{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:ASAKX5XH6K3MK7HRATWIO66RFT","short_pith_number":"pith:ASAKX5XH","canonical_record":{"source":{"id":"1906.08952","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2019-06-21T05:29:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a779c111cbd5de4a085036b318483ae93fcf591b1205e8cf4554a026d97ba70e","abstract_canon_sha256":"aec3c2e850ff4fe3d8f58e346295895683f77aab9b60f5607ff2fb558b2f7404"},"schema_version":"1.0"},"canonical_sha256":"0480abf6e7f2b6c57cf104ec877bd12cc74cdc596a6268d6a30e8c33da66b3ce","source":{"kind":"arxiv","id":"1906.08952","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.08952","created_at":"2026-05-17T23:42:47Z"},{"alias_kind":"arxiv_version","alias_value":"1906.08952v1","created_at":"2026-05-17T23:42:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.08952","created_at":"2026-05-17T23:42:47Z"},{"alias_kind":"pith_short_12","alias_value":"ASAKX5XH6K3M","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"ASAKX5XH6K3MK7HR","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"ASAKX5XH","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:ASAKX5XH6K3MK7HRATWIO66RFT","target":"record","payload":{"canonical_record":{"source":{"id":"1906.08952","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2019-06-21T05:29:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a779c111cbd5de4a085036b318483ae93fcf591b1205e8cf4554a026d97ba70e","abstract_canon_sha256":"aec3c2e850ff4fe3d8f58e346295895683f77aab9b60f5607ff2fb558b2f7404"},"schema_version":"1.0"},"canonical_sha256":"0480abf6e7f2b6c57cf104ec877bd12cc74cdc596a6268d6a30e8c33da66b3ce","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:47.290974Z","signature_b64":"676EKh6rRgs5HAFmJnwucsLoPZoQ8pB7oMJbRGaouJ3eXplOJGSSAxt8eWTtxm122rO93aqza71Y43/ymHl5BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0480abf6e7f2b6c57cf104ec877bd12cc74cdc596a6268d6a30e8c33da66b3ce","last_reissued_at":"2026-05-17T23:42:47.290268Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:47.290268Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.08952","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":"2026-05-17T23:42:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eEIualRbSuArKjqyd/opAqBEW9D+fvJhkraXX3bhjeYZQcupUUv4anTotVL/mp/kr8zDJfVu8BLjWVIm/VnAAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:42:28.269946Z"},"content_sha256":"93b0bb764bb22e44e97e8e9f651900a514ae738557dc5bfa19dab77f1733378a","schema_version":"1.0","event_id":"sha256:93b0bb764bb22e44e97e8e9f651900a514ae738557dc5bfa19dab77f1733378a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:ASAKX5XH6K3MK7HRATWIO66RFT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hiroyuki Toda, Maya Okawa, Naonori Ueda, Takeshi Kurashima, Tomoharu Iwata, Yusuke Tanaka","submitted_at":"2019-06-21T05:29:40Z","abstract_excerpt":"Predicting when and where events will occur in cities, like taxi pick-ups, crimes, and vehicle collisions, is a challenging and important problem with many applications in fields such as urban planning, transportation optimization and location-based marketing. Though many point processes have been proposed to model events in a continuous spatio-temporal space, none of them allow for the consideration of the rich contextual factors that affect event occurrence, such as weather, social activities, geographical characteristics, and traffic. In this paper, we propose \\textsf{DMPP} (Deep Mixture Po"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.08952","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":""},"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":"2026-05-17T23:42:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n190Dt6UZeSCEUNwBXrcjjZhlJQvZthtu/RQhvuHNJ6CND2L4fJWQo2n8ZRq88LtJbSK0JspiRGHvlt9q3q2Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:42:28.270605Z"},"content_sha256":"1569b9d7c44c6fc69060c0b7f9de4f5c865afbca1978c0637c8c29c3d0013ec2","schema_version":"1.0","event_id":"sha256:1569b9d7c44c6fc69060c0b7f9de4f5c865afbca1978c0637c8c29c3d0013ec2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ASAKX5XH6K3MK7HRATWIO66RFT/bundle.json","state_url":"https://pith.science/pith/ASAKX5XH6K3MK7