{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:QNFMRZYJV7TNVHJGRODISA3GC5","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":"a622b32157590cfb1e2e190deac80e717eadaa8c98336a982c659a75261f1fe1","cross_cats_sorted":["cs.CV","cs.HC"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-29T22:56:19Z","title_canon_sha256":"f432fd077a1b430c3a8e0a9d05906999082676208a72a9d36b4f84988270854d"},"schema_version":"1.0","source":{"id":"2310.00180","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.00180","created_at":"2026-07-05T06:56:02Z"},{"alias_kind":"arxiv_version","alias_value":"2310.00180v1","created_at":"2026-07-05T06:56:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.00180","created_at":"2026-07-05T06:56:02Z"},{"alias_kind":"pith_short_12","alias_value":"QNFMRZYJV7TN","created_at":"2026-07-05T06:56:02Z"},{"alias_kind":"pith_short_16","alias_value":"QNFMRZYJV7TNVHJG","created_at":"2026-07-05T06:56:02Z"},{"alias_kind":"pith_short_8","alias_value":"QNFMRZYJ","created_at":"2026-07-05T06:56:02Z"}],"graph_snapshots":[{"event_id":"sha256:ea75558ff7195d70f87edf05b5fa34ce2907f55d16769ceb43af86367fc7294c","target":"graph","created_at":"2026-07-05T06:56:02Z","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/2310.00180/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Building archetypes, representative models of building stock, are crucial for precise energy simulations in Urban Building Energy Modeling. The current widely adopted building archetypes are developed on a nationwide scale, potentially neglecting the impact of local buildings' geometric specificities. We present Multi-scale Archetype Representation Learning (MARL), an approach that leverages representation learning to extract geometric features from a specific building stock. Built upon VQ-AE, MARL encodes building footprints and purifies geometric information into latent vectors constrained b","authors_text":"Luisa Caldas, Wentao Zeng, Xinwei Zhuang, Zixun Huang","cross_cats":["cs.CV","cs.HC"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-29T22:56:19Z","title":"MARL: Multi-scale Archetype Representation Learning for Urban Building Energy Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.00180","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":"sha256:08173c00d26a763a50b20fa5b2328a2edbff2e73d52d1e8fe346a52417818f57","target":"record","created_at":"2026-07-05T06:56:02Z","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":"a622b32157590cfb1e2e190deac80e717eadaa8c98336a982c659a75261f1fe1","cross_cats_sorted":["cs.CV","cs.HC"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-29T22:56:19Z","title_canon_sha256":"f432fd077a1b430c3a8e0a9d05906999082676208a72a9d36b4f84988270854d"},"schema_version":"1.0","source":{"id":"2310.00180","kind":"arxiv","version":1}},"canonical_sha256":"834ac8e709afe6da9d268b868903661767fa43f50b57ef33bedc1f5d9a54c90b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"834ac8e709afe6da9d268b868903661767fa43f50b57ef33bedc1f5d9a54c90b","first_computed_at":"2026-07-05T06:56:02.399603Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:56:02.399603Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hjid5pbLYUqKsclX0DK0eGp+b6Nzdz/8UuNlMjPh7y1Ap2GOswScGL1zidyj+SXqnTAKLRZ+W0jic604E5DlBw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:56:02.400054Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.00180","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:08173c00d26a763a50b20fa5b2328a2edbff2e73d52d1e8fe346a52417818f57","sha256:ea75558ff7195d70f87edf05b5fa34ce2907f55d16769ceb43af86367fc7294c"],"state_sha256":"5a0a61ba6947e6f38d424370fe5d013cb983c8a4d6358ef0e6415b99bbdc78a5"}