{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:7KZBCWJM2PMRXYSGOKQY3X562Z","short_pith_number":"pith:7KZBCWJM","schema_version":"1.0","canonical_sha256":"fab211592cd3d91be24672a18ddfbed67ea489fd01cd9ecdcf507df0894d9dae","source":{"kind":"arxiv","id":"2007.05996","version":1},"attestation_state":"computed","paper":{"title":"Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV","physics.ao-ph"],"primary_cat":"cs.CV","authors_text":"Christopher S. Edwards, Gautam Dasarathy, John Janiczek, Parth Thaker, Philip Christensen, Suren Jayasuriya","submitted_at":"2020-07-12T14:16:35Z","abstract_excerpt":"Hyperspectral unmixing is an important remote sensing task with applications including material identification and analysis. Characteristic spectral features make many pure materials identifiable from their visible-to-infrared spectra, but quantifying their presence within a mixture is a challenging task due to nonlinearities and factors of variation. In this paper, spectral variation is considered from a physics-based approach and incorporated into an end-to-end spectral unmixing algorithm via differentiable programming. The dispersion model is introduced to simulate realistic spectral variat"},"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":"2007.05996","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-12T14:16:35Z","cross_cats_sorted":["eess.IV","physics.ao-ph"],"title_canon_sha256":"29ccfeda9f157227ccb3bebfdd9140e38941aa595eaa92fcee10e9e7a968accf","abstract_canon_sha256":"799114f33b03ac0f2b5cfd5b1790b5146957daf87e25c0ee932b7f330f2fcb73"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:18:08.985006Z","signature_b64":"gW298yRmWY33wQ4Sb0g4qvMIFkC5aAPp9szkdo9ChyA1Qd4kRuYmbKigr+lZCTy/oktWZnkoP4edXBjmLLKQCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fab211592cd3d91be24672a18ddfbed67ea489fd01cd9ecdcf507df0894d9dae","last_reissued_at":"2026-07-05T01:18:08.984610Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:18:08.984610Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV","physics.ao-ph"],"primary_cat":"cs.CV","authors_text":"Christopher S. Edwards, Gautam Dasarathy, John Janiczek, Parth Thaker, Philip Christensen, Suren Jayasuriya","submitted_at":"2020-07-12T14:16:35Z","abstract_excerpt":"Hyperspectral unmixing is an important remote sensing task with applications including material identification and analysis. Characteristic spectral features make many pure materials identifiable from their visible-to-infrared spectra, but quantifying their presence within a mixture is a challenging task due to nonlinearities and factors of variation. In this paper, spectral variation is considered from a physics-based approach and incorporated into an end-to-end spectral unmixing algorithm via differentiable programming. The dispersion model is introduced to simulate realistic spectral variat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.05996","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/2007.05996/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":"2007.05996","created_at":"2026-07-05T01:18:08.984674+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.05996v1","created_at":"2026-07-05T01:18:08.984674+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.05996","created_at":"2026-07-05T01:18:08.984674+00:00"},{"alias_kind":"pith_short_12","alias_value":"7KZBCWJM2PMR","created_at":"2026-07-05T01:18:08.984674+00:00"},{"alias_kind":"pith_short_16","alias_value":"7KZBCWJM2PMRXYSG","created_at":"2026-07-05T01:18:08.984674+00:00"},{"alias_kind":"pith_short_8","alias_value":"7KZBCWJM","created_at":"2026-07-05T01:18:08.984674+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/7KZBCWJM2PMRXYSGOKQY3X562Z","json":"https://pith.science/pith/7KZBCWJM2PMRXYSGOKQY3X562Z.json","graph_json":"https://pith.science/api/pith-number/7KZBCWJM2PMRXYSGOKQY3X562Z/graph.json","events_json":"https://pith.science/api/pith-number/7KZBCWJM2PMRXYSGOKQY3X562Z/events.json","paper":"https://pith.science/paper/7KZBCWJM"},"agent_actions":{"view_html":"https://pith.science/pith/7KZBCWJM2PMRXYSGOKQY3X562Z","download_json":"https://pith.science/pith/7KZBCWJM2PMRXYSGOKQY3X562Z.json","view_paper":"https://pith.science/paper/7KZBCWJM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.05996&json=true","fetch_graph":"https://pith.science/api/pith-number/7KZBCWJM2PMRXYSGOKQY3X562Z/graph.json","fetch_events":"https://pith.science/api/pith-number/7KZBCWJM2PMRXYSGOKQY3X562Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7KZBCWJM2PMRXYSGOKQY3X562Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7KZBCWJM2PMRXYSGOKQY3X562Z/action/storage_attestation","attest_author":"https://pith.science/pith/7KZBCWJM2PMRXYSGOKQY3X562Z/action/author_attestation","sign_citation":"https://pith.science/pith/7KZBCWJM2PMRXYSGOKQY3X562Z/action/citation_signature","submit_replication":"https://pith.science/pith/7KZBCWJM2PMRXYSGOKQY3X562Z/action/replication_record"}},"created_at":"2026-07-05T01:18:08.984674+00:00","updated_at":"2026-07-05T01:18:08.984674+00:00"}