{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:MAFSWZXSCSP46PQT34RTXRHVHE","short_pith_number":"pith:MAFSWZXS","canonical_record":{"source":{"id":"2308.10453","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-21T03:58:04Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"9a4126716377d2688e14ab5ac6a318e1da3e0563d8fb9e7f6c7e4b8e0e381fe1","abstract_canon_sha256":"f76be8a701edd8a153f60f0403829e7a6dfd69b19c1181ba49f984ba66de60cf"},"schema_version":"1.0"},"canonical_sha256":"600b2b66f2149fcf3e13df233bc4f539315fdcec10ce2e5bcdd4f091792d8c03","source":{"kind":"arxiv","id":"2308.10453","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.10453","created_at":"2026-07-05T06:43:09Z"},{"alias_kind":"arxiv_version","alias_value":"2308.10453v1","created_at":"2026-07-05T06:43:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.10453","created_at":"2026-07-05T06:43:09Z"},{"alias_kind":"pith_short_12","alias_value":"MAFSWZXSCSP4","created_at":"2026-07-05T06:43:09Z"},{"alias_kind":"pith_short_16","alias_value":"MAFSWZXSCSP46PQT","created_at":"2026-07-05T06:43:09Z"},{"alias_kind":"pith_short_8","alias_value":"MAFSWZXS","created_at":"2026-07-05T06:43:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:MAFSWZXSCSP46PQT34RTXRHVHE","target":"record","payload":{"canonical_record":{"source":{"id":"2308.10453","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-21T03:58:04Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"9a4126716377d2688e14ab5ac6a318e1da3e0563d8fb9e7f6c7e4b8e0e381fe1","abstract_canon_sha256":"f76be8a701edd8a153f60f0403829e7a6dfd69b19c1181ba49f984ba66de60cf"},"schema_version":"1.0"},"canonical_sha256":"600b2b66f2149fcf3e13df233bc4f539315fdcec10ce2e5bcdd4f091792d8c03","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:43:09.463422Z","signature_b64":"2kYPJIDgO6q9Ld1B3tULTImIlefM34SavHaNQt08vTQJdWAanCBUqVE9Be5DBh8cY2M/GmdsG+MOVBix1yWKDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"600b2b66f2149fcf3e13df233bc4f539315fdcec10ce2e5bcdd4f091792d8c03","last_reissued_at":"2026-07-05T06:43:09.462928Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:43:09.462928Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.10453","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":"2026-07-05T06:43:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nnO3jmnfIW1n6gm/3EI5gGlZjK9REKD6hoezjjIoZ0L+tvbYuFbX3JP8ziltj5q/oL9hEDJAFyf9Xl8/lULHAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T18:10:45.795688Z"},"content_sha256":"bc3c6775752459566cc29fbbdfc27393cf1a063cebe8ac4ccfc97f23b4465b0a","schema_version":"1.0","event_id":"sha256:bc3c6775752459566cc29fbbdfc27393cf1a063cebe8ac4ccfc97f23b4465b0a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:MAFSWZXSCSP46PQT34RTXRHVHE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Adam J. Woods, Alejandro Albizu, Aprinda Indahlastari, Kevin Brink, Kyle Volle, Matthew Hale, Ruogu Fang, Skylar E. Stolte","submitted_at":"2023-08-21T03:58:04Z","abstract_excerpt":"Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model's training data. DL models that perform well on in-domain test data could struggle on OOD data. Overcoming this discrepancy is essential to the reliable deployment of DL. Proper model calibration decreases the number of spurious connections that are made between model features and class outputs. Hence, calibrated DL can improve OOD generalization by only learning features that are truly indicative of the respective class"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.10453","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/2308.10453/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":"2026-07-05T06:43:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jGrOxnn1jje1IDDtJ7y9M6/ZXWTaq6mLonBnrWyn04MTdBefR9m9zTu2uJxwEJAAXKLTXvdYqYhTkTBwpkYQAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T18:10:45.796059Z"},"content_sha256":"abf7b112e30429ff799bb166e9a7de4caf63b91f6036c172ee468ef7ae6a32d8","schema_version":"1.0","event_id":"sha256:abf7b112e30429ff799bb166e9a7de4caf63b91f6036c172ee468ef7ae6a32d8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MAFSWZXSCSP46PQT34RTXRHVHE/bundle.json","state_url":"https://pith.science