{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:B2VPCHQYIYXMTFWUCJLCZPV4RM","short_pith_number":"pith:B2VPCHQY","canonical_record":{"source":{"id":"2001.05849","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-12-28T01:26:20Z","cross_cats_sorted":[],"title_canon_sha256":"0dcd19c9fa192eb7b56f12c3855aad666f3637e595eca72e061412f41c8cf33e","abstract_canon_sha256":"443e7a5958d71f0f0da5d6af9d0165c064b86821e48f38eac69f47032ce140ee"},"schema_version":"1.0"},"canonical_sha256":"0eaaf11e18462ec996d412562cbebc8b1827297189e3bbeec8944e7011e662b4","source":{"kind":"arxiv","id":"2001.05849","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.05849","created_at":"2026-07-05T02:47:38Z"},{"alias_kind":"arxiv_version","alias_value":"2001.05849v2","created_at":"2026-07-05T02:47:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.05849","created_at":"2026-07-05T02:47:38Z"},{"alias_kind":"pith_short_12","alias_value":"B2VPCHQYIYXM","created_at":"2026-07-05T02:47:38Z"},{"alias_kind":"pith_short_16","alias_value":"B2VPCHQYIYXMTFWU","created_at":"2026-07-05T02:47:38Z"},{"alias_kind":"pith_short_8","alias_value":"B2VPCHQY","created_at":"2026-07-05T02:47:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:B2VPCHQYIYXMTFWUCJLCZPV4RM","target":"record","payload":{"canonical_record":{"source":{"id":"2001.05849","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-12-28T01:26:20Z","cross_cats_sorted":[],"title_canon_sha256":"0dcd19c9fa192eb7b56f12c3855aad666f3637e595eca72e061412f41c8cf33e","abstract_canon_sha256":"443e7a5958d71f0f0da5d6af9d0165c064b86821e48f38eac69f47032ce140ee"},"schema_version":"1.0"},"canonical_sha256":"0eaaf11e18462ec996d412562cbebc8b1827297189e3bbeec8944e7011e662b4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:47:38.407561Z","signature_b64":"X5GgqyBC91gSKxJFT/KYXtGhPHdse5YphyN0gB6zHB7TTljfNd8a5iw5sk58eCz+YwJl+bRswwRoWKW0aJ89AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0eaaf11e18462ec996d412562cbebc8b1827297189e3bbeec8944e7011e662b4","last_reissued_at":"2026-07-05T02:47:38.407148Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:47:38.407148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2001.05849","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:47:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VaVXvBTTvVboP2/FwnIy+et0QcG6g0CwMy2L3EGJ2ONspVRw81GB/4qE7qhpLz9Q/WCVFGQMFs1+BtmMR3RxCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:05:21.215618Z"},"content_sha256":"6b98bb80b3065b107abb0733537029e193ce66290ebddfaa2fbeb527bd64b6e8","schema_version":"1.0","event_id":"sha256:6b98bb80b3065b107abb0733537029e193ce66290ebddfaa2fbeb527bd64b6e8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:B2VPCHQYIYXMTFWUCJLCZPV4RM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Application of Deep Learning in Generating Desired Design Options: Experiments Using Synthetic Training Dataset","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Wei Yan, Zohreh Shaghaghian","submitted_at":"2019-12-28T01:26:20Z","abstract_excerpt":"Most design methods contain a forward framework, asking for primary specifications of a building to generate an output or assess its performance. However, architects urge for specific objectives though uncertain of the proper design parameters. Deep Learning (DL) algorithms provide an intelligent workflow in which the system can learn from sequential training experiments. This study applies a method using DL algorithms towards generating demanded design options. In this study, an object recognition problem is investigated to initially predict the label of unseen sample images based on training"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.05849","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2001.05849/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:47:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"71fsQCGl1mMOyD5qCkqmUOJgo+H1k9/WY8rmYNqTpMy5rOI/WOeNwE30zDfqh1SIHgvvk/et9rWs/ZoG2P6UBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:05:21.215989Z"},"content_sha256":"21f8882839c3b0eab958168f8bcbf2e1c4f03ff736c3dfcd5f3e8b44c4f2a8b1","schema_version":"1.0","event_id":"sha256:21f8882839c3b0eab958168f8bcbf2e1c4f03ff736c3dfcd5f3e8b44c4f2a8b1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B2VPCHQYIYXMTFWUCJLCZPV4RM/bundle.json","state_url":"https://pith.science/pith/B2VPCHQYIYXMTFWUCJLCZPV4RM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B2VPCHQYIYXMTFWUCJLCZPV4RM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T23:05:21Z","links":{"resolver":"https://pith.science/pith/B2VPCHQYIYXMTFWUCJLCZPV4RM","bundle":"https://pith.science/pith/B2VPCHQYIYXMTFWUCJLCZPV4RM/bundle.json","state":"https://pith.science/pith/B2VPCHQYIYXMTFWUCJLCZPV4RM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B2VPCHQYIYXMTFWUCJLCZPV4RM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:B2VPCHQYIYXMTFWUCJLCZPV4RM","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":"443e7a5958d71f0f0da5d6af9d0165c064b86821e48f38eac69f47032ce140ee","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-12-28T01:26:20Z","title_canon_sha256":"0dcd19c9fa192eb7b56f12c3855aad666f3637e595eca72e061412f41c8cf33e"},"schema_version":"1.0","source":{"id":"2001.05849","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.05849","created_at":"2026-07-05T02:47:38Z"},{"alias_kind":"arxiv_version","alias_value":"2001.05849v2","created_at":"2026-07-05T02:47:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.05849","created_at":"2026-07-05T02:47:38Z"},{"alias_kind":"pith_short_12","alias_value":"B2VPCHQYIYXM","created_at":"2026-07-05T02:47:38Z"},{"alias_kind":"pith_short_16","alias_value":"B2VPCHQYIYXMTFWU","created_at":"2026-07-05T02:47:38Z"},{"alias_kind":"pith_short_8","alias_value":"B2VPCHQY","created_at":"2026-07-05T02:47:38Z"}],"graph_snapshots":[{"event_id":"sha256:21f8882839c3b0eab958168f8bcbf2e1c4f03ff736c3dfcd5f3e8b44c4f2a8b1","target":"graph","created_at":"2026-07-05T02:47:38Z","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/2001.05849/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most design methods contain a forward framework, asking for primary specifications of a building to generate an output or assess its performance. However, architects urge for specific objectives though uncertain of the proper design parameters. Deep Learning (DL) algorithms provide an intelligent workflow in which the system can learn from sequential training experiments. This study applies a method using DL algorithms towards generating demanded design options. In this study, an object recognition problem is investigated to initially predict the label of unseen sample images based on training","authors_text":"Wei Yan, Zohreh Shaghaghian","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-12-28T01:26:20Z","title":"Application of Deep Learning in Generating Desired Design Options: Experiments Using Synthetic Training Dataset"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.05849","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":"sha256:6b98bb80b3065b107abb0733537029e193ce66290ebddfaa2fbeb527bd64b6e8","target":"record","created_at":"2026-07-05T02:47:38Z","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":"443e7a5958d71f0f0da5d6af9d0165c064b86821e48f38eac69f47032ce140ee","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-12-28T01:26:20Z","title_canon_sha256":"0dcd19c9fa192eb7b56f12c3855aad666f3637e595eca72e061412f41c8cf33e"},"schema_version":"1.0","source":{"id":"2001.05849","kind":"arxiv","version":2}},"canonical_sha256":"0eaaf11e18462ec996d412562cbebc8b1827297189e3bbeec8944e7011e662b4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0eaaf11e18462ec996d412562cbebc8b1827297189e3bbeec8944e7011e662b4","first_computed_at":"2026-07-05T02:47:38.407148Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:47:38.407148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"X5GgqyBC91gSKxJFT/KYXtGhPHdse5YphyN0gB6zHB7TTljfNd8a5iw5sk58eCz+YwJl+bRswwRoWKW0aJ89AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:47:38.407561Z","signed_message":"canonical_sha256_bytes"},"source_id":"2001.05849","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6b98bb80b3065b107abb0733537029e193ce66290ebddfaa2fbeb527bd64b6e8","sha256:21f8882839c3b0eab958168f8bcbf2e1c4f03ff736c3dfcd5f3e8b44c4f2a8b1"],"state_sha256":"86315cb7b55e51633473786fd400c8c0eca1d552a191c34e0b10585028be47d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vrUqH9ME+/kDJGRX+CYmBt10yRYY0opCcRQB7ak3CAZYMnXzGyhWLQD3+LH/JpOqGlq6y6D9S/83sctEOrKPCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T23:05:21.217948Z","bundle_sha256":"2660acba555881a394e9f35637825a5453314decf42c3f0eb04bac60f34f7fe6"}}