{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:HXFJHSA6UZFJ3ZQGISU5J7MAWV","short_pith_number":"pith:HXFJHSA6","canonical_record":{"source":{"id":"2106.02930","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-05T16:51:54Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"f1c8f8a69bd289e0d016de275a986f21ddeefdb9503e48b89fed858cea801dbf","abstract_canon_sha256":"c1a32425000801502ac7db2cb7f95b5a0fd630988a0187ebd3507ef6f181c149"},"schema_version":"1.0"},"canonical_sha256":"3dca93c81ea64a9de60644a9d4fd80b5695d810c00368ee392971bd583f552ee","source":{"kind":"arxiv","id":"2106.02930","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.02930","created_at":"2026-07-05T02:46:31Z"},{"alias_kind":"arxiv_version","alias_value":"2106.02930v1","created_at":"2026-07-05T02:46:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.02930","created_at":"2026-07-05T02:46:31Z"},{"alias_kind":"pith_short_12","alias_value":"HXFJHSA6UZFJ","created_at":"2026-07-05T02:46:31Z"},{"alias_kind":"pith_short_16","alias_value":"HXFJHSA6UZFJ3ZQG","created_at":"2026-07-05T02:46:31Z"},{"alias_kind":"pith_short_8","alias_value":"HXFJHSA6","created_at":"2026-07-05T02:46:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:HXFJHSA6UZFJ3ZQGISU5J7MAWV","target":"record","payload":{"canonical_record":{"source":{"id":"2106.02930","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-05T16:51:54Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"f1c8f8a69bd289e0d016de275a986f21ddeefdb9503e48b89fed858cea801dbf","abstract_canon_sha256":"c1a32425000801502ac7db2cb7f95b5a0fd630988a0187ebd3507ef6f181c149"},"schema_version":"1.0"},"canonical_sha256":"3dca93c81ea64a9de60644a9d4fd80b5695d810c00368ee392971bd583f552ee","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:46:31.867965Z","signature_b64":"OwMb98Egs4/azwOesVMZECcKsujxu2aDp7gcOc8mQHyL5b65qTbiRa0ydbALphNMfVxkYld4cbkycUZ8pqjeDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3dca93c81ea64a9de60644a9d4fd80b5695d810c00368ee392971bd583f552ee","last_reissued_at":"2026-07-05T02:46:31.867468Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:46:31.867468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.02930","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":"2026-07-05T02:46:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vp5zZkUsedjZ0d4yUvk0algybk6bNoatze2PNDcvk810uL7YfKkYGtobAXVYEoJnV0aDN+arR4B0c3evOQ9jCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T14:13:34.009509Z"},"content_sha256":"dad68580a96fcd251dbe3232e7b3de6a4890acd2029ae9518023cbdf2728c721","schema_version":"1.0","event_id":"sha256:dad68580a96fcd251dbe3232e7b3de6a4890acd2029ae9518023cbdf2728c721"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:HXFJHSA6UZFJ3ZQGISU5J7MAWV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spectral Temporal Graph Neural Network for Trajectory Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Defu Cao, Hengbo Ma, Jiachen Li, Masayoshi Tomizuka","submitted_at":"2021-06-05T16:51:54Z","abstract_excerpt":"An effective understanding of the contextual environment and accurate motion forecasting of surrounding agents is crucial for the development of autonomous vehicles and social mobile robots. This task is challenging since the behavior of an autonomous agent is not only affected by its own intention, but also by the static environment and surrounding dynamically interacting agents. Previous works focused on utilizing the spatial and temporal information in time domain while not sufficiently taking advantage of the cues in frequency domain. To this end, we propose a Spectral Temporal Graph Neura"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.02930","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/2106.02930/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":"2026-07-05T02:46:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eorytASm0IgIYqKJIFCIDH6XbgR20kXttkTdLe60pKMRFtvZ2cLcXS/L3l5WDrSbdQlstHxEgoD9DNbi//cGBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T14:13:34.010381Z"},"content_sha256":"140b2107613d2f4e5f9948d99f51c24939f6b0ad4a575baf1f79234272dea16f","schema_version":"1.0","event_id":"sha256:140b2107613d2f4e5f9948d99f51c24939f6b0ad4a575baf1f79234272dea16f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HXFJHSA6UZFJ3ZQGISU5J7MAWV/bundle.json","state_url":"https://pith.science/