{"as_of":"2026-08-09T18:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:909bcfd5c270a4437a6a0990be56c61b92aab96df6413ce708429463bf333a91","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T08:47:11.825296Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T23:09:43.968802Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T15:57:07.174101Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"cited_work":{"arxiv_id":"2510.19161","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2510.19161","snapshot_observed_at":"2026-07-29T02:25:09.668309Z","title":"Extreme event aware (η-)learning","venue":null,"work_id":"26732e52-d8a2-4649-abec-c8a2f777c90e","year":2025},"citing_paper":{"arxiv_id":"2605.22692","last_updated":"2026-05-21T16:36:44Z","snapshot_observed_at":"2026-08-01T18:14:00.230377Z","submitted_at":"2026-05-21T16:36:44Z","title":"Mechanisms and Pathways of Extreme Events in Partially-Observed Stochastic Dynamical Systems","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-22T03:37:29.991899Z"},"links":{"cited_paper":"/paper/2510.19161","citing_paper":"/paper/2605.22692"},"observation_digest":"sha256:2ea2148c165721fd76b3db7a22fa961d75986858cac9a06846597113c1e674f6","observation_id":"dca76823-57c8-41e7-ae19-1296e8f8f6e4","resolution":{"observed_at":"2026-07-29T02:25:09.668309Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"cited_work":{"arxiv_id":"2510.19161","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2510.19161","snapshot_observed_at":"2026-07-29T02:25:09.668309Z","title":"Extreme event aware (η-)learning","venue":null,"work_id":"26732e52-d8a2-4649-abec-c8a2f777c90e","year":2025},"citing_paper":{"arxiv_id":"2606.05618","last_updated":"2026-06-04T02:41:47Z","snapshot_observed_at":"2026-08-07T11:44:23.670972Z","submitted_at":"2026-06-04T02:41:47Z","title":"Uncovering Extreme Event Mechanisms for Prediction and Control with Sensitivity-Balanced Projections","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-27T23:09:43.968802Z"},"links":{"cited_paper":"/paper/2510.19161","citing_paper":"/paper/2606.05618"},"observation_digest":"sha256:8c010464127d21b1c647386bd972ad7ab3e8acad11c29929aaa442cee86b685a","observation_id":"2e64d295-2f0a-4f86-b9ec-cabf7bdcedd4","resolution":{"observed_at":"2026-07-29T02:25:09.668309Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2510.19161/citation-record","integrity":"/paper/2510.19161/integrity","json":"/paper/2510.19161/citation-record.json","paper":"/paper/2510.19161"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:08.151121Z","title":"MIT press, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:08.151121Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:7d27449d2864d9148859c18e0f1a8f63445b4ac1d634f216c6dca8f86b5609b8","observation_id":"0433fc86-f634-4134-a0f1-c9577847d2cd","resolution":{"observed_at":"2026-08-04T08:47:08.151121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:08.300472Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:08.300472Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:8971556bdee089d51157d0f53f1d8cd634004b3c0124eb07fdc8db86bd0a682e","observation_id":"d651cf22-5f00-4049-a69d-32767311999f","resolution":{"observed_at":"2026-08-04T08:47:08.300472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:08.429801Z","title":"Distribution tail structure and extreme value analysis of constrained piecewise linear oscillators","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:08.429801Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:e10dae9d9794c70629f1aa291b9b52643464bd8f018482dc16d5059c017ef9fc","observation_id":"b8324774-c70d-4f3c-a145-88a6b3871323","resolution":{"observed_at":"2026-08-04T08:47:08.429801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:08.609199Z","title":"Bayesian optimization with output- weighted optimal sampling.Journal of Computational Physics, 425:109901, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:08.609199Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:0ba31e0eeed1ab2a0d0228a0d23456d249af3392d7c6b98256441ea4fd7ccc49","observation_id":"c1465b7b-33cd-4a95-ad2f-85487ae47e28","resolution":{"observed_at":"2026-08-04T08:47:08.609199Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.18163","last_updated":"2024-11-07T16:31:48Z","snapshot_observed_at":"2026-08-07T23:20:50.265483Z","submitted_at":"2024-07-25T16:25:10Z","title":"Statistical optimal transport","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.18163","snapshot_observed_at":"2026-08-04T08:47:08.746850Z","title":"Statistical optimal transport","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:08.746850Z"},"links":{"cited_paper":"/paper/2407.18163","citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:d8dd9d56430a334a65daeb657bd409f5ce94cf22922711436dcbf4a1d1addb6d","observation_id":"4c7b6bca-b16a-4109-b132-30caee51352f","resolution":{"observed_at":"2026-08-04T08:47:08.746850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:08.869671Z","title":"Springer, 2001","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:08.869671Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:9d84843298b5beb937332b69fc1d4366e24ca6523ad7baa5696bdf865ff2c605","observation_id":"cdd4dbfb-48b4-496c-b87a-9861fd6c21ca","resolution":{"observed_at":"2026-08-04T08:47:08.869671Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:09.046691Z","title":"Cousins