{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:7W25TKYYDHBGJUMDKR6BB6BAYN","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":"5b1e66e3ddf1cfece71e2ba0e61a58d7e12a3ab05c02cdd8cac7e92fde8f28d2","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-20T21:22:56Z","title_canon_sha256":"524cf0cfff425c81a628e83afb7f09afac0e66c7bb4fefd0ef595e268d9ef2e1"},"schema_version":"1.0","source":{"id":"2204.09788","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.09788","created_at":"2026-07-05T04:16:42Z"},{"alias_kind":"arxiv_version","alias_value":"2204.09788v1","created_at":"2026-07-05T04:16:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.09788","created_at":"2026-07-05T04:16:42Z"},{"alias_kind":"pith_short_12","alias_value":"7W25TKYYDHBG","created_at":"2026-07-05T04:16:42Z"},{"alias_kind":"pith_short_16","alias_value":"7W25TKYYDHBGJUMD","created_at":"2026-07-05T04:16:42Z"},{"alias_kind":"pith_short_8","alias_value":"7W25TKYY","created_at":"2026-07-05T04:16:42Z"}],"graph_snapshots":[{"event_id":"sha256:7bd735837cbb373ee8a03d026a5c879abadc9194b53a4c19444e3c639e6fed68","target":"graph","created_at":"2026-07-05T04:16:42Z","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/2204.09788/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate scene understanding from multiple sensors mounted on cars is a key requirement for autonomous driving systems. Nowadays, this task is mainly performed through data-hungry deep learning techniques that need very large amounts of data to be trained. Due to the high cost of performing segmentation labeling, many synthetic datasets have been proposed. However, most of them miss the multi-sensor nature of the data, and do not capture the significant changes introduced by the variation of daytime and weather conditions. To fill these gaps, we introduce SELMA, a novel synthetic dataset for s","authors_text":"Francesco Barbato, Marco Giordani, Michele Zorzi, Paolo Testolina, Pietro Zanuttigh, Umberto Michieli","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-20T21:22:56Z","title":"SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.09788","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":"sha256:2a0c0cf0242b0209ecb76c57fa6a2d0107346a9e52f634fb6041338ded706845","target":"record","created_at":"2026-07-05T04:16:42Z","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":"5b1e66e3ddf1cfece71e2ba0e61a58d7e12a3ab05c02cdd8cac7e92fde8f28d2","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-20T21:22:56Z","title_canon_sha256":"524cf0cfff425c81a628e83afb7f09afac0e66c7bb4fefd0ef595e268d9ef2e1"},"schema_version":"1.0","source":{"id":"2204.09788","kind":"arxiv","version":1}},"canonical_sha256":"fdb5d9ab1819c264d183547c10f820c3595b4980c968317f60fcb8957e67f884","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fdb5d9ab1819c264d183547c10f820c3595b4980c968317f60fcb8957e67f884","first_computed_at":"2026-07-05T04:16:42.950677Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:16:42.950677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VMQzqrUxrd13zkNX9mw0cMO96P54cKSL11yMxcRE1E8o2wF9pM6R3zcW1JIPOT4xcPj8ZJB6dBLyO8xZrFB+DA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:16:42.951049Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.09788","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2a0c0cf0242b0209ecb76c57fa6a2d0107346a9e52f634fb6041338ded706845","sha256:7bd735837cbb373ee8a03d026a5c879abadc9194b53a4c19444e3c639e6fed68"],"state_sha256":"2cd5dc8f01cf5e9ece0d42507c267203d260b6d5335d8f20a52f49284bc1936f"}