pith/HXFJHSA6UZFJ3ZQGISU5J7MAWV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HXFJHSA6UZFJ3ZQGISU5J7MAWV/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":"2026-08-14T14:13:34Z","links":{"resolver":"https://pith.science/pith/HXFJHSA6UZFJ3ZQGISU5J7MAWV","bundle":"https://pith.science/pith/HXFJHSA6UZFJ3ZQGISU5J7MAWV/bundle.json","state":"https://pith.science/pith/HXFJHSA6UZFJ3ZQGISU5J7MAWV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HXFJHSA6UZFJ3ZQGISU5J7MAWV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:HXFJHSA6UZFJ3ZQGISU5J7MAWV","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":"c1a32425000801502ac7db2cb7f95b5a0fd630988a0187ebd3507ef6f181c149","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-05T16:51:54Z","title_canon_sha256":"f1c8f8a69bd289e0d016de275a986f21ddeefdb9503e48b89fed858cea801dbf"},"schema_version":"1.0","source":{"id":"2106.02930","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.02930","created_at":"2026-07-05T02:46:31Z"},{"alias_kind":"arxiv_version","alias_value":"2106.02930v1","created_at":"2026-07-05T02:46:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.02930","created_at":"2026-07-05T02:46:31Z"},{"alias_kind":"pith_short_12","alias_value":"HXFJHSA6UZFJ","created_at":"2026-07-05T02:46:31Z"},{"alias_kind":"pith_short_16","alias_value":"HXFJHSA6UZFJ3ZQG","created_at":"2026-07-05T02:46:31Z"},{"alias_kind":"pith_short_8","alias_value":"HXFJHSA6","created_at":"2026-07-05T02:46:31Z"}],"graph_snapshots":[{"event_id":"sha256:140b2107613d2f4e5f9948d99f51c24939f6b0ad4a575baf1f79234272dea16f","target":"graph","created_at":"2026-07-05T02:46:31Z","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/2106.02930/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"An effective understanding of the contextual environment and accurate motion forecasting of surrounding agents is crucial for the development of autonomous vehicles and social mobile robots. This task is challenging since the behavior of an autonomous agent is not only affected by its own intention, but also by the static environment and surrounding dynamically interacting agents. Previous works focused on utilizing the spatial and temporal information in time domain while not sufficiently taking advantage of the cues in frequency domain. To this end, we propose a Spectral Temporal Graph Neura","authors_text":"Defu Cao, Hengbo Ma, Jiachen Li, Masayoshi Tomizuka","cross_cats":["cs.AI","cs.LG","cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-05T16:51:54Z","title":"Spectral Temporal Graph Neural Network for Trajectory Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.02930","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:dad68580a96fcd251dbe3232e7b3de6a4890acd2029ae9518023cbdf2728c721","target":"record","created_at":"2026-07-05T02:46:31Z","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":"c1a32425000801502ac7db2cb7f95b5a0fd630988a0187ebd3507ef6f181c149","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-05T16:51:54Z","title_canon_sha256":"f1c8f8a69bd289e0d016de275a986f21ddeefdb9503e48b89fed858cea801dbf"},"schema_version":"1.0","source":{"id":"2106.02930","kind":"arxiv","version":1}},"canonical_sha256":"3dca93c81ea64a9de60644a9d4fd80b5695d810c00368ee392971bd583f552ee","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3dca93c81ea64a9de60644a9d4fd80b5695d810c00368ee392971bd583f552ee","first_computed_at":"2026-07-05T02:46:31.867468Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:46:31.867468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OwMb98Egs4/azwOesVMZECcKsujxu2aDp7gcOc8mQHyL5b65qTbiRa0ydbALphNMfVxkYld4cbkycUZ8pqjeDw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:46:31.867965Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.02930","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dad68580a96fcd251dbe3232e7b3de6a4890acd2029ae9518023cbdf2728c721","sha256:140b2107613d2f4e5f9948d99f51c24939f6b0ad4a575baf1f79234272dea16f"],"state_sha256":"1d1217889b4e650b067c565e6e72373d907d3223a01e0698edbe762a312a3128"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cbNsOhkYpxOt6dw0UruhDGCRsu0Z+QzctKxxQD2a0OK7uRxiOUhXuXrVuozZb/LxJqAXqQksAR9E65bVBjCkDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T14:13:34.016150Z","bundle_sha256":"199c586f2b416919f34a9a94d38f41b6c95e7acf2f11ab8fc6342124fa83ddd2"}}