{"as_of":"2026-08-21T11:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9186e03b0980040c0d6541fba31bf0165a05d5c420c9d468594c378b0e554a4c","coverage":[{"denominator":121,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T20:02:26.253579Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2508.11597/citation-record","integrity":"/paper/2508.11597/integrity","json":"/paper/2508.11597/citation-record.json","paper":"/paper/2508.11597"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:17.405705Z","title":null,"venue":null,"work_id":null,"year":1979},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:17.405705Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:7ef2dd4652109161256a2bb15a4f1f244cdbcaef7f12cb51b109c447e09ec289","observation_id":"6e22e7d1-1ff7-4013-881f-933b2efe1d85","resolution":{"observed_at":"2026-08-05T20:02:17.405705Z","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":"2026-08-05T20:02:17.485338Z","title":null,"venue":null,"work_id":null,"year":2099},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:17.485338Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:636e88edd3a23f3a12424868b3bfb4bbd300dd09a1c52f1c8414331d560d3dd5","observation_id":"ec2cc114-dad5-4b9d-986b-b627107cf5ee","resolution":{"observed_at":"2026-08-05T20:02:17.485338Z","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":"2026-08-05T20:02:17.345566Z","title":"We focus on the case ofH (2,2), discussed for the SSH model in Sec","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:17.345566Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:c64d8ad111a4b12bbd3329a7b8f57a7cd4c3a24c028a3d023ddf213b57be3f5f","observation_id":"c424226d-38c3-4f12-82ca-6ca8bf4227c0","resolution":{"observed_at":"2026-08-05T20:02:17.345566Z","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":"2026-08-05T20:02:17.586314Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:17.586314Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:85eac1a9605de91a54fdc5d2ee27de58823499ebde3638782739c8bdb5c0c5e0","observation_id":"c3f060ea-569e-4e1a-b872-8eb2ecc85b79","resolution":{"observed_at":"2026-08-05T20:02:17.586314Z","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":"2026-08-05T20:02:17.641363Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:17.641363Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:4e770fdb05909e69eb695670bf0c1f0ba4b8444c877570f690b3018f5ad87880","observation_id":"5f09529b-9150-4be2-9d0d-da7bb085698f","resolution":{"observed_at":"2026-08-05T20:02:17.641363Z","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":"2026-08-05T20:02:17.685731Z","title":"Kitaev, Periodic table for topological insulators and superconductors, AIP Conf","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:17.685731Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:fc537c7b27cc7b5668f8d97726f77b383cb14e4f97715ae017c4349eb96d5551","observation_id":"9012c4e6-029b-4aef-9818-d1c76ebcd70a","resolution":{"observed_at":"2026-08-05T20:02:17.685731Z","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":"2026-08-05T20:02:17.800032Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:17.800032Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:3a0842dec0371d16e576b8aed2c318a1d14ce4170096ef9f54231f49836994ff","observation_id":"623b46e4-2041-4093-bb5a-1e69fa5ad3c2","resolution":{"observed_at":"2026-08-05T20:02:17.800032Z","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":"2026-08-05T20:02:17.898903Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:17.898903Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:e76912434e0697b4b995bdb1349b39c23065364e7483058ad15afa488d829104","observation_id":"0d8b968e-06e1-46c9-aa22-1b5726f65ed8","resolution":{"observed_at":"2026-08-05T20:02:17.898903Z","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":"2026-08-05T20:02:17.978531Z","title":null,"venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:17.978531Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:68e80759f99bd7506cbf8c5c329808df08589047692142401937698384794d99","observation_id":"28c5ac9a-1977-4386-b8f7-3c44b7aff07e","resolution":{"observed_at":"2026-08-05T20:02:17.978531Z","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":"2026-08-05T20:02:18.046902Z","title":null,"venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:18.046902Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:2645b425015a3d908d4969da04769f4f9d02c31f76e2f0e1851be6a4559a711d","observation_id":"15d9d3d2-2ce5-4c34-92cd-b7e901afc15a","resolution":{"observed_at":"2026-08-05T20:02:18.046902Z","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":"2026-08-05T20:02:18.151085Z","title":"Liang and G.-Y","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:18.151085Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:ba3e0ffcc593e3fa76a4ec6583f506742f060dfee3e8eba323720c6a8f9569d6","observation_id":"1857a17b-8c90-402f-889b-b84d2a4c0a26","resolution":{"observed_at":"2026-08-05T20:02:18.151085Z","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":"2026-08-05T20:02:18.260769Z","title":"Schomerus, Topologically protected midgap states in complex photonic lattices, Optics Letters38, 1912 (2013)","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:18.260769Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:bb8acb1ae680174f09a269e4e1f7a345b4fe4423e77026404edea9a0efe3afef","observation_id":"c0291196-50e5-4b08-ba40-3f1d4e9f83d5","resolution":{"observed_at":"2026-08-05T20:02:18.260769Z","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":"2026-08-05T20:02:18.340661Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:18.340661Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:06d4092ec3db665b0bdc5976e8aba578e314fa0cfb9e6a87c5fbc5256dd525f8","observation_id":"a2f2b75c-eddd-40b1-bc26-0a6223082326","resolution":{"observed_at":"2026-08-05T20:02:18.340661Z","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":"2026-08-05T20:02:18.418217Z","title":"Weimann, M","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:18.418217Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:3e1af97f47fcd196cecd70e65851c59bc40415b5df360e9ef18bd93ba8465bc6","observation_id":"d3256eb4-3b5d-457d-a75a-3b177032731a","resolution":{"observed_at":"2026-08-05T20:02:18.418217Z","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":"2026-08-05T20:02:18.498277Z","title":"Klett, H","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:18.498277Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:1d76afeb0ba3c36d0d899d722ee1748f73704bed9ba00906c4e60dd7bb2a512f","observation_id":"c1d6e115-e573-46b9-a19f-296101b33dd7","resolution":{"observed_at":"2026-08-05T20:02:18.498277Z","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":"2026-08-05T20:02:18.597967Z","title":"Lieu, Topological phases in the non-Hermitian Su- Schrieffer-Heeger model, Phys","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:18.597967Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:07f779b147ae78ecb8eb3adac4be73c057ea9fb2c3b1f40a7b370a2c51e43417","observation_id":"a14edb2c-dc78-4681-a7e4-b205583ef740","resolution":{"observed_at":"2026-08-05T20:02:18.597967Z","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":"2026-08-05T20:02:18.675708Z","title":"Halder, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:18.675708Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:89b6a1bc8da991b9e53f33362f177c0e7d64e2f79627f011f7a766246b79c2ce","observation_id":"843eb3bd-4667-4aca-a769-ab37887ea23d","resolution":{"observed_at":"2026-08-05T20:02:18.675708Z","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":"2026-08-05T20:02:18.804055Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:18.804055Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:23f1559cebcc60e23d68d20409c36360243fca1442f4ca99a7578c8e6d8346e3","observation_id":"4c3afbc7-5380-475f-9c6c-aefc187cd39d","resolution":{"observed_at":"2026-08-05T20:02:18.804055Z","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":"2026-08-05T20:02:18.878711Z","title":"Slootman, W","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:18.878711Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:2f80089515f5136de57ac0a53b9c0a9c667e8122026d5cfd0ce7803a932af98b","observation_id":"16cfcb97-fa72-4899-b652-5b9b07422c8d","resolution":{"observed_at":"2026-08-05T20:02:18.878711Z","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":"2026-08-05T20:02:18.980976Z","title":"Hatano and D","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:18.980976Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:5b75dbc8f8a1c7bbff0fe04f832639b0403b805487b3cb4e6769f93c7f073441","observation_id":"339c95c0-bb47-40f5-8b2e-d72ba9b41ebc","resolution":{"observed_at":"2026-08-05T20:02:18.980976Z","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":"2026-08-05T20:02:19.087262Z","title":"Hatano and D","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:19.087262Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:a27619befdc5a4ce630c0dfc2bc50c692cbc7e0f9ae4532413142fa7fb154375","observation_id":"8539a3a7-4202-4ac2-83f1-87b46dd936bf","resolution":{"observed_at":"2026-08-05T20:02:19.087262Z","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":"2026-08-05T20:02:19.156146Z","title":"Hatano and D","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:19.156146Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:1971184b3d25181c420f8be17808d69c7306aef628a6a7f791af9fc2f38c881b","observation_id":"f35656ef-53c8-476e-a128-b8f12301f1c0","resolution":{"observed_at":"2026-08-05T20:02:19.156146Z","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":"2026-08-05T20:02:19.260022Z","title":"Longhi, D","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:19.260022Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:68bc790cd92c9a8f14d2c2963bf069833d784e2270c070c7a37a9d4672932e55","observation_id":"10adfb1e-eb2b-4438-ba04-52a576395547","resolution":{"observed_at":"2026-08-05T20:02:19.260022Z","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":"2026-08-05T20:02:19.369762Z","title":"Yao and Z","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:19.369762Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:f5c1978a99b11785ee15a2e9e78a1ad72a0c45e7672c8ae546b8d8a48651a330","observation_id":"e7611ed9-4fa3-4486-b6d9-3c2f1c88c795","resolution":{"observed_at":"2026-08-05T20:02:19.369762Z","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":"2026-08-05T20:02:19.433993Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:19.433993Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:10fe301cd57d2861ce2db29117912ed75cfa755a3e9643bcfcc8af626d95ea4d","observation_id":"88ba2571-1335-4271-8d23-362dd626c2d3","resolution":{"observed_at":"2026-08-05T20:02:19.433993Z","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":"2026-08-05T20:02:19.577324Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:19.577324Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:4eed47212cef0275e8528bf4bb5cc08f814ae6fc729d4d9b6abf27707a7eea89","observation_id":"19ad282e-aa4b-4a16-b8e9-28f0ed223426","resolution":{"observed_at":"2026-08-05T20:02:19.577324Z","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":"2026-08-05T20:02:19.685463Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:19.685463Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:aa7996e2bb10ca60fc475279335d506ce8ef9ad0b2af53329bbe4ce8d6d4ca1a","observation_id":"0cea3c26-7af8-49ee-9b9e-d91a20729ca2","resolution":{"observed_at":"2026-08-05T20:02:19.685463Z","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":"2026-08-05T20:02:19.814046Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:19.814046Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:f815598affccd917cb87a3efa8b7718ee9492c9a2d837fde64b705f3dfd77050","observation_id":"6a641a5a-8a88-475d-aa9c-ca69a236fab8","resolution":{"observed_at":"2026-08-05T20:02:19.814046Z","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":"2026-08-05T20:02:19.885684Z","title":"Ryu, J.