{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:47CGZQSIVTF4ZT2SVESMVHZYQ7","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":"230db74b8ee610e6a70fe261b830140093bb369b351779e43806f47d84cc8159","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-06T09:33:56Z","title_canon_sha256":"d1112a290a13a525b8c2d5ea7ef96248109d4473a820598c90da2b7ff2ae621d"},"schema_version":"1.0","source":{"id":"2311.03414","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.03414","created_at":"2026-07-05T07:09:58Z"},{"alias_kind":"arxiv_version","alias_value":"2311.03414v1","created_at":"2026-07-05T07:09:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.03414","created_at":"2026-07-05T07:09:58Z"},{"alias_kind":"pith_short_12","alias_value":"47CGZQSIVTF4","created_at":"2026-07-05T07:09:58Z"},{"alias_kind":"pith_short_16","alias_value":"47CGZQSIVTF4ZT2S","created_at":"2026-07-05T07:09:58Z"},{"alias_kind":"pith_short_8","alias_value":"47CGZQSI","created_at":"2026-07-05T07:09:58Z"}],"graph_snapshots":[{"event_id":"sha256:6aa94e9a5d99936b1feeb1483b35a041c5b637713a18da1ea16df9ab3cf60a87","target":"graph","created_at":"2026-07-05T07:09:58Z","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/2311.03414/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"One of the most promising developments in computer vision in recent years is the use of generative neural networks for functionality condition-based 3D design reconstruction and generation. Here, neural networks learn dependencies between functionalities and a geometry in a very effective way. For a neural network the functionalities are translated in conditions to a certain geometry. But the more conditions the design generation needs to reflect, the more difficult it is to learn clear dependencies. This leads to a multi criteria design problem due various conditions, which are not considered","authors_text":"Christoph Petroll, Oliver Niggemann, Philipp Hoefer, Sebastian Eilermann","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-06T09:33:56Z","title":"A Generative Neural Network Approach for 3D Multi-Criteria Design Generation and Optimization of an Engine Mount for an Unmanned Air Vehicle"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.03414","kind":"arxiv","version":1},"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:b1cef21488fb4c31e1dd0f2824cd97bcc7183eb9a20448d7b313d3a1752f4f94","target":"record","created_at":"2026-07-05T07:09:58Z","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":"230db74b8ee610e6a70fe261b830140093bb369b351779e43806f47d84cc8159","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-06T09:33:56Z","title_canon_sha256":"d1112a290a13a525b8c2d5ea7ef96248109d4473a820598c90da2b7ff2ae621d"},"schema_version":"1.0","source":{"id":"2311.03414","kind":"arxiv","version":1}},"canonical_sha256":"e7c46cc248accbcccf52a924ca9f3887f3a49ba72601e206e66050b8a555e129","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e7c46cc248accbcccf52a924ca9f3887f3a49ba72601e206e66050b8a555e129","first_computed_at":"2026-07-05T07:09:58.020877Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:09:58.020877Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZXwvRUMvqG+ehdXp64G9ig1PZrNAO+BUOce/pocfmUXukIbucubXhd5+xawDLhics2dtJsffDfhrtV49UEvzDw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:09:58.021356Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.03414","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b1cef21488fb4c31e1dd0f2824cd97bcc7183eb9a20448d7b313d3a1752f4f94","sha256:6aa94e9a5d99936b1feeb1483b35a041c5b637713a18da1ea16df9ab3cf60a87"],"state_sha256":"923348ffe46c3449abf566e656500c06d811c3fdac63f7de334a3ead8e54a34e"}