{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:F4M4EJRYEMJDBUF5744YNMDTLI","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":"9b6f40dd568dc06be417614afff9d119c8010ceb0d519a05084678ef5ae16751","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-11T12:33:45Z","title_canon_sha256":"04b539b4aa32e6dabd284b97102792d16e14333c1c6488ba25a6601bbcea0adb"},"schema_version":"1.0","source":{"id":"1907.05146","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.05146","created_at":"2026-05-17T23:40:10Z"},{"alias_kind":"arxiv_version","alias_value":"1907.05146v2","created_at":"2026-05-17T23:40:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.05146","created_at":"2026-05-17T23:40:10Z"},{"alias_kind":"pith_short_12","alias_value":"F4M4EJRYEMJD","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"F4M4EJRYEMJDBUF5","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"F4M4EJRY","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:daf82b53ad6efc0b79aad7a0a505e290a328057c2498f8401ee72d296819a396","target":"graph","created_at":"2026-05-17T23:40:10Z","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":"Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce costs. Here a common baseline may be derived by fitting a probability density function to past lifetimes and then utilizing the (conditional) expected remaining useful life as a prognostic. This approach finds widespread use in practice because of its high explanatory power. A m","authors_text":"Mathias Kraus, Stefan Feuerriegel","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-11T12:33:45Z","title":"Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.05146","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":"sha256:4766073382c74f64bf7e2d65314250347abf2c6763c75ccf3bd1524d110a20d5","target":"record","created_at":"2026-05-17T23:40:10Z","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":"9b6f40dd568dc06be417614afff9d119c8010ceb0d519a05084678ef5ae16751","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-11T12:33:45Z","title_canon_sha256":"04b539b4aa32e6dabd284b97102792d16e14333c1c6488ba25a6601bbcea0adb"},"schema_version":"1.0","source":{"id":"1907.05146","kind":"arxiv","version":2}},"canonical_sha256":"2f19c22638231230d0bdff3986b0735a27fe00e6aa2c6b6ae1451d9e447ef2d3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f19c22638231230d0bdff3986b0735a27fe00e6aa2c6b6ae1451d9e447ef2d3","first_computed_at":"2026-05-17T23:40:10.640594Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:40:10.640594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BzLD8rrbgl8f6ZgRcD/D5RUdy8n7xy4PvBagkevMMEc8QQbdU7dU4iwOMZZp33jKyfLZSeOPffgV4UZPAaWpBA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:40:10.641261Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.05146","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4766073382c74f64bf7e2d65314250347abf2c6763c75ccf3bd1524d110a20d5","sha256:daf82b53ad6efc0b79aad7a0a505e290a328057c2498f8401ee72d296819a396"],"state_sha256":"c8673f7a79739a0cd5517a45da9ce803e7c663b23251b0a160b940c27867aae4"}