{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:YOKVLFESEHJVP755P467C7PDZ2","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":"b1e50507508cd0a27de2fcf5ee3818cff831150154017fccdc3ad09ae976c9f0","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-07T07:03:59Z","title_canon_sha256":"cfc71e24233f2a2b8dac41105a15f4790b8f01843c3f4e829021ce0dda6f7b8b"},"schema_version":"1.0","source":{"id":"2412.05566","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.05566","created_at":"2026-07-05T09:46:05Z"},{"alias_kind":"arxiv_version","alias_value":"2412.05566v1","created_at":"2026-07-05T09:46:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05566","created_at":"2026-07-05T09:46:05Z"},{"alias_kind":"pith_short_12","alias_value":"YOKVLFESEHJV","created_at":"2026-07-05T09:46:05Z"},{"alias_kind":"pith_short_16","alias_value":"YOKVLFESEHJVP755","created_at":"2026-07-05T09:46:05Z"},{"alias_kind":"pith_short_8","alias_value":"YOKVLFES","created_at":"2026-07-05T09:46:05Z"}],"graph_snapshots":[{"event_id":"sha256:6817d0953bbdb0de00221ff09de7c0247fd4750e6309b46f0d0d741991c0ec67","target":"graph","created_at":"2026-07-05T09:46:05Z","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/2412.05566/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the fast-fashion industry, overproduction and unsold inventory create significant environmental problems. Precise sales forecasts for unreleased items could drastically improve the efficiency and profits of industries. However, predicting the success of entirely new styles is difficult due to the absence of past data and ever-changing trends. Specifically, currently used deterministic models struggle with domain shifts when encountering items outside their training data. The recently proposed diffusion models address this issue using a continuous-time diffusion process. Specifically, these ","authors_text":"Andrea Avogaro, Franco Fummi, Luigi Capogrosso, Marco Cristani","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-07T07:03:59Z","title":"Dif4FF: Leveraging Multimodal Diffusion Models and Graph Neural Networks for Accurate New Fashion Product Performance Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05566","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":"sha256:6c5b0ca9f77d820479d427de9459207cbd929f04585961ba2f3d207a26262ae2","target":"record","created_at":"2026-07-05T09:46:05Z","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":"b1e50507508cd0a27de2fcf5ee3818cff831150154017fccdc3ad09ae976c9f0","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-07T07:03:59Z","title_canon_sha256":"cfc71e24233f2a2b8dac41105a15f4790b8f01843c3f4e829021ce0dda6f7b8b"},"schema_version":"1.0","source":{"id":"2412.05566","kind":"arxiv","version":1}},"canonical_sha256":"c39555949221d357ffbd7f3df17de3ce9f5dd09cf7a7222da4db4bcd9d22505f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c39555949221d357ffbd7f3df17de3ce9f5dd09cf7a7222da4db4bcd9d22505f","first_computed_at":"2026-07-05T09:46:05.506048Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:46:05.506048Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9IGcBK+8WJzJtwGFCQWrfsEhvYyaPWGa0rPfNOv8hN3jxQx9zyphR1NiEBSEz5N/xwPp0cSoNCWk2VaDm++nAg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:46:05.506523Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.05566","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6c5b0ca9f77d820479d427de9459207cbd929f04585961ba2f3d207a26262ae2","sha256:6817d0953bbdb0de00221ff09de7c0247fd4750e6309b46f0d0d741991c0ec67"],"state_sha256":"cee0a4985cd3bcad74d993530a5197405277b848e3f7cf6fd670aa43e780f3b0"}