{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:OGOH4SMC6OEX7IRL35MPTSNXEH","short_pith_number":"pith:OGOH4SMC","canonical_record":{"source":{"id":"2303.12091","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-21T09:07:15Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"124dcac930b795f3bdfbb2dee742cb379d2c66d223dbe7d0e602edd3e4446efe","abstract_canon_sha256":"06fb2cd5bd7a09611565db21ba4b129243f5b907be9ddbc174938484d76f8d78"},"schema_version":"1.0"},"canonical_sha256":"719c7e4982f3897fa22bdf58f9c9b721e6677b589f668ceba063513f70f5413c","source":{"kind":"arxiv","id":"2303.12091","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.12091","created_at":"2026-07-05T08:07:27Z"},{"alias_kind":"arxiv_version","alias_value":"2303.12091v4","created_at":"2026-07-05T08:07:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.12091","created_at":"2026-07-05T08:07:27Z"},{"alias_kind":"pith_short_12","alias_value":"OGOH4SMC6OEX","created_at":"2026-07-05T08:07:27Z"},{"alias_kind":"pith_short_16","alias_value":"OGOH4SMC6OEX7IRL","created_at":"2026-07-05T08:07:27Z"},{"alias_kind":"pith_short_8","alias_value":"OGOH4SMC","created_at":"2026-07-05T08:07:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:OGOH4SMC6OEX7IRL35MPTSNXEH","target":"record","payload":{"canonical_record":{"source":{"id":"2303.12091","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-21T09:07:15Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"124dcac930b795f3bdfbb2dee742cb379d2c66d223dbe7d0e602edd3e4446efe","abstract_canon_sha256":"06fb2cd5bd7a09611565db21ba4b129243f5b907be9ddbc174938484d76f8d78"},"schema_version":"1.0"},"canonical_sha256":"719c7e4982f3897fa22bdf58f9c9b721e6677b589f668ceba063513f70f5413c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:07:27.048942Z","signature_b64":"qrTXMo5tZMj13jk7BbqTHyu+0yFs3T66RPLolymCWluEyyOob6ZxcwNX4CLkYp27AKS4owBMbbQgL3iOpnREDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"719c7e4982f3897fa22bdf58f9c9b721e6677b589f668ceba063513f70f5413c","last_reissued_at":"2026-07-05T08:07:27.048448Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:07:27.048448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.12091","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:07:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JF9iEl929nEnU04fVGZgKvBKG0gwHLjFJDVZZCWYUVTjlUWrodKBYq76UhXrScolS063giRY2rN1L/O3H8aZDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T23:55:36.051153Z"},"content_sha256":"2b15244e3bbc4f2d982c80a16cf7fd71220f0055717eec94b8cf8ddb4a550d3a","schema_version":"1.0","event_id":"sha256:2b15244e3bbc4f2d982c80a16cf7fd71220f0055717eec94b8cf8ddb4a550d3a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:OGOH4SMC6OEX7IRL35MPTSNXEH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Danruo Deng, Furui Liu, Guangyong Chen, Pheng-Ann Heng, Qi Dou, Yang Yu, Yueming Jin","submitted_at":"2023-03-21T09:07:15Z","abstract_excerpt":"Semi-supervised learning (SSL) methods assume that labeled data, unlabeled data and test data are from the same distribution. Open-set semi-supervised learning (Open-set SSL) considers a more practical scenario, where unlabeled data and test data contain new categories (outliers) not observed in labeled data (inliers). Most previous works focused on outlier detection via binary classifiers, which suffer from insufficient scalability and inability to distinguish different types of uncertainty. In this paper, we propose a novel framework, Adaptive Negative Evidential Deep Learning (ANEDL) to tac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.12091","kind":"arxiv","version":4},"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/2303.12091/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":"2026-07-05T08:07:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fzk/NcN2d+wBiFTBB/adBNKGhc27lPeru0rgCfrSml0AtQP/rq9TFhhaBKVQJb+95XUTJc3zZ1pXz4p7gPGLAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T23:55:36.052123Z"},"content_sha256":"18678b47cdf6fc1c98883f2d6f102203afacfad4970314e061fce7b6d8ad53f4","schema_version":"1.0","event_id":"sha256:18678b47cdf6fc1c98883f2d6f102203afacfad4970314e061fce7b6d8ad53f4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OGOH4SMC6OEX7IRL35MPTSNXEH/bundle.json","state_url":"https://pith.science/pith/OGOH4SMC6OEX7IRL35