{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:PPTIA24RJTF4R43NTOQY6NIDWZ","short_pith_number":"pith:PPTIA24R","canonical_record":{"source":{"id":"2003.00895","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-28T12:21:31Z","cross_cats_sorted":[],"title_canon_sha256":"e905bacac114c36f50ca7616b285d71b9edc498fb6d34302ee307b5709af1e82","abstract_canon_sha256":"bc0b4d85fb4bc79a5beed92d054a6a4f194e62e9594cb51c5df402dd918ba897"},"schema_version":"1.0"},"canonical_sha256":"7be6806b914ccbc8f36d9ba18f3503b65bf53e16100ef47bc22ad74e364e4db4","source":{"kind":"arxiv","id":"2003.00895","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.00895","created_at":"2026-07-05T01:11:45Z"},{"alias_kind":"arxiv_version","alias_value":"2003.00895v2","created_at":"2026-07-05T01:11:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.00895","created_at":"2026-07-05T01:11:45Z"},{"alias_kind":"pith_short_12","alias_value":"PPTIA24RJTF4","created_at":"2026-07-05T01:11:45Z"},{"alias_kind":"pith_short_16","alias_value":"PPTIA24RJTF4R43N","created_at":"2026-07-05T01:11:45Z"},{"alias_kind":"pith_short_8","alias_value":"PPTIA24R","created_at":"2026-07-05T01:11:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:PPTIA24RJTF4R43NTOQY6NIDWZ","target":"record","payload":{"canonical_record":{"source":{"id":"2003.00895","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-28T12:21:31Z","cross_cats_sorted":[],"title_canon_sha256":"e905bacac114c36f50ca7616b285d71b9edc498fb6d34302ee307b5709af1e82","abstract_canon_sha256":"bc0b4d85fb4bc79a5beed92d054a6a4f194e62e9594cb51c5df402dd918ba897"},"schema_version":"1.0"},"canonical_sha256":"7be6806b914ccbc8f36d9ba18f3503b65bf53e16100ef47bc22ad74e364e4db4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:11:45.801109Z","signature_b64":"ow2wVys296vn5SjHHZLTZj2Ei0VXyJN36497caW4MyBxWiatnHdwTp1MK/UkYMgcgmikJEMnByO5KeVSGECwAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7be6806b914ccbc8f36d9ba18f3503b65bf53e16100ef47bc22ad74e364e4db4","last_reissued_at":"2026-07-05T01:11:45.800707Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:11:45.800707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2003.00895","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":"2026-07-05T01:11:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BVJ9ZQqCT3OVqNceAfX5bBvzsnVfwm8qB8auykhJ6nkZ17X3LHyKhnTFxUFGh+T+zgJtk2RuJvo1ggICfavlCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:44:06.343898Z"},"content_sha256":"0fa3a7331a3f56da1696f5086ac3cef4d3aa468554cce88d60a7fd9049baf729","schema_version":"1.0","event_id":"sha256:0fa3a7331a3f56da1696f5086ac3cef4d3aa468554cce88d60a7fd9049baf729"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:PPTIA24RJTF4R43NTOQY6NIDWZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"David S. Rosenblum, Junbo Zhang, Kun Ouyang, Ye Liu, Yiwei Wang, Yuxuan Liang, Yu Zheng","submitted_at":"2020-02-28T12:21:31Z","abstract_excerpt":"Citywide crowd flow analytics is of great importance to smart city efforts. It aims to model the crowd flow (e.g., inflow and outflow) of each region in a city based on historical observations. Nowadays, Convolutional Neural Networks (CNNs) have been widely adopted in raster-based crowd flow analytics by virtue of their capability in capturing spatial dependencies. After revisiting CNN-based methods for different analytics tasks, we expose two common critical drawbacks in the existing uses: 1) inefficiency in learning global spatial dependencies, and 2) overlooking latent region functions. To "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.00895","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/2003.00895/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":"2026-07-05T01:11:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aLqyhXZPpvwTa+kT121yxshDjPS0HZUtW/4BRFku7wHjqFRcMYt/U2DHomcIQ8b1d2+JLZBRegQRdiDUfh39BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:44:06.344274Z"},"content_sha256":"354a6359ea0d5381aae766833be14e69d32d046c9d734dacd8b659ccaf5341b8","schema_version":"1.0","event_id":"sha256:354a6359ea0d5381aae766833be14e69d32d046c9d734dacd8b659ccaf5341b8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PPTIA24RJTF4R43NTOQY6NIDWZ/bundle.json","state_url":"https://pith.science/pith/PPTIA24RJTF4R43