-H","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:19.885684Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:979dbf671e12f79295a2686b057d957382d597fcd6aff80b35549029051392db","observation_id":"1e8e4ead-152a-4f5c-af95-7e5f4402d28e","resolution":{"observed_at":"2026-08-05T20:02:19.885684Z","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":"2026-08-05T20:02:19.986264Z","title":"Kato,Perturbation Theory for Linear Operators (Springer, New York, 1966)","venue":null,"work_id":null,"year":1966},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:19.986264Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:caf6f7eef38aac08751dfcd37a6bd35d019ee5572e03b9ebce27d585b8997860","observation_id":"65f05ccb-96d7-4771-af01-313945983822","resolution":{"observed_at":"2026-08-05T20:02:19.986264Z","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":"2026-08-05T20:02:20.084609Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:20.084609Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:8858fee4602d126a45ff1fffaffb3056c59b24a423cf34221f279064351dc43e","observation_id":"927a6953-f5ac-48be-8520-cb60607ad12c","resolution":{"observed_at":"2026-08-05T20:02:20.084609Z","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":"2026-08-05T20:02:20.192324Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:20.192324Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:cf781fa610d83627a2293916f4233cec625dd516b8e0967f2cb6434e76d13645","observation_id":"bbde1031-e42d-46b2-9834-62643126efc3","resolution":{"observed_at":"2026-08-05T20:02:20.192324Z","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":"2026-08-05T20:02:20.259108Z","title":"Klaiman, U","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:20.259108Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:8eaacc8886154c823a2cdf7af0952109dbe5289237dafabb959ccc6e1e89833f","observation_id":"fc4bb01d-ceb5-4563-bb9d-1ab46394fcef","resolution":{"observed_at":"2026-08-05T20:02:20.259108Z","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":"2026-08-05T20:02:20.339266Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:20.339266Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:0dc96ad835e431a2d6cfd7e4c71f91171f39ac425139fc7fe125ab4becf9d48f","observation_id":"b28c1490-4c24-4d94-8cef-3cddac887659","resolution":{"observed_at":"2026-08-05T20:02:20.339266Z","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":"2026-08-05T20:02:20.467291Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:20.467291Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:1d302916846a9902602332430bc721894fa6f00820f038bb12e42957675bbd7e","observation_id":"e94bb0a9-bbc0-4d3b-8550-68ef48f14f85","resolution":{"observed_at":"2026-08-05T20:02:20.467291Z","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":"2026-08-05T20:02:20.545100Z","title":"Kawabata, T","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:20.545100Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:5c30cc7b73dc9eb8be6699e58fb62b1135f3d384ef297a562d791394b1a89166","observation_id":"f63f120e-b7f4-45a8-b7e3-6b28fddbd4a0","resolution":{"observed_at":"2026-08-05T20:02:20.545100Z","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":"2026-08-05T20:02:20.618621Z","title":"Miri and A","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:20.618621Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:e000b8857c9cf9468c9d4766b1e7303698fb99ce3e395f11de4efb362a3cc885","observation_id":"f99fbf6f-4f88-4e2b-a9be-c1030782356a","resolution":{"observed_at":"2026-08-05T20:02:20.618621Z","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":"2026-08-05T20:02:20.658605Z","title":"Wiersig, Response strengths of open systems at excep- tional points, Phys","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:20.658605Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:1a2c4a84e5317212e3c7ac0a81dd629a9756d6889d68894bcb0780f1ad430cca","observation_id":"b1ac6f05-d8fc-4aa5-a627-597a58c57be7","resolution":{"observed_at":"2026-08-05T20:02:20.658605Z","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":"2026-08-05T20:02:20.754671Z","title":"Wiersig, Distance between exceptional points and dia- bolic points and its implication for the response strength of non-Hermitian systems, Phys","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:20.754671Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:ae785847f9ea5314a770272b7e7455125a41cf35d99c0bbc70a94d64def2e184","observation_id":"1dcf5191-450c-46b9-a855-61acf30b3b2b","resolution":{"observed_at":"2026-08-05T20:02:20.754671Z","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":"2026-08-05T20:02:20.816007Z","title":"Manna and