{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2OTFVIQ3UNVLONUQKQXRLDVWMW","short_pith_number":"pith:2OTFVIQ3","canonical_record":{"source":{"id":"2503.08155","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-11T08:10:03Z","cross_cats_sorted":[],"title_canon_sha256":"bf3ee5e82f893fcd748c1eb60e72c7dfd953215dfef9d72f12e890d94bce6947","abstract_canon_sha256":"6d142483dd6139f93ea1e75197496f78d9b50641a6642e319099a4aadfa5f86e"},"schema_version":"1.0"},"canonical_sha256":"d3a65aa21ba36ab73690542f158eb665949cd00c9bfeec5d7f9f9edf5e673374","source":{"kind":"arxiv","id":"2503.08155","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.08155","created_at":"2026-07-05T10:28:52Z"},{"alias_kind":"arxiv_version","alias_value":"2503.08155v1","created_at":"2026-07-05T10:28:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.08155","created_at":"2026-07-05T10:28:52Z"},{"alias_kind":"pith_short_12","alias_value":"2OTFVIQ3UNVL","created_at":"2026-07-05T10:28:52Z"},{"alias_kind":"pith_short_16","alias_value":"2OTFVIQ3UNVLONUQ","created_at":"2026-07-05T10:28:52Z"},{"alias_kind":"pith_short_8","alias_value":"2OTFVIQ3","created_at":"2026-07-05T10:28:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2OTFVIQ3UNVLONUQKQXRLDVWMW","target":"record","payload":{"canonical_record":{"source":{"id":"2503.08155","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-11T08:10:03Z","cross_cats_sorted":[],"title_canon_sha256":"bf3ee5e82f893fcd748c1eb60e72c7dfd953215dfef9d72f12e890d94bce6947","abstract_canon_sha256":"6d142483dd6139f93ea1e75197496f78d9b50641a6642e319099a4aadfa5f86e"},"schema_version":"1.0"},"canonical_sha256":"d3a65aa21ba36ab73690542f158eb665949cd00c9bfeec5d7f9f9edf5e673374","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:28:52.421564Z","signature_b64":"XdnL9AB2FjHXamxke2nuZUdOngt7urriN8jB2yDhlmixYHgyi1C1N4UtCBNtz8he7FMmIODA4eit1ykUyUoYAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d3a65aa21ba36ab73690542f158eb665949cd00c9bfeec5d7f9f9edf5e673374","last_reissued_at":"2026-07-05T10:28:52.421038Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:28:52.421038Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.08155","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":"2026-07-05T10:28:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+bQnzIyhidnz7R3JuVDssYwqobfoljgS5BIhxQa3PFGccgX8Kp7AY4MSGPEFG3tE1pRNtYRZxvVfMTEtUQTrDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T03:23:04.836976Z"},"content_sha256":"63afd52a4159805b624efe0733ffaeb6afbc74b3b846294d90f465fbb5e0d8af","schema_version":"1.0","event_id":"sha256:63afd52a4159805b624efe0733ffaeb6afbc74b3b846294d90f465fbb5e0d8af"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2OTFVIQ3UNVLONUQKQXRLDVWMW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Domain Adaptation and Entanglement: an Optimal Transport Perspective","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alexander Soen, Chao-Kai Chiang, Masashi Sugiyama, Okan Ko\\c{c}","submitted_at":"2025-03-11T08:10:03Z","abstract_excerpt":"Current machine learning systems are brittle in the face of distribution shifts (DS), where the target distribution that the system is tested on differs from the source distribution used to train the system. This problem of robustness to DS has been studied extensively in the field of domain adaptation. For deep neural networks, a popular framework for unsupervised domain adaptation (UDA) is domain matching, in which algorithms\n  try to align the marginal distributions in the feature or output space.\n  The current theoretical understanding of these methods, however, is limited and existing the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.08155","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/2503.08155/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":"2026-07-05T10:28:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2+3BJrWQBU61cZ5zI9ZlArHk9EOMcBxvAE0W7Bb7OGjiU3bdpIQhfUG4vQZk5JS/bL3goqbsvgdG6IKF1ZkPCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T03:23:04.837389Z"},"content_sha256":"0cfc0064237a57e6b869e682baaf82de4b6e521dca97e649924bdce5b3e4124b","schema_version":"1.0","event_id":"sha256:0cfc0064237a57e6b869e682baaf82de4b6e521dca97e649924bdce5b3e4124b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2OTFVIQ3UNVLONUQKQXRLDVWMW/bundle.json","state_url":"https://pith.