{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:MAXDVQSQIFIA6P4WR66GRGANAZ","short_pith_number":"pith:MAXDVQSQ","canonical_record":{"source":{"id":"2308.15081","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-29T07:29:30Z","cross_cats_sorted":[],"title_canon_sha256":"b7cd5457a39f115b06a11994482e097cdf7a47db47848591b18a79b02796b44a","abstract_canon_sha256":"1711bebf637b867da0d0ac68abcbf66118c33d463089311d73f12a0af6c831c9"},"schema_version":"1.0"},"canonical_sha256":"602e3ac25041500f3f968fbc68980d0658757f4988a17cc5f1371f4f35dbd7db","source":{"kind":"arxiv","id":"2308.15081","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.15081","created_at":"2026-07-05T10:18:32Z"},{"alias_kind":"arxiv_version","alias_value":"2308.15081v1","created_at":"2026-07-05T10:18:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.15081","created_at":"2026-07-05T10:18:32Z"},{"alias_kind":"pith_short_12","alias_value":"MAXDVQSQIFIA","created_at":"2026-07-05T10:18:32Z"},{"alias_kind":"pith_short_16","alias_value":"MAXDVQSQIFIA6P4W","created_at":"2026-07-05T10:18:32Z"},{"alias_kind":"pith_short_8","alias_value":"MAXDVQSQ","created_at":"2026-07-05T10:18:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:MAXDVQSQIFIA6P4WR66GRGANAZ","target":"record","payload":{"canonical_record":{"source":{"id":"2308.15081","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-29T07:29:30Z","cross_cats_sorted":[],"title_canon_sha256":"b7cd5457a39f115b06a11994482e097cdf7a47db47848591b18a79b02796b44a","abstract_canon_sha256":"1711bebf637b867da0d0ac68abcbf66118c33d463089311d73f12a0af6c831c9"},"schema_version":"1.0"},"canonical_sha256":"602e3ac25041500f3f968fbc68980d0658757f4988a17cc5f1371f4f35dbd7db","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:18:32.683099Z","signature_b64":"UeFh8pIE4pckD5aVKJfDhxrrXaKJaieJdsUCcAtmF2Hg71FT0dWp+kC8MyDktBl2LwuL4sGvAUk2675BGwj0BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"602e3ac25041500f3f968fbc68980d0658757f4988a17cc5f1371f4f35dbd7db","last_reissued_at":"2026-07-05T10:18:32.682595Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:18:32.682595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.15081","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":"2026-07-05T10:18:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ySqlU451EaeymiJYWDdfdabFTpEkEdbRWIUrr095WLZSYF1r9iSs8QXsO4b3z84LyirIJIhhoqCcI4VFpt/KDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T02:31:54.086926Z"},"content_sha256":"9c1e9f443196900cff571d3a9aef3b30f8ce1970947591eca55771c452285b0d","schema_version":"1.0","event_id":"sha256:9c1e9f443196900cff571d3a9aef3b30f8ce1970947591eca55771c452285b0d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:MAXDVQSQIFIA6P4WR66GRGANAZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hengwei Zhao, Jingtao Li, Xinyu Wang, Yanfei Zhong","submitted_at":"2023-08-29T07:29:30Z","abstract_excerpt":"Positive-unlabeled learning (PU learning) in hyperspectral remote sensing imagery (HSI) is aimed at learning a binary classifier from positive and unlabeled data, which has broad prospects in various earth vision applications. However, when PU learning meets limited labeled HSI, the unlabeled data may dominate the optimization process, which makes the neural networks overfit the unlabeled data. In this paper, a Taylor variational loss is proposed for HSI PU learning, which reduces the weight of the gradient of the unlabeled data by Taylor series expansion to enable the network to find a balanc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.15081","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/2308.15081/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":"2026-07-05T10:18:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YhyYZu4T4C3SIvde5fRIqRHJB7rRk+xYn1+BYPp0oCSneUWpv1T606V3Dt6A9fd+NzzwxrFoSNlkBzxUa1IVCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T02:31:54.087595Z"},"content_sha256":"80f5bf7ff46a1cc394b9b008c9527f0866775f6e25214c7bf5ea393b90fa5e15","schema_version":"1.0","event_id":"sha256:80f5bf7ff46a1cc394b9b008c9527f0866775f6e25214c7bf5ea393b90fa5e15"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MAXDVQSQIFIA6P4WR66GRGANAZ/bundle.json","state_url":"https://pith.science/pith/MAXDVQSQIFIA