B","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:20.816007Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:38c3731044536ebcfe952095eff2d948fbddd61ed0f6b389a03dab954b87a787","observation_id":"16cbc8f6-9f20-40a7-a9de-8e746a214d3b","resolution":{"observed_at":"2026-08-05T20:02:20.816007Z","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":"2026-08-05T20:02:20.911441Z","title":"Wiersig, Petermann factors and phase rigidities near exceptional points, Phys","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:20.911441Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:54735fb50b7562d03e4006ec10aa8c7295f149058660fb948d742eb382651997","observation_id":"c4397b04-b04a-4186-8663-72587c34d2ae","resolution":{"observed_at":"2026-08-05T20:02:20.911441Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:28.121290Z","title":"Sayyad, M","venue":null,"work_id":"bcf82db8-1c46-425a-b81e-eb084f381891","year":2023},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:20.967201Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:b42235e5567296128391639919f6ac8579ef88be2b12dca06af00691c37aeaf5","observation_id":"c2a3ba65-a038-4a72-a3e7-446c6f6ecf6c","resolution":{"observed_at":"2026-08-05T20:02:28.124462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:21.025936Z","title":"Schomerus, Eigenvalue sensitivity from eigenstate geometry near and beyond arbitrary-order exceptional points, Phys","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:21.025936Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:58313e7f85a319002c2a09f1af8c98772f9a50109dc083b58485f3ee6c3fb49f","observation_id":"e0b018ba-c964-4e14-b9e4-9839bdee3839","resolution":{"observed_at":"2026-08-05T20:02:21.025936Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:28.104615Z","title":"Bid and H","venue":null,"work_id":"7bc7d460-ad77-4097-b3cc-cd6c244d32e5","year":2025},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:21.143268Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:3ff00b45ae6b1fa5c3579c353efc91a7c8903e85dcd5b1d3f8099dfd15c972ee","observation_id":"59ec08ca-9a3d-435c-bd3b-9cc73eae59e4","resolution":{"observed_at":"2026-08-05T20:02:28.107614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:28.095085Z","title":"Het´ enyi and B","venue":null,"work_id":"1691d812-75da-40ec-8dd5-c61370cd4694","year":2025},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:21.264295Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:69a752209c977c0fb598a8ea1f556ebd1f619f497bfb146797240cfd1dafab53","observation_id":"827b1fac-468f-43e3-a9c2-b2360e6b3220","resolution":{"observed_at":"2026-08-05T20:02:28.098195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:28.084499Z","title":"Kullig, J","venue":null,"work_id":"3f50df02-eca8-481c-901d-38315555ebd9","year":2025},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:21.360525Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:f4cdb52c010e11f9e360ca54473515d0c5c28ea8cbb74b1cc744179e1fc732ed","observation_id":"d4bd197e-726d-4976-a870-8a30567cdc1a","resolution":{"observed_at":"2026-08-05T20:02:28.088136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:28.074382Z","title":"Bid and H","venue":null,"work_id":"fc9466a1-2200-4e1e-a219-4c92028c5217","year":2025},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:21.519019Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:4cabecdacc46bb49c0084fa4ce224e893074c3d17430196dd9a3c84e6d166046","observation_id":"c8971188-6454-45f0-b4c3-8dff25d5b7a9","resolution":{"observed_at":"2026-08-05T20:02:28.077631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2508.11490","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.386303Z","title":"Bid and H","venue":null,"work_id":"6973d6e6-3072-4937-ba16-e2da11050c88","year":null},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:21.617168Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:8b8e05a53802dd1e4624d3b02c456b37ea0a1d80f8b6e8b80d6425d4d376fe54","observation_id":"ee383d9c-c9cc-4d8b-8f57-7922c9e300eb","resolution":{"observed_at":"2026-08-05T20:02:27.391660Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12018","last_updated":"2024-11-08T15:49:10Z","snapshot_observed_at":"2026-08-16T14:08:41.394206Z","submitted_at":"2024-03-18T17:53:33Z","title":"Exceptional points of any order in a generalized Hatano-Nelson model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.12018","snapshot_observed_at":"2026-08-05T20:02:21.733244Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:21.733244Z"},"links":{"cited_paper":"/paper/2403.12018","citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:f8ce268c8c2395c6793691f67eb42b24f3d51920b81009cae886210c0ecdf8e8","observation_id":"86cfbafc-8e0b-4745-a7d6-459368dd13eb","resolution":{"observed_at":"2026-08-05T20:02:21.733244Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:28.064408Z","title":"Fendley, Free