{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:EM64GBMGYSAGTL57PI3JKSDPUW","short_pith_number":"pith:EM64GBMG","canonical_record":{"source":{"id":"2202.03954","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-08T16:04:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"02d572930966203fe311ce2c0712fd4d660f9362bcea0a20689441b1ae4c58ce","abstract_canon_sha256":"0ac9dc789d9cbff3d1e2edcc453f5f14ea582637bad76c4ce433aec71032a2ca"},"schema_version":"1.0"},"canonical_sha256":"233dc30586c48069afbf7a3695486fa598917053e281f9bcaed53d706d0a8dd7","source":{"kind":"arxiv","id":"2202.03954","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.03954","created_at":"2026-07-05T03:55:22Z"},{"alias_kind":"arxiv_version","alias_value":"2202.03954v1","created_at":"2026-07-05T03:55:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.03954","created_at":"2026-07-05T03:55:22Z"},{"alias_kind":"pith_short_12","alias_value":"EM64GBMGYSAG","created_at":"2026-07-05T03:55:22Z"},{"alias_kind":"pith_short_16","alias_value":"EM64GBMGYSAGTL57","created_at":"2026-07-05T03:55:22Z"},{"alias_kind":"pith_short_8","alias_value":"EM64GBMG","created_at":"2026-07-05T03:55:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:EM64GBMGYSAGTL57PI3JKSDPUW","target":"record","payload":{"canonical_record":{"source":{"id":"2202.03954","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-08T16:04:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"02d572930966203fe311ce2c0712fd4d660f9362bcea0a20689441b1ae4c58ce","abstract_canon_sha256":"0ac9dc789d9cbff3d1e2edcc453f5f14ea582637bad76c4ce433aec71032a2ca"},"schema_version":"1.0"},"canonical_sha256":"233dc30586c48069afbf7a3695486fa598917053e281f9bcaed53d706d0a8dd7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:55:22.094842Z","signature_b64":"Se1y1WRusGnB59tD5AROSNV5ETklqX4cLtTFPZLeF2FiOrGm3kiv9E81HCk5sBXMzpiOScz1NZNSxAk1l1HmBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"233dc30586c48069afbf7a3695486fa598917053e281f9bcaed53d706d0a8dd7","last_reissued_at":"2026-07-05T03:55:22.094339Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:55:22.094339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.03954","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":"2026-07-05T03:55:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"74+QpA3/rMi2+sFGjTkvrAqzrxN6y0HHTGeyTWv4Riqsyky6bReJ66WLYrHa66atiSsNmqkbhF95t8Jt+F/XDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T03:20:41.175974Z"},"content_sha256":"0ec9fac9199fe639913b5819abca162e9989aed8755f36cae9812a6a54251db8","schema_version":"1.0","event_id":"sha256:0ec9fac9199fe639913b5819abca162e9989aed8755f36cae9812a6a54251db8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:EM64GBMGYSAGTL57PI3JKSDPUW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Social-DualCVAE: Multimodal Trajectory Forecasting Based on Social Interactions Pattern Aware and Dual Conditional Variational Auto-Encoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"James J.Q. Yu, Jiashi Gao, Xinming Shi","submitted_at":"2022-02-08T16:04:47Z","abstract_excerpt":"Pedestrian trajectory forecasting is a fundamental task in multiple utility areas, such as self-driving, autonomous robots, and surveillance systems. The future trajectory forecasting is multi-modal, influenced by physical interaction with scene contexts and intricate social interactions among pedestrians. The mainly existing literature learns representations of social interactions by deep learning networks, while the explicit interaction patterns are not utilized. Different interaction patterns, such as following or collision avoiding, will generate different trends of next movement, thus, th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.03954","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/2202.03954/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":"2026-07-05T03:55:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9bzoyJIQHD0jIAxG90h8vnA58TopfiQQVoCx0hzlxLyFpP/mYFj57N11V44rxa/FMb54Trk3wVaUT21+6qi5AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T03:20:41.176985Z"},"content_sha256":"2b86ae32b921f1a9415f94f38323a680327e65b6896b7c22ce424e3e185e95e6","schema_version":"1.0","event_id":"sha256:2b86ae32b921f1a9415f94f38323a680327e65b6896b7c22ce424e3e185e95e6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EM64GBMGYSAGTL57PI3JKSDPUW/bundle.json","state_url":"https://pith.