{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:TIVTXCXKVWE3JQQLG5D5W6EUKK","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":"008b6042a01eb4245446e9ca45bd7fc94f2f87d364c94ccdda4362de91743cf8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-02-03T15:05:01Z","title_canon_sha256":"ccc29ac1fa14640a2bb1eab9983a5403a1b094aef3765f2eb04a80d4a4ec3463"},"schema_version":"1.0","source":{"id":"1802.00985","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.00985","created_at":"2026-05-18T00:23:44Z"},{"alias_kind":"arxiv_version","alias_value":"1802.00985v2","created_at":"2026-05-18T00:23:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.00985","created_at":"2026-05-18T00:23:44Z"},{"alias_kind":"pith_short_12","alias_value":"TIVTXCXKVWE3","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TIVTXCXKVWE3JQQL","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TIVTXCXK","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:eb2ac3eee2058966783c7c6d068c51257dc8de05309d11415a737a61a3ee1fe1","target":"graph","created_at":"2026-05-18T00:23:44Z","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"},"paper":{"abstract_excerpt":"Cross-modal information retrieval aims to find heterogeneous data of various modalities from a given query of one modality. The main challenge is to map different modalities into a common semantic space, in which distance between concepts in different modalities can be well modeled. For cross-modal information retrieval between images and texts, existing work mostly uses off-the-shelf Convolutional Neural Network (CNN) for image feature extraction. For texts, word-level features such as bag-of-words or word2vec are employed to build deep learning models to represent texts. Besides word-level s","authors_text":"Jianlong Tan, Jing Yu, Li Guo, Weifeng Zhang, Yanbing Liu, Yuhang Lu, Zengchang Qin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-02-03T15:05:01Z","title":"Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.00985","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":"sha256:1a1b44da01fb441f15f1d4176ee7cfcd056d477967e080238b359dfcc01d2298","target":"record","created_at":"2026-05-18T00:23:44Z","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":"008b6042a01eb4245446e9ca45bd7fc94f2f87d364c94ccdda4362de91743cf8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-02-03T15:05:01Z","title_canon_sha256":"ccc29ac1fa14640a2bb1eab9983a5403a1b094aef3765f2eb04a80d4a4ec3463"},"schema_version":"1.0","source":{"id":"1802.00985","kind":"arxiv","version":2}},"canonical_sha256":"9a2b3b8aeaad89b4c20b3747db789452b27927c4183b26656f8c3d799fab5e0e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9a2b3b8aeaad89b4c20b3747db789452b27927c4183b26656f8c3d799fab5e0e","first_computed_at":"2026-05-18T00:23:44.753859Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:23:44.753859Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6c9Qp/cbu552J7n6tU8UfWMdhPwhKH4FqE4sC9ltA0riORhD/ABSHBd6+cg0S6hUR+fyqXl72zWPAszZ1wGtDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:23:44.754507Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.00985","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1a1b44da01fb441f15f1d4176ee7cfcd056d477967e080238b359dfcc01d2298","sha256:eb2ac3eee2058966783c7c6d068c51257dc8de05309d11415a737a61a3ee1fe1"],"state_sha256":"c01ee284d5d00d60a2d16f705defc63f59de85890a967e20e02c2723e4526edf"}