{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:XROLIXRNNJW4BTRI4BI2BGPGOZ","short_pith_number":"pith:XROLIXRN","canonical_record":{"source":{"id":"2509.09006","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-10T21:09:20Z","cross_cats_sorted":[],"title_canon_sha256":"05674ff81a59e708b2cee57edf749ad603dfd40be528ef919b274ec9976f3567","abstract_canon_sha256":"00608690da993820584a47b8b92039d9e978af59bc1864b987f7555a7f494aad"},"schema_version":"1.0"},"canonical_sha256":"bc5cb45e2d6a6dc0ce28e051a099e67658e6e2fb57874842d2bc71e666ac920c","source":{"kind":"arxiv","id":"2509.09006","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.09006","created_at":"2026-07-05T12:09:27Z"},{"alias_kind":"arxiv_version","alias_value":"2509.09006v1","created_at":"2026-07-05T12:09:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09006","created_at":"2026-07-05T12:09:27Z"},{"alias_kind":"pith_short_12","alias_value":"XROLIXRNNJW4","created_at":"2026-07-05T12:09:27Z"},{"alias_kind":"pith_short_16","alias_value":"XROLIXRNNJW4BTRI","created_at":"2026-07-05T12:09:27Z"},{"alias_kind":"pith_short_8","alias_value":"XROLIXRN","created_at":"2026-07-05T12:09:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:XROLIXRNNJW4BTRI4BI2BGPGOZ","target":"record","payload":{"canonical_record":{"source":{"id":"2509.09006","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-10T21:09:20Z","cross_cats_sorted":[],"title_canon_sha256":"05674ff81a59e708b2cee57edf749ad603dfd40be528ef919b274ec9976f3567","abstract_canon_sha256":"00608690da993820584a47b8b92039d9e978af59bc1864b987f7555a7f494aad"},"schema_version":"1.0"},"canonical_sha256":"bc5cb45e2d6a6dc0ce28e051a099e67658e6e2fb57874842d2bc71e666ac920c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:09:27.464518Z","signature_b64":"ZSmBbsG6OsdGU70bS7SzIdP5vNaIfhs/kR05trxWbQZ9MeOKUkQJeHNWWFZ5CGhXZbXHfB5UZlLDHfxwAmljAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc5cb45e2d6a6dc0ce28e051a099e67658e6e2fb57874842d2bc71e666ac920c","last_reissued_at":"2026-07-05T12:09:27.463985Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:09:27.463985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.09006","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":"2026-07-05T12:09:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WwyrWdncyUxyNcyUxCrZSvLJtEVFFRYy+YPHbXFlPznkZ/QQYFXua+jsbDbQ4YXIkjfhGI28feM0s9qWg1fCDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:26:29.088788Z"},"content_sha256":"a7be54b70a507a3db4a89a157240d7fae8de6a13f6602ef77d6bfddc17049ea5","schema_version":"1.0","event_id":"sha256:a7be54b70a507a3db4a89a157240d7fae8de6a13f6602ef77d6bfddc17049ea5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:XROLIXRNNJW4BTRI4BI2BGPGOZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"E-MLNet: Enhanced Mutual Learning for Universal Domain Adaptation with Sample-Specific Weighting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jurandy Almeida, Samuel Felipe dos Santos, Tiago Agostinho de Almeida","submitted_at":"2025-09-10T21:09:20Z","abstract_excerpt":"Universal Domain Adaptation (UniDA) seeks to transfer knowledge from a labeled source to an unlabeled target domain without assuming any relationship between their label sets, requiring models to classify known samples while rejecting unknown ones. Advanced methods like Mutual Learning Network (MLNet) use a bank of one-vs-all classifiers adapted via Open-set Entropy Minimization (OEM). However, this strategy treats all classifiers equally, diluting the learning signal. We propose the Enhanced Mutual Learning Network (E-MLNet), which integrates a dynamic weighting strategy to OEM. By leveraging"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09006","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/2509.09006/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":"2026-07-05T12:09:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v6MSKiQvAZZn3PC1gzEWliTHfJNcuqUIaSf6Dsb1vqTOU2FK854hIUGJzlgmitVKZ5796nhzsIm6x12ShtsHAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:26:29.089431Z"},"content_sha256":"f91f92120de0c670c5a7e5484b8c8108e18a7e78aabe0d684d9f152843c33d0d","schema_version":"1.0","event_id":"sha256:f91f92120de0c670c5a7e5484b8c8108e18a7e78aabe0d684d9f152843c33d0d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XROLIXRNNJW4BTRI4BI2BGPGOZ/bundle.json","state_url":"https://pith.science