{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:5OLEZYENBVOJBXKMTITYGZ42DX","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":"3457fbb61ae6eba42c77c873082823545468f3f3dd46f5c30f6407ef1205451a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-23T12:48:19Z","title_canon_sha256":"d44af88e00befbba91e1bea81673364c10e9bc83b984604198ecaf8012c4014f"},"schema_version":"1.0","source":{"id":"2506.18587","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.18587","created_at":"2026-07-05T11:25:52Z"},{"alias_kind":"arxiv_version","alias_value":"2506.18587v1","created_at":"2026-07-05T11:25:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.18587","created_at":"2026-07-05T11:25:52Z"},{"alias_kind":"pith_short_12","alias_value":"5OLEZYENBVOJ","created_at":"2026-07-05T11:25:52Z"},{"alias_kind":"pith_short_16","alias_value":"5OLEZYENBVOJBXKM","created_at":"2026-07-05T11:25:52Z"},{"alias_kind":"pith_short_8","alias_value":"5OLEZYEN","created_at":"2026-07-05T11:25:52Z"}],"graph_snapshots":[{"event_id":"sha256:21c242a6869788eb233a309458e1fa7e0be95141685db9e2b4d0eba685555aa4","target":"graph","created_at":"2026-07-05T11:25:52Z","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/2506.18587/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Given the abundance of unlabeled Satellite Image Time Series (SITS) and the scarcity of labeled data, contrastive self-supervised pretraining emerges as a natural tool to leverage this vast quantity of unlabeled data. However, designing effective data augmentations for contrastive learning remains challenging for time series. We introduce a novel resampling-based augmentation strategy that generates positive pairs by upsampling time series and extracting disjoint subsequences while preserving temporal coverage. We validate our approach on multiple agricultural classification benchmarks using S","authors_text":"Antoine Cornu\\'ejols, Antoine Saget, Baptiste Lafabregue, Pierre Gan\\c{c}arski","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-23T12:48:19Z","title":"Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.18587","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:ed787eb065eec4707ff50825c3220671459741cd415b6e6fbf47366bf63dc5ee","target":"record","created_at":"2026-07-05T11:25:52Z","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":"3457fbb61ae6eba42c77c873082823545468f3f3dd46f5c30f6407ef1205451a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-23T12:48:19Z","title_canon_sha256":"d44af88e00befbba91e1bea81673364c10e9bc83b984604198ecaf8012c4014f"},"schema_version":"1.0","source":{"id":"2506.18587","kind":"arxiv","version":1}},"canonical_sha256":"eb964ce08d0d5c90dd4c9a2783679a1dec6650fc0cd1d2633da4e2229be3bc84","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eb964ce08d0d5c90dd4c9a2783679a1dec6650fc0cd1d2633da4e2229be3bc84","first_computed_at":"2026-07-05T11:25:52.569803Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:25:52.569803Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wCAQ007h3aj5gQfm0WRZ9nPUyqai/qUEh/O4Y7jbnKkY4PPlnKC1REtF5c5R83XtyhYTSghbJWBRhpkjGBkZBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:25:52.570244Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.18587","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ed787eb065eec4707ff50825c3220671459741cd415b6e6fbf47366bf63dc5ee","sha256:21c242a6869788eb233a309458e1fa7e0be95141685db9e2b4d0eba685555aa4"],"state_sha256":"ce1260db877d232fe64c7523e22dc1c79aee2eac6d8d737d53f487df2565a2bc"}