science/pith/2OTFVIQ3UNVLONUQKQXRLDVWMW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2OTFVIQ3UNVLONUQKQXRLDVWMW/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":"2026-07-07T03:23:04Z","links":{"resolver":"https://pith.science/pith/2OTFVIQ3UNVLONUQKQXRLDVWMW","bundle":"https://pith.science/pith/2OTFVIQ3UNVLONUQKQXRLDVWMW/bundle.json","state":"https://pith.science/pith/2OTFVIQ3UNVLONUQKQXRLDVWMW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2OTFVIQ3UNVLONUQKQXRLDVWMW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2OTFVIQ3UNVLONUQKQXRLDVWMW","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":"6d142483dd6139f93ea1e75197496f78d9b50641a6642e319099a4aadfa5f86e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-11T08:10:03Z","title_canon_sha256":"bf3ee5e82f893fcd748c1eb60e72c7dfd953215dfef9d72f12e890d94bce6947"},"schema_version":"1.0","source":{"id":"2503.08155","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.08155","created_at":"2026-07-05T10:28:52Z"},{"alias_kind":"arxiv_version","alias_value":"2503.08155v1","created_at":"2026-07-05T10:28:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.08155","created_at":"2026-07-05T10:28:52Z"},{"alias_kind":"pith_short_12","alias_value":"2OTFVIQ3UNVL","created_at":"2026-07-05T10:28:52Z"},{"alias_kind":"pith_short_16","alias_value":"2OTFVIQ3UNVLONUQ","created_at":"2026-07-05T10:28:52Z"},{"alias_kind":"pith_short_8","alias_value":"2OTFVIQ3","created_at":"2026-07-05T10:28:52Z"}],"graph_snapshots":[{"event_id":"sha256:0cfc0064237a57e6b869e682baaf82de4b6e521dca97e649924bdce5b3e4124b","target":"graph","created_at":"2026-07-05T10:28:52Z","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/2503.08155/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current machine learning systems are brittle in the face of distribution shifts (DS), where the target distribution that the system is tested on differs from the source distribution used to train the system. This problem of robustness to DS has been studied extensively in the field of domain adaptation. For deep neural networks, a popular framework for unsupervised domain adaptation (UDA) is domain matching, in which algorithms\n  try to align the marginal distributions in the feature or output space.\n  The current theoretical understanding of these methods, however, is limited and existing the","authors_text":"Alexander Soen, Chao-Kai Chiang, Masashi Sugiyama, Okan Ko\\c{c}","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-11T08:10:03Z","title":"Domain Adaptation and Entanglement: an Optimal Transport Perspective"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.08155","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":"sha256:63afd52a4159805b624efe0733ffaeb6afbc74b3b846294d90f465fbb5e0d8af","target":"record","created_at":"2026-07-05T10:28:52Z","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":"6d142483dd6139f93ea1e75197496f78d9b50641a6642e319099a4aadfa5f86e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-11T08:10:03Z","title_canon_sha256":"bf3ee5e82f893fcd748c1eb60e72c7dfd953215dfef9d72f12e890d94bce6947"},"schema_version":"1.0","source":{"id":"2503.08155","kind":"arxiv","version":1}},"canonical_sha256":"d3a65aa21ba36ab73690542f158eb665949cd00c9bfeec5d7f9f9edf5e673374","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d3a65aa21ba36ab73690542f158eb665949cd00c9bfeec5d7f9f9edf5e673374","first_computed_at":"2026-07-05T10:28:52.421038Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:28:52.421038Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XdnL9AB2FjHXamxke2nuZUdOngt7urriN8jB2yDhlmixYHgyi1C1N4UtCBNtz8he7FMmIODA4eit1ykUyUoYAg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:28:52.421564Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.08155","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:63afd52a4159805b624efe0733ffaeb6afbc74b3b846294d90f465fbb5e0d8af","sha256:0cfc0064237a57e6b869e682baaf82de4b6e521dca97e649924bdce5b3e4124b"],"state_sha256":"50d15bf86df95366afb250d4df6d798f9bfdbcd94ca64d97d62ce6625d2e37b7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QBT8LB7cRy39UF+Ja3w8xu2R1N1iq2TrBiCS9nMCrVIwsRrlZC23g3jMKIY4JGM8CoeQsdJ2ycYpfinZ1sllCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T03:23:04.839396Z","bundle_sha256":"c1f45979633052878a2ca29ef0e4ba6706036db760367a1339020e43e130e8bf"}}