{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:57GV2XTH75K2JEBLJV7I6UJIVB","short_pith_number":"pith:57GV2XTH","canonical_record":{"source":{"id":"1812.10788","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-27T18:31:25Z","cross_cats_sorted":[],"title_canon_sha256":"0cd954ffcfdec4d46be6be083a3568333b13386167f24f8d0043ad09b6bb5bc9","abstract_canon_sha256":"db4977b680b3a0f8c431618941988f5c837651d22348daedb28ba0cbc1180d3c"},"schema_version":"1.0"},"canonical_sha256":"efcd5d5e67ff55a4902b4d7e8f5128a865f287916865b311e8f47262ed51ce9b","source":{"kind":"arxiv","id":"1812.10788","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.10788","created_at":"2026-05-17T23:57:19Z"},{"alias_kind":"arxiv_version","alias_value":"1812.10788v1","created_at":"2026-05-17T23:57:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.10788","created_at":"2026-05-17T23:57:19Z"},{"alias_kind":"pith_short_12","alias_value":"57GV2XTH75K2","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"57GV2XTH75K2JEBL","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"57GV2XTH","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:57GV2XTH75K2JEBLJV7I6UJIVB","target":"record","payload":{"canonical_record":{"source":{"id":"1812.10788","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-27T18:31:25Z","cross_cats_sorted":[],"title_canon_sha256":"0cd954ffcfdec4d46be6be083a3568333b13386167f24f8d0043ad09b6bb5bc9","abstract_canon_sha256":"db4977b680b3a0f8c431618941988f5c837651d22348daedb28ba0cbc1180d3c"},"schema_version":"1.0"},"canonical_sha256":"efcd5d5e67ff55a4902b4d7e8f5128a865f287916865b311e8f47262ed51ce9b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:19.972722Z","signature_b64":"ftWsSQAizUQqLa9KYzfL7bNOjhgNT0CDHEk4J1fGiC8NaQj9M7/nwV8WyvEm/+7lXUFSoh/I95Y22ZMMTvDZDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efcd5d5e67ff55a4902b4d7e8f5128a865f287916865b311e8f47262ed51ce9b","last_reissued_at":"2026-05-17T23:57:19.972205Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:19.972205Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.10788","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":"2026-05-17T23:57:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5m/hTeyQOkNA2CI57/SwGct06sc4pUtJCsVmhHWmAKsM8634u3hOePhIuB8RWwqTR0+jPJuPx8gIULaj6UHZDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T06:31:53.316230Z"},"content_sha256":"510833b4a75324b96f6f45a85a771ce33a123db58dbf5a0718d531e3f314dc79","schema_version":"1.0","event_id":"sha256:510833b4a75324b96f6f45a85a771ce33a123db58dbf5a0718d531e3f314dc79"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:57GV2XTH75K2JEBLJV7I6UJIVB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hyperspectral Unmixing Based on Clustered Multitask Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hadi Zayyani, Roozbeh Rajabi, Sara Khoshsokhan","submitted_at":"2018-12-27T18:31:25Z","abstract_excerpt":"Hyperspectral remote sensing is a prominent research topic in data processing. Most of the spectral unmixing algorithms are developed by adopting the linear mixing models. Nonnegative matrix factorization (NMF) and its developments are used widely for estimation of signatures and fractional abundances in the SU problem. Sparsity constraints was added to NMF, and was regularized by $ L_ {q} $ norm. In this paper, at first hyperspectral images are clustered by fuzzy c- means method, and then a new algorithm based on sparsity constrained distributed optimization is used for spectral unmixing. In "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.10788","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":""},"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":"2026-05-17T23:57:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"METzB+kSEOjXWqQKrHiDs9usAFoX+524WZ9AIXqerrVAuA+t3SS1dT94PyRNygmy2X7xFmIA18MLQm5AM4hLBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T06:31:53.316581Z"},"content_sha256":"6af903d2d0ba94e01564d96b1c326a855676033f9c070747c8f65feabb0a8106","schema_version":"1.0","event_id":"sha256:6af903d2d0ba94e01564d96b1c326a855676033f9c070747c8f65feabb0a8106"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/57GV2XTH75K2JEBLJV7I6UJIVB/bundle.json","state_url":"https