{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:SXDST4QDZVVUX4EUHN4O36IO62","short_pith_number":"pith:SXDST4QD","canonical_record":{"source":{"id":"2201.03246","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-01-10T10:02:40Z","cross_cats_sorted":[],"title_canon_sha256":"b76e11040225dd22809b5755c8405d1603d159db844b375e2cee746b45e70499","abstract_canon_sha256":"136c58a84f221a9fbbd3af3b77c5f1766a7907d77a1818c13ed5f12619cd83e5"},"schema_version":"1.0"},"canonical_sha256":"95c729f203cd6b4bf0943b78edf90ef6a456323eda3865715b9772aabfbcac9f","source":{"kind":"arxiv","id":"2201.03246","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.03246","created_at":"2026-07-05T05:29:25Z"},{"alias_kind":"arxiv_version","alias_value":"2201.03246v3","created_at":"2026-07-05T05:29:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.03246","created_at":"2026-07-05T05:29:25Z"},{"alias_kind":"pith_short_12","alias_value":"SXDST4QDZVVU","created_at":"2026-07-05T05:29:25Z"},{"alias_kind":"pith_short_16","alias_value":"SXDST4QDZVVUX4EU","created_at":"2026-07-05T05:29:25Z"},{"alias_kind":"pith_short_8","alias_value":"SXDST4QD","created_at":"2026-07-05T05:29:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:SXDST4QDZVVUX4EUHN4O36IO62","target":"record","payload":{"canonical_record":{"source":{"id":"2201.03246","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-01-10T10:02:40Z","cross_cats_sorted":[],"title_canon_sha256":"b76e11040225dd22809b5755c8405d1603d159db844b375e2cee746b45e70499","abstract_canon_sha256":"136c58a84f221a9fbbd3af3b77c5f1766a7907d77a1818c13ed5f12619cd83e5"},"schema_version":"1.0"},"canonical_sha256":"95c729f203cd6b4bf0943b78edf90ef6a456323eda3865715b9772aabfbcac9f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:29:25.688512Z","signature_b64":"eW3ocHLfCtCFd8d+1v8FCW9kmdouOY+bEqz1AJTOgCIL6pVB8l9buCaIy7VfQmQQvstM7O1hF1NNs+cO5NKzAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"95c729f203cd6b4bf0943b78edf90ef6a456323eda3865715b9772aabfbcac9f","last_reissued_at":"2026-07-05T05:29:25.688061Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:29:25.688061Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2201.03246","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:29:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RAkCuX8ARCAB0s/26vnV4/Xs29d3HbcZyeahKAhrk/ZbAnAKldqMCVCImHw/N+K7KauLhwGpcPozlgnRd4NlDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T04:11:26.686852Z"},"content_sha256":"9564c2580e14e202ec34510839426a1c2658ee0f20e134abbf1f0b8745be66cb","schema_version":"1.0","event_id":"sha256:9564c2580e14e202ec34510839426a1c2658ee0f20e134abbf1f0b8745be66cb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:SXDST4QDZVVUX4EUHN4O36IO62","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander Rast, Andrew Bradley, Fabio Cuzzolin, Izzeddin Teeti, Salman Khan, Valentina Musat","submitted_at":"2022-01-10T10:02:40Z","abstract_excerpt":"In an autonomous driving system, perception - identification of features and objects from the environment - is crucial. In autonomous racing, high speeds and small margins demand rapid and accurate detection systems. During the race, the weather can change abruptly, causing significant degradation in perception, resulting in ineffective manoeuvres. In order to improve detection in adverse weather, deep-learning-based models typically require extensive datasets captured in such conditions - the collection of which is a tedious, laborious, and costly process. However, recent developments in Cycl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.03246","kind":"arxiv","version":3},"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/2201.03246/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":"2026-07-05T05:29:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XlFY44N37gELqBNp6EeQisBmudI19tGMfID/csoYTsPA7OV4lYLGeUJL/IPlON4C0vdbQmRtTnJDTvoQoG1IDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T04:11:26.687240Z"},"content_sha256":"26e857fa57d38eefca8dcfeaaf6568775f5e4235df827d3478a13e1b26f989a0","schema_version":"1.0","event_id":"sha256:26e857fa57d38eefca8dcfeaaf6568775f5e4235df827d3478a13e1b26f989a0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SXDST4QDZVVUX4EUHN4O36IO62/bundle.json","state_url":"https://pith.science/pith/SXDST4QDZVVUX4EUHN4O36IO62/