{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:4CZ5LNIUMBZ6OP6IDTFKQHQMHX","short_pith_number":"pith:4CZ5LNIU","schema_version":"1.0","canonical_sha256":"e0b3d5b5146073e73fc81ccaa81e0c3dfd1f8f93969e2eafc6b56a09ce21383b","source":{"kind":"arxiv","id":"1709.09883","version":2},"attestation_state":"computed","paper":{"title":"The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.acc-ph","physics.ins-det"],"primary_cat":"cs.LG","authors_text":"Andrzej Skocze\\'n, Ernesto De Matteis, Maciej Wielgosz, Matej Mertik","submitted_at":"2017-09-28T10:19:40Z","abstract_excerpt":"This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection. Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the 231 MB dataset composed of the signals acquired from HiLumi magne"},"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":"1709.09883","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-28T10:19:40Z","cross_cats_sorted":["physics.acc-ph","physics.ins-det"],"title_canon_sha256":"8772ac650975bf2cccd4fe431c3971bfea9de984f6643a23fc72011c60e8273a","abstract_canon_sha256":"6766f78be538a87ed1ef32d3c6514fca1f4b05473b873e020dda870257ee731e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:09.889386Z","signature_b64":"hDu40vOCJU0GOLdA6ofYq6nlS//f81cEqvhhQuohONqvA7slnVTdboAk4109QimsL7yL2Azpd8mV0OEf3j9dBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0b3d5b5146073e73fc81ccaa81e0c3dfd1f8f93969e2eafc6b56a09ce21383b","last_reissued_at":"2026-05-18T00:09:09.888916Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:09.888916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.acc-ph","physics.ins-det"],"primary_cat":"cs.LG","authors_text":"Andrzej Skocze\\'n, Ernesto De Matteis, Maciej Wielgosz, Matej Mertik","submitted_at":"2017-09-28T10:19:40Z","abstract_excerpt":"This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection. Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the 231 MB dataset composed of the signals acquired from HiLumi magne"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.09883","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1709.09883","created_at":"2026-05-18T00:09:09.888992+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.09883v2","created_at":"2026-05-18T00:09:09.888992+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.09883","created_at":"2026-05-18T00:09:09.888992+00:00"},{"alias_kind":"pith_short_12","alias_value":"4CZ5LNIUMBZ6","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"4CZ5LNIUMBZ6OP6I","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"4CZ5LNIU","created_at":"2026-05-18T12:30:58.224056+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4CZ5LNIUMBZ6OP6IDTFKQHQMHX","json":"https://pith.science/pith/4CZ5LNIUMBZ6OP6IDTFKQHQMHX.json","graph_json":"https://pith.science/api/pith-number/4CZ5LNIUMBZ6OP6IDTFKQHQMHX/graph.json","events_json":"https://pith.science/api/pith-number/4CZ5LNIUMBZ6OP6IDTFKQHQMHX/events.json","paper":"https://pith.science/paper/4CZ5LNIU"},"agent_actions":{"view_html":"https://pith.science/pith/4CZ5LNIUMBZ6OP6IDTFKQHQMHX","download_json":"https://pith.science/pith/4CZ5LNIUMBZ6OP6IDTFKQHQMHX.json","view_paper":"https://pith.science/paper/4CZ5LNIU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.09883&json=true","fetch_graph":"https://pith.science/api/pith-number/4CZ5LNIUMBZ6OP6IDTFKQHQMHX/graph.json","fetch_events":"https://pith.science/api/pith-number/4CZ5LNIUMBZ6OP6IDTFKQHQMHX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4CZ5LNIUMBZ6OP6IDTFKQHQMHX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4CZ5LNIUMBZ6OP6IDTFKQHQMHX/action/storage_attestation","attest_author":"https://pith.science/pith/4CZ5LNIUMBZ6OP6IDTFKQHQMHX/action/author_attestation","sign_citation":"https://pith.science/pith/4CZ5LNIUMBZ6OP6IDTFKQHQMHX/action/citation_signature","submit_replication":"https://pith.science/pith/4CZ5LNIUMBZ6OP6IDTFKQHQMHX/action/replication_record"}},"created_at":"2026-05-18T00:09:09.888992+00:00","updated_at":"2026-05-18T00:09:09.888992+00:00"}