{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:D3MLV3ZYMX62V7ACCG2C4CDDZ7","short_pith_number":"pith:D3MLV3ZY","canonical_record":{"source":{"id":"2202.05330","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.DS","submitted_at":"2022-02-10T21:19:23Z","cross_cats_sorted":[],"title_canon_sha256":"4e4aa438c778410913a03ca20b8520a51c84dbc82272d48a93085680344d0c47","abstract_canon_sha256":"93b0df191ec81d4d34902627a61275106aa08f171c62dd764a6116b190557293"},"schema_version":"1.0"},"canonical_sha256":"1ed8baef3865fdaafc0211b42e0863cfca1f1aa1bddbd31ce837ebf79a93923f","source":{"kind":"arxiv","id":"2202.05330","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.05330","created_at":"2026-07-05T03:56:07Z"},{"alias_kind":"arxiv_version","alias_value":"2202.05330v1","created_at":"2026-07-05T03:56:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.05330","created_at":"2026-07-05T03:56:07Z"},{"alias_kind":"pith_short_12","alias_value":"D3MLV3ZYMX62","created_at":"2026-07-05T03:56:07Z"},{"alias_kind":"pith_short_16","alias_value":"D3MLV3ZYMX62V7AC","created_at":"2026-07-05T03:56:07Z"},{"alias_kind":"pith_short_8","alias_value":"D3MLV3ZY","created_at":"2026-07-05T03:56:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:D3MLV3ZYMX62V7ACCG2C4CDDZ7","target":"record","payload":{"canonical_record":{"source":{"id":"2202.05330","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.DS","submitted_at":"2022-02-10T21:19:23Z","cross_cats_sorted":[],"title_canon_sha256":"4e4aa438c778410913a03ca20b8520a51c84dbc82272d48a93085680344d0c47","abstract_canon_sha256":"93b0df191ec81d4d34902627a61275106aa08f171c62dd764a6116b190557293"},"schema_version":"1.0"},"canonical_sha256":"1ed8baef3865fdaafc0211b42e0863cfca1f1aa1bddbd31ce837ebf79a93923f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:56:07.104307Z","signature_b64":"IPAmUjx5MP4qRz8xOO0C1Jy2bkkg/+TYDnfI40EP+ErhWY+o8IgZ6oY2l94lqrogLMLgYMqVrh6Y6EEB5lQ2Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ed8baef3865fdaafc0211b42e0863cfca1f1aa1bddbd31ce837ebf79a93923f","last_reissued_at":"2026-07-05T03:56:07.103853Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:56:07.103853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.05330","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:56:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vLjJzcP5v4dgaPgKXK9Wg2Zx5P3jxYIyAboQJRQHLYwIs35TF8PXAugDFCR1nSrxeRMLDP8mD3+9u325svGhDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T06:58:22.191483Z"},"content_sha256":"6e4d52f145915fbbba3a6d100813129502d0f501846ab590ac0b5649790f96bc","schema_version":"1.0","event_id":"sha256:6e4d52f145915fbbba3a6d100813129502d0f501846ab590ac0b5649790f96bc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:D3MLV3ZYMX62V7ACCG2C4CDDZ7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Data-driven sensor placement with shallow decoder networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.DS","authors_text":"Jan Williams, J. Nathan Kutz, Olivia Zahn","submitted_at":"2022-02-10T21:19:23Z","abstract_excerpt":"Sensor placement is an important and ubiquitous problem across the engineering and physical sciences for tasks such as reconstruction, forecasting and control. Surprisingly, there are few principled mathematical techniques developed to date for optimizing sensor locations, with the leading sensor placement algorithms often based upon the discovery of linear, low-rank sub-spaces and the QR algorithm. QR is a computationally efficient greedy search algorithm which selects sensor locations from candidate positions with maximal variance exhibited in a training data set. More recently, neural netwo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.05330","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2202.05330/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:56:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O3al8OgXcXTUjzON5kgzAKICObZPDmFoUvcYAnapxlj5QEaerj3zso5YDeY7OBXPMg1NSr2sbwyU/AGiDMAvDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T06:58:22.192264Z"},"content_sha256":"9ca6e2d05439acf3909cc3312c522a37e0cb97f5455bc57772030ee3475d3425","schema_version":"1.0","event_id":"sha256:9ca6e2d05439acf3909cc3312c522a37e0cb97f5455bc57772030ee3475d3425"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D3MLV3ZYMX62V7ACCG2C4CDDZ7/bundle.json","state_url":"https://pith.science/pith/D3MLV3ZYMX62V7ACCG