{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:GRFBWDIEN3N5PFQCK2HHFQ47UG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"50c0a747f4930ae2c849114468980f5a854454c399c936b17d61ff105500ec57","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-01-26T03:42:19Z","title_canon_sha256":"1155698a877df12929f0b98c8fb2e478056cd15558b2f38ab81c9864f7416738"},"schema_version":"1.0","source":{"id":"1701.07569","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.07569","created_at":"2026-06-04T18:10:26Z"},{"alias_kind":"arxiv_version","alias_value":"1701.07569v2","created_at":"2026-06-04T18:10:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.07569","created_at":"2026-06-04T18:10:26Z"},{"alias_kind":"pith_short_12","alias_value":"GRFBWDIEN3N5","created_at":"2026-06-04T18:10:26Z"},{"alias_kind":"pith_short_16","alias_value":"GRFBWDIEN3N5PFQC","created_at":"2026-06-04T18:10:26Z"},{"alias_kind":"pith_short_8","alias_value":"GRFBWDIE","created_at":"2026-06-04T18:10:26Z"}],"graph_snapshots":[{"event_id":"sha256:866f1243327e9086a9d190bb01e3e0889a195653d62213ae4edd6d82f756f8a7","target":"graph","created_at":"2026-06-04T18:10:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1701.07569/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Optimal sensor placement is a central challenge in the design, prediction, estimation, and control of high-dimensional systems. High-dimensional states can often leverage a latent low-dimensional representation, and this inherent compressibility enables sparse sensing. This article explores optimized sensor placement for signal reconstruction based on a tailored library of features extracted from training data. Sparse point sensors are discovered using the singular value decomposition and QR pivoting, which are two ubiquitous matrix computations that underpin modern linear dimensionality reduc","authors_text":"Bingni W. Brunton, J. Nathan Kutz, Krithika Manohar, Steven L. Brunton","cross_cats":["cs.SY","eess.SY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-01-26T03:42:19Z","title":"Data-Driven Sparse Sensor Placement for Reconstruction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.07569","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:53ce5673695a60de7584ec78f20e48737738275a9c0421b7c4be7ee9237eabe6","target":"record","created_at":"2026-06-04T18:10:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"50c0a747f4930ae2c849114468980f5a854454c399c936b17d61ff105500ec57","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-01-26T03:42:19Z","title_canon_sha256":"1155698a877df12929f0b98c8fb2e478056cd15558b2f38ab81c9864f7416738"},"schema_version":"1.0","source":{"id":"1701.07569","kind":"arxiv","version":2}},"canonical_sha256":"344a1b0d046edbd79602568e72c39fa1959821103b4f768fd2c82fd9101eb327","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"344a1b0d046edbd79602568e72c39fa1959821103b4f768fd2c82fd9101eb327","first_computed_at":"2026-06-04T18:10:26.461741Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T18:10:26.461741Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uJf9XuhMrLeg0bAtosoiFfNo2ItT3+0N1LxXeIto7zAGBmb0SvuRc9x2o7dmEJs+xVEdTROvPtkPRH4ImXVbDg==","signature_status":"signed_v1","signed_at":"2026-06-04T18:10:26.462165Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.07569","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:53ce5673695a60de7584ec78f20e48737738275a9c0421b7c4be7ee9237eabe6","sha256:866f1243327e9086a9d190bb01e3e0889a195653d62213ae4edd6d82f756f8a7"],"state_sha256":"f2da1255b258a109c7c3a2458764dc43eeef908142ba3919d714802d7d820a94"}