{"as_of":"2026-08-10T17:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:49cbfb2141a1722dea65dd26073c66471cae2f73e6b7815f8751c3e6c47eff4a","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T18:27:52.177771Z","state":"measured"},{"denominator":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2502.05679/citation-record","integrity":"/paper/2502.05679/integrity","json":"/paper/2502.05679/citation-record.json","paper":"/paper/2502.05679"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:27:52.099166Z","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.099166Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:6eaff4582d8046836bcef6cc20cbaabfc72c88c17ebf0049e0376dac4bd6fb34","observation_id":"43523b4b-8878-445c-9bf9-f7844fd7dee7","resolution":{"observed_at":"2026-08-08T18:27:52.099166Z","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":"2026-08-08T18:27:52.661419Z","title":"The “echo state","venue":null,"work_id":"83a30053-728c-4df8-be7d-e86aaeede68e","year":2001},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.104299Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:df906197855bad10e601ce287d57674815b0039cbcba8aa2e2821bb58558a9b0","observation_id":"f44c4cd1-dc89-4305-b7c4-17adc2a1554c","resolution":{"observed_at":"2026-08-08T18:27:52.664925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T18:27:52.650932Z","title":"Federated reservoir computing neural networks,","venue":null,"work_id":"466766bc-30b7-43a8-81d5-0716d2a39810","year":2021},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.107415Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:17e4eb54948ced0f5fdb40a1c58a3ee4854449c699e26098dd87ef114170aba9","observation_id":"35640cff-bea5-4344-bbd5-ffc0a4d6df0d","resolution":{"observed_at":"2026-08-08T18:27:52.654675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T18:27:52.640222Z","title":"Decentralized incremental federated learning with echo state networks,","venue":null,"work_id":"1d1ff595-3c10-4e5a-96d1-7a4fa3f5f356","year":2024},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.110475Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:d9e25e7fa313c059475893493b13d185648edb6f394a1c54b6ec33f502ac9a6e","observation_id":"afa515c9-b400-4bcb-8a8d-1334c083f5a9","resolution":{"observed_at":"2026-08-08T18:27:52.644155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T18:27:52.629699Z","title":"Decentralized federated learning for industrial iot with deep echo state networks,","venue":null,"work_id":"ba7ac1a1-037a-4611-bd37-e061a2b6b0b5","year":2023},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.113640Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:af8107ccd53d0d725b8baa70fd1d5b6092335285b810210ca1b56629605e3271","observation_id":"33492302-1959-44f6-810c-58ae5f5ed044","resolution":{"observed_at":"2026-08-08T18:27:52.633375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T18:27:52.116573Z","title":"Mahalanobis distance of reservoir states for online time-series anomaly detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.116573Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:28cf4bd11d479b7d73dec04ab8094e061ea2faba1c5bd69d73f809a0b3046470","observation_id":"6439fd68-1b76-4f1e-8019-c02700f09673","resolution":{"observed_at":"2026-08-08T18:27:52.116573Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-08T18:27:52.120052Z","title":"Anomaly detection in time series: a comprehensive evaluation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.120052Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:bb1853c626f1124d608044169812ddd7093e77d19375f84a16dc9f5bf7d3a01f","observation_id":"56aa250e-a804-4b55-b143-0e4bcfb03b55","resolution":{"observed_at":"2026-08-08T18:27:52.120052Z","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":"2026-08-08T18:27:52.613077Z","title":"Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines,","venue":null,"work_id":"28e4c744-4af6-48f9-848a-bce028591315","year":2021},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.122657Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:f71218a0d04061ca8759591a258195c45887904ade30847f4a4759521dedd565","observation_id":"2ced8772-6567-49a2-a502-c09d3e332289","resolution":{"observed_at":"2026-08-08T18:27:52.616755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T18:27:52.602722Z","title":"Reservoir computing approaches to recurrent neural network