://pith.science/pith/57GV2XTH75K2JEBLJV7I6UJIVB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/57GV2XTH75K2JEBLJV7I6UJIVB/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":"2026-06-05T06:31:53Z","links":{"resolver":"https://pith.science/pith/57GV2XTH75K2JEBLJV7I6UJIVB","bundle":"https://pith.science/pith/57GV2XTH75K2JEBLJV7I6UJIVB/bundle.json","state":"https://pith.science/pith/57GV2XTH75K2JEBLJV7I6UJIVB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/57GV2XTH75K2JEBLJV7I6UJIVB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:57GV2XTH75K2JEBLJV7I6UJIVB","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":"db4977b680b3a0f8c431618941988f5c837651d22348daedb28ba0cbc1180d3c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-27T18:31:25Z","title_canon_sha256":"0cd954ffcfdec4d46be6be083a3568333b13386167f24f8d0043ad09b6bb5bc9"},"schema_version":"1.0","source":{"id":"1812.10788","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.10788","created_at":"2026-05-17T23:57:19Z"},{"alias_kind":"arxiv_version","alias_value":"1812.10788v1","created_at":"2026-05-17T23:57:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.10788","created_at":"2026-05-17T23:57:19Z"},{"alias_kind":"pith_short_12","alias_value":"57GV2XTH75K2","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"57GV2XTH75K2JEBL","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"57GV2XTH","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:6af903d2d0ba94e01564d96b1c326a855676033f9c070747c8f65feabb0a8106","target":"graph","created_at":"2026-05-17T23:57:19Z","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"},"paper":{"abstract_excerpt":"Hyperspectral remote sensing is a prominent research topic in data processing. Most of the spectral unmixing algorithms are developed by adopting the linear mixing models. Nonnegative matrix factorization (NMF) and its developments are used widely for estimation of signatures and fractional abundances in the SU problem. Sparsity constraints was added to NMF, and was regularized by $ L_ {q} $ norm. In this paper, at first hyperspectral images are clustered by fuzzy c- means method, and then a new algorithm based on sparsity constrained distributed optimization is used for spectral unmixing. In ","authors_text":"Hadi Zayyani, Roozbeh Rajabi, Sara Khoshsokhan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-27T18:31:25Z","title":"Hyperspectral Unmixing Based on Clustered Multitask Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.10788","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":"sha256:510833b4a75324b96f6f45a85a771ce33a123db58dbf5a0718d531e3f314dc79","target":"record","created_at":"2026-05-17T23:57:19Z","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":"db4977b680b3a0f8c431618941988f5c837651d22348daedb28ba0cbc1180d3c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-27T18:31:25Z","title_canon_sha256":"0cd954ffcfdec4d46be6be083a3568333b13386167f24f8d0043ad09b6bb5bc9"},"schema_version":"1.0","source":{"id":"1812.10788","kind":"arxiv","version":1}},"canonical_sha256":"efcd5d5e67ff55a4902b4d7e8f5128a865f287916865b311e8f47262ed51ce9b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"efcd5d5e67ff55a4902b4d7e8f5128a865f287916865b311e8f47262ed51ce9b","first_computed_at":"2026-05-17T23:57:19.972205Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:57:19.972205Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ftWsSQAizUQqLa9KYzfL7bNOjhgNT0CDHEk4J1fGiC8NaQj9M7/nwV8WyvEm/+7lXUFSoh/I95Y22ZMMTvDZDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:57:19.972722Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.10788","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:510833b4a75324b96f6f45a85a771ce33a123db58dbf5a0718d531e3f314dc79","sha256:6af903d2d0ba94e01564d96b1c326a855676033f9c070747c8f65feabb0a8106"],"state_sha256":"515ca1e0d0a82997eb58c0051819a4269704f195afee41386f583a81a77928e6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EqAyn6hK7sNLqDDn22M94nViC2R0jZQ/+1FOl8TQducbPqEdEeVvZGg5TNBJ2E5U+MJpZGewbPWLRpGwiSNgCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T06:31:53.318542Z","bundle_sha256":"9cc58c06bfa1df9bb577572410265123e7bf60c1b839be7f078be166e626b8f2"}}