{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2HUVIH5AE7YBKWJ6GIE3ATEQQ2","short_pith_number":"pith:2HUVIH5A","schema_version":"1.0","canonical_sha256":"d1e9541fa027f015593e3209b04c908685548067511a868a9a15499885861414","source":{"kind":"arxiv","id":"2505.18190","version":5},"attestation_state":"computed","paper":{"title":"PhySense: Sensor Placement Optimization for Accurate Physics Sensing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.SP","authors_text":"Haixu Wu, Hang Zhou, Huikun Weng, Jianmin Wang, Mingsheng Long, Yuezhou Ma","submitted_at":"2025-05-19T14:59:11Z","abstract_excerpt":"Physics sensing plays a central role in many scientific and engineering domains, which inherently involves two coupled tasks: reconstructing dense physical fields from sparse observations and optimizing scattered sensor placements to observe maximum information. While deep learning has made rapid advances in sparse-data reconstruction, existing methods generally omit optimization of sensor placements, leaving the mutual enhancement between reconstruction and placement on the shelf. To change this suboptimal practice, we propose PhySense, a synergistic two-stage framework that learns to jointly"},"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":"2505.18190","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2025-05-19T14:59:11Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"ba4841c7b91a9525dde65f2cab9ffbcea218a1707946117634c860a22e9f7a98","abstract_canon_sha256":"2aa3860f95347ffd4691a1b0f636d1c29b5cedcf8ac31f24245d51cbfdb82788"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:03:50.943502Z","signature_b64":"xx8FoBlbfEgE1TrY6zpqWnAXxofuOkEJ6E0ckAW9loSYq+fRW+FkmxTwPtsVthvY3/+fjfiDGblsbP+TQzzfBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d1e9541fa027f015593e3209b04c908685548067511a868a9a15499885861414","last_reissued_at":"2026-05-26T02:03:50.942401Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:03:50.942401Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PhySense: Sensor Placement Optimization for Accurate Physics Sensing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.SP","authors_text":"Haixu Wu, Hang Zhou, Huikun Weng, Jianmin Wang, Mingsheng Long, Yuezhou Ma","submitted_at":"2025-05-19T14:59:11Z","abstract_excerpt":"Physics sensing plays a central role in many scientific and engineering domains, which inherently involves two coupled tasks: reconstructing dense physical fields from sparse observations and optimizing scattered sensor placements to observe maximum information. While deep learning has made rapid advances in sparse-data reconstruction, existing methods generally omit optimization of sensor placements, leaving the mutual enhancement between reconstruction and placement on the shelf. To change this suboptimal practice, we propose PhySense, a synergistic two-stage framework that learns to jointly"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.18190","kind":"arxiv","version":5},"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/2505.18190/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":"2505.18190","created_at":"2026-05-26T02:03:50.942539+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.18190v5","created_at":"2026-05-26T02:03:50.942539+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.18190","created_at":"2026-05-26T02:03:50.942539+00:00"},{"alias_kind":"pith_short_12","alias_value":"2HUVIH5AE7YB","created_at":"2026-05-26T02:03:50.942539+00:00"},{"alias_kind":"pith_short_16","alias_value":"2HUVIH5AE7YBKWJ6","created_at":"2026-05-26T02:03:50.942539+00:00"},{"alias_kind":"pith_short_8","alias_value":"2HUVIH5A","created_at":"2026-05-26T02:03:50.942539+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2605.18866","citing_title":"FLUIDSPLAT: Reconstructing Physical Fields from Sparse Sensors via Gaussian Primitives","ref_index":22,"is_internal_anchor":true},{"citing_arxiv_id":"2605.03548","citing_title":"PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics","ref_index":20,"is_internal_anchor":true},{"citing_arxiv_id":"2605.03548","citing_title":"PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2HUVIH5AE7YBKWJ6GIE3ATEQQ2","json":"https://pith.science/pith/2HUVIH5AE7YBKWJ6GIE3ATEQQ2.json","graph_json":"https://pith.science/api/pith-number/2HUVIH5AE7YBKWJ6GIE3ATEQQ2/graph.json","events_json":"https://pith.science/api/pith-number/2HUVIH5AE7YBKWJ6GIE3ATEQQ2/events.json","paper":"https://pith.science/paper/2HUVIH5A"},"agent_actions":{"view_html":"https://pith.science/pith/2HUVIH5AE7YBKWJ6GIE3ATEQQ2","download_json":"https://pith.science/pith/2HUVIH5AE7YBKWJ6GIE3ATEQQ2.json","view_paper":"https://pith.science/paper/2HUVIH5A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.18190&json=true","fetch_graph":"https://pith.science/api/pith-number/2HUVIH5AE7YBKWJ6GIE3ATEQQ2/graph.json","fetch_events":"https://pith.science/api/pith-number/2HUVIH5AE7YBKWJ6GIE3ATEQQ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2HUVIH5AE7YBKWJ6GIE3ATEQQ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2HUVIH5AE7YBKWJ6GIE3ATEQQ2/action/storage_attestation","attest_author":"https://pith.science/pith/2HUVIH5AE7YBKWJ6GIE3ATEQQ2/action/author_attestation","sign_citation":"https://pith.science/pith/2HUVIH5AE7YBKWJ6GIE3ATEQQ2/action/citation_signature","submit_replication":"https://pith.science/pith/2HUVIH5AE7YBKWJ6GIE3ATEQQ2/action/replication_record"}},"created_at":"2026-05-26T02:03:50.942539+00:00","updated_at":"2026-05-26T02:03:50.942539+00:00"}