{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:64IAAK3JF3EMWZCZYFHNRNBKXR","short_pith_number":"pith:64IAAK3J","canonical_record":{"source":{"id":"2506.06853","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-07T16:18:37Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"f8ea5de54b1ebd093807966947481a6e04e853d826e5a14cafcd097e7f51f1cf","abstract_canon_sha256":"063a17540a6f8dc5d323ac6b84009a6ea0332e8fac5f89d7e963adb1772edeec"},"schema_version":"1.0"},"canonical_sha256":"f710002b692ec8cb6459c14ed8b42abc7c4a22800393c67d0dc8035fc9f6c813","source":{"kind":"arxiv","id":"2506.06853","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.06853","created_at":"2026-07-05T11:17:56Z"},{"alias_kind":"arxiv_version","alias_value":"2506.06853v1","created_at":"2026-07-05T11:17:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06853","created_at":"2026-07-05T11:17:56Z"},{"alias_kind":"pith_short_12","alias_value":"64IAAK3JF3EM","created_at":"2026-07-05T11:17:56Z"},{"alias_kind":"pith_short_16","alias_value":"64IAAK3JF3EMWZCZ","created_at":"2026-07-05T11:17:56Z"},{"alias_kind":"pith_short_8","alias_value":"64IAAK3J","created_at":"2026-07-05T11:17:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:64IAAK3JF3EMWZCZYFHNRNBKXR","target":"record","payload":{"canonical_record":{"source":{"id":"2506.06853","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-07T16:18:37Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"f8ea5de54b1ebd093807966947481a6e04e853d826e5a14cafcd097e7f51f1cf","abstract_canon_sha256":"063a17540a6f8dc5d323ac6b84009a6ea0332e8fac5f89d7e963adb1772edeec"},"schema_version":"1.0"},"canonical_sha256":"f710002b692ec8cb6459c14ed8b42abc7c4a22800393c67d0dc8035fc9f6c813","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:56.954692Z","signature_b64":"AUXqhkDl7KEdSIfJbSNXJQHZ5JY/mLg7hbepT/vA6y5rpBqdT3RDRngeLTCYqLwAziPJcH/9JGIvYifdXnWfCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f710002b692ec8cb6459c14ed8b42abc7c4a22800393c67d0dc8035fc9f6c813","last_reissued_at":"2026-07-05T11:17:56.952549Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:56.952549Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.06853","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":"2026-07-05T11:17:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7uhjgjKOsLVc8wP+mfGLRtvz256BoI6lee48LVRzK6XKJRxZOtM3XtFKDO1CqhZH+gJIGKAo5oRq7f6TJdfZBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T02:40:35.085278Z"},"content_sha256":"e5c146b7d3dab120a67a79deb1b9fb3c03357f8249e9d9ecb4c7d6c12ba7679c","schema_version":"1.0","event_id":"sha256:e5c146b7d3dab120a67a79deb1b9fb3c03357f8249e9d9ecb4c7d6c12ba7679c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:64IAAK3JF3EMWZCZYFHNRNBKXR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Curvature Enhanced Data Augmentation for Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ilya Kaufman Sirot, Omri Azencot","submitted_at":"2025-06-07T16:18:37Z","abstract_excerpt":"Deep learning models with a large number of parameters, often referred to as over-parameterized models, have achieved exceptional performance across various tasks. Despite concerns about overfitting, these models frequently generalize well to unseen data, thanks to effective regularization techniques, with data augmentation being among the most widely used. While data augmentation has shown great success in classification tasks using label-preserving transformations, its application in regression problems has received less attention. Recently, a novel \\emph{manifold learning} approach for gene"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06853","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/2506.06853/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":"2026-07-05T11:17:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TXl7TK+srwHbx7ynr/TCqcP2tgIR8hZOamwBkkmlur/YSU6phIHBHhuYazNxLJT39m2IjtizpnX0ejUC7SUvCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T02:40:35.085772Z"},"content_sha256":"949439ebe701ae9980ee844fb3fc69e968ae3dfa8ce2be45710f1683c65fa462","schema_version":"1.0","event_id":"sha256:949439ebe701ae9980ee844fb3fc69e968ae3dfa8ce2be45710f1683c65fa462"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/64IAAK3JF3EMWZCZYFHNRNBKXR/bundle.json","state_url":"https