science/pith/EM64GBMGYSAGTL57PI3JKSDPUW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EM64GBMGYSAGTL57PI3JKSDPUW/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":"2026-08-23T03:20:41Z","links":{"resolver":"https://pith.science/pith/EM64GBMGYSAGTL57PI3JKSDPUW","bundle":"https://pith.science/pith/EM64GBMGYSAGTL57PI3JKSDPUW/bundle.json","state":"https://pith.science/pith/EM64GBMGYSAGTL57PI3JKSDPUW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EM64GBMGYSAGTL57PI3JKSDPUW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:EM64GBMGYSAGTL57PI3JKSDPUW","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":"0ac9dc789d9cbff3d1e2edcc453f5f14ea582637bad76c4ce433aec71032a2ca","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-08T16:04:47Z","title_canon_sha256":"02d572930966203fe311ce2c0712fd4d660f9362bcea0a20689441b1ae4c58ce"},"schema_version":"1.0","source":{"id":"2202.03954","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.03954","created_at":"2026-07-05T03:55:22Z"},{"alias_kind":"arxiv_version","alias_value":"2202.03954v1","created_at":"2026-07-05T03:55:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.03954","created_at":"2026-07-05T03:55:22Z"},{"alias_kind":"pith_short_12","alias_value":"EM64GBMGYSAG","created_at":"2026-07-05T03:55:22Z"},{"alias_kind":"pith_short_16","alias_value":"EM64GBMGYSAGTL57","created_at":"2026-07-05T03:55:22Z"},{"alias_kind":"pith_short_8","alias_value":"EM64GBMG","created_at":"2026-07-05T03:55:22Z"}],"graph_snapshots":[{"event_id":"sha256:2b86ae32b921f1a9415f94f38323a680327e65b6896b7c22ce424e3e185e95e6","target":"graph","created_at":"2026-07-05T03:55:22Z","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/2202.03954/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pedestrian trajectory forecasting is a fundamental task in multiple utility areas, such as self-driving, autonomous robots, and surveillance systems. The future trajectory forecasting is multi-modal, influenced by physical interaction with scene contexts and intricate social interactions among pedestrians. The mainly existing literature learns representations of social interactions by deep learning networks, while the explicit interaction patterns are not utilized. Different interaction patterns, such as following or collision avoiding, will generate different trends of next movement, thus, th","authors_text":"James J.Q. Yu, Jiashi Gao, Xinming Shi","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-08T16:04:47Z","title":"Social-DualCVAE: Multimodal Trajectory Forecasting Based on Social Interactions Pattern Aware and Dual Conditional Variational Auto-Encoder"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.03954","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":"sha256:0ec9fac9199fe639913b5819abca162e9989aed8755f36cae9812a6a54251db8","target":"record","created_at":"2026-07-05T03:55:22Z","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":"0ac9dc789d9cbff3d1e2edcc453f5f14ea582637bad76c4ce433aec71032a2ca","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-08T16:04:47Z","title_canon_sha256":"02d572930966203fe311ce2c0712fd4d660f9362bcea0a20689441b1ae4c58ce"},"schema_version":"1.0","source":{"id":"2202.03954","kind":"arxiv","version":1}},"canonical_sha256":"233dc30586c48069afbf7a3695486fa598917053e281f9bcaed53d706d0a8dd7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"233dc30586c48069afbf7a3695486fa598917053e281f9bcaed53d706d0a8dd7","first_computed_at":"2026-07-05T03:55:22.094339Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:55:22.094339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Se1y1WRusGnB59tD5AROSNV5ETklqX4cLtTFPZLeF2FiOrGm3kiv9E81HCk5sBXMzpiOScz1NZNSxAk1l1HmBw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:55:22.094842Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.03954","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0ec9fac9199fe639913b5819abca162e9989aed8755f36cae9812a6a54251db8","sha256:2b86ae32b921f1a9415f94f38323a680327e65b6896b7c22ce424e3e185e95e6"],"state_sha256":"dded998e566aef5cdefbd1aac36623fa5bfaa1ae856d535a5b914305f5e0e587"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gm6zWTEus+7rVekgkGzCwGE2OoS+F2RPtMATI1sI3KC2UvdbiMOi2pjy5kKiiJJ4isU9oT6FlFUxw0GPF5PzAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T03:20:41.183352Z","bundle_sha256":"b240e0cd2034c1a64f4ba7b6eeb6a5bb1ce22bd54f2a66267dffb95197e80ebd"}}