{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:AO7N22E66UJHPUH4Q5LHCGV4KH","short_pith_number":"pith:AO7N22E6","canonical_record":{"source":{"id":"2412.14537","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-19T05:33:55Z","cross_cats_sorted":[],"title_canon_sha256":"02240d6516112f6029311be3612eee23da8656669139277e14f7295737604579","abstract_canon_sha256":"33be12dab53a190c357975bd08f1ae24283ce807ba9345e7d3dbdf13ca314322"},"schema_version":"1.0"},"canonical_sha256":"03bedd689ef51277d0fc8756711abc51f92c814c5085bf611d881539d2bf8bd6","source":{"kind":"arxiv","id":"2412.14537","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.14537","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"arxiv_version","alias_value":"2412.14537v1","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.14537","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"pith_short_12","alias_value":"AO7N22E66UJH","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"pith_short_16","alias_value":"AO7N22E66UJHPUH4","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"pith_short_8","alias_value":"AO7N22E6","created_at":"2026-07-05T09:51:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:AO7N22E66UJHPUH4Q5LHCGV4KH","target":"record","payload":{"canonical_record":{"source":{"id":"2412.14537","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-19T05:33:55Z","cross_cats_sorted":[],"title_canon_sha256":"02240d6516112f6029311be3612eee23da8656669139277e14f7295737604579","abstract_canon_sha256":"33be12dab53a190c357975bd08f1ae24283ce807ba9345e7d3dbdf13ca314322"},"schema_version":"1.0"},"canonical_sha256":"03bedd689ef51277d0fc8756711abc51f92c814c5085bf611d881539d2bf8bd6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:49.036391Z","signature_b64":"WwedNkY3xUYFdkY9f5hVrcRN8aEgJ3eT6IVMaUZh/734GvkpANVXnwQEhJ1zJaljvv12rV/JpNdh/AWGIPtcAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03bedd689ef51277d0fc8756711abc51f92c814c5085bf611d881539d2bf8bd6","last_reissued_at":"2026-07-05T09:51:49.035854Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:49.035854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.14537","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":"2026-07-05T09:51:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fzAJJ7lTtuKG5EyY63zWsUeEtx+ZmPGgwnvMtIrL6HOYKTfL1HNbPD3J47o+62GeSG4liYxXOfhV+WnjLkK/Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T19:29:09.631832Z"},"content_sha256":"8512c07f12dc8960ea43808ced827520478a2f5f9bcb812c90d8055287f1f5df","schema_version":"1.0","event_id":"sha256:8512c07f12dc8960ea43808ced827520478a2f5f9bcb812c90d8055287f1f5df"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:AO7N22E66UJHPUH4Q5LHCGV4KH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Qi Zheng, Yaying Zhang, Zihao Yao","submitted_at":"2024-12-19T05:33:55Z","abstract_excerpt":"Spatial-temporal forecasting is crucial and widely applicable in various domains such as traffic, energy, and climate. Benefiting from the abundance of unlabeled spatial-temporal data, self-supervised methods are increasingly adapted to learn spatial-temporal representations. However, it encounters three key challenges: 1) the difficulty in selecting reliable negative pairs due to the homogeneity of variables, hindering contrastive learning methods; 2) overlooking spatial correlations across variables over time; 3) limitations of efficiency and scalability in existing self-supervised learning "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.14537","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/2412.14537/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":"2026-07-05T09:51:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mQL/tUs5Niqgxr33AKw16fyO8mkTZ/n8C1D1CR7P9DELOyIrslHzWQG8pDiiT92OetHSmWe14XVZZx6fFWQeDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T19:29:09.632333Z"},"content_sha256":"158629b6ba9ee953b6a926067d56d01618bd154a9e0bfaa9e51f89d401434f83","schema_version":"1.0","event_id":"sha256:158629b6ba9ee953b6a926067d56d01618bd154a9e0bfaa9e51f89d401434f83"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AO7N22E66UJHPUH4Q5LHCGV4KH/bundle.json","state_url":"https://pith.science/pith/AO7N22E66UJHPUH4