NTOQY6NIDWZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PPTIA24RJTF4R43NTOQY6NIDWZ/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":"2026-07-07T15:44:06Z","links":{"resolver":"https://pith.science/pith/PPTIA24RJTF4R43NTOQY6NIDWZ","bundle":"https://pith.science/pith/PPTIA24RJTF4R43NTOQY6NIDWZ/bundle.json","state":"https://pith.science/pith/PPTIA24RJTF4R43NTOQY6NIDWZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PPTIA24RJTF4R43NTOQY6NIDWZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:PPTIA24RJTF4R43NTOQY6NIDWZ","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":"bc0b4d85fb4bc79a5beed92d054a6a4f194e62e9594cb51c5df402dd918ba897","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-28T12:21:31Z","title_canon_sha256":"e905bacac114c36f50ca7616b285d71b9edc498fb6d34302ee307b5709af1e82"},"schema_version":"1.0","source":{"id":"2003.00895","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.00895","created_at":"2026-07-05T01:11:45Z"},{"alias_kind":"arxiv_version","alias_value":"2003.00895v2","created_at":"2026-07-05T01:11:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.00895","created_at":"2026-07-05T01:11:45Z"},{"alias_kind":"pith_short_12","alias_value":"PPTIA24RJTF4","created_at":"2026-07-05T01:11:45Z"},{"alias_kind":"pith_short_16","alias_value":"PPTIA24RJTF4R43N","created_at":"2026-07-05T01:11:45Z"},{"alias_kind":"pith_short_8","alias_value":"PPTIA24R","created_at":"2026-07-05T01:11:45Z"}],"graph_snapshots":[{"event_id":"sha256:354a6359ea0d5381aae766833be14e69d32d046c9d734dacd8b659ccaf5341b8","target":"graph","created_at":"2026-07-05T01:11:45Z","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/2003.00895/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Citywide crowd flow analytics is of great importance to smart city efforts. It aims to model the crowd flow (e.g., inflow and outflow) of each region in a city based on historical observations. Nowadays, Convolutional Neural Networks (CNNs) have been widely adopted in raster-based crowd flow analytics by virtue of their capability in capturing spatial dependencies. After revisiting CNN-based methods for different analytics tasks, we expose two common critical drawbacks in the existing uses: 1) inefficiency in learning global spatial dependencies, and 2) overlooking latent region functions. To ","authors_text":"David S. Rosenblum, Junbo Zhang, Kun Ouyang, Ye Liu, Yiwei Wang, Yuxuan Liang, Yu Zheng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-28T12:21:31Z","title":"Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.00895","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":"sha256:0fa3a7331a3f56da1696f5086ac3cef4d3aa468554cce88d60a7fd9049baf729","target":"record","created_at":"2026-07-05T01:11:45Z","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":"bc0b4d85fb4bc79a5beed92d054a6a4f194e62e9594cb51c5df402dd918ba897","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-28T12:21:31Z","title_canon_sha256":"e905bacac114c36f50ca7616b285d71b9edc498fb6d34302ee307b5709af1e82"},"schema_version":"1.0","source":{"id":"2003.00895","kind":"arxiv","version":2}},"canonical_sha256":"7be6806b914ccbc8f36d9ba18f3503b65bf53e16100ef47bc22ad74e364e4db4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7be6806b914ccbc8f36d9ba18f3503b65bf53e16100ef47bc22ad74e364e4db4","first_computed_at":"2026-07-05T01:11:45.800707Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:11:45.800707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ow2wVys296vn5SjHHZLTZj2Ei0VXyJN36497caW4MyBxWiatnHdwTp1MK/UkYMgcgmikJEMnByO5KeVSGECwAA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:11:45.801109Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.00895","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0fa3a7331a3f56da1696f5086ac3cef4d3aa468554cce88d60a7fd9049baf729","sha256:354a6359ea0d5381aae766833be14e69d32d046c9d734dacd8b659ccaf5341b8"],"state_sha256":"7c17f573e8796aa4976e83d1c17d6b894bb976142c1644556ba4f7ebdb852f38"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JhYK/zc5ALV2qMhtiBZ9de+quKqlxXgUvZ8Mc0zPU6g0DQ9/2t7vEuP2xPKcXnjrdhEH+eXknjo+Jkg/scORAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T15:44:06.346280Z","bundle_sha256":"54fd80e52d85ed0f020e149d8b7bbf65d7efd2387bfbba5204f8603a09fd35eb"}}