{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:WVTRVFSTPJBFXVLEJ6FUKPO42W","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2feb47901586c9b4d61f982768053bb3cb0d49364d0fd38bd7b53d645667f71a","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2022-04-09T01:13:26Z","title_canon_sha256":"6b75d3b6e18aa411e9748fd2848512b1d0d01f3a2c461dc886b9e39cb29ca4da"},"schema_version":"1.0","source":{"id":"2204.08990","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.08990","created_at":"2026-07-05T04:22:07Z"},{"alias_kind":"arxiv_version","alias_value":"2204.08990v1","created_at":"2026-07-05T04:22:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.08990","created_at":"2026-07-05T04:22:07Z"},{"alias_kind":"pith_short_12","alias_value":"WVTRVFSTPJBF","created_at":"2026-07-05T04:22:07Z"},{"alias_kind":"pith_short_16","alias_value":"WVTRVFSTPJBFXVLE","created_at":"2026-07-05T04:22:07Z"},{"alias_kind":"pith_short_8","alias_value":"WVTRVFST","created_at":"2026-07-05T04:22:07Z"}],"graph_snapshots":[{"event_id":"sha256:8b8fb143f457acd4fdcba01ef15722e8e6e67d01526f690d63bd239c9459070a","target":"graph","created_at":"2026-07-05T04:22:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2204.08990/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper proposes a unified sparsity-aware robust recursive least-squares RLS (S-RRLS) algorithm for the identification of sparse systems under impulsive noise. The proposed algorithm generalizes multiple algorithms only by replacing the specified criterion of robustness and sparsity-aware penalty. Furthermore, by jointly optimizing the forgetting factor and the sparsity penalty parameter, we develop the jointly-optimized S-RRLS (JO-S-RRLS) algorithm, which not only exhibits low misadjustment but also can track well sudden changes of a sparse system. Simulations in impulsive noise scenarios ","authors_text":"B. Chen, L. Lu, R. C. de Lamare, Y. Yu, Y. Zakharov","cross_cats":["cs.IT","cs.LG","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2022-04-09T01:13:26Z","title":"Study of Robust Sparsity-Aware RLS algorithms with Jointly-Optimized Parameters for Impulsive Noise Environments"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.08990","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:61c136387f2a4ce454124e9918a0ed4f0d04ba74446917a77864ec0c3bd8a9a0","target":"record","created_at":"2026-07-05T04:22:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2feb47901586c9b4d61f982768053bb3cb0d49364d0fd38bd7b53d645667f71a","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2022-04-09T01:13:26Z","title_canon_sha256":"6b75d3b6e18aa411e9748fd2848512b1d0d01f3a2c461dc886b9e39cb29ca4da"},"schema_version":"1.0","source":{"id":"2204.08990","kind":"arxiv","version":1}},"canonical_sha256":"b5671a96537a425bd5644f8b453ddcd58f2ee0333a0fd35c54b284d184597c80","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b5671a96537a425bd5644f8b453ddcd58f2ee0333a0fd35c54b284d184597c80","first_computed_at":"2026-07-05T04:22:07.781765Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:22:07.781765Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"38ofHjCDmE1XEjXaN0AjIHCltGovAgp49qbkxrfzS4xZtxf7axUw+l2mdDIgCW73fDP9x4YTuNNYQH8jVm+cDw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:22:07.782197Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.08990","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:61c136387f2a4ce454124e9918a0ed4f0d04ba74446917a77864ec0c3bd8a9a0","sha256:8b8fb143f457acd4fdcba01ef15722e8e6e67d01526f690d63bd239c9459070a"],"state_sha256":"43edb6494a8160f16f1adfee7e7b8a908b9d1e9cbf25d6d501acdbb2a1c3843d"}