{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LMBRTDB4QGJOSMIAYLHMOKYCJI","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":"9842e58fa61ab661a608623619996fb2fe5adebe8c0b8270bf76b37325579191","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2024-06-28T14:05:47Z","title_canon_sha256":"e11dabf32c9eff2662c5acaac43d01917ccfd7c47d5be3550665229194b36e01"},"schema_version":"1.0","source":{"id":"2406.19936","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.19936","created_at":"2026-07-05T09:06:42Z"},{"alias_kind":"arxiv_version","alias_value":"2406.19936v2","created_at":"2026-07-05T09:06:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.19936","created_at":"2026-07-05T09:06:42Z"},{"alias_kind":"pith_short_12","alias_value":"LMBRTDB4QGJO","created_at":"2026-07-05T09:06:42Z"},{"alias_kind":"pith_short_16","alias_value":"LMBRTDB4QGJOSMIA","created_at":"2026-07-05T09:06:42Z"},{"alias_kind":"pith_short_8","alias_value":"LMBRTDB4","created_at":"2026-07-05T09:06:42Z"}],"graph_snapshots":[{"event_id":"sha256:d49f7add503d70dbae7e06b7fb33e698c3352b02b6f3b6092649c0880fefab5e","target":"graph","created_at":"2026-07-05T09:06:42Z","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/2406.19936/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The study of geometric extremes, where extremal dependence properties are inferred from the deterministic limiting shapes of scaled sample clouds, provides an exciting approach to modelling the extremes of multivariate data. These shapes, termed limit sets, link together several popular extremal dependence modelling frameworks. Although the geometric approach is becoming an increasingly popular modelling tool, current inference techniques are limited to a low dimensional setting (d < 5), and generally require rigid modelling assumptions. In this work, we propose a range of novel theoretical re","authors_text":"Callum J. R. Murphy-Barltrop, Jordan Richards, Reetam Majumder","cross_cats":["math.ST","stat.ML","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2024-06-28T14:05:47Z","title":"Deep Learning of Multivariate Extremes via a Geometric Representation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.19936","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":"sha256:23dc7afaf9c630135339a4a7a001c18471545021be09dd4085fa917d43804cbf","target":"record","created_at":"2026-07-05T09:06:42Z","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":"9842e58fa61ab661a608623619996fb2fe5adebe8c0b8270bf76b37325579191","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2024-06-28T14:05:47Z","title_canon_sha256":"e11dabf32c9eff2662c5acaac43d01917ccfd7c47d5be3550665229194b36e01"},"schema_version":"1.0","source":{"id":"2406.19936","kind":"arxiv","version":2}},"canonical_sha256":"5b03198c3c8192e93100c2cec72b024a27ff8d4189eeef690834983eb025ba21","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5b03198c3c8192e93100c2cec72b024a27ff8d4189eeef690834983eb025ba21","first_computed_at":"2026-07-05T09:06:42.224860Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:06:42.224860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"a9qRNaghnAl4vQWbhLqyAK4lGmj0JRYzJKbvRAaWFxKqcc/GpALZblcCpXOPMYOPibKfHa1ymcOdbvrCfT3MCg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:06:42.225349Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.19936","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:23dc7afaf9c630135339a4a7a001c18471545021be09dd4085fa917d43804cbf","sha256:d49f7add503d70dbae7e06b7fb33e698c3352b02b6f3b6092649c0880fefab5e"],"state_sha256":"337eb43ed12677017165239773877ecedcecfbf0ae35ed680afd4535c4c59818"}