/pith/XROLIXRNNJW4BTRI4BI2BGPGOZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XROLIXRNNJW4BTRI4BI2BGPGOZ/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":"2026-08-05T14:26:29Z","links":{"resolver":"https://pith.science/pith/XROLIXRNNJW4BTRI4BI2BGPGOZ","bundle":"https://pith.science/pith/XROLIXRNNJW4BTRI4BI2BGPGOZ/bundle.json","state":"https://pith.science/pith/XROLIXRNNJW4BTRI4BI2BGPGOZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XROLIXRNNJW4BTRI4BI2BGPGOZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:XROLIXRNNJW4BTRI4BI2BGPGOZ","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":"00608690da993820584a47b8b92039d9e978af59bc1864b987f7555a7f494aad","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-10T21:09:20Z","title_canon_sha256":"05674ff81a59e708b2cee57edf749ad603dfd40be528ef919b274ec9976f3567"},"schema_version":"1.0","source":{"id":"2509.09006","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.09006","created_at":"2026-07-05T12:09:27Z"},{"alias_kind":"arxiv_version","alias_value":"2509.09006v1","created_at":"2026-07-05T12:09:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09006","created_at":"2026-07-05T12:09:27Z"},{"alias_kind":"pith_short_12","alias_value":"XROLIXRNNJW4","created_at":"2026-07-05T12:09:27Z"},{"alias_kind":"pith_short_16","alias_value":"XROLIXRNNJW4BTRI","created_at":"2026-07-05T12:09:27Z"},{"alias_kind":"pith_short_8","alias_value":"XROLIXRN","created_at":"2026-07-05T12:09:27Z"}],"graph_snapshots":[{"event_id":"sha256:f91f92120de0c670c5a7e5484b8c8108e18a7e78aabe0d684d9f152843c33d0d","target":"graph","created_at":"2026-07-05T12:09:27Z","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/2509.09006/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Universal Domain Adaptation (UniDA) seeks to transfer knowledge from a labeled source to an unlabeled target domain without assuming any relationship between their label sets, requiring models to classify known samples while rejecting unknown ones. Advanced methods like Mutual Learning Network (MLNet) use a bank of one-vs-all classifiers adapted via Open-set Entropy Minimization (OEM). However, this strategy treats all classifiers equally, diluting the learning signal. We propose the Enhanced Mutual Learning Network (E-MLNet), which integrates a dynamic weighting strategy to OEM. By leveraging","authors_text":"Jurandy Almeida, Samuel Felipe dos Santos, Tiago Agostinho de Almeida","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-10T21:09:20Z","title":"E-MLNet: Enhanced Mutual Learning for Universal Domain Adaptation with Sample-Specific Weighting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09006","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:a7be54b70a507a3db4a89a157240d7fae8de6a13f6602ef77d6bfddc17049ea5","target":"record","created_at":"2026-07-05T12:09:27Z","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":"00608690da993820584a47b8b92039d9e978af59bc1864b987f7555a7f494aad","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-10T21:09:20Z","title_canon_sha256":"05674ff81a59e708b2cee57edf749ad603dfd40be528ef919b274ec9976f3567"},"schema_version":"1.0","source":{"id":"2509.09006","kind":"arxiv","version":1}},"canonical_sha256":"bc5cb45e2d6a6dc0ce28e051a099e67658e6e2fb57874842d2bc71e666ac920c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bc5cb45e2d6a6dc0ce28e051a099e67658e6e2fb57874842d2bc71e666ac920c","first_computed_at":"2026-07-05T12:09:27.463985Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:09:27.463985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZSmBbsG6OsdGU70bS7SzIdP5vNaIfhs/kR05trxWbQZ9MeOKUkQJeHNWWFZ5CGhXZbXHfB5UZlLDHfxwAmljAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T12:09:27.464518Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.09006","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a7be54b70a507a3db4a89a157240d7fae8de6a13f6602ef77d6bfddc17049ea5","sha256:f91f92120de0c670c5a7e5484b8c8108e18a7e78aabe0d684d9f152843c33d0d"],"state_sha256":"e7ed203ba19ff269d1d38a6f506b7edcf435cd0682eb33532397110a6982a548"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V1JLMozgnFAbL3/aquqqaVH75bO8ofcUxi5hr9opxYSi2Hwlxk8pQ8R2XgMxFHfW2Jnkraza3fM6U0qJ0zRcBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T14:26:29.095125Z","bundle_sha256":"1cb36f7cfa2abd47ffed4d235e64ae84cdcc1cde5a73e3959f9d12f30dd48c70"}}