training,","venue":null,"work_id":"5a579271-9950-42d4-b1dd-a74dfe7d216f","year":2009},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.125314Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:9f2eacaacbdfe3e38591e4b15de1edef56c91975772f432715adeb0ae1eee02f","observation_id":"022b35a9-563f-465b-a68d-335d006e0f44","resolution":{"observed_at":"2026-08-08T18:27:52.606351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T18:27:52.128775Z","title":"Optimization and applications of echo state networks with leaky-integrator neurons,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.128775Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:75330abe24c941d0bc00c33f85cc912805b3730d80abf7d7909e949f018eefd4","observation_id":"998f386b-09da-4016-8242-f8a91df50602","resolution":{"observed_at":"2026-08-08T18:27:52.128775Z","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":"2026-08-08T18:27:52.585961Z","title":"A survey on federated learning: The journey from centralized to distributed on-site learning and beyond,","venue":null,"work_id":"5be344df-8229-42fb-8657-b00731037dd6","year":2020},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.132155Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:39739bc7921ede5ac770f94c32270577772645e70648b14416272102d2a30e72","observation_id":"b72e7332-72ff-4cad-970d-db6da1df4ac9","resolution":{"observed_at":"2026-08-08T18:27:52.589678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T18:27:52.575850Z","title":"Federated learning for beginners: Types, simulation environments, and open challenges,","venue":null,"work_id":"ed06c6ad-946e-44c8-9054-c450d89b051c","year":2023},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.135394Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:d8b35022c2ad07d1aad673281ad902ea66bf65ebabd130231d939e4007dca751","observation_id":"4873668a-5979-4ab1-84cd-5ba87a974152","resolution":{"observed_at":"2026-08-08T18:27:52.579512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.06127","last_updated":"2020-04-21T18:58:23Z","snapshot_observed_at":"2026-08-10T11:19:21.991368Z","submitted_at":"2018-12-14T19:28:29Z","title":"Federated Optimization in Heterogeneous Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.06127","snapshot_observed_at":"2026-08-08T18:27:52.139099Z","title":"On the convergence of federated optimization in heterogeneous networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.139099Z"},"links":{"cited_paper":"/paper/1812.06127","citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:8e340676ed231e4cbe4784a5f00f383d9e91a559e07f6aa17c1149dd542fbf45","observation_id":"ff920be7-8c1d-41b4-92f6-0080704bdc08","resolution":{"observed_at":"2026-08-08T18:27:52.139099Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.06378","last_updated":"2021-04-09T16:21:59Z","snapshot_observed_at":"2026-07-06T08:29:27.968839Z","submitted_at":"2019-10-14T18:49:20Z","title":"SCAFFOLD: Stochastic Controlled Averaging for Federated Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.06378","snapshot_observed_at":"2026-08-08T18:27:52.142973Z","title":"SCAFFOLD: stochastic controlled averaging for on-device federated learning,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.142973Z"},"links":{"cited_paper":"/paper/1910.06378","citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:6ca5c2307a7cf464ff31dee49c5a1ae6e25b5702b8a7713c955ce79d16ca6aa1","observation_id":"7b178609-8325-43a6-8228-7297a8bf3330","resolution":{"observed_at":"2026-08-08T18:27:52.142973Z","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":"2026-08-08T18:27:52.565607Z","title":"Model-contrastive federated learning,","venue":null,"work_id":"1a33d1dc-2cee-4ac5-a4b9-e97afa1c293d","year":2021},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.146585Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:7b0dc20d93ef14e974051b90b6fb4ff8a1a550a290f5ca9094a7c38d7a45780d","observation_id":"4c131ed4-3aa7-4daa-8ece-d96fd802058e","resolution":{"observed_at":"2026-08-08T18:27:52.569025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T18:27:52.556032Z","title":"Fedtadbench: Federated time-series anomaly detection bench- mark,","venue":null,"work_id":"160faf44-ca59-462e-be7d-efe7e03c535b","year":2022},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.149951Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:1c89b3a818598009674569cad43a33d62d31f991f52353d69942e429cc845067","observation_id":"e2d1bbe9-c4e6-4ed1-b953-986b548b8d60","resolution":{"observed_at":"2026-08-08T18:27:52.559064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T18:27:52.153214Z","title":"Robust anomaly detection for multivariate time series through stochastic recurrent neural