parafermions, J","venue":null,"work_id":"3cb84fc7-ce6a-4799-8a97-cfbd88b3cdb8","year":2014},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:21.841936Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:7532246d9447ef5e1f85f0c55f228fdf1b437fbcb5c10f9ad196c395d5ba71ab","observation_id":"21cc6e31-2f93-49ac-990b-d119c8bd9d25","resolution":{"observed_at":"2026-08-05T20:02:28.067601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:28.054145Z","title":null,"venue":null,"work_id":"7556bb57-b27b-4589-aa47-f7a532521d76","year":2022},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:21.948086Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:54d41c33cf1640f08a6e3fb83f32409ba841e8bfd82db77cfc9cc40b95893691","observation_id":"16f21c6a-b57a-4752-b422-d94c7d5686fc","resolution":{"observed_at":"2026-08-05T20:02:28.057105Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:28.044295Z","title":"Viedma, A","venue":null,"work_id":"5c4ecc26-0f1a-4dfc-96b8-2e5ee607d4c5","year":2024},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:22.095396Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:236e8ae8d719b9b4e4b7ca29fd2f87e63cc8f2930470f4aed11ed114417b247a","observation_id":"eb7569e9-2d47-478b-9dff-ac493f6ac033","resolution":{"observed_at":"2026-08-05T20:02:28.047411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:28.034524Z","title":"Longhi, D","venue":null,"work_id":"48e33e1f-a46d-44d0-9062-97f414f92de6","year":2015},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:22.219641Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:a4b1809f5aeefd73e4f5291ff75827d4f49f61082a55b5afc743c0c2656ce82f","observation_id":"ea60ed92-0f21-4446-bc63-d038dd21b2a4","resolution":{"observed_at":"2026-08-05T20:02:28.037759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:22.321015Z","title":"Weidemann, M","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:22.321015Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:7207415344cc1d3925856ab7943568cd1685c8cc28ccb6915965b8b3c0d19707","observation_id":"7c03aa55-7245-44c2-85e7-bbf9ff1c39fd","resolution":{"observed_at":"2026-08-05T20:02:22.321015Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:28.017034Z","title":null,"venue":null,"work_id":"35ab4bf9-1736-40c5-8a80-267a94f8d27b","year":2020},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:22.436335Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:89cf43520b839a5f0f4fefb4d3e7ba463a8bc00ae8ad33001d60d4aea60ec63e","observation_id":"a19f0547-998e-4704-aefd-a33c03824ba4","resolution":{"observed_at":"2026-08-05T20:02:28.020543Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:28.006869Z","title":null,"venue":null,"work_id":"0fe46c1c-5ad5-4019-ae85-b8446daf964e","year":2021},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:22.577022Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:8b7cbbab24212bf1b4f0329511ffaba2dca8f07560ed379bcd906dcc9a15b5af","observation_id":"2e8cf509-163c-4d3b-a73f-2c66a6d379f0","resolution":{"observed_at":"2026-08-05T20:02:28.010014Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.996760Z","title":"Weidemann, M","venue":null,"work_id":"3a9228e1-7ade-4b1c-8e1e-1e863136e33f","year":2022},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:22.686888Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:a9865c030168b9a77c0a9e493d5174439fafd02fdac23234719262608d334502","observation_id":"1dfb30c0-155a-42c0-a92b-602b9dac9d28","resolution":{"observed_at":"2026-08-05T20:02:28.000024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.986697Z","title":null,"venue":null,"work_id":"d11d6c75-3d6f-4186-b36e-51ca7fefe0d7","year":2022},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:22.780179Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:0fc5e4b6acb9dd9012132d3f28135550ca0ec7ca579bf62d03caba1cfdda7388","observation_id":"98b5251c-0b6b-4ca0-bf17-13abd76e5347","resolution":{"observed_at":"2026-08-05T20:02:27.990064Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.976398Z","title":null,"venue":null,"work_id":"ea6e0e9f-3ccf-47a0-9f75-ef0ef7a7fae8","year":2023},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:22.849053Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:d20865735cf1fef24a31c36f7725b23f7834a7c6f8328a6851281f812d724668","observation_id":"bd481ab4-97e9-4b64-bcca-81fa6b6136c7","resolution":{"observed_at":"2026-08-05T20:02:27.979555Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.966528Z","title":null,"venue":null,"work_id":"7d9f3f7e-069c-4347-978f-50c8f1273275","year":2023},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:22.910734Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:d436fe34195f22d6937c738fad76b1d7c392b89bf43e8479c8929bfad247cd7a","observation_id":"df1bfb03-511d-423d-80d6-f79afdae60d3","resolution":{"observed_at":"2026-08-05T20:02:27.969620Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:22.991417Z","title":"Zhang, Y","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:22.991417Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:e5c35b905cd082d6ab4505dbb6123549ba8ff0dbf8086771a0600a5012524da1","observation_id":"645b17c6-4302-4daa-985c-f89ef4d7da4e","resolution":{"observed_at":"2026-08-05T20:02:22.991417Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.950111Z","title":null,"venue":null,"work_id":"b0dbab03-19aa-4d3c-bb3b-1dec40303707","year":2022},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:23.074292Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:9847ac9138af9aa01e846ac5c79869b2f8b7abc5500836f3200edfad23ed3bb5","observation_id":"8af3df19-91dd-4185-950a-aa6ec3a8a741","resolution":{"observed_at":"2026-08-05T20:02:27.953631Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.940264Z","title":null,"venue":null,"work_id":"54dc3f16-6a13-4577-9798-0887e36f16d0","year":2022},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:23.162146Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:a229c8878e88d671d9854abb6d428c3cc45dbc50a88f1d28ccb8dd7ae8de055b","observation_id":"9de18b3d-95c0-4935-975d-7a5f0f356827","resolution":{"observed_at":"2026-08-05T20:02:27.943516Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.929641Z","title":null,"venue":null,"work_id":"8c5be4fc-7309-4a84-9b12-9cb598d31854","year":2014},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:23.249815Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:10069830631c52d8b513e96d4dcaa378fb136e1b5406d5840444e29a717b2a11","observation_id":"46db9b7e-4dcf-402f-b578-741accb037b3","resolution":{"observed_at":"2026-08-05T20:02:27.932859Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.918854Z","title":null,"venue":null,"work_id":"39d2fb33-2e3a-4638-9d65-f85050ceb8a8","year":2019},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:23.329711Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:425df3da7c8dc7d6e69928bcced741949224fdc0554492ca98db57b9178cdebc","observation_id":"acc8c797-15bd-42ac-9dd0-e16ac81a4b1d","resolution":{"observed_at":"2026-08-05T20:02:27.922765Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:23.416454Z","title":"Liu, Y.