{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:YYOC54JOIMRERAXEDJIUHUECG3","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":"abf1104db0450e0c061243e840e14e080352c0a9d3c868957c3fb3e9767fa0f2","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.acc-ph","submitted_at":"2021-02-21T05:16:18Z","title_canon_sha256":"0e8e99d67e30161385386fbf36500ac5ee54f90e8526fefb49f061d26e4aedc8"},"schema_version":"1.0","source":{"id":"2102.10510","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.10510","created_at":"2026-07-05T02:25:51Z"},{"alias_kind":"arxiv_version","alias_value":"2102.10510v2","created_at":"2026-07-05T02:25:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.10510","created_at":"2026-07-05T02:25:51Z"},{"alias_kind":"pith_short_12","alias_value":"YYOC54JOIMRE","created_at":"2026-07-05T02:25:51Z"},{"alias_kind":"pith_short_16","alias_value":"YYOC54JOIMRERAXE","created_at":"2026-07-05T02:25:51Z"},{"alias_kind":"pith_short_8","alias_value":"YYOC54JO","created_at":"2026-07-05T02:25:51Z"}],"graph_snapshots":[{"event_id":"sha256:fd15a5afe2df8af7d84e522e4b06cd302614642c8e98d0f1db96ca2b644fd351","target":"graph","created_at":"2026-07-05T02:25:51Z","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/2102.10510/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning (ML) tools such as encoder-decoder deep convolutional neural networks (CNN) are able to extract relationships between inputs and outputs of large complex systems directly from raw data. For time-varying systems the predictive capabilities of ML tools degrade as the systems are no longer accurately represented by the data sets with which the ML models were trained. Re-training is possible, but only if the changes are slow and if new input-output training data measurements can be made online non-invasively. In this work we present an approach to deep learning for time-varying sy","authors_text":"Alexander Scheinker, Daniele Filippetto, Frederick Cropp, Sergio Paiagua","cross_cats":["math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.acc-ph","submitted_at":"2021-02-21T05:16:18Z","title":"Adaptive deep learning for time-varying systems with hidden parameters: Predicting changing input beam distributions of compact particle accelerators"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.10510","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:d52f9d55af29ae02a6ea55016bb82b1632ca86b2a47fc69e332f9692b97aa0fa","target":"record","created_at":"2026-07-05T02:25:51Z","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":"abf1104db0450e0c061243e840e14e080352c0a9d3c868957c3fb3e9767fa0f2","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.acc-ph","submitted_at":"2021-02-21T05:16:18Z","title_canon_sha256":"0e8e99d67e30161385386fbf36500ac5ee54f90e8526fefb49f061d26e4aedc8"},"schema_version":"1.0","source":{"id":"2102.10510","kind":"arxiv","version":2}},"canonical_sha256":"c61c2ef12e43224882e41a5143d08236cd3991d66c4fa58d5a51c179ba960b6c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c61c2ef12e43224882e41a5143d08236cd3991d66c4fa58d5a51c179ba960b6c","first_computed_at":"2026-07-05T02:25:51.161163Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:25:51.161163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lxXidftOY1CqxjL6PkjCEGCmwi9Cl/RnvZDUytkCL3PChkZZ+h+4QdPMhvpULOh5YW+qA/EdhXZJU2ampbIGBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:25:51.161614Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.10510","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d52f9d55af29ae02a6ea55016bb82b1632ca86b2a47fc69e332f9692b97aa0fa","sha256:fd15a5afe2df8af7d84e522e4b06cd302614642c8e98d0f1db96ca2b644fd351"],"state_sha256":"7defca6ecd835526aed42391775db178082239de4ff0f61987b00c4d428a88fd"}