{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:GMHTVSB6WNMJRZ53K2NGHW5WW4","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":"da14fa4b72324e9ffb2dc3c8984daacc7c336d7f08ff32db0074568b8018a125","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-29T21:34:02Z","title_canon_sha256":"329f920cc0d53cb99e0794d862948a82f8c552c3336b14c3751afb625b875af8"},"schema_version":"1.0","source":{"id":"2010.15955","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.15955","created_at":"2026-07-05T05:47:00Z"},{"alias_kind":"arxiv_version","alias_value":"2010.15955v2","created_at":"2026-07-05T05:47:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.15955","created_at":"2026-07-05T05:47:00Z"},{"alias_kind":"pith_short_12","alias_value":"GMHTVSB6WNMJ","created_at":"2026-07-05T05:47:00Z"},{"alias_kind":"pith_short_16","alias_value":"GMHTVSB6WNMJRZ53","created_at":"2026-07-05T05:47:00Z"},{"alias_kind":"pith_short_8","alias_value":"GMHTVSB6","created_at":"2026-07-05T05:47:00Z"}],"graph_snapshots":[{"event_id":"sha256:a724e047771d6dc27373b4c6ad6f1b7052b19aa5f6fb548445be5c1c329474f1","target":"graph","created_at":"2026-07-05T05:47:00Z","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/2010.15955/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Optimization in engineering requires appropriate models. In this article, a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity constraints, is presented. Incorporating such information is particularly useful when the available data sets are small or do not cover the entire input space, as is often the case in manufacturing applications. The regression subject to the considered monotonicity constraints is set up as a semi-infinite optimization problem, and an adaptive solution algori","authors_text":"Anke Stoll, Ingo Schmidt, Jochen Schmid, Lukas Morand, Martin von Kurnatowski, Patrick Link, Rebekka Zache, Torsten Kraft","cross_cats":["math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-29T21:34:02Z","title":"Compensating data shortages in manufacturing with monotonicity knowledge"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.15955","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:dff4314dc0e625cb515629f2cd484e56e57f1faf57846718d6855b1c79d93fe2","target":"record","created_at":"2026-07-05T05:47:00Z","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":"da14fa4b72324e9ffb2dc3c8984daacc7c336d7f08ff32db0074568b8018a125","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-29T21:34:02Z","title_canon_sha256":"329f920cc0d53cb99e0794d862948a82f8c552c3336b14c3751afb625b875af8"},"schema_version":"1.0","source":{"id":"2010.15955","kind":"arxiv","version":2}},"canonical_sha256":"330f3ac83eb35898e7bb569a63dbb6b707fbd0ae78c0f12105305f7418a3c6fd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"330f3ac83eb35898e7bb569a63dbb6b707fbd0ae78c0f12105305f7418a3c6fd","first_computed_at":"2026-07-05T05:47:00.712164Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:47:00.712164Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ShMgx2bmwuVRSE7iRPSJP3+l/wk3SVxgQ6SRGxeiqoVamExiHNJxxIMBDW/4IU53KwByUSUUgcP48Jes0h1jBg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:47:00.712671Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.15955","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dff4314dc0e625cb515629f2cd484e56e57f1faf57846718d6855b1c79d93fe2","sha256:a724e047771d6dc27373b4c6ad6f1b7052b19aa5f6fb548445be5c1c329474f1"],"state_sha256":"11af5668c57476c3a74e18d762434f56c57aa089011afbe2a9e8676b44964a8f"}