{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:IWVDL4GJGC6Y4IZWESAOVEO32X","short_pith_number":"pith:IWVDL4GJ","schema_version":"1.0","canonical_sha256":"45aa35f0c930bd8e23362480ea91dbd5fee7f2516bfc4b7ffaadf3347c8a4842","source":{"kind":"arxiv","id":"1611.10257","version":1},"attestation_state":"computed","paper":{"title":"Signal Detection in Singular Value Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Mohsen Rakhshan","submitted_at":"2016-11-29T00:16:10Z","abstract_excerpt":"We develop an Iterative version of the Singular Value Decomposition (ISVD) that jointly analyzes a finite number of data matrices to identify signals that correlate among the rows of matrices. It will be illustrated how the supervised analysis of a big data set by another complex, multi-dimensional phenotype using the ISVD algorithm could lead to signal detection."},"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":"1611.10257","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-11-29T00:16:10Z","cross_cats_sorted":[],"title_canon_sha256":"fae0f5571209cc036a16f1ab8f351708dd56b2e36cc81c618f72e0d4b57efd28","abstract_canon_sha256":"7fd00194a749b36baa29628dfdbbc3fb8d4eeb1186da18f92c370b3969c1870e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:56:10.877701Z","signature_b64":"OSFXQGpCzkGg2NxcIvZqji7f7DSJUKvJFcLwVdFcgZLnUHmn2GVsMe0oCNt0jjiFFxDs54boj9vEEGgeLd2UAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45aa35f0c930bd8e23362480ea91dbd5fee7f2516bfc4b7ffaadf3347c8a4842","last_reissued_at":"2026-05-18T00:56:10.877239Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:56:10.877239Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Signal Detection in Singular Value Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Mohsen Rakhshan","submitted_at":"2016-11-29T00:16:10Z","abstract_excerpt":"We develop an Iterative version of the Singular Value Decomposition (ISVD) that jointly analyzes a finite number of data matrices to identify signals that correlate among the rows of matrices. It will be illustrated how the supervised analysis of a big data set by another complex, multi-dimensional phenotype using the ISVD algorithm could lead to signal detection."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.10257","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":"1611.10257","created_at":"2026-05-18T00:56:10.877332+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.10257v1","created_at":"2026-05-18T00:56:10.877332+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.10257","created_at":"2026-05-18T00:56:10.877332+00:00"},{"alias_kind":"pith_short_12","alias_value":"IWVDL4GJGC6Y","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_16","alias_value":"IWVDL4GJGC6Y4IZW","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_8","alias_value":"IWVDL4GJ","created_at":"2026-05-18T12:30:22.444734+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/IWVDL4GJGC6Y4IZWESAOVEO32X","json":"https://pith.science/pith/IWVDL4GJGC6Y4IZWESAOVEO32X.json","graph_json":"https://pith.science/api/pith-number/IWVDL4GJGC6Y4IZWESAOVEO32X/graph.json","events_json":"https://pith.science/api/pith-number/IWVDL4GJGC6Y4IZWESAOVEO32X/events.json","paper":"https://pith.science/paper/IWVDL4GJ"},"agent_actions":{"view_html":"https://pith.science/pith/IWVDL4GJGC6Y4IZWESAOVEO32X","download_json":"https://pith.science/pith/IWVDL4GJGC6Y4IZWESAOVEO32X.json","view_paper":"https://pith.science/paper/IWVDL4GJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.10257&json=true","fetch_graph":"https://pith.science/api/pith-number/IWVDL4GJGC6Y4IZWESAOVEO32X/graph.json","fetch_events":"https://pith.science/api/pith-number/IWVDL4GJGC6Y4IZWESAOVEO32X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IWVDL4GJGC6Y4IZWESAOVEO32X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IWVDL4GJGC6Y4IZWESAOVEO32X/action/storage_attestation","attest_author":"https://pith.science/pith/IWVDL4GJGC6Y4IZWESAOVEO32X/action/author_attestation","sign_citation":"https://pith.science/pith/IWVDL4GJGC6Y4IZWESAOVEO32X/action/citation_signature","submit_replication":"https://pith.science/pith/IWVDL4GJGC6Y4IZWESAOVEO32X/action/replication_record"}},"created_at":"2026-05-18T00:56:10.877332+00:00","updated_at":"2026-05-18T00:56:10.877332+00:00"}