{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:AIQL4N3LC6U6AYKXHB4GEEYTSP","short_pith_number":"pith:AIQL4N3L","canonical_record":{"source":{"id":"2504.15944","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-04-22T14:42:16Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"ca6f49385f663654642d1f0977c35b571f930b75ab3b712d3daa10f196909748","abstract_canon_sha256":"08dae8eb8ace71b1edbd32f096ede723e9266d2a37f929766134ff214851eafb"},"schema_version":"1.0"},"canonical_sha256":"0220be376b17a9e06157387862131393e56080989b4a33ac3dba3dbe2b89af20","source":{"kind":"arxiv","id":"2504.15944","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.15944","created_at":"2026-07-05T10:52:32Z"},{"alias_kind":"arxiv_version","alias_value":"2504.15944v1","created_at":"2026-07-05T10:52:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.15944","created_at":"2026-07-05T10:52:32Z"},{"alias_kind":"pith_short_12","alias_value":"AIQL4N3LC6U6","created_at":"2026-07-05T10:52:32Z"},{"alias_kind":"pith_short_16","alias_value":"AIQL4N3LC6U6AYKX","created_at":"2026-07-05T10:52:32Z"},{"alias_kind":"pith_short_8","alias_value":"AIQL4N3L","created_at":"2026-07-05T10:52:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:AIQL4N3LC6U6AYKXHB4GEEYTSP","target":"record","payload":{"canonical_record":{"source":{"id":"2504.15944","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-04-22T14:42:16Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"ca6f49385f663654642d1f0977c35b571f930b75ab3b712d3daa10f196909748","abstract_canon_sha256":"08dae8eb8ace71b1edbd32f096ede723e9266d2a37f929766134ff214851eafb"},"schema_version":"1.0"},"canonical_sha256":"0220be376b17a9e06157387862131393e56080989b4a33ac3dba3dbe2b89af20","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:52:32.402764Z","signature_b64":"uwosvfjsUHeBWSz+Xpd1MHWss8RRN6uYyY0KxgzFOPJWIkCocKmy9wTGOzCJxZ5ZRVveBKSr/IDWGITeHvtrDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0220be376b17a9e06157387862131393e56080989b4a33ac3dba3dbe2b89af20","last_reissued_at":"2026-07-05T10:52:32.402326Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:52:32.402326Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.15944","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":"2026-07-05T10:52:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BZ5W3ZMeeltq+oxPe3y0vp681x8Mi3uz7+PNvVDRFP/3JdXA7ZjR3rGxsIB9MCgrMmCJXRBNWN1PhGUpZl4uAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T17:38:20.594636Z"},"content_sha256":"a027704e243f17ffbea667c92b1a47decc7c6556191299a4241a4e5c4f3df5c4","schema_version":"1.0","event_id":"sha256:a027704e243f17ffbea667c92b1a47decc7c6556191299a4241a4e5c4f3df5c4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:AIQL4N3LC6U6AYKXHB4GEEYTSP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep learning of point processes for modeling high-frequency data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Ioane Muni Toke, nakahiro yoshida, Yoshihiro Gyotoku","submitted_at":"2025-04-22T14:42:16Z","abstract_excerpt":"We investigate applications of deep neural networks to a point process having an intensity with mixing covariates processes as input. Our generic model includes Cox-type models and marked point processes as well as multivariate point processes. An oracle inequality and a rate of convergence are derived for the prediction error. A simulation study shows that the marked point process can be superior to the simple multivariate model in prediction. We apply the marked ratio model to real limit order book data"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.15944","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/2504.15944/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":"2026-07-05T10:52:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iV2swig4rYNCG8ZS5rK2pTzzs0mEvRHMACe6TKM3Sz0dHmXJjO65cE37Gix4SZQDTBntMB4Zx6+7Sfd+mRRlCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T17:38:20.595176Z"},"content_sha256":"862431f4f15b52f9a741de3501904508a03bcdfcc4f64dc44ab3224d1b3c4983","schema_version":"1.0","event_id":"sha256:862431f4f15b52f9a741de3501904508a03bcdfcc4f64dc44ab3224d1b3c4983"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AIQL4N3LC6U6AYKXHB4GEEYTSP/bundle.json","state_url":"https://pith.science