{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:IXKUPEG4DDGUVS2VD7LVBCTYNX","short_pith_number":"pith:IXKUPEG4","canonical_record":{"source":{"id":"1903.05631","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-13T17:57:12Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7b7363b4aedfce0c548b3249c19a4fb153b60505697c2bd01f2af3d4e3d2d86c","abstract_canon_sha256":"33af43172b3ee06632081160f7ed890352a4b7c151347e79e977c9dc7a5f26af"},"schema_version":"1.0"},"canonical_sha256":"45d54790dc18cd4acb551fd7508a786df55456b45968386cff090a3619c37a68","source":{"kind":"arxiv","id":"1903.05631","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.05631","created_at":"2026-07-05T02:49:07Z"},{"alias_kind":"arxiv_version","alias_value":"1903.05631v2","created_at":"2026-07-05T02:49:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.05631","created_at":"2026-07-05T02:49:07Z"},{"alias_kind":"pith_short_12","alias_value":"IXKUPEG4DDGU","created_at":"2026-07-05T02:49:07Z"},{"alias_kind":"pith_short_16","alias_value":"IXKUPEG4DDGUVS2V","created_at":"2026-07-05T02:49:07Z"},{"alias_kind":"pith_short_8","alias_value":"IXKUPEG4","created_at":"2026-07-05T02:49:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:IXKUPEG4DDGUVS2VD7LVBCTYNX","target":"record","payload":{"canonical_record":{"source":{"id":"1903.05631","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-13T17:57:12Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7b7363b4aedfce0c548b3249c19a4fb153b60505697c2bd01f2af3d4e3d2d86c","abstract_canon_sha256":"33af43172b3ee06632081160f7ed890352a4b7c151347e79e977c9dc7a5f26af"},"schema_version":"1.0"},"canonical_sha256":"45d54790dc18cd4acb551fd7508a786df55456b45968386cff090a3619c37a68","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:49:07.975449Z","signature_b64":"BFk2OqxzaHReEaRqD3G74NJQJbAQkMPkc28vPTAvwVUZiIpyb3HgAG6Z5tIIZ8OJ+sIf6XS3CqFVm5z7kGJACQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45d54790dc18cd4acb551fd7508a786df55456b45968386cff090a3619c37a68","last_reissued_at":"2026-07-05T02:49:07.975021Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:49:07.975021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.05631","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:49:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MmWdiHsJObbc+6YIdj7uEuCJuHs61+DqpAZYUv5ficJnnEWRToABXoy9pfZS+nOIcMbWCKgLzp/qUZujmpTcDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T19:01:45.129497Z"},"content_sha256":"d6f611e6949954ce1831b18fd9e6bb72ee74d3e31efa2a87e3a53681432f9f6c","schema_version":"1.0","event_id":"sha256:d6f611e6949954ce1831b18fd9e6bb72ee74d3e31efa2a87e3a53681432f9f6c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:IXKUPEG4DDGUVS2VD7LVBCTYNX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bing Yu, Haoteng Yin, Zhanxing Zhu","submitted_at":"2019-03-13T17:57:12Z","abstract_excerpt":"The spatio-temporal graph learning is becoming an increasingly important object of graph study. Many application domains involve highly dynamic graphs where temporal information is crucial, e.g. traffic networks and financial transaction graphs. Despite the constant progress made on learning structured data, there is still a lack of effective means to extract dynamic complex features from spatio-temporal structures. Particularly, conventional models such as convolutional networks or recurrent neural networks are incapable of revealing the temporal patterns in short or long terms and exploring "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.05631","kind":"arxiv","version":2},"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/1903.05631/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":"2026-07-05T02:49:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N9somPsyRDHK6J0N0sgFPw4CfY2omUc+YXRGgW67Ih0A7crXTzNVOXhFsrRtWMW2Bb4J8rRUN6QKu0o5lZ3GDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T19:01:45.130006Z"},"content_sha256":"e5ce096f3eb4843ff39089fec9e1e4e303d194118361d219276c392e018f45fa","schema_version":"1.0","event_id":"sha256:e5ce096f3eb4843ff39089fec9e1e4e303d194118361d219276c392e018f45fa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IXKUPEG4DDGUVS2VD7LVBCTYNX/bundle.json","state_url":"https://pith.science/pith/IXKUPEG4DDGUVS2VD7LVBCTYNX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IXKUPEG4DDGUVS2VD7LVBCTYNX/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":"2026-08-16T19:01:45Z","links":{"resolver":"https://pith.science/pith/IXKUPEG4DDGUVS2VD7LVBCTYNX","bundle":"https://pith.science/pith/IXKUPEG4DDGUVS2VD7LVBCTYNX/bundle.json","state":"https://pith.science/pith/IXKUPEG4DDGUVS2VD7LVBCTYNX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IXKUPEG4DDGUVS2VD7LVBCTYNX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:IXKUPEG4DDGUVS2VD7LVBCTYNX","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":"33af43172b3ee06632081160f7ed890352a4b7c151347e79e977c9dc7a5f26af","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-13T17:57:12Z","title_canon_sha256":"7b7363b4aedfce0c548b3249c19a4fb153b60505697c2bd01f2af3d4e3d2d86c"},"schema_version":"1.0","source":{"id":"1903.05631","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.05631","created_at":"2026-07-05T02:49:07Z"},{"alias_kind":"arxiv_version","alias_value":"1903.05631v2","created_at":"2026-07-05T02:49:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.05631","created_at":"2026-07-05T02:49:07Z"},{"alias_kind":"pith_short_12","alias_value":"IXKUPEG4DDGU","created_at":"2026-07-05T02:49:07Z"},{"alias_kind":"pith_short_16","alias_value":"IXKUPEG4DDGUVS2V","created_at":"2026-07-05T02:49:07Z"},{"alias_kind":"pith_short_8","alias_value":"IXKUPEG4","created_at":"2026-07-05T02:49:07Z"}],"graph_snapshots":[{"event_id":"sha256:e5ce096f3eb4843ff39089fec9e1e4e303d194118361d219276c392e018f45fa","target":"graph","created_at":"2026-07-05T02:49:07Z","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/1903.05631/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The spatio-temporal graph learning is becoming an increasingly important object of graph study. Many application domains involve highly dynamic graphs where temporal information is crucial, e.g. traffic networks and financial transaction graphs. Despite the constant progress made on learning structured data, there is still a lack of effective means to extract dynamic complex features from spatio-temporal structures. Particularly, conventional models such as convolutional networks or recurrent neural networks are incapable of revealing the temporal patterns in short or long terms and exploring ","authors_text":"Bing Yu, Haoteng Yin, Zhanxing Zhu","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-13T17:57:12Z","title":"ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.05631","kind":"arxiv","version":2},"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:d6f611e6949954ce1831b18fd9e6bb72ee74d3e31efa2a87e3a53681432f9f6c","target":"record","created_at":"2026-07-05T02:49:07Z","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":"33af43172b3ee06632081160f7ed890352a4b7c151347e79e977c9dc7a5f26af","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-13T17:57:12Z","title_canon_sha256":"7b7363b4aedfce0c548b3249c19a4fb153b60505697c2bd01f2af3d4e3d2d86c"},"schema_version":"1.0","source":{"id":"1903.05631","kind":"arxiv","version":2}},"canonical_sha256":"45d54790dc18cd4acb551fd7508a786df55456b45968386cff090a3619c37a68","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"45d54790dc18cd4acb551fd7508a786df55456b45968386cff090a3619c37a68","first_computed_at":"2026-07-05T02:49:07.975021Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:49:07.975021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BFk2OqxzaHReEaRqD3G74NJQJbAQkMPkc28vPTAvwVUZiIpyb3HgAG6Z5tIIZ8OJ+sIf6XS3CqFVm5z7kGJACQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:49:07.975449Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.05631","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d6f611e6949954ce1831b18fd9e6bb72ee74d3e31efa2a87e3a53681432f9f6c","sha256:e5ce096f3eb4843ff39089fec9e1e4e303d194118361d219276c392e018f45fa"],"state_sha256":"ecbb2515e06a2124335e9baa00d04f5a2aa96a88c0cbf39080a6a2dc8c4e84ce"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"96cZglcAiRA4MnY27XjW4cfLW4u/5Lipr98iI9pFZG9GAGDRBFeb894qKHKMMZ3glC6wHXUAr4iZOPSel83jDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T19:01:45.135358Z","bundle_sha256":"675051d5b373d195062bef8a3a7d10c13c0f93cbecccfd8e95dcad0a1712527c"}}