{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:FLNHDKBONLXJ5FOGMABLYEFMPI","short_pith_number":"pith:FLNHDKBO","canonical_record":{"source":{"id":"1801.01717","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-01-05T11:22:44Z","cross_cats_sorted":[],"title_canon_sha256":"e8022bbeda56c253f980492ace9865cabc96ddf3cc26c970d662c958156b0a9d","abstract_canon_sha256":"5cdb813266a16f0c220f44035c8eeb1742e1affd903a0e625d2e5a58957a6f74"},"schema_version":"1.0"},"canonical_sha256":"2ada71a82e6aee9e95c66002bc10ac7a1f6e9322d4c3661457c61c2c12b0f4ec","source":{"kind":"arxiv","id":"1801.01717","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.01717","created_at":"2026-05-18T00:01:53Z"},{"alias_kind":"arxiv_version","alias_value":"1801.01717v2","created_at":"2026-05-18T00:01:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.01717","created_at":"2026-05-18T00:01:53Z"},{"alias_kind":"pith_short_12","alias_value":"FLNHDKBONLXJ","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"FLNHDKBONLXJ5FOG","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"FLNHDKBO","created_at":"2026-05-18T12:32:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:FLNHDKBONLXJ5FOGMABLYEFMPI","target":"record","payload":{"canonical_record":{"source":{"id":"1801.01717","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-01-05T11:22:44Z","cross_cats_sorted":[],"title_canon_sha256":"e8022bbeda56c253f980492ace9865cabc96ddf3cc26c970d662c958156b0a9d","abstract_canon_sha256":"5cdb813266a16f0c220f44035c8eeb1742e1affd903a0e625d2e5a58957a6f74"},"schema_version":"1.0"},"canonical_sha256":"2ada71a82e6aee9e95c66002bc10ac7a1f6e9322d4c3661457c61c2c12b0f4ec","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:53.497756Z","signature_b64":"IfJ31lCzYtdrbIXhE7ZN1Kcx9+wvDZHIOSGISRWXnrOhMFPGE2dUu8EjiIihAi9xEli/CvSallTedo51MFrUBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ada71a82e6aee9e95c66002bc10ac7a1f6e9322d4c3661457c61c2c12b0f4ec","last_reissued_at":"2026-05-18T00:01:53.497351Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:53.497351Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1801.01717","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:01:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"64wC7k96pItNgco0iWi1CoNh1rcA99LzbKma+zdAg/3EJRlzW5ratX/fwKRfSolaULUGh619gKse0+eNDdPmCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T22:15:43.688775Z"},"content_sha256":"9ce65afe6f1ff6ced5844863c148f0c2689ee33df00d35f6194137f7d05940f3","schema_version":"1.0","event_id":"sha256:9ce65afe6f1ff6ced5844863c148f0c2689ee33df00d35f6194137f7d05940f3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:FLNHDKBONLXJ5FOGMABLYEFMPI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Diffusion Leaky Zero Attracting Least Mean Square Algorithm and Its Performance Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Haiquan Zhao, Long Shi","submitted_at":"2018-01-05T11:22:44Z","abstract_excerpt":"Recently, the leaky diffusion least-mean-square (DLMS) algorithm has obtained much attention because of its good performance for high input eigenvalue spread and low signal-to-noise ratio (SNR). However, the leaky DLMS algorithm may suffer from performance deterioration in the sparse system. To overcome this drawback, the leaky zero attracting DLMS (LZA-DLMS) algorithm is developed in this paper, which adds an l1-norm penalty to the cost function to exploit the property of sparse system. The leaky reweighted zero attracting DLMS (LRZA-DLMS) algorithm is also put forward, which can improve the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.01717","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:01:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fbffg/DX0dfB7565sujKUQUZ9FL6+RJqq4Nbc36VsccQeSZa7EM3K7sa6VTnEQzwr5sZuBDmwgV7wpyLg7pEDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T22:15:43.689619Z"},"content_sha256":"715b5a1bd9205370cdaf308942a69b255cce1a51d0f6edad76e9579fc538a5b8","schema_version":"1.0","event_id":"sha256:715b5a1bd9205370cdaf308942a69b255cce1a51d0f6edad76e9579fc538a5b8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FLNHDKBONLXJ5FOGMABLYEFMPI/bundle.json","state_url":"https://pith.science/pith/FLNHDKBONLXJ5FOGMABLYEFMPI