2C4CDDZ7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D3MLV3ZYMX62V7ACCG2C4CDDZ7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T06:58:22Z","links":{"resolver":"https://pith.science/pith/D3MLV3ZYMX62V7ACCG2C4CDDZ7","bundle":"https://pith.science/pith/D3MLV3ZYMX62V7ACCG2C4CDDZ7/bundle.json","state":"https://pith.science/pith/D3MLV3ZYMX62V7ACCG2C4CDDZ7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D3MLV3ZYMX62V7ACCG2C4CDDZ7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:D3MLV3ZYMX62V7ACCG2C4CDDZ7","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":"93b0df191ec81d4d34902627a61275106aa08f171c62dd764a6116b190557293","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.DS","submitted_at":"2022-02-10T21:19:23Z","title_canon_sha256":"4e4aa438c778410913a03ca20b8520a51c84dbc82272d48a93085680344d0c47"},"schema_version":"1.0","source":{"id":"2202.05330","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.05330","created_at":"2026-07-05T03:56:07Z"},{"alias_kind":"arxiv_version","alias_value":"2202.05330v1","created_at":"2026-07-05T03:56:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.05330","created_at":"2026-07-05T03:56:07Z"},{"alias_kind":"pith_short_12","alias_value":"D3MLV3ZYMX62","created_at":"2026-07-05T03:56:07Z"},{"alias_kind":"pith_short_16","alias_value":"D3MLV3ZYMX62V7AC","created_at":"2026-07-05T03:56:07Z"},{"alias_kind":"pith_short_8","alias_value":"D3MLV3ZY","created_at":"2026-07-05T03:56:07Z"}],"graph_snapshots":[{"event_id":"sha256:9ca6e2d05439acf3909cc3312c522a37e0cb97f5455bc57772030ee3475d3425","target":"graph","created_at":"2026-07-05T03:56:07Z","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/2202.05330/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Sensor placement is an important and ubiquitous problem across the engineering and physical sciences for tasks such as reconstruction, forecasting and control. Surprisingly, there are few principled mathematical techniques developed to date for optimizing sensor locations, with the leading sensor placement algorithms often based upon the discovery of linear, low-rank sub-spaces and the QR algorithm. QR is a computationally efficient greedy search algorithm which selects sensor locations from candidate positions with maximal variance exhibited in a training data set. More recently, neural netwo","authors_text":"Jan Williams, J. Nathan Kutz, Olivia Zahn","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.DS","submitted_at":"2022-02-10T21:19:23Z","title":"Data-driven sensor placement with shallow decoder networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.05330","kind":"arxiv","version":1},"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":"sha256:6e4d52f145915fbbba3a6d100813129502d0f501846ab590ac0b5649790f96bc","target":"record","created_at":"2026-07-05T03:56:07Z","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":"93b0df191ec81d4d34902627a61275106aa08f171c62dd764a6116b190557293","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.DS","submitted_at":"2022-02-10T21:19:23Z","title_canon_sha256":"4e4aa438c778410913a03ca20b8520a51c84dbc82272d48a93085680344d0c47"},"schema_version":"1.0","source":{"id":"2202.05330","kind":"arxiv","version":1}},"canonical_sha256":"1ed8baef3865fdaafc0211b42e0863cfca1f1aa1bddbd31ce837ebf79a93923f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1ed8baef3865fdaafc0211b42e0863cfca1f1aa1bddbd31ce837ebf79a93923f","first_computed_at":"2026-07-05T03:56:07.103853Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:56:07.103853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IPAmUjx5MP4qRz8xOO0C1Jy2bkkg/+TYDnfI40EP+ErhWY+o8IgZ6oY2l94lqrogLMLgYMqVrh6Y6EEB5lQ2Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T03:56:07.104307Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.05330","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6e4d52f145915fbbba3a6d100813129502d0f501846ab590ac0b5649790f96bc","sha256:9ca6e2d05439acf3909cc3312c522a37e0cb97f5455bc57772030ee3475d3425"],"state_sha256":"4a82712d6fd25e587c6f4bf4c92fd4be4897e43a70daa32821e505fba822e58a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mgoGawq9AhzDG1FYuPRwhHi1S9S2PzRu9P9dWGBDWJcYi4aGsO6WU+abCmN40UIIvmzYgSdBZ23MITTv2msXBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T06:58:22.201541Z","bundle_sha256":"e70b001229934228e9658bda9310018fac1535d37a1008d88495268d00e68753"}}