/pith/MAFSWZXSCSP46PQT34RTXRHVHE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MAFSWZXSCSP46PQT34RTXRHVHE/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":"2026-07-08T18:10:45Z","links":{"resolver":"https://pith.science/pith/MAFSWZXSCSP46PQT34RTXRHVHE","bundle":"https://pith.science/pith/MAFSWZXSCSP46PQT34RTXRHVHE/bundle.json","state":"https://pith.science/pith/MAFSWZXSCSP46PQT34RTXRHVHE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MAFSWZXSCSP46PQT34RTXRHVHE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:MAFSWZXSCSP46PQT34RTXRHVHE","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":"f76be8a701edd8a153f60f0403829e7a6dfd69b19c1181ba49f984ba66de60cf","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-21T03:58:04Z","title_canon_sha256":"9a4126716377d2688e14ab5ac6a318e1da3e0563d8fb9e7f6c7e4b8e0e381fe1"},"schema_version":"1.0","source":{"id":"2308.10453","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.10453","created_at":"2026-07-05T06:43:09Z"},{"alias_kind":"arxiv_version","alias_value":"2308.10453v1","created_at":"2026-07-05T06:43:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.10453","created_at":"2026-07-05T06:43:09Z"},{"alias_kind":"pith_short_12","alias_value":"MAFSWZXSCSP4","created_at":"2026-07-05T06:43:09Z"},{"alias_kind":"pith_short_16","alias_value":"MAFSWZXSCSP46PQT","created_at":"2026-07-05T06:43:09Z"},{"alias_kind":"pith_short_8","alias_value":"MAFSWZXS","created_at":"2026-07-05T06:43:09Z"}],"graph_snapshots":[{"event_id":"sha256:abf7b112e30429ff799bb166e9a7de4caf63b91f6036c172ee468ef7ae6a32d8","target":"graph","created_at":"2026-07-05T06:43:09Z","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/2308.10453/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model's training data. DL models that perform well on in-domain test data could struggle on OOD data. Overcoming this discrepancy is essential to the reliable deployment of DL. Proper model calibration decreases the number of spurious connections that are made between model features and class outputs. Hence, calibrated DL can improve OOD generalization by only learning features that are truly indicative of the respective class","authors_text":"Adam J. Woods, Alejandro Albizu, Aprinda Indahlastari, Kevin Brink, Kyle Volle, Matthew Hale, Ruogu Fang, Skylar E. Stolte","cross_cats":["cs.LG","eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-21T03:58:04Z","title":"DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.10453","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:bc3c6775752459566cc29fbbdfc27393cf1a063cebe8ac4ccfc97f23b4465b0a","target":"record","created_at":"2026-07-05T06:43:09Z","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":"f76be8a701edd8a153f60f0403829e7a6dfd69b19c1181ba49f984ba66de60cf","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-21T03:58:04Z","title_canon_sha256":"9a4126716377d2688e14ab5ac6a318e1da3e0563d8fb9e7f6c7e4b8e0e381fe1"},"schema_version":"1.0","source":{"id":"2308.10453","kind":"arxiv","version":1}},"canonical_sha256":"600b2b66f2149fcf3e13df233bc4f539315fdcec10ce2e5bcdd4f091792d8c03","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"600b2b66f2149fcf3e13df233bc4f539315fdcec10ce2e5bcdd4f091792d8c03","first_computed_at":"2026-07-05T06:43:09.462928Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:43:09.462928Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2kYPJIDgO6q9Ld1B3tULTImIlefM34SavHaNQt08vTQJdWAanCBUqVE9Be5DBh8cY2M/GmdsG+MOVBix1yWKDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:43:09.463422Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.10453","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bc3c6775752459566cc29fbbdfc27393cf1a063cebe8ac4ccfc97f23b4465b0a","sha256:abf7b112e30429ff799bb166e9a7de4caf63b91f6036c172ee468ef7ae6a32d8"],"state_sha256":"2118cf7e59cf3c443210f37eeab7c35e64c74232cea85e0a8a3700e5bcda19bd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XZr+OVt146QPJ5CSKrBGdgVc+yBlGcDu+jDfpLkmR3ZgkCVrzNUP6PrABaITUz50BMOwgGPUOxckFpWT5EcRBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T18:10:45.798198Z","bundle_sha256":"297896f4fb4af4ce31552f2055fee5bff16123f816def83704d932b8560e25f7"}}