{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:H7JVMDRZUVYPYJQREBV622L2GP","short_pith_number":"pith:H7JVMDRZ","canonical_record":{"source":{"id":"2505.24383","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-05-30T09:12:49Z","cross_cats_sorted":["stat.ML","stat.TH"],"title_canon_sha256":"60801f34f1492b12f0853f9394165df50ace544f01fe3d75ea728937aaf2517b","abstract_canon_sha256":"c87a0abc57bd2cfe38140d73626bc81ae7bcb47c2905a3b553b4d262a6f41ed2"},"schema_version":"1.0"},"canonical_sha256":"3fd3560e39a570fc2611206bed697a33f8f61db7ec60ff6da5151f69adda40bd","source":{"kind":"arxiv","id":"2505.24383","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.24383","created_at":"2026-07-05T11:12:48Z"},{"alias_kind":"arxiv_version","alias_value":"2505.24383v1","created_at":"2026-07-05T11:12:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.24383","created_at":"2026-07-05T11:12:48Z"},{"alias_kind":"pith_short_12","alias_value":"H7JVMDRZUVYP","created_at":"2026-07-05T11:12:48Z"},{"alias_kind":"pith_short_16","alias_value":"H7JVMDRZUVYPYJQR","created_at":"2026-07-05T11:12:48Z"},{"alias_kind":"pith_short_8","alias_value":"H7JVMDRZ","created_at":"2026-07-05T11:12:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:H7JVMDRZUVYPYJQREBV622L2GP","target":"record","payload":{"canonical_record":{"source":{"id":"2505.24383","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-05-30T09:12:49Z","cross_cats_sorted":["stat.ML","stat.TH"],"title_canon_sha256":"60801f34f1492b12f0853f9394165df50ace544f01fe3d75ea728937aaf2517b","abstract_canon_sha256":"c87a0abc57bd2cfe38140d73626bc81ae7bcb47c2905a3b553b4d262a6f41ed2"},"schema_version":"1.0"},"canonical_sha256":"3fd3560e39a570fc2611206bed697a33f8f61db7ec60ff6da5151f69adda40bd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:12:48.084192Z","signature_b64":"Lhh7tVpUKYc6hTZ7SyO724QpK8UwbF4/KbaMh5ctQl+C7P78SB6Rh1IT9CZEih+0d8UQlhm6aFGvfsGneqf5CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3fd3560e39a570fc2611206bed697a33f8f61db7ec60ff6da5151f69adda40bd","last_reissued_at":"2026-07-05T11:12:48.083683Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:12:48.083683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.24383","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":"2026-07-05T11:12:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C8xHe77CtwN1IFuNHzcRtlAcCvXTtu3NHmAJw8XnQ1thReK+Q/xS4aacVi4l4OokSMnfQfDWEvfQQYbmfPA3DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T19:43:18.365469Z"},"content_sha256":"904f0f114f99812f7b0cf10f5ab6ad09831daaed988328582373084af1aa15a4","schema_version":"1.0","event_id":"sha256:904f0f114f99812f7b0cf10f5ab6ad09831daaed988328582373084af1aa15a4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:H7JVMDRZUVYPYJQREBV622L2GP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neural Drift Estimation for Ergodic Diffusions: Non-parametric Analysis and Numerical Exploration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Francesco Iafrate, Simone Di Gregorio","submitted_at":"2025-05-30T09:12:49Z","abstract_excerpt":"We take into consideration generalization bounds for the problem of the estimation of the drift component for ergodic stochastic differential equations, when the estimator is a ReLU neural network and the estimation is non-parametric with respect to the statistical model. We show a practical way to enforce the theoretical estimation procedure, enabling inference on noisy and rough functional data. Results are shown for a simulated It\\^o-Taylor approximation of the sample paths."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.24383","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/2505.24383/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":"2026-07-05T11:12:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"suz5RAv2WZA8vINQT4PXpcvDhwmUw/cKzFe/KIJH81QO3/OQoZxKfT0UhpSrM77hcNpXZGCVeBqcbDSQCHUKAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T19:43:18.365954Z"},"content_sha256":"9e49a71a026476f6386a32d63d943cad435b9218c7407284b66d5e570d3f46f8","schema_version":"1.0","event_id":"sha256:9e49a71a026476f6386a32d63d943cad435b9218c7407284b66d5e570d3f46f8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H7JVMDRZUVYPYJQREBV622L2GP/bundle.json","state_url":"https://pith.science/pith/H7JVMDRZUVYPYJQREBV622L2GP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H7JVMDRZUVYPYJQREBV622L2GP/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":"2026-08-08T19:43:18Z","links":{"resolver":"https://pith.science/pith/H7JVMDRZUVYPYJQREBV622L2GP","bundle":"https://pith.science/pith/H7JVMDRZUVYPYJQREBV622L2GP/bundle.json","state":"https://pith.science/pith/H7JVMDRZUVYPYJQREBV622L2GP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H7JVMDRZUVYPYJQREBV622L2GP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:H7JVMDRZUVYPYJQREBV622L2GP","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":"c87a0abc57bd2cfe38140d73626bc81ae7bcb47c2905a3b553b4d262a6f41ed2","cross_cats_sorted":["stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-05-30T09:12:49Z","title_canon_sha256":"60801f34f1492b12f0853f9394165df50ace544f01fe3d75ea728937aaf2517b"},"schema_version":"1.0","source":{"id":"2505.24383","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.24383","created_at":"2026-07-05T11:12:48Z"},{"alias_kind":"arxiv_version","alias_value":"2505.24383v1","created_at":"2026-07-05T11:12:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.24383","created_at":"2026-07-05T11:12:48Z"},{"alias_kind":"pith_short_12","alias_value":"H7JVMDRZUVYP","created_at":"2026-07-05T11:12:48Z"},{"alias_kind":"pith_short_16","alias_value":"H7JVMDRZUVYPYJQR","created_at":"2026-07-05T11:12:48Z"},{"alias_kind":"pith_short_8","alias_value":"H7JVMDRZ","created_at":"2026-07-05T11:12:48Z"}],"graph_snapshots":[{"event_id":"sha256:9e49a71a026476f6386a32d63d943cad435b9218c7407284b66d5e570d3f46f8","target":"graph","created_at":"2026-07-05T11:12:48Z","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/2505.24383/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We take into consideration generalization bounds for the problem of the estimation of the drift component for ergodic stochastic differential equations, when the estimator is a ReLU neural network and the estimation is non-parametric with respect to the statistical model. We show a practical way to enforce the theoretical estimation procedure, enabling inference on noisy and rough functional data. Results are shown for a simulated It\\^o-Taylor approximation of the sample paths.","authors_text":"Francesco Iafrate, Simone Di Gregorio","cross_cats":["stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-05-30T09:12:49Z","title":"Neural Drift Estimation for Ergodic Diffusions: Non-parametric Analysis and Numerical Exploration"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.24383","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":"sha256:904f0f114f99812f7b0cf10f5ab6ad09831daaed988328582373084af1aa15a4","target":"record","created_at":"2026-07-05T11:12:48Z","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":"c87a0abc57bd2cfe38140d73626bc81ae7bcb47c2905a3b553b4d262a6f41ed2","cross_cats_sorted":["stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-05-30T09:12:49Z","title_canon_sha256":"60801f34f1492b12f0853f9394165df50ace544f01fe3d75ea728937aaf2517b"},"schema_version":"1.0","source":{"id":"2505.24383","kind":"arxiv","version":1}},"canonical_sha256":"3fd3560e39a570fc2611206bed697a33f8f61db7ec60ff6da5151f69adda40bd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3fd3560e39a570fc2611206bed697a33f8f61db7ec60ff6da5151f69adda40bd","first_computed_at":"2026-07-05T11:12:48.083683Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:12:48.083683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Lhh7tVpUKYc6hTZ7SyO724QpK8UwbF4/KbaMh5ctQl+C7P78SB6Rh1IT9CZEih+0d8UQlhm6aFGvfsGneqf5CA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:12:48.084192Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.24383","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:904f0f114f99812f7b0cf10f5ab6ad09831daaed988328582373084af1aa15a4","sha256:9e49a71a026476f6386a32d63d943cad435b9218c7407284b66d5e570d3f46f8"],"state_sha256":"d3f1fec1d835d40e659668ab2a1d6ca14090e3031f2a18c9fa8e15b482d409fd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eWESfQ1jmn/BVEKVoAibDDAi8NTMW3BavRiYZFfx8JUZofPLUf1O472AzRD53JbpYWzEbPKTA1La3D1mo9QmDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T19:43:18.370666Z","bundle_sha256":"9755ecfc07e5b4acf447e5113fc45a2cc7097be9290ffe9ffef6f0a36ba63c8b"}}