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FLNHDKBONLXJ5FOGMABLYEFMPI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-31T22:15:43Z","links":{"resolver":"https://pith.science/pith/FLNHDKBONLXJ5FOGMABLYEFMPI","bundle":"https://pith.science/pith/FLNHDKBONLXJ5FOGMABLYEFMPI/bundle.json","state":"https://pith.science/pith/FLNHDKBONLXJ5FOGMABLYEFMPI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FLNHDKBONLXJ5FOGMABLYEFMPI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:FLNHDKBONLXJ5FOGMABLYEFMPI","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":"5cdb813266a16f0c220f44035c8eeb1742e1affd903a0e625d2e5a58957a6f74","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-01-05T11:22:44Z","title_canon_sha256":"e8022bbeda56c253f980492ace9865cabc96ddf3cc26c970d662c958156b0a9d"},"schema_version":"1.0","source":{"id":"1801.01717","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.01717","created_at":"2026-05-18T00:01:53Z"},{"alias_kind":"arxiv_version","alias_value":"1801.01717v2","created_at":"2026-05-18T00:01:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.01717","created_at":"2026-05-18T00:01:53Z"},{"alias_kind":"pith_short_12","alias_value":"FLNHDKBONLXJ","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"FLNHDKBONLXJ5FOG","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"FLNHDKBO","created_at":"2026-05-18T12:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:715b5a1bd9205370cdaf308942a69b255cce1a51d0f6edad76e9579fc538a5b8","target":"graph","created_at":"2026-05-18T00:01:53Z","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"},"paper":{"abstract_excerpt":"Recently, the leaky diffusion least-mean-square (DLMS) algorithm has obtained much attention because of its good performance for high input eigenvalue spread and low signal-to-noise ratio (SNR). However, the leaky DLMS algorithm may suffer from performance deterioration in the sparse system. To overcome this drawback, the leaky zero attracting DLMS (LZA-DLMS) algorithm is developed in this paper, which adds an l1-norm penalty to the cost function to exploit the property of sparse system. The leaky reweighted zero attracting DLMS (LRZA-DLMS) algorithm is also put forward, which can improve the ","authors_text":"Haiquan Zhao, Long Shi","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-01-05T11:22:44Z","title":"Diffusion Leaky Zero Attracting Least Mean Square Algorithm and Its Performance Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.01717","kind":"arxiv","version":2},"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":"sha256:9ce65afe6f1ff6ced5844863c148f0c2689ee33df00d35f6194137f7d05940f3","target":"record","created_at":"2026-05-18T00:01:53Z","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":"5cdb813266a16f0c220f44035c8eeb1742e1affd903a0e625d2e5a58957a6f74","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-01-05T11:22:44Z","title_canon_sha256":"e8022bbeda56c253f980492ace9865cabc96ddf3cc26c970d662c958156b0a9d"},"schema_version":"1.0","source":{"id":"1801.01717","kind":"arxiv","version":2}},"canonical_sha256":"2ada71a82e6aee9e95c66002bc10ac7a1f6e9322d4c3661457c61c2c12b0f4ec","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2ada71a82e6aee9e95c66002bc10ac7a1f6e9322d4c3661457c61c2c12b0f4ec","first_computed_at":"2026-05-18T00:01:53.497351Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:01:53.497351Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IfJ31lCzYtdrbIXhE7ZN1Kcx9+wvDZHIOSGISRWXnrOhMFPGE2dUu8EjiIihAi9xEli/CvSallTedo51MFrUBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:01:53.497756Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.01717","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9ce65afe6f1ff6ced5844863c148f0c2689ee33df00d35f6194137f7d05940f3","sha256:715b5a1bd9205370cdaf308942a69b255cce1a51d0f6edad76e9579fc538a5b8"],"state_sha256":"080e928291208753820e7f5155a5e4d0eca2e9c27dec2fce2fd719f5c9973f09"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3ETQ8YZoai5YWKCP+SZSM6fi6M3xh6LjC6Vmxlmwf9wpldPcgBP+m8SjnvnAcPK1RFZvJv/rzNNXusDWdWO6Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T22:15:43.694395Z","bundle_sha256":"e083a6503bcee2fb31b6b642539feac5b32aa37f1d051e33a4a69163ec7b13df"}}