and T","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:09.046691Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:f4e7ad7e3c7bec6c9691bc7bc2636ddb165d686db24defa855bde5fc3560bf5a","observation_id":"44bdac86-c29f-48cc-be56-7a5d3b475a2b","resolution":{"observed_at":"2026-08-04T08:47:09.046691Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:09.198504Z","title":"Sinkhorn distances: Lightspeed computation of optimal transport.Ad- vances in neural information processing systems, 26, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:09.198504Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:bd04d8efd2b6f3931f4dbf6b61d6a79e68c15a10af9bbfa58df00f15751184e0","observation_id":"abea00dc-0e26-4a2f-8e1d-86ed0fc07fd2","resolution":{"observed_at":"2026-08-04T08:47:09.198504Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:09.294741Z","title":"Rogue waves and large deviations in deep sea.Proceedings of the National Academy of Sciences, 115(5):855– 860, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:09.294741Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:d3ae5666629f0d1cbe94298533e7e78ec472f0eef057c2f26d008f756ae222ec","observation_id":"1473e1a2-58b1-4495-be8d-2e8d549f95c8","resolution":{"observed_at":"2026-08-04T08:47:09.294741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:09.374746Z","title":"Asymptotic theory for the probability density functions in burgers turbulence.Physical Review Letters, 83(13):2572–2575, 1999","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:09.374746Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:f194753fcd3a99dd92f453886a103ee6314781c97544b22fe2b934f1bb1fc467","observation_id":"94f37125-1e51-4cc5-a6ee-515a2d3d9a92","resolution":{"observed_at":"2026-08-04T08:47:09.374746Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:09.539766Z","title":"Transition-path theory and path-finding algorithms for the study of rare events.Annual review of physical chemistry, 61:391–420, jan 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:09.539766Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:af0114ac17596e29372add8797faf93a4afe6630388ea59c9544d8cab8aad231","observation_id":"bd9308e0-07e1-41ec-8650-2176a5c22d15","resolution":{"observed_at":"2026-08-04T08:47:09.539766Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:09.638791Z","title":"John Wiley & Sons, 1999","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:09.638791Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:55859555daa30bac288cb0cd74efba5b7cd36ba1ba25589df9921a77d2d76707","observation_id":"e49fa9c4-f130-4ae7-88b5-cda6d7573e1f","resolution":{"observed_at":"2026-08-04T08:47:09.638791Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:09.772200Z","title":"Cambridge University Press, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:09.772200Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:d9ab7633d6fb4b99bbae1a1f12c7ba3c78141cef9038a9a4ccb0b1a4f239c13a","observation_id":"27486d98-4095-4eb2-9b73-b6ce8436efb1","resolution":{"observed_at":"2026-08-04T08:47:09.772200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:09.888760Z","title":"On choosing and bounding probability metrics","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:09.888760Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:eb5139756aa1b9d63774a163a29480e487621d21b9ec63ba417047a7d456ffea","observation_id":"ae13834b-1f88-4da1-91c4-5505a5c54a09","resolution":{"observed_at":"2026-08-04T08:47:09.888760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:09.983055Z","title":"MIT press, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:09.983055Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:9fad5134a489276fea80e663bba18f83ce5feae3d16b32c23f9d2c5e3fd0dfc5","observation_id":"b601bd11-cc90-4bbd-8ca6-c300c6757b70","resolution":{"observed_at":"2026-08-04T08:47:09.983055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-04T08:47:10.050433Z","title":"Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:10.050433Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:0fcaed6b1bcd1c493a2c0c28c36a4f588ddeb4fcb0111b150a1dc5756d3961a6","observation_id":"58833dc8-4721-46bc-8ff4-bfcf5f1ed0f4","resolution":{"observed_at":"2026-08-04T08:47:10.050433Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:10.127463Z","title":"A Tutorial on Energy-Based Learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:10.127463Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:dcd48d2d794cf87fd5304edd838ab384afa1fdce4cad151bd05fbb46c8a79fca","observation_id":"a3e01529-a75b-41eb-a96b-4904852cfef1","resolution":{"observed_at":"2026-08-04T08:47:10.127463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02747","last_updated":"2023-02-08T15:46:05Z","snapshot_observed_at":"2026-08-02T18:24:58.914589Z","submitted_at":"2022-10-06T08:32:20Z","title":"Flow Matching for Generative Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02747","snapshot_observed_at":"2026-08-04T08:47:10.204123Z","title":"Flow matching for generative modeling.arXiv preprint arXiv:2210.02747, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:10.204123Z"},"links":{"cited_paper":"/paper/2210.02747","citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:a63f403381add793886ca24f18cd3f5ebc4a4f0af94cb6a3c3b2b529f288b4fb","observation_id":"4b3339f3-5143-4618-a956-b0264e9897b5","resolution":{"observed_at":"2026-08-04T08:47:10.204123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:10.278989Z","title":"A