{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:ZVAJ26IIDO53GNADFAS2N3XU75","short_pith_number":"pith:ZVAJ26II","canonical_record":{"source":{"id":"1603.08474","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-03-28T18:38:46Z","cross_cats_sorted":["cs.CV","cs.LG","cs.NE"],"title_canon_sha256":"3be259299ceee3709fc481937d82a630de17abe77c5fb9823ff8590b0f8d5d91","abstract_canon_sha256":"a93ab10c6ac76530236a16a7a942089aedd14df9ae923fca893ccab489138b82"},"schema_version":"1.0"},"canonical_sha256":"cd409d79081bbbb334032825a6eef4ff5506d94d7db58994236f7d69ec8c280d","source":{"kind":"arxiv","id":"1603.08474","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.08474","created_at":"2026-05-18T01:18:10Z"},{"alias_kind":"arxiv_version","alias_value":"1603.08474v1","created_at":"2026-05-18T01:18:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.08474","created_at":"2026-05-18T01:18:10Z"},{"alias_kind":"pith_short_12","alias_value":"ZVAJ26IIDO53","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"ZVAJ26IIDO53GNAD","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"ZVAJ26II","created_at":"2026-05-18T12:30:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:ZVAJ26IIDO53GNADFAS2N3XU75","target":"record","payload":{"canonical_record":{"source":{"id":"1603.08474","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-03-28T18:38:46Z","cross_cats_sorted":["cs.CV","cs.LG","cs.NE"],"title_canon_sha256":"3be259299ceee3709fc481937d82a630de17abe77c5fb9823ff8590b0f8d5d91","abstract_canon_sha256":"a93ab10c6ac76530236a16a7a942089aedd14df9ae923fca893ccab489138b82"},"schema_version":"1.0"},"canonical_sha256":"cd409d79081bbbb334032825a6eef4ff5506d94d7db58994236f7d69ec8c280d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:18:10.679478Z","signature_b64":"iI+cLWvEnM9vdPrk9nfuSLwTdegoHmOxi6mZB/aHjP+ZzHPaIptrJtX4TbBNMNWVM61oLwnpt7/JZfNXguAgBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd409d79081bbbb334032825a6eef4ff5506d94d7db58994236f7d69ec8c280d","last_reissued_at":"2026-05-18T01:18:10.678901Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:18:10.678901Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1603.08474","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:18:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N1APZRrXJIqUzHFNI14Ot/Ww1mEG5wY6tvm+cjmgz5mtlC0+RgPaj7XAsD/+Gr338CUFG1/Mn7aM6Me6S0tbCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T20:26:06.073668Z"},