{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:XAFI2EEO3Y5VNESBTOQ2CTWP72","short_pith_number":"pith:XAFI2EEO","schema_version":"1.0","canonical_sha256":"b80a8d108ede3b5692419ba1a14ecffe80c7fad4289dcf42bcfb4ba43e3dacf9","source":{"kind":"arxiv","id":"2304.05106","version":2},"attestation_state":"computed","paper":{"title":"Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Beihao Xia, Conghao Wong, Duanquan Xu, Qinmu Peng, Xinge You","submitted_at":"2023-04-11T10:04:16Z","abstract_excerpt":"With the fast development of AI-related techniques, the applications of trajectory prediction are no longer limited to easier scenes and trajectories. More and more trajectories with different forms, such as coordinates, bounding boxes, and even high-dimensional human skeletons, need to be analyzed and forecasted. Among these heterogeneous trajectories, interactions between different elements within a frame of trajectory, which we call ``Dimension-wise Interactions'', would be more complex and challenging. However, most previous approaches focus mainly on a specific form of trajectories, and p"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2304.05106","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-11T10:04:16Z","cross_cats_sorted":[],"title_canon_sha256":"26836a69b943c2fa16dd02cc97a81d1d7c7be4aaefffcd14878eaf5583572ccc","abstract_canon_sha256":"c2b7d0a5740ae29df4252d87d3ffc80bde978ef08c6372f52e8feb1cfea6d2dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:43:22.266482Z","signature_b64":"e2ZpwftF/rtT5mmuUB5tyWW4CNSgKFhC3hZO0wIE9Jj2KvBE5iRVuYOGfw2XVbNf3RYCP1yQxc6IRGqMOBefCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b80a8d108ede3b5692419ba1a14ecffe80c7fad4289dcf42bcfb4ba43e3dacf9","last_reissued_at":"2026-07-05T09:43:22.265805Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:43:22.265805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Beihao Xia, Conghao Wong, Duanquan Xu, Qinmu Peng, Xinge You","submitted_at":"2023-04-11T10:04:16Z","abstract_excerpt":"With the fast development of AI-related techniques, the applications of trajectory prediction are no longer limited to easier scenes and trajectories. More and more trajectories with different forms, such as coordinates, bounding boxes, and even high-dimensional human skeletons, need to be analyzed and forecasted. Among these heterogeneous trajectories, interactions between different elements within a frame of trajectory, which we call ``Dimension-wise Interactions'', would be more complex and challenging. However, most previous approaches focus mainly on a specific form of trajectories, and p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.05106","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/2304.05106/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2304.05106","created_at":"2026-07-05T09:43:22.265896+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.05106v2","created_at":"2026-07-05T09:43:22.265896+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.05106","created_at":"2026-07-05T09:43:22.265896+00:00"},{"alias_kind":"pith_short_12","alias_value":"XAFI2EEO3Y5V","created_at":"2026-07-05T09:43:22.265896+00:00"},{"alias_kind":"pith_short_16","alias_value":"XAFI2EEO3Y5VNESB","created_at":"2026-07-05T09:43:22.265896+00:00"},{"alias_kind":"pith_short_8","alias_value":"XAFI2EEO","created_at":"2026-07-05T09:43:22.265896+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2506.14831","citing_title":"Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review","ref_index":156,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XAFI2EEO3Y5VNESBTOQ2CTWP72","json":"https://pith.science/pith/XAFI2EEO3Y5VNESBTOQ2CTWP72.json","graph_json":"https://pith.science/api/pith-number/XAFI2EEO3Y5VNESBTOQ2CTWP72/graph.json","events_json":"https://pith.science/api/pith-number/XAFI2EEO3Y5VNESBTOQ2CTWP72/events.json","paper":"https://pith.science/paper/XAFI2EEO"},"agent_actions":{"view_html":"https://pith.science/pith/XAFI2EEO3Y5VNESBTOQ2CTWP72","download_json":"https://pith.science/pith/XAFI2EEO3Y5VNESBTOQ2CTWP72.json","view_paper":"https://pith.science/paper/XAFI2EEO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.05106&json=true","fetch_graph":"https://pith.science/api/pith-number/XAFI2EEO3Y5VNESBTOQ2CTWP72/graph.json","fetch_events":"https://pith.science/api/pith-number/XAFI2EEO3Y5VNESBTOQ2CTWP72/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XAFI2EEO3Y5VNESBTOQ2CTWP72/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XAFI2EEO3Y5VNESBTOQ2CTWP72/action/storage_attestation","attest_author":"https://pith.science/pith/XAFI2EEO3Y5VNESBTOQ2CTWP72/action/author_attestation","sign_citation":"https://pith.science/pith/XAFI2EEO3Y5VNESBTOQ2CTWP72/action/citation_signature","submit_replication":"https://pith.science/pith/XAFI2EEO3Y5VNESBTOQ2CTWP72/action/replication_record"}},"created_at":"2026-07-05T09:43:22.265896+00:00","updated_at":"2026-07-05T09:43:22.265896+00:00"}