://pith.science/pith/64IAAK3JF3EMWZCZYFHNRNBKXR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/64IAAK3JF3EMWZCZYFHNRNBKXR/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":"2026-08-09T02:40:35Z","links":{"resolver":"https://pith.science/pith/64IAAK3JF3EMWZCZYFHNRNBKXR","bundle":"https://pith.science/pith/64IAAK3JF3EMWZCZYFHNRNBKXR/bundle.json","state":"https://pith.science/pith/64IAAK3JF3EMWZCZYFHNRNBKXR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/64IAAK3JF3EMWZCZYFHNRNBKXR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:64IAAK3JF3EMWZCZYFHNRNBKXR","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":"063a17540a6f8dc5d323ac6b84009a6ea0332e8fac5f89d7e963adb1772edeec","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-07T16:18:37Z","title_canon_sha256":"f8ea5de54b1ebd093807966947481a6e04e853d826e5a14cafcd097e7f51f1cf"},"schema_version":"1.0","source":{"id":"2506.06853","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.06853","created_at":"2026-07-05T11:17:56Z"},{"alias_kind":"arxiv_version","alias_value":"2506.06853v1","created_at":"2026-07-05T11:17:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06853","created_at":"2026-07-05T11:17:56Z"},{"alias_kind":"pith_short_12","alias_value":"64IAAK3JF3EM","created_at":"2026-07-05T11:17:56Z"},{"alias_kind":"pith_short_16","alias_value":"64IAAK3JF3EMWZCZ","created_at":"2026-07-05T11:17:56Z"},{"alias_kind":"pith_short_8","alias_value":"64IAAK3J","created_at":"2026-07-05T11:17:56Z"}],"graph_snapshots":[{"event_id":"sha256:949439ebe701ae9980ee844fb3fc69e968ae3dfa8ce2be45710f1683c65fa462","target":"graph","created_at":"2026-07-05T11:17:56Z","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/2506.06853/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning models with a large number of parameters, often referred to as over-parameterized models, have achieved exceptional performance across various tasks. Despite concerns about overfitting, these models frequently generalize well to unseen data, thanks to effective regularization techniques, with data augmentation being among the most widely used. While data augmentation has shown great success in classification tasks using label-preserving transformations, its application in regression problems has received less attention. Recently, a novel \\emph{manifold learning} approach for gene","authors_text":"Ilya Kaufman Sirot, Omri Azencot","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-07T16:18:37Z","title":"Curvature Enhanced Data Augmentation for Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06853","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:e5c146b7d3dab120a67a79deb1b9fb3c03357f8249e9d9ecb4c7d6c12ba7679c","target":"record","created_at":"2026-07-05T11:17:56Z","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":"063a17540a6f8dc5d323ac6b84009a6ea0332e8fac5f89d7e963adb1772edeec","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-07T16:18:37Z","title_canon_sha256":"f8ea5de54b1ebd093807966947481a6e04e853d826e5a14cafcd097e7f51f1cf"},"schema_version":"1.0","source":{"id":"2506.06853","kind":"arxiv","version":1}},"canonical_sha256":"f710002b692ec8cb6459c14ed8b42abc7c4a22800393c67d0dc8035fc9f6c813","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f710002b692ec8cb6459c14ed8b42abc7c4a22800393c67d0dc8035fc9f6c813","first_computed_at":"2026-07-05T11:17:56.952549Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:17:56.952549Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AUXqhkDl7KEdSIfJbSNXJQHZ5JY/mLg7hbepT/vA6y5rpBqdT3RDRngeLTCYqLwAziPJcH/9JGIvYifdXnWfCA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:17:56.954692Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.06853","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e5c146b7d3dab120a67a79deb1b9fb3c03357f8249e9d9ecb4c7d6c12ba7679c","sha256:949439ebe701ae9980ee844fb3fc69e968ae3dfa8ce2be45710f1683c65fa462"],"state_sha256":"f29d2422e5167f8ad2ae1bb60f190194b8f6994bbd5a2cb48702622a4d665fcc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wsknFm/KFLyo05eZYT0okaR5BgAys3F2KZkB2HCCDTbMreovyuXjfUetuwDkA4SGcaUcUxmuWnK65bTYmdA9Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T02:40:35.089353Z","bundle_sha256":"91a54193e56d7cd2793b9bc5329239be500bef66655fbf033859dd324febf84a"}}