-R","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:23.416454Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:8a2a691711198858c71460256e3cdb520f3cd81d4f61914eb47445ebed090fbb","observation_id":"30a44106-a6f7-42a1-8e1a-317433bd1571","resolution":{"observed_at":"2026-08-05T20:02:23.416454Z","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":"2026-08-05T20:02:23.495308Z","title":"Hofmann, T","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:23.495308Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:c20992d7897f9c3c454abc1a511600e046362c36526aeafda49265f99b5bafda","observation_id":"c0db39ea-855a-4add-b008-b0fa7049ac51","resolution":{"observed_at":"2026-08-05T20:02:23.495308Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.895274Z","title":"Helbig, T","venue":null,"work_id":"3ff21eb8-715a-4bdc-9658-6a1043a54d32","year":2020},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:23.561904Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:99ee177ab0aeb31b697edd83c7b796c58775033fce5e0c7ed01bf9b83736c5b2","observation_id":"7da28793-653c-4228-afac-c77f61ee18e8","resolution":{"observed_at":"2026-08-05T20:02:27.898575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.884729Z","title":null,"venue":null,"work_id":"c695da93-69be-4b9a-90f4-cf7c2c94c94b","year":2021},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:23.625012Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:0f01b2ec4ed2f77d108972affb41ec8f2a41acc951e50b9235c6636969eb0336","observation_id":"d911ad72-b3a5-41c6-947e-12e5a5a784bb","resolution":{"observed_at":"2026-08-05T20:02:27.888233Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.874177Z","title":null,"venue":null,"work_id":"ec136a36-34ba-4213-b8d2-1702bafcaa20","year":2021},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:23.703379Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:f0db224bbd7545d6ab9f7d004e6e6105e6a2627709465fcc613b81282f802287","observation_id":"9a405f00-bb7e-409e-87e2-cd842c0054a4","resolution":{"observed_at":"2026-08-05T20:02:27.877482Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.864365Z","title":"Zhang, K","venue":null,"work_id":"c4cb2062-0b5e-47a6-af99-6017fece5aad","year":2021},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:23.797643Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:4b151f816184e687465747e47035a45c048eb1e818c2c05f49f63afadd66f59f","observation_id":"05f1abb3-ce79-48ad-8c51-c4a4d41b4c2f","resolution":{"observed_at":"2026-08-05T20:02:27.867503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.853208Z","title":null,"venue":null,"work_id":"e5e94cec-5f21-4344-8e15-b1001f08d114","year":2021},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:23.872142Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:5575011f4eeea57ec53b5ef403576dd54829965678108eb089257aca09b257ac","observation_id":"1a3c0fea-1224-487a-8de2-8a08094cefaf","resolution":{"observed_at":"2026-08-05T20:02:27.857205Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.843198Z","title":null,"venue":null,"work_id":"c0fc4556-f8a1-496d-b1ad-c69f9eae40cf","year":2025},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:23.959406Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:2335221c369620b2d876efc514a20005cecb89f097f20c52ea6461f01740b3bb","observation_id":"146fc290-b944-4b96-94dd-8d79326270cc","resolution":{"observed_at":"2026-08-05T20:02:27.846252Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.833136Z","title":"Sahin, M","venue":null,"work_id":"487c8500-4b3a-42e3-b326-39376c2a68fa","year":2025},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:24.033222Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:0cecbd56f77726918bd95239939ac9924abec5605c9ea7279582d3a890bbd060","observation_id":"c4eac3bb-3903-4915-bf9d-b73ab650b1cb","resolution":{"observed_at":"2026-08-05T20:02:27.836293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:24.134105Z","title":"Arkinstall, M","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:24.134105Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:5d1d8c02b051ed3c17e65254416bfd457312badb185a8510f7672a43ce7cc8eb","observation_id":"a00cf784-6be7-4238-9ff3-4d9554660415","resolution":{"observed_at":"2026-08-05T20:02:24.134105Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.816028Z","title":"Pelegr ´ ı, A","venue":null,"work_id":"e212b4ff-b9c7-4086-94f9-e14e97d873e0","year":2019},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:24.202644Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:cd88d3a106019ecc50a412eac17b57cff420f959ca56c16e07499c3029f9ca01","observation_id":"7a10f59d-cb2c-40a2-85eb-1b297e12f234","resolution":{"observed_at":"2026-08-05T20:02:27.819247Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.805196Z","title":"Kremer, I","venue":null,"work_id":"0ef476e2-2a22-4cb7-a0b6-5eff1e217e2a","year":2020},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:24.271674Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:ae1547931c8715d47c6a1b35efc7816a01c8753acf4c70fe8af33d845614c347","observation_id":"939f8b27-81e6-4109-be30-9a4787a2a9a6","resolution":{"observed_at":"2026-08-05T20:02:27.808523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.794304Z","title":"Ezawa, Systematic