{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:22BOSX4HCJUJ3CG65A56UWUA2W","short_pith_number":"pith:22BOSX4H","schema_version":"1.0","canonical_sha256":"d682e95f8712689d88dee83bea5a80d5b3b29efcad367042c543f18fa877773e","source":{"kind":"arxiv","id":"1811.06472","version":1},"attestation_state":"computed","paper":{"title":"Oversampled Adaptive Sensing with Random Projections: Analysis and Algorithmic Approaches","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Ali Bereyhi, Christoph F. Mecklenbr\\\"auker, Ralf R. M\\\"uller","submitted_at":"2018-11-15T17:04:12Z","abstract_excerpt":"Oversampled adaptive sensing (OAS) is a recently proposed Bayesian framework which sequentially adapts the sensing basis. In OAS, estimation quality is, in each step, measured by conditional mean squared errors (MSEs), and the basis for the next sensing step is adapted accordingly. For given average sensing time, OAS reduces the MSE compared to non-adaptive schemes, when the signal is sparse. This paper studies the asymptotic performance of Bayesian OAS, for unitarily invariant random projections. For sparse signals, it is shown that OAS with Bayesian recovery and hard adaptation significantly"},"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":"1811.06472","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-11-15T17:04:12Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"35c6e820dfca0ac40546ec90b1db278828ada312c192d82d70994a10376cfc09","abstract_canon_sha256":"ac68452b854ad5e7187d4c9f34f91a75e2b27879b1b642bea877ec546093df67"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:37.622449Z","signature_b64":"RS1QDufe+ajjCHeJns8cTqTClHBVzImagfshIjHA+b81XhK9f7nM1uOpudcnFpY7ow2L7U1nYYK65+xTYSxpDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d682e95f8712689d88dee83bea5a80d5b3b29efcad367042c543f18fa877773e","last_reissued_at":"2026-05-18T00:00:37.621921Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:37.621921Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Oversampled Adaptive Sensing with Random Projections: Analysis and Algorithmic Approaches","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Ali Bereyhi, Christoph F. Mecklenbr\\\"auker, Ralf R. M\\\"uller","submitted_at":"2018-11-15T17:04:12Z","abstract_excerpt":"Oversampled adaptive sensing (OAS) is a recently proposed Bayesian framework which sequentially adapts the sensing basis. In OAS, estimation quality is, in each step, measured by conditional mean squared errors (MSEs), and the basis for the next sensing step is adapted accordingly. For given average sensing time, OAS reduces the MSE compared to non-adaptive schemes, when the signal is sparse. This paper studies the asymptotic performance of Bayesian OAS, for unitarily invariant random projections. For sparse signals, it is shown that OAS with Bayesian recovery and hard adaptation significantly"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.06472","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":"1811.06472","created_at":"2026-05-18T00:00:37.621998+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.06472v1","created_at":"2026-05-18T00:00:37.621998+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.06472","created_at":"2026-05-18T00:00:37.621998+00:00"},{"alias_kind":"pith_short_12","alias_value":"22BOSX4HCJUJ","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"22BOSX4HCJUJ3CG6","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"22BOSX4H","created_at":"2026-05-18T12:31:59.375834+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/22BOSX4HCJUJ3CG65A56UWUA2W","json":"https://pith.science/pith/22BOSX4HCJUJ3CG65A56UWUA2W.json","graph_json":"https://pith.science/api/pith-number/22BOSX4HCJUJ3CG65A56UWUA2W/graph.json","events_json":"https://pith.science/api/pith-number/22BOSX4HCJUJ3CG65A56UWUA2W/events.json","paper":"https://pith.science/paper/22BOSX4H"},"agent_actions":{"view_html":"https://pith.science/pith/22BOSX4HCJUJ3CG65A56UWUA2W","download_json":"https://pith.science/pith/22BOSX4HCJUJ3CG65A56UWUA2W.json","view_paper":"https://pith.science/paper/22BOSX4H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.06472&json=true","fetch_graph":"https://pith.science/api/pith-number/22BOSX4HCJUJ3CG65A56UWUA2W/graph.json","fetch_events":"https://pith.science/api/pith-number/22BOSX4HCJUJ3CG65A56UWUA2W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/22BOSX4HCJUJ3CG65A56UWUA2W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/22BOSX4HCJUJ3CG65A56UWUA2W/action/storage_attestation","attest_author":"https://pith.science/pith/22BOSX4HCJUJ3CG65A56UWUA2W/action/author_attestation","sign_citation":"https://pith.science/pith/22BOSX4HCJUJ3CG65A56UWUA2W/action/citation_signature","submit_replication":"https://pith.science/pith/22BOSX4HCJUJ3CG65A56UWUA2W/action/replication_record"}},"created_at":"2026-05-18T00:00:37.621998+00:00","updated_at":"2026-05-18T00:00:37.621998+00:00"}