Q5LHCGV4KH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AO7N22E66UJHPUH4Q5LHCGV4KH/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":"2026-08-22T19:29:09Z","links":{"resolver":"https://pith.science/pith/AO7N22E66UJHPUH4Q5LHCGV4KH","bundle":"https://pith.science/pith/AO7N22E66UJHPUH4Q5LHCGV4KH/bundle.json","state":"https://pith.science/pith/AO7N22E66UJHPUH4Q5LHCGV4KH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AO7N22E66UJHPUH4Q5LHCGV4KH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:AO7N22E66UJHPUH4Q5LHCGV4KH","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":"33be12dab53a190c357975bd08f1ae24283ce807ba9345e7d3dbdf13ca314322","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-19T05:33:55Z","title_canon_sha256":"02240d6516112f6029311be3612eee23da8656669139277e14f7295737604579"},"schema_version":"1.0","source":{"id":"2412.14537","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.14537","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"arxiv_version","alias_value":"2412.14537v1","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.14537","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"pith_short_12","alias_value":"AO7N22E66UJH","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"pith_short_16","alias_value":"AO7N22E66UJHPUH4","created_at":"2026-07-05T09:51:49Z"},{"alias_kind":"pith_short_8","alias_value":"AO7N22E6","created_at":"2026-07-05T09:51:49Z"}],"graph_snapshots":[{"event_id":"sha256:158629b6ba9ee953b6a926067d56d01618bd154a9e0bfaa9e51f89d401434f83","target":"graph","created_at":"2026-07-05T09:51:49Z","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/2412.14537/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Spatial-temporal forecasting is crucial and widely applicable in various domains such as traffic, energy, and climate. Benefiting from the abundance of unlabeled spatial-temporal data, self-supervised methods are increasingly adapted to learn spatial-temporal representations. However, it encounters three key challenges: 1) the difficulty in selecting reliable negative pairs due to the homogeneity of variables, hindering contrastive learning methods; 2) overlooking spatial correlations across variables over time; 3) limitations of efficiency and scalability in existing self-supervised learning ","authors_text":"Qi Zheng, Yaying Zhang, Zihao Yao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-19T05:33:55Z","title":"ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.14537","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":"sha256:8512c07f12dc8960ea43808ced827520478a2f5f9bcb812c90d8055287f1f5df","target":"record","created_at":"2026-07-05T09:51:49Z","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":"33be12dab53a190c357975bd08f1ae24283ce807ba9345e7d3dbdf13ca314322","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-19T05:33:55Z","title_canon_sha256":"02240d6516112f6029311be3612eee23da8656669139277e14f7295737604579"},"schema_version":"1.0","source":{"id":"2412.14537","kind":"arxiv","version":1}},"canonical_sha256":"03bedd689ef51277d0fc8756711abc51f92c814c5085bf611d881539d2bf8bd6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"03bedd689ef51277d0fc8756711abc51f92c814c5085bf611d881539d2bf8bd6","first_computed_at":"2026-07-05T09:51:49.035854Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:51:49.035854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WwedNkY3xUYFdkY9f5hVrcRN8aEgJ3eT6IVMaUZh/734GvkpANVXnwQEhJ1zJaljvv12rV/JpNdh/AWGIPtcAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:51:49.036391Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.14537","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8512c07f12dc8960ea43808ced827520478a2f5f9bcb812c90d8055287f1f5df","sha256:158629b6ba9ee953b6a926067d56d01618bd154a9e0bfaa9e51f89d401434f83"],"state_sha256":"8f35497818c7be89d27c1e443bba0ba92bac205a89d9131cd5ef253af8ca4d15"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4P5KA9wUb0UW7l9cwkkf90BKYeMoAejzIXOkBC1jZ2Q3rLkxGa3BM/1cvIdZt45blGgs62RRqwBdbKTAq6wdBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T19:29:09.637307Z","bundle_sha256":"fcaa9b398527b841f805862bfcb5216d3fc8756c2e457b6270be0c7b4c56c3d3"}}