one-dimensional model for dispersive wave turbulence.Journal of Nonlinear Science, 7:9–44, 1997","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:10.278989Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:c60dae90d54389c00d20f177c0ebd2a488424e1f706ffa11cf61a7c8b7946750","observation_id":"70798070-5b09-433d-84dd-3b0a29080b69","resolution":{"observed_at":"2026-08-04T08:47:10.278989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:10.372593Z","title":"Sequential sampling strategy for extreme event statistics in nonlinear dynamical systems.Proceedings of the National Academy of Sciences, 115(44):11138–11143, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:10.372593Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:35ea858a9dd7829f5bdf717053837a9a275026859e834859b08307c135637a39","observation_id":"fa6094c7-5fdc-4edb-a3db-44efbbd2a2a6","resolution":{"observed_at":"2026-08-04T08:47:10.372593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:10.461499Z","title":"Era5-land: A state-of-the-art global reanalysis dataset for land applications.Earth system science data, 13(9):4349–4383, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:10.461499Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:689df15fdf25764e9f1e568ab8a094f0ffff887a6b802c48e34d846aa2e66f3e","observation_id":"0b826934-26e1-4b07-8325-fb43856d4bfd","resolution":{"observed_at":"2026-08-04T08:47:10.461499Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:10.537495Z","title":"Univariate stable distributions.Springer Series in Operations Research and Financial Engineering, 10:978–3, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:10.537495Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:c45626de047ca0fb5b424a319ea44fc40da2846f10f995aecc73799e261750b1","observation_id":"276c3053-b87e-4822-955b-da16280a66a9","resolution":{"observed_at":"2026-08-04T08:47:10.537495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:10.591269Z","title":"Pytorch: An imperative style, high-performance deep learning library.Advances in neural infor- mation processing systems, 32, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:10.591269Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:6c6a6e764c67b2bc50d80206b5d1a32387eef88a01c570602a8b88704a1412d6","observation_id":"79093cfd-86eb-4ba9-b965-e10d0a67ebb1","resolution":{"observed_at":"2026-08-04T08:47:10.591269Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:10.686771Z","title":"Discovering and forecasting extreme events via active learning in neural operators.Na- ture Computational Science, 2(12):823–833, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:10.686771Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:63753261db090c2b220b668197a12a708316968ebec114f8a483a506e417bbe1","observation_id":"91882142-876c-4e0b-a612-3b1a65c186c8","resolution":{"observed_at":"2026-08-04T08:47:10.686771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:10.740008Z","title":"U-net: Convolutional networks for biomedical image segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:10.740008Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:724773958a313c5dbed28980fee5404f436544acfa5e2208f234721d0e0310cc","observation_id":"c6f9aa32-da5b-4c7f-bb77-77c645cc97ab","resolution":{"observed_at":"2026-08-04T08:47:10.740008Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:10.817175Z","title":"The Wasserstein distances between pushed-forward measures with ap- plications to uncertainty quantification.Communications in Mathematical Sciences, 18(3):707–724, June 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:10.817175Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:e1addd703b155db6515d659ce0baf473952c0888d00acd3e3a43669cdcd36dd4","observation_id":"5991903a-47bc-4260-8101-ee8700669f3d","resolution":{"observed_at":"2026-08-04T08:47:10.817175Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:10.893841Z","title":"Statistics of extreme events in fluid flows and waves.Annual Review of Fluid Mechanics, 53(1):85–111, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:10.893841Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:62c8e3c1a9d99b78a98641693e992b5f258ca4648b9565a8f4fbd1d106e25eda","observation_id":"656c0a51-525e-4d23-8f7c-580de5a6a24f","resolution":{"observed_at":"2026-08-04T08:47:10.893841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:10.942405Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:10.942405Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:a1b2abb9760a6b53c6968d6887dd1f2139e3e64a3021e9064e177720be26651a","observation_id":"150cfe59-8df2-48dc-b403-ba4ed71e9633","resolution":{"observed_at":"2026-08-04T08:47:10.942405Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:11.020179Z","title":"Weather and climate extreme events in a changing climate.Climate Change 2021: The Physical Science Basis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:11.020179Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:b6940989dd6093cc7386c07cc48afbf2de709d7f129a5c377c28541f29a0d87b","observation_id":"45fe4304-a229-4672-8826-2b8c3492facc","resolution":{"observed_at":"2026-08-04T08:47:11.020179Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:11.102080Z","title":"Sobczyk.Stochastic