{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RNN36IC2MW2XHPE4C2D3KKAKVC","short_pith_number":"pith:RNN36IC2","schema_version":"1.0","canonical_sha256":"8b5bbf205a65b573bc9c1687b5280aa89ccaedf2065f61dd34214a1a90a6add1","source":{"kind":"arxiv","id":"2506.12045","version":1},"attestation_state":"computed","paper":{"title":"From Proxies to Fields: Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Diab Abueidda, Kazuma Kobayashi, Samrendra Roy, Seid Koric, Syed Bahauddin Alam","submitted_at":"2025-05-24T16:24:10Z","abstract_excerpt":"Accurate reconstruction of latent environmental fields from sparse and indirect observations is a foundational challenge across scientific domains-from atmospheric science and geophysics to public health and aerospace safety. Traditional approaches rely on physics-based simulators or dense sensor networks, both constrained by high computational cost, latency, or limited spatial coverage. We present the Temporal Radiation Operator Network (TRON), a spatiotemporal neural operator architecture designed to infer continuous global scalar fields from sequences of sparse, non-uniform proxy measuremen"},"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":"2506.12045","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-24T16:24:10Z","cross_cats_sorted":["cs.AI","eess.SP"],"title_canon_sha256":"a34cc6b972f9510c1b89d3617ea37811c86cab5e0a98bd55f386486db5df1612","abstract_canon_sha256":"a235f607c4576373b50c04798942f8d45e88380ee1b68a21c59c319fba2e3dfa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:16.167799Z","signature_b64":"S24xBsAC+QqC+cR2i9aoFafiV4NK4e7T1WQ/3C94py/NCrFeXIQjV/KUHV0vFLUCyiikWMEPaECYRE/q2jVXCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b5bbf205a65b573bc9c1687b5280aa89ccaedf2065f61dd34214a1a90a6add1","last_reissued_at":"2026-07-05T11:21:16.167306Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:16.167306Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Proxies to Fields: Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Diab Abueidda, Kazuma Kobayashi, Samrendra Roy, Seid Koric, Syed Bahauddin Alam","submitted_at":"2025-05-24T16:24:10Z","abstract_excerpt":"Accurate reconstruction of latent environmental fields from sparse and indirect observations is a foundational challenge across scientific domains-from atmospheric science and geophysics to public health and aerospace safety. Traditional approaches rely on physics-based simulators or dense sensor networks, both constrained by high computational cost, latency, or limited spatial coverage. We present the Temporal Radiation Operator Network (TRON), a spatiotemporal neural operator architecture designed to infer continuous global scalar fields from sequences of sparse, non-uniform proxy measuremen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12045","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/2506.12045/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":"2506.12045","created_at":"2026-07-05T11:21:16.167380+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.12045v1","created_at":"2026-07-05T11:21:16.167380+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12045","created_at":"2026-07-05T11:21:16.167380+00:00"},{"alias_kind":"pith_short_12","alias_value":"RNN36IC2MW2X","created_at":"2026-07-05T11:21:16.167380+00:00"},{"alias_kind":"pith_short_16","alias_value":"RNN36IC2MW2XHPE4","created_at":"2026-07-05T11:21:16.167380+00:00"},{"alias_kind":"pith_short_8","alias_value":"RNN36IC2","created_at":"2026-07-05T11:21:16.167380+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.11625","citing_title":"SCNO: Spiking Compositional Neural Operator -- Towards a Neuromorphic Foundation Model for Nuclear PDE Solving","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13316","citing_title":"Beyond Uniform Sampling: Synergistic Active Learning and Input Denoising for Robust Neural Operators","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RNN36IC2MW2XHPE4C2D3KKAKVC","json":"https://pith.science/pith/RNN36IC2MW2XHPE4C2D3KKAKVC.json","graph_json":"https://pith.science/api/pith-number/RNN36IC2MW2XHPE4C2D3KKAKVC/graph.json","events_json":"https://pith.science/api/pith-number/RNN36IC2MW2XHPE4C2D3KKAKVC/events.json","paper":"https://pith.science/paper/RNN36IC2"},"agent_actions":{"view_html":"https://pith.science/pith/RNN36IC2MW2XHPE4C2D3KKAKVC","download_json":"https://pith.science/pith/RNN36IC2MW2XHPE4C2D3KKAKVC.json","view_paper":"https://pith.science/paper/RNN36IC2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.12045&json=true","fetch_graph":"https://pith.science/api/pith-number/RNN36IC2MW2XHPE4C2D3KKAKVC/graph.json","fetch_events":"https://pith.science/api/pith-number/RNN36IC2MW2XHPE4C2D3KKAKVC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RNN36IC2MW2XHPE4C2D3KKAKVC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RNN36IC2MW2XHPE4C2D3KKAKVC/action/storage_attestation","attest_author":"https://pith.science/pith/RNN36IC2MW2XHPE4C2D3KKAKVC/action/author_attestation","sign_citation":"https://pith.science/pith/RNN36IC2MW2XHPE4C2D3KKAKVC/action/citation_signature","submit_replication":"https://pith.science/pith/RNN36IC2MW2XHPE4C2D3KKAKVC/action/replication_record"}},"created_at":"2026-07-05T11:21:16.167380+00:00","updated_at":"2026-07-05T11:21:16.167380+00:00"}