{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ALGSGKVYCUVEIPNBW3LGRRXD53","short_pith_number":"pith:ALGSGKVY","schema_version":"1.0","canonical_sha256":"02cd232ab8152a443da1b6d668c6e3eeee2000490b72e68c666bfd3b941afb5a","source":{"kind":"arxiv","id":"2405.09973","version":4},"attestation_state":"computed","paper":{"title":"Ensemble Control for Stochastic Systems with Asymmetric Laplace Noises","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Shiliang Zhang, Tingwen Huang, Xubing Shi, Xuehui Ma, Yajie Yu, Yushuai Li, Zhuzhu Wang","submitted_at":"2024-05-16T10:37:40Z","abstract_excerpt":"This paper presents an adaptive ensemble control for stochastic systems subject to asymmetric noises and outliers. Asymmetric noises skew system observations, and outliers with large amplitude deteriorate the observations even further. Such disturbances induce poor system estimation and degraded stochastic system control. In this work, we model the asymmetric noises and outliers by mixed asymmetric Laplace distributions (ALDs), and propose an optimal control for stochastic systems with mixed ALD noises. Particularly, we segregate the system disturbed by mixed ALD noises into subsystems, each o"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2405.09973","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.OC","submitted_at":"2024-05-16T10:37:40Z","cross_cats_sorted":[],"title_canon_sha256":"0acf5dfbae8c24505daddccbea40151efee5707a5d089c57af5b78abf3949630","abstract_canon_sha256":"203a92bcdcbd7690640def1f6ead8c505962fbba4448064132f8bb3d3773e635"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:20.682331Z","signature_b64":"WSNCXVMpVEraIkCVBfvHyt1BTdM6WC0Pl+umpQQxe2hYCCa9TJE5D6n1RfxAYtIulxMEzg0AcQ7gCNv/e5M+CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02cd232ab8152a443da1b6d668c6e3eeee2000490b72e68c666bfd3b941afb5a","last_reissued_at":"2026-07-05T09:58:20.681881Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:20.681881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ensemble Control for Stochastic Systems with Asymmetric Laplace Noises","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Shiliang Zhang, Tingwen Huang, Xubing Shi, Xuehui Ma, Yajie Yu, Yushuai Li, Zhuzhu Wang","submitted_at":"2024-05-16T10:37:40Z","abstract_excerpt":"This paper presents an adaptive ensemble control for stochastic systems subject to asymmetric noises and outliers. Asymmetric noises skew system observations, and outliers with large amplitude deteriorate the observations even further. Such disturbances induce poor system estimation and degraded stochastic system control. In this work, we model the asymmetric noises and outliers by mixed asymmetric Laplace distributions (ALDs), and propose an optimal control for stochastic systems with mixed ALD noises. Particularly, we segregate the system disturbed by mixed ALD noises into subsystems, each o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.09973","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2405.09973/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2405.09973","created_at":"2026-07-05T09:58:20.681931+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.09973v4","created_at":"2026-07-05T09:58:20.681931+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.09973","created_at":"2026-07-05T09:58:20.681931+00:00"},{"alias_kind":"pith_short_12","alias_value":"ALGSGKVYCUVE","created_at":"2026-07-05T09:58:20.681931+00:00"},{"alias_kind":"pith_short_16","alias_value":"ALGSGKVYCUVEIPNB","created_at":"2026-07-05T09:58:20.681931+00:00"},{"alias_kind":"pith_short_8","alias_value":"ALGSGKVY","created_at":"2026-07-05T09:58:20.681931+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.24572","citing_title":"Fine-tuning for Data-enabled Predictive Control of Noisy Systems by Reinforcement Learning","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ALGSGKVYCUVEIPNBW3LGRRXD53","json":"https://pith.science/pith/ALGSGKVYCUVEIPNBW3LGRRXD53.json","graph_json":"https://pith.science/api/pith-number/ALGSGKVYCUVEIPNBW3LGRRXD53/graph.json","events_json":"https://pith.science/api/pith-number/ALGSGKVYCUVEIPNBW3LGRRXD53/events.json","paper":"https://pith.science/paper/ALGSGKVY"},"agent_actions":{"view_html":"https://pith.science/pith/ALGSGKVYCUVEIPNBW3LGRRXD53","download_json":"https://pith.science/pith/ALGSGKVYCUVEIPNBW3LGRRXD53.json","view_paper":"https://pith.science/paper/ALGSGKVY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.09973&json=true","fetch_graph":"https://pith.science/api/pith-number/ALGSGKVYCUVEIPNBW3LGRRXD53/graph.json","fetch_events":"https://pith.science/api/pith-number/ALGSGKVYCUVEIPNBW3LGRRXD53/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ALGSGKVYCUVEIPNBW3LGRRXD53/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ALGSGKVYCUVEIPNBW3LGRRXD53/action/storage_attestation","attest_author":"https://pith.science/pith/ALGSGKVYCUVEIPNBW3LGRRXD53/action/author_attestation","sign_citation":"https://pith.science/pith/ALGSGKVYCUVEIPNBW3LGRRXD53/action/citation_signature","submit_replication":"https://pith.science/pith/ALGSGKVYCUVEIPNBW3LGRRXD53/action/replication_record"}},"created_at":"2026-07-05T09:58:20.681931+00:00","updated_at":"2026-07-05T09:58:20.681931+00:00"}