/pith/AIQL4N3LC6U6AYKXHB4GEEYTSP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AIQL4N3LC6U6AYKXHB4GEEYTSP/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":"2026-08-19T17:38:20Z","links":{"resolver":"https://pith.science/pith/AIQL4N3LC6U6AYKXHB4GEEYTSP","bundle":"https://pith.science/pith/AIQL4N3LC6U6AYKXHB4GEEYTSP/bundle.json","state":"https://pith.science/pith/AIQL4N3LC6U6AYKXHB4GEEYTSP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AIQL4N3LC6U6AYKXHB4GEEYTSP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:AIQL4N3LC6U6AYKXHB4GEEYTSP","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":"08dae8eb8ace71b1edbd32f096ede723e9266d2a37f929766134ff214851eafb","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-04-22T14:42:16Z","title_canon_sha256":"ca6f49385f663654642d1f0977c35b571f930b75ab3b712d3daa10f196909748"},"schema_version":"1.0","source":{"id":"2504.15944","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.15944","created_at":"2026-07-05T10:52:32Z"},{"alias_kind":"arxiv_version","alias_value":"2504.15944v1","created_at":"2026-07-05T10:52:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.15944","created_at":"2026-07-05T10:52:32Z"},{"alias_kind":"pith_short_12","alias_value":"AIQL4N3LC6U6","created_at":"2026-07-05T10:52:32Z"},{"alias_kind":"pith_short_16","alias_value":"AIQL4N3LC6U6AYKX","created_at":"2026-07-05T10:52:32Z"},{"alias_kind":"pith_short_8","alias_value":"AIQL4N3L","created_at":"2026-07-05T10:52:32Z"}],"graph_snapshots":[{"event_id":"sha256:862431f4f15b52f9a741de3501904508a03bcdfcc4f64dc44ab3224d1b3c4983","target":"graph","created_at":"2026-07-05T10:52:32Z","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/2504.15944/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We investigate applications of deep neural networks to a point process having an intensity with mixing covariates processes as input. Our generic model includes Cox-type models and marked point processes as well as multivariate point processes. An oracle inequality and a rate of convergence are derived for the prediction error. A simulation study shows that the marked point process can be superior to the simple multivariate model in prediction. We apply the marked ratio model to real limit order book data","authors_text":"Ioane Muni Toke, nakahiro yoshida, Yoshihiro Gyotoku","cross_cats":["stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-04-22T14:42:16Z","title":"Deep learning of point processes for modeling high-frequency data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.15944","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:a027704e243f17ffbea667c92b1a47decc7c6556191299a4241a4e5c4f3df5c4","target":"record","created_at":"2026-07-05T10:52:32Z","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":"08dae8eb8ace71b1edbd32f096ede723e9266d2a37f929766134ff214851eafb","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-04-22T14:42:16Z","title_canon_sha256":"ca6f49385f663654642d1f0977c35b571f930b75ab3b712d3daa10f196909748"},"schema_version":"1.0","source":{"id":"2504.15944","kind":"arxiv","version":1}},"canonical_sha256":"0220be376b17a9e06157387862131393e56080989b4a33ac3dba3dbe2b89af20","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0220be376b17a9e06157387862131393e56080989b4a33ac3dba3dbe2b89af20","first_computed_at":"2026-07-05T10:52:32.402326Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:52:32.402326Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uwosvfjsUHeBWSz+Xpd1MHWss8RRN6uYyY0KxgzFOPJWIkCocKmy9wTGOzCJxZ5ZRVveBKSr/IDWGITeHvtrDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:52:32.402764Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.15944","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a027704e243f17ffbea667c92b1a47decc7c6556191299a4241a4e5c4f3df5c4","sha256:862431f4f15b52f9a741de3501904508a03bcdfcc4f64dc44ab3224d1b3c4983"],"state_sha256":"6d16aa5fba7efd4290da8d880ccffb359a4018594574bf26fa41049aed6a89b8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6+M0VzkeMgkX2z09FYKYwliL+nlo6Em/dgLe4YttOYylqODkI5oJ+emu9LtjIcdztOw+LKpQxpVOFvc65xkYCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T17:38:20.600367Z","bundle_sha256":"8e7ea9f9e754a9b6db14f2b507ab6c26abb12f6a103ad5ae5b85dbc9842122e3"}}