construction of square-root topo- logical insulators and superconductors, Phys","venue":null,"work_id":"73f6871d-af34-4c1d-94af-0a4c4f058883","year":2020},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:24.360488Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:b95523d238ed6a06afa09f364c7215ccb0836dcdfad4b09b014771cfd0c59138","observation_id":"7e77c05d-4e6b-49aa-b3cf-dcd3c2f67744","resolution":{"observed_at":"2026-08-05T20:02:27.797636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.783736Z","title":null,"venue":null,"work_id":"fae339d8-bfca-47c1-aead-d5cca348bbbe","year":2021},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:24.428838Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:8ea923c7c602a30f2f4e65b277e5088593efa4db65e05bea1089b21e044dc3c1","observation_id":"8bad1e15-0e4e-47a6-bb4c-b868bcda6d72","resolution":{"observed_at":"2026-08-05T20:02:27.787171Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.772092Z","title":null,"venue":null,"work_id":"9cbe6cba-8ca7-4be6-a844-134596c2e26b","year":2024},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:24.502362Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:f3dc3354b2863f42294b16792e6505c15680491654387c86c5fe14872bbc5379","observation_id":"dca9d250-ff3d-4692-9f1f-e49d8552b205","resolution":{"observed_at":"2026-08-05T20:02:27.776372Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.761062Z","title":"Huang, M","venue":null,"work_id":"9a69efea-9134-49c8-b95f-24ce6a183c75","year":2025},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:24.558865Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:9105a960d9a305f9657d6e72aaa9277518b9a0be76068b2d4a45a81f404fe8a1","observation_id":"bcc793eb-335d-4d3e-a07c-5c7c6df10a4b","resolution":{"observed_at":"2026-08-05T20:02:27.764339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.750698Z","title":null,"venue":null,"work_id":"62e2bb75-261d-4adb-8323-019bac3f88e0","year":2025},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:24.625337Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:56196a689e0cb61158b4f078b7ac64b0959751d929a4c8ceedc6e2d145fff69f","observation_id":"07df2a76-b5c6-4d10-b6d8-d4b9543035e1","resolution":{"observed_at":"2026-08-05T20:02:27.754176Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.740527Z","title":null,"venue":null,"work_id":"c19d0dea-8c71-4e47-8b22-48b2a2113b48","year":2025},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:24.712545Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:33adc2c72a0dac38c4585c50756f56a234c19ef6835b17ece8e07a41e983e830","observation_id":"467aa3d6-5603-4ddb-8e01-3045878d75d0","resolution":{"observed_at":"2026-08-05T20:02:27.743783Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.730422Z","title":null,"venue":null,"work_id":"278d4b21-58a8-402c-8546-36ac564c60f4","year":1989},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:24.783110Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:507816f189e8a374c9064febd172d2c009e3ce77dc7e9c655fdae52eb5971513","observation_id":"14d682a2-b141-4d1d-871b-94e168466483","resolution":{"observed_at":"2026-08-05T20:02:27.733934Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.720769Z","title":null,"venue":null,"work_id":"d110a7c0-5f3a-49f8-bc44-05816d416774","year":1989},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:24.855069Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:0d278ca8bf18f7fb069134e2023cac6a5e037c3fa651b2293372de5dd2921b87","observation_id":"89e33045-d360-4ae7-8747-39a5e44dc089","resolution":{"observed_at":"2026-08-05T20:02:27.724257Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:24.916380Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:24.916380Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:db373898b144097ef55342a8b8286eda19594bc928f8307e903d42ae4f92ca1a","observation_id":"985b775d-06b6-46ab-bb00-fd2ac6c13adc","resolution":{"observed_at":"2026-08-05T20:02:24.916380Z","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":"2026-08-05T20:02:25.001377Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:25.001377Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:b20003d0ecc3174bab0942e16f9f8fda09a6a562a9630cf6706cfbbaf72299ba","observation_id":"1081988b-bd4c-4d82-a008-9dc85998874f","resolution":{"observed_at":"2026-08-05T20:02:25.001377Z","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":"2026-08-05T20:02:25.073481Z","title":"McCann and M","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:25.073481Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:91062c42ef82fce2f92aefe0b9309de903c1ca38efd08b9f74e9394a89b42bcc","observation_id":"14eb3c80-f92b-477a-a724-cdca7d1e02a0","resolution":{"observed_at":"2026-08-05T20:02:25.073481Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.690254Z","title":null,"venue":null,"work_id":"32923259-c326-4409-9dd7-403666349efb","year":1984},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:25.110164Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:4b9cf848a55eadcc22a7b1cc07a6d6a23f828d2bc4654f14c1f8b787d63b88cb","observation_id":"78ca2b7c-ebcc-49ba-806c-35bc6905c7f9","resolution":{"observed_at":"2026-08-05T20:02:27.694380Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.680850Z","title":null,"venue":null,"work_id":"5c79f865-5440-41c7-9865-2944a0992899","year":2018},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:25.195119Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:892c8298d3b217224e4e4acc802aac8af0898a9f8375855ea3a2d0522316b6fb","observation_id":"15435e6c-2bb3-4c83-9bd2-ed306da84d4d","resolution":{"observed_at":"2026-08-05T20:02:27.684004Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.670984Z","title":"Fan, G.