOWAAQDEIUUMT36PJAG","json":"https://pith.science/pith/TCL4PPHJOWAAQDEIUUMT36PJAG.json","graph_json":"https://pith.science/api/pith-number/TCL4PPHJOWAAQDEIUUMT36PJAG/graph.json","events_json":"https://pith.science/api/pith-number/TCL4PPHJOWAAQDEIUUMT36PJAG/events.json","paper":"https://pith.science/paper/TCL4PPHJ"},"agent_actions":{"view_html":"https://pith.science/pith/TCL4PPHJOWAAQDEIUUMT36PJAG","download_json":"https://pith.science/pith/TCL4PPHJOWAAQDEIUUMT36PJAG.json","view_paper":"https://pith.science/paper/TCL4PPHJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.02100&json=true","fetch_graph":"https://pith.science/api/pith-number/TCL4PPHJOWAAQDEIUUMT36PJAG/graph.json","fetch_events":"https://pith.science/api/pith-number/TCL4PPHJOWAAQDEIUUMT36PJAG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TCL4PPHJOWAAQDEIUUMT36PJAG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TCL4PPHJOWAAQDEIUUMT36PJAG/action/storage_attestation","attest_author":"https://pith.science/pith/TCL4PPHJOWAAQDEIUUMT36PJAG/action/author_attestation","sign_citation":"https://pith.science/pith/TCL4PPHJOWAAQDEIUUMT36PJAG/action/citation_signature","submit_replication":"https://pith.science/pith/TCL4PPHJOWAAQDEIUUMT36PJAG/action/replication_record"}},"created_at":"2026-06-04T17:10:09.947437+00:00","updated_at":"2026-06-04T17:10:09.947437+00:00"}