6P4WR66GRGANAZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MAXDVQSQIFIA6P4WR66GRGANAZ/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":"2026-08-11T02:31:54Z","links":{"resolver":"https://pith.science/pith/MAXDVQSQIFIA6P4WR66GRGANAZ","bundle":"https://pith.science/pith/MAXDVQSQIFIA6P4WR66GRGANAZ/bundle.json","state":"https://pith.science/pith/MAXDVQSQIFIA6P4WR66GRGANAZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MAXDVQSQIFIA6P4WR66GRGANAZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:MAXDVQSQIFIA6P4WR66GRGANAZ","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":"1711bebf637b867da0d0ac68abcbf66118c33d463089311d73f12a0af6c831c9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-29T07:29:30Z","title_canon_sha256":"b7cd5457a39f115b06a11994482e097cdf7a47db47848591b18a79b02796b44a"},"schema_version":"1.0","source":{"id":"2308.15081","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.15081","created_at":"2026-07-05T10:18:32Z"},{"alias_kind":"arxiv_version","alias_value":"2308.15081v1","created_at":"2026-07-05T10:18:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.15081","created_at":"2026-07-05T10:18:32Z"},{"alias_kind":"pith_short_12","alias_value":"MAXDVQSQIFIA","created_at":"2026-07-05T10:18:32Z"},{"alias_kind":"pith_short_16","alias_value":"MAXDVQSQIFIA6P4W","created_at":"2026-07-05T10:18:32Z"},{"alias_kind":"pith_short_8","alias_value":"MAXDVQSQ","created_at":"2026-07-05T10:18:32Z"}],"graph_snapshots":[{"event_id":"sha256:80f5bf7ff46a1cc394b9b008c9527f0866775f6e25214c7bf5ea393b90fa5e15","target":"graph","created_at":"2026-07-05T10:18:32Z","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/2308.15081/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Positive-unlabeled learning (PU learning) in hyperspectral remote sensing imagery (HSI) is aimed at learning a binary classifier from positive and unlabeled data, which has broad prospects in various earth vision applications. However, when PU learning meets limited labeled HSI, the unlabeled data may dominate the optimization process, which makes the neural networks overfit the unlabeled data. In this paper, a Taylor variational loss is proposed for HSI PU learning, which reduces the weight of the gradient of the unlabeled data by Taylor series expansion to enable the network to find a balanc","authors_text":"Hengwei Zhao, Jingtao Li, Xinyu Wang, Yanfei Zhong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-29T07:29:30Z","title":"Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.15081","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":"sha256:9c1e9f443196900cff571d3a9aef3b30f8ce1970947591eca55771c452285b0d","target":"record","created_at":"2026-07-05T10:18:32Z","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":"1711bebf637b867da0d0ac68abcbf66118c33d463089311d73f12a0af6c831c9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-29T07:29:30Z","title_canon_sha256":"b7cd5457a39f115b06a11994482e097cdf7a47db47848591b18a79b02796b44a"},"schema_version":"1.0","source":{"id":"2308.15081","kind":"arxiv","version":1}},"canonical_sha256":"602e3ac25041500f3f968fbc68980d0658757f4988a17cc5f1371f4f35dbd7db","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"602e3ac25041500f3f968fbc68980d0658757f4988a17cc5f1371f4f35dbd7db","first_computed_at":"2026-07-05T10:18:32.682595Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:18:32.682595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UeFh8pIE4pckD5aVKJfDhxrrXaKJaieJdsUCcAtmF2Hg71FT0dWp+kC8MyDktBl2LwuL4sGvAUk2675BGwj0BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:18:32.683099Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.15081","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9c1e9f443196900cff571d3a9aef3b30f8ce1970947591eca55771c452285b0d","sha256:80f5bf7ff46a1cc394b9b008c9527f0866775f6e25214c7bf5ea393b90fa5e15"],"state_sha256":"d7c9f5745a9e0830e55df6f17467abcf70e0fedaf28f01977a9450ea6505b059"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nTuSpDf4gRpG2d821HhtvVgP3DvdwoR52TVEkqUFl7hYR2M7ZEFujeg8Bzx0NLEPhokvVoGs881s7tBMRVLqBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T02:31:54.092767Z","bundle_sha256":"3f9bb4e8ee4edb1e86009e5ee60702dd8bae9989dd96607b68ee791528897ee7"}}