Differential Equations","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:11.102080Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:4b27204734a4a7f48e53797f662564377770a6f9d5be882f9caf3fa61dc6abab","observation_id":"ef6e0391-0e6e-4469-ad09-e7e08a91dc3d","resolution":{"observed_at":"2026-08-04T08:47:11.102080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:11.177272Z","title":"Soong and M","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:11.177272Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:796a8e3818d3b175c56b3eaa7950af440683a476808f8740286b5bb8a02c5feb","observation_id":"5e72c939-3487-4b9b-9d08-c7821049309c","resolution":{"observed_at":"2026-08-04T08:47:11.177272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.02688","last_updated":"2024-11-22T17:59:47Z","snapshot_observed_at":"2026-08-09T17:32:39.991025Z","submitted_at":"2024-08-02T18:34:30Z","title":"A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.02688","snapshot_observed_at":"2026-08-04T08:47:11.247893Z","title":"A probabilistic framework for learn- ing non-intrusive corrections to long-time climate simulations from short-time training data.arXiv preprint arXiv:2408.02688, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:11.247893Z"},"links":{"cited_paper":"/paper/2408.02688","citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:a2d913ae4f5fee6562df9e80b56746a7a03f9e8f125b52ff58e346193a93a9ab","observation_id":"314af4a9-4fc1-4aa6-a4ac-658d380188d6","resolution":{"observed_at":"2026-08-04T08:47:11.247893Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:11.341340Z","title":"Sullivan, M","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:11.341340Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:5edf1ff4b729127439473ffb87b83bf064b3a87339abdd9aef4bb05272dc2800","observation_id":"fbf52e60-d090-4da4-8e2b-7eb647b2641e","resolution":{"observed_at":"2026-08-04T08:47:11.341340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:11.442222Z","title":null,"venue":null,"work_id":null,"year":1980},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:11.442222Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:9e5aa1632484b29ea0be70db973a54acd64b300582a9191cf53ba9993023ff14","observation_id":"333081c0-ca1d-4d8c-9d83-722b2e42b33d","resolution":{"observed_at":"2026-08-04T08:47:11.442222Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:11.484800Z","title":"Large deviation theory-based adaptive importance sampling for rare events in high dimensions.SIAM/ASA Journal on Uncertainty Quan- tification, 11(3):788–813, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:11.484800Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:f4355237da9424873ea9919d6c15f7ef12df51c502c85d3cd83a83ac5697843c","observation_id":"4426ebbe-4ce5-4125-a61f-a323bc8b0cb8","resolution":{"observed_at":"2026-08-04T08:47:11.484800Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:11.552440Z","title":"Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St´ efan J","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:11.552440Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:42ac6645158d116eac2f1b97a4e16a9e4c1d1eb644216baad9b3d7270d441a91","observation_id":"a8e34ede-e1c0-41b8-9462-457ec4741b76","resolution":{"observed_at":"2026-08-04T08:47:11.552440Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:11.623601Z","title":"Practicalities and Algorithmic Details","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:11.623601Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:706986e829319be5f95f20603ac2b341b0cb0d39bccbfac68c3c11ffa81d99f0","observation_id":"082175af-5209-4468-913d-0413e5887b71","resolution":{"observed_at":"2026-08-04T08:47:11.623601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:11.675164Z","title":null,"venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:11.675164Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:1d205f81a179041b709cea6950744c8c3cf797cb4f47ae3ab8c1e784147b3c14","observation_id":"eea7bb6d-a875-4d66-acf6-97e1d969b340","resolution":{"observed_at":"2026-08-04T08:47:11.675164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:11.738588Z","title":"The window sizeωin Algorithm 1 is selected to be 30, and we train the network for 150 epochs","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:11.738588Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:ba899cf7bc81b62176cf18fb0eb5bf3f33599f311010c1406633698f2aec9c7c","observation_id":"d3842bd6-8a0e-487e-9db3-00b693f7024a","resolution":{"observed_at":"2026-08-04T08:47:11.738588Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:47:11.825296Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T08:47:11.825296Z"},"links":{"citing_paper":"/paper/2510.19161"},"observation_digest":"sha256:361692e8707a1138b534803ba287219a1a9be325ecd90429fd5bea3d2afbb173","observation_id":"3d4986ff-79ec-4c34-9873-7e59145aa5ee","resolution":{"observed_at":"2026-08-04T08:47:11.825296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2510.19161","last_updated":"2026-07-28T16:41:48Z","latest_version":2,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-09T17:32:54.611730Z","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":40,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":40},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 2 inbound Pith citation observations for arXiv:2510.19161."}