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SXDST4QDZVVUX4EUHN4O36IO62/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":"2026-07-21T04:11:26Z","links":{"resolver":"https://pith.science/pith/SXDST4QDZVVUX4EUHN4O36IO62","bundle":"https://pith.science/pith/SXDST4QDZVVUX4EUHN4O36IO62/bundle.json","state":"https://pith.science/pith/SXDST4QDZVVUX4EUHN4O36IO62/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SXDST4QDZVVUX4EUHN4O36IO62/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:SXDST4QDZVVUX4EUHN4O36IO62","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":"136c58a84f221a9fbbd3af3b77c5f1766a7907d77a1818c13ed5f12619cd83e5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-01-10T10:02:40Z","title_canon_sha256":"b76e11040225dd22809b5755c8405d1603d159db844b375e2cee746b45e70499"},"schema_version":"1.0","source":{"id":"2201.03246","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.03246","created_at":"2026-07-05T05:29:25Z"},{"alias_kind":"arxiv_version","alias_value":"2201.03246v3","created_at":"2026-07-05T05:29:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.03246","created_at":"2026-07-05T05:29:25Z"},{"alias_kind":"pith_short_12","alias_value":"SXDST4QDZVVU","created_at":"2026-07-05T05:29:25Z"},{"alias_kind":"pith_short_16","alias_value":"SXDST4QDZVVUX4EU","created_at":"2026-07-05T05:29:25Z"},{"alias_kind":"pith_short_8","alias_value":"SXDST4QD","created_at":"2026-07-05T05:29:25Z"}],"graph_snapshots":[{"event_id":"sha256:26e857fa57d38eefca8dcfeaaf6568775f5e4235df827d3478a13e1b26f989a0","target":"graph","created_at":"2026-07-05T05:29:25Z","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/2201.03246/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In an autonomous driving system, perception - identification of features and objects from the environment - is crucial. In autonomous racing, high speeds and small margins demand rapid and accurate detection systems. During the race, the weather can change abruptly, causing significant degradation in perception, resulting in ineffective manoeuvres. In order to improve detection in adverse weather, deep-learning-based models typically require extensive datasets captured in such conditions - the collection of which is a tedious, laborious, and costly process. However, recent developments in Cycl","authors_text":"Alexander Rast, Andrew Bradley, Fabio Cuzzolin, Izzeddin Teeti, Salman Khan, Valentina Musat","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-01-10T10:02:40Z","title":"Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.03246","kind":"arxiv","version":3},"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":"sha256:9564c2580e14e202ec34510839426a1c2658ee0f20e134abbf1f0b8745be66cb","target":"record","created_at":"2026-07-05T05:29:25Z","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":"136c58a84f221a9fbbd3af3b77c5f1766a7907d77a1818c13ed5f12619cd83e5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-01-10T10:02:40Z","title_canon_sha256":"b76e11040225dd22809b5755c8405d1603d159db844b375e2cee746b45e70499"},"schema_version":"1.0","source":{"id":"2201.03246","kind":"arxiv","version":3}},"canonical_sha256":"95c729f203cd6b4bf0943b78edf90ef6a456323eda3865715b9772aabfbcac9f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"95c729f203cd6b4bf0943b78edf90ef6a456323eda3865715b9772aabfbcac9f","first_computed_at":"2026-07-05T05:29:25.688061Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:29:25.688061Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eW3ocHLfCtCFd8d+1v8FCW9kmdouOY+bEqz1AJTOgCIL6pVB8l9buCaIy7VfQmQQvstM7O1hF1NNs+cO5NKzAw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:29:25.688512Z","signed_message":"canonical_sha256_bytes"},"source_id":"2201.03246","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9564c2580e14e202ec34510839426a1c2658ee0f20e134abbf1f0b8745be66cb","sha256:26e857fa57d38eefca8dcfeaaf6568775f5e4235df827d3478a13e1b26f989a0"],"state_sha256":"8345af13fa03158e6a725c033a630bd6b29c34b42dcc847710980c7c1fb947da"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6inh/SSGtP+5ZquV+QKSt+BpQq24RZnkr8LCrFsft/kyoawKNmsXZBDq/J4vwlg3KAJ3acDHl3+V7sTdvsMUAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-21T04:11:26.689495Z","bundle_sha256":"0d1b424efe54602fd4ed0ae1e7122b7888ae21777b2ffdcc08fc03da887f2f4c"}}