-Y","venue":null,"work_id":"991288d6-3be2-46f6-94a6-82f5303694fe","year":2020},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:25.252246Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:fad1ea4e692bfd59b494d6affe8b5064a4e35123d3900d3096a8217a0fc11190","observation_id":"bffb7f60-8640-4278-96c8-bbcab2684873","resolution":{"observed_at":"2026-08-05T20:02:27.674002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.661163Z","title":"Tsubota, H","venue":null,"work_id":"43b537aa-f2a6-4737-8c8a-7eb4b6f3c186","year":2022},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:25.340876Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:d84f5f561823e6c9b4915765fec5b84c16b6bed8152465899a66b9882238bef4","observation_id":"e28fa4a8-5fd4-4fb9-8ab4-aab0bc34786a","resolution":{"observed_at":"2026-08-05T20:02:27.664402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.651310Z","title":"Longhi and L","venue":null,"work_id":"fdbc4310-2c83-4651-bd76-59c9938697af","year":2023},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:25.391694Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:43c8b399605f2d79ecd1bad010c925eed22ec13bf50b754128b4fe9f27be8817","observation_id":"1ea534c2-7b20-4ee5-8780-8a1d7906fe1d","resolution":{"observed_at":"2026-08-05T20:02:27.654686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.641711Z","title":"Lee, S.-C","venue":null,"work_id":"afe2df3e-4c5b-4086-b78c-bb82b3196b9d","year":2008},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:25.476668Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:2f62582a4e394342899508b5f22abc28e35c8dda6605bb6466396e44ef1504ea","observation_id":"cbf3f6e5-d723-4eb8-8c2a-3b6bb69c4665","resolution":{"observed_at":"2026-08-05T20:02:27.644984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.632181Z","title":null,"venue":null,"work_id":"129fd9dd-9497-4c7e-8060-2b31b46b7b52","year":2016},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:25.601003Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:c277ef5fc970f1fbf99f7b750b7e9397d402dc262c67e9955448037c509e6619","observation_id":"da04d4b1-425a-47d2-a0b7-3d8e82ad20f0","resolution":{"observed_at":"2026-08-05T20:02:27.635265Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:25.730750Z","title":"Kawabata, S","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:25.730750Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:692bd0c1bc1573142cdb0f91b2624ac47a58507bbb3b545bf285b18cf98b4dc7","observation_id":"eefb7071-52e5-4589-87e0-99ea30c0ae36","resolution":{"observed_at":"2026-08-05T20:02:25.730750Z","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":"2026-08-05T20:02:25.863205Z","title":"Kawabata, K","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:25.863205Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:6a21a1db08376b93345ea2924b5d29a52db6430c546cfc1c3cd7a4f2c720b6d1","observation_id":"bf8ac77f-4fe3-44b4-bc50-84f871fd8f45","resolution":{"observed_at":"2026-08-05T20:02:25.863205Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.609093Z","title":"Mostafazadeh, Pseudo-Hermiticity versus PT Symme- try: The Necessary Condition for the Reality of the Spec- trum of a Non-Hermitian Hamiltonian, J","venue":null,"work_id":"80bd79d2-d63d-46d1-92ea-98ccb4128dec","year":2002},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:25.989666Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:b30b6870082ef2d703a0df39222e9444c71c642242b0ddcde2e705e21d7bcdb6","observation_id":"ce962d8f-2420-490c-ae47-7a6a1cf875a4","resolution":{"observed_at":"2026-08-05T20:02:27.612314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.599207Z","title":"Leykam, S","venue":null,"work_id":"18636f3a-86b4-4d7b-8832-22f95331b8af","year":2013},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:26.121735Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:03d8ec22f759073bf2420641e2c47e1cdf9bac148e08483adf1b7a960973270f","observation_id":"d34f1b28-c839-4f8e-b5a5-bf4c8964379d","resolution":{"observed_at":"2026-08-05T20:02:27.602374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:02:27.589186Z","title":"Flach, D","venue":null,"work_id":"bddf0ae6-df8c-401b-9b07-66b279d699c3","year":2014},"citing_paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-05T20:02:26.253579Z"},"links":{"citing_paper":"/paper/2508.11597"},"observation_digest":"sha256:54d05c98bb101443636b6caf7ec6ced19feb742fe8765a4ded650c7d5e6abd73","observation_id":"b588c284-c328-4a92-b188-d645aa0d62af","resolution":{"observed_at":"2026-08-05T20:02:27.592506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.11597","last_updated":"2025-08-15T17:01:59Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-15T01:06:00.193468Z","submitted_at":"2025-08-15T17:01:59Z","title":"Nonparametric learning of stochastic differential equations from sparse and noisy data"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":76,"verified_exact":1,"verified_fuzzy":23},"total_outbound_references":121},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 100 of 121 outbound references and 0 inbound Pith citation observations for arXiv:2508.11597."}