network,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.153214Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:6cef51b0ae11afdd164c564aa51426ad5f6d53cd0a196e9b694de14670b8116b","observation_id":"7b5ff0e6-f77f-47c6-9d4b-496a4971617e","resolution":{"observed_at":"2026-08-08T18:27:52.153214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.04431","last_updated":"2018-06-06T20:39:29Z","snapshot_observed_at":"2026-07-06T06:23:05.095698Z","submitted_at":"2018-02-13T02:09:32Z","title":"Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.04431","snapshot_observed_at":"2026-08-08T18:27:52.157425Z","title":"Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.157425Z"},"links":{"cited_paper":"/paper/1802.04431","citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:039fc46ed928fdd3433731b769664947a4bd19c0f2ccb9120996074badb8ce4e","observation_id":"e3ea75a1-287e-48c3-80b1-54ff69d9cf30","resolution":{"observed_at":"2026-08-08T18:27:52.157425Z","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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:27:52.474972Z","title":"Practical approach to asynchronous multivariate time series anomaly detection and localization,","venue":null,"work_id":"e1e9e292-12fd-4c8a-b7a1-8c633312f5fa","year":2021},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.161118Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:5a9725631697f37c5726e6700a81f927b3a4077cc1721ba30d7c2c4c373c62ba","observation_id":"74672cbc-da46-4e88-9730-de5ac8e7ee12","resolution":{"observed_at":"2026-08-08T18:27:52.478847Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T18:27:52.546910Z","title":"V olume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection,","venue":null,"work_id":"681957c5-5558-4ef0-ac70-0c4fd3dce4a6","year":2022},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.164459Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:47a217678a19e2281ead6f4d1deb8904f3274e9bf5eee4bac912357c58048a8f","observation_id":"ec60a070-88a5-4868-b5d5-190000c1b51a","resolution":{"observed_at":"2026-08-08T18:27:52.549928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:27:52.434670Z","title":"Pate: Proximity- aware time series anomaly evaluation,","venue":null,"work_id":"7943bb70-8990-4a32-b8fa-5a72779f9539","year":2024},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.167919Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:c648f300a15620aa67cc570ddcc5c83c7beb0929680e047852f5a85a33eb7652","observation_id":"8a679f88-f644-4182-a56c-eba16021ca02","resolution":{"observed_at":"2026-08-08T18:27:52.441712Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T18:27:52.537179Z","title":"TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data,","venue":null,"work_id":"48a3c7ee-7e3b-49f0-98c1-8680ea8f9433","year":2022},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.171151Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:655b80fe1d9fc33c6f3fa964a66678a25243b7641b218de9a671570029be823b","observation_id":"44134308-da74-420e-9550-051295681bd4","resolution":{"observed_at":"2026-08-08T18:27:52.540317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T18:27:52.526388Z","title":"Lstm-based encoder-decoder for multi-sensor anomaly de- tection,","venue":null,"work_id":"3fcb55b5-bff1-4adb-879d-021bf636eab6","year":2016},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.174496Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:63dce4c7278b3a173e0051cf497aa1cc8ef74406ebd66e6f852c17cb0fb2e98d","observation_id":"b27bc991-ff87-4310-a873-8dea61d65e16","resolution":{"observed_at":"2026-08-08T18:27:52.530021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T18:27:52.515650Z","title":"Reconstructive reservoir computing for anomaly detection in time-series signals,","venue":null,"work_id":"dc2a77ad-812e-4ee1-b6f6-f2a79eda9baa","year":2024},"citing_paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T18:27:52.177771Z"},"links":{"citing_paper":"/paper/2502.05679"},"observation_digest":"sha256:40ee9110887df47fbc81434b77a3cb0528e56d97df85c28aeed6bb4a09832089","observation_id":"b3b2cefd-84fe-462e-9392-7092942881d6","resolution":{"observed_at":"2026-08-08T18:27:52.519474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.05679","last_updated":"2025-02-14T08:34:44Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T11:19:56.329962Z","submitted_at":"2025-02-08T20:00:23Z","title":"Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":2,"verified_fuzzy":14},"total_outbound_references":24},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2502.05679."}