MPTSNXEH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OGOH4SMC6OEX7IRL35MPTSNXEH/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":"2026-08-05T23:55:36Z","links":{"resolver":"https://pith.science/pith/OGOH4SMC6OEX7IRL35MPTSNXEH","bundle":"https://pith.science/pith/OGOH4SMC6OEX7IRL35MPTSNXEH/bundle.json","state":"https://pith.science/pith/OGOH4SMC6OEX7IRL35MPTSNXEH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OGOH4SMC6OEX7IRL35MPTSNXEH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:OGOH4SMC6OEX7IRL35MPTSNXEH","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":"06fb2cd5bd7a09611565db21ba4b129243f5b907be9ddbc174938484d76f8d78","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-21T09:07:15Z","title_canon_sha256":"124dcac930b795f3bdfbb2dee742cb379d2c66d223dbe7d0e602edd3e4446efe"},"schema_version":"1.0","source":{"id":"2303.12091","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.12091","created_at":"2026-07-05T08:07:27Z"},{"alias_kind":"arxiv_version","alias_value":"2303.12091v4","created_at":"2026-07-05T08:07:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.12091","created_at":"2026-07-05T08:07:27Z"},{"alias_kind":"pith_short_12","alias_value":"OGOH4SMC6OEX","created_at":"2026-07-05T08:07:27Z"},{"alias_kind":"pith_short_16","alias_value":"OGOH4SMC6OEX7IRL","created_at":"2026-07-05T08:07:27Z"},{"alias_kind":"pith_short_8","alias_value":"OGOH4SMC","created_at":"2026-07-05T08:07:27Z"}],"graph_snapshots":[{"event_id":"sha256:18678b47cdf6fc1c98883f2d6f102203afacfad4970314e061fce7b6d8ad53f4","target":"graph","created_at":"2026-07-05T08:07:27Z","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/2303.12091/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Semi-supervised learning (SSL) methods assume that labeled data, unlabeled data and test data are from the same distribution. Open-set semi-supervised learning (Open-set SSL) considers a more practical scenario, where unlabeled data and test data contain new categories (outliers) not observed in labeled data (inliers). Most previous works focused on outlier detection via binary classifiers, which suffer from insufficient scalability and inability to distinguish different types of uncertainty. In this paper, we propose a novel framework, Adaptive Negative Evidential Deep Learning (ANEDL) to tac","authors_text":"Danruo Deng, Furui Liu, Guangyong Chen, Pheng-Ann Heng, Qi Dou, Yang Yu, Yueming Jin","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-21T09:07:15Z","title":"Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.12091","kind":"arxiv","version":4},"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":"sha256:2b15244e3bbc4f2d982c80a16cf7fd71220f0055717eec94b8cf8ddb4a550d3a","target":"record","created_at":"2026-07-05T08:07:27Z","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":"06fb2cd5bd7a09611565db21ba4b129243f5b907be9ddbc174938484d76f8d78","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-21T09:07:15Z","title_canon_sha256":"124dcac930b795f3bdfbb2dee742cb379d2c66d223dbe7d0e602edd3e4446efe"},"schema_version":"1.0","source":{"id":"2303.12091","kind":"arxiv","version":4}},"canonical_sha256":"719c7e4982f3897fa22bdf58f9c9b721e6677b589f668ceba063513f70f5413c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"719c7e4982f3897fa22bdf58f9c9b721e6677b589f668ceba063513f70f5413c","first_computed_at":"2026-07-05T08:07:27.048448Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:07:27.048448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qrTXMo5tZMj13jk7BbqTHyu+0yFs3T66RPLolymCWluEyyOob6ZxcwNX4CLkYp27AKS4owBMbbQgL3iOpnREDA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:07:27.048942Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.12091","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2b15244e3bbc4f2d982c80a16cf7fd71220f0055717eec94b8cf8ddb4a550d3a","sha256:18678b47cdf6fc1c98883f2d6f102203afacfad4970314e061fce7b6d8ad53f4"],"state_sha256":"30157cdd7f5fd7c7a15c0f72432b911059678fa8ca35ea6443ae15f8542dbf34"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+WbJhXsIxjM59avzT3IQjEJuHlFO2PHesRDWXn5W+712aowq9fCnoP8Vb00aV4I1wj3isHhVRl75VOdk1cu2Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T23:55:36.057564Z","bundle_sha256":"74dcb603ce55357a69e40469737bd952bb14129fcba1909b33ca91cc34ef3cab"}}