{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DLOPPOETWUHWOS4W4T7RNMWIDU","short_pith_number":"pith:DLOPPOET","schema_version":"1.0","canonical_sha256":"1adcf7b893b50f674b96e4ff16b2c81d2d56982067b1c7412702d1321b9dfe90","source":{"kind":"arxiv","id":"2310.02284","version":1},"attestation_state":"computed","paper":{"title":"PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation gating for fine-grained crowd flow prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Cheonbok Park, Chung Park, Jaegul Choo, Junui Hong, Minsung Choi, Taesan Kim","submitted_at":"2023-10-02T14:10:42Z","abstract_excerpt":"Understanding the movement patterns of objects (e.g., humans and vehicles) in a city is essential for many applications, including city planning and management. This paper proposes a method for predicting future city-wide crowd flows by modeling the spatio-temporal patterns of historical crowd flows in fine-grained city-wide maps. We introduce a novel neural network named PArallel Spatio-Temporal Attention with spatial auto-correlation gating (PASTA) that effectively captures the irregular spatio-temporal patterns of fine-grained maps. The novel components in our approach include spatial auto-"},"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":"2310.02284","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-02T14:10:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5ec60d141abff374267dbfcee541c4fd114e5662f0c929da4316443c8bcac318","abstract_canon_sha256":"4d46560686422eb90c514fc5f049fd8cbdff445cfa571573545388c95a36fd4a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:56:55.030586Z","signature_b64":"x9HkkkMidgiY9NfaXZYkIarh/KBmb+JJ+mT3uB2bB3kqFDeQ5K8H1pkBhMs3YWsRfJN0WawyP/kgZsRWQjDkCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1adcf7b893b50f674b96e4ff16b2c81d2d56982067b1c7412702d1321b9dfe90","last_reissued_at":"2026-07-05T06:56:55.030225Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:56:55.030225Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation gating for fine-grained crowd flow prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Cheonbok Park, Chung Park, Jaegul Choo, Junui Hong, Minsung Choi, Taesan Kim","submitted_at":"2023-10-02T14:10:42Z","abstract_excerpt":"Understanding the movement patterns of objects (e.g., humans and vehicles) in a city is essential for many applications, including city planning and management. This paper proposes a method for predicting future city-wide crowd flows by modeling the spatio-temporal patterns of historical crowd flows in fine-grained city-wide maps. We introduce a novel neural network named PArallel Spatio-Temporal Attention with spatial auto-correlation gating (PASTA) that effectively captures the irregular spatio-temporal patterns of fine-grained maps. The novel components in our approach include spatial auto-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.02284","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/2310.02284/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":"2310.02284","created_at":"2026-07-05T06:56:55.030281+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.02284v1","created_at":"2026-07-05T06:56:55.030281+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.02284","created_at":"2026-07-05T06:56:55.030281+00:00"},{"alias_kind":"pith_short_12","alias_value":"DLOPPOETWUHW","created_at":"2026-07-05T06:56:55.030281+00:00"},{"alias_kind":"pith_short_16","alias_value":"DLOPPOETWUHWOS4W","created_at":"2026-07-05T06:56:55.030281+00:00"},{"alias_kind":"pith_short_8","alias_value":"DLOPPOET","created_at":"2026-07-05T06:56:55.030281+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DLOPPOETWUHWOS4W4T7RNMWIDU","json":"https://pith.science/pith/DLOPPOETWUHWOS4W4T7RNMWIDU.json","graph_json":"https://pith.science/api/pith-number/DLOPPOETWUHWOS4W4T7RNMWIDU/graph.json","events_json":"https://pith.science/api/pith-number/DLOPPOETWUHWOS4W4T7RNMWIDU/events.json","paper":"https://pith.science/paper/DLOPPOET"},"agent_actions":{"view_html":"https://pith.science/pith/DLOPPOETWUHWOS4W4T7RNMWIDU","download_json":"https://pith.science/pith/DLOPPOETWUHWOS4W4T7RNMWIDU.json","view_paper":"https://pith.science/paper/DLOPPOET","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.02284&json=true","fetch_graph":"https://pith.science/api/pith-number/DLOPPOETWUHWOS4W4T7RNMWIDU/graph.json","fetch_events":"https://pith.science/api/pith-number/DLOPPOETWUHWOS4W4T7RNMWIDU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DLOPPOETWUHWOS4W4T7RNMWIDU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DLOPPOETWUHWOS4W4T7RNMWIDU/action/storage_attestation","attest_author":"https://pith.science/pith/DLOPPOETWUHWOS4W4T7RNMWIDU/action/author_attestation","sign_citation":"https://pith.science/pith/DLOPPOETWUHWOS4W4T7RNMWIDU/action/citation_signature","submit_replication":"https://pith.science/pith/DLOPPOETWUHWOS4W4T7RNMWIDU/action/replication_record"}},"created_at":"2026-07-05T06:56:55.030281+00:00","updated_at":"2026-07-05T06:56:55.030281+00:00"}