HRATWIO66RFT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ASAKX5XH6K3MK7HRATWIO66RFT/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":"2026-05-26T03:42:28Z","links":{"resolver":"https://pith.science/pith/ASAKX5XH6K3MK7HRATWIO66RFT","bundle":"https://pith.science/pith/ASAKX5XH6K3MK7HRATWIO66RFT/bundle.json","state":"https://pith.science/pith/ASAKX5XH6K3MK7HRATWIO66RFT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ASAKX5XH6K3MK7HRATWIO66RFT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:ASAKX5XH6K3MK7HRATWIO66RFT","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":"aec3c2e850ff4fe3d8f58e346295895683f77aab9b60f5607ff2fb558b2f7404","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2019-06-21T05:29:40Z","title_canon_sha256":"a779c111cbd5de4a085036b318483ae93fcf591b1205e8cf4554a026d97ba70e"},"schema_version":"1.0","source":{"id":"1906.08952","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.08952","created_at":"2026-05-17T23:42:47Z"},{"alias_kind":"arxiv_version","alias_value":"1906.08952v1","created_at":"2026-05-17T23:42:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.08952","created_at":"2026-05-17T23:42:47Z"},{"alias_kind":"pith_short_12","alias_value":"ASAKX5XH6K3M","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"ASAKX5XH6K3MK7HR","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"ASAKX5XH","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:1569b9d7c44c6fc69060c0b7f9de4f5c865afbca1978c0637c8c29c3d0013ec2","target":"graph","created_at":"2026-05-17T23:42:47Z","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"},"paper":{"abstract_excerpt":"Predicting when and where events will occur in cities, like taxi pick-ups, crimes, and vehicle collisions, is a challenging and important problem with many applications in fields such as urban planning, transportation optimization and location-based marketing. Though many point processes have been proposed to model events in a continuous spatio-temporal space, none of them allow for the consideration of the rich contextual factors that affect event occurrence, such as weather, social activities, geographical characteristics, and traffic. In this paper, we propose \\textsf{DMPP} (Deep Mixture Po","authors_text":"Hiroyuki Toda, Maya Okawa, Naonori Ueda, Takeshi Kurashima, Tomoharu Iwata, Yusuke Tanaka","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2019-06-21T05:29:40Z","title":"Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.08952","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":"sha256:93b0bb764bb22e44e97e8e9f651900a514ae738557dc5bfa19dab77f1733378a","target":"record","created_at":"2026-05-17T23:42:47Z","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":"aec3c2e850ff4fe3d8f58e346295895683f77aab9b60f5607ff2fb558b2f7404","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2019-06-21T05:29:40Z","title_canon_sha256":"a779c111cbd5de4a085036b318483ae93fcf591b1205e8cf4554a026d97ba70e"},"schema_version":"1.0","source":{"id":"1906.08952","kind":"arxiv","version":1}},"canonical_sha256":"0480abf6e7f2b6c57cf104ec877bd12cc74cdc596a6268d6a30e8c33da66b3ce","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0480abf6e7f2b6c57cf104ec877bd12cc74cdc596a6268d6a30e8c33da66b3ce","first_computed_at":"2026-05-17T23:42:47.290268Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:42:47.290268Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"676EKh6rRgs5HAFmJnwucsLoPZoQ8pB7oMJbRGaouJ3eXplOJGSSAxt8eWTtxm122rO93aqza71Y43/ymHl5BQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:42:47.290974Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.08952","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:93b0bb764bb22e44e97e8e9f651900a514ae738557dc5bfa19dab77f1733378a","sha256:1569b9d7c44c6fc69060c0b7f9de4f5c865afbca1978c0637c8c29c3d0013ec2"],"state_sha256":"4211fac6dd66c217cc133ba1bd265fb90936c117befefb6bbdac3034e8d6496d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LHUy3GjGZV+ywigcPxdnpNkjkxCSjGXzWRKKsKt7fBGv4RB1QAaQncmNrVSWus6pS/9rc106mxyvh5p6sB2gAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T03:42:28.274105Z","bundle_sha256":"f080316c808e21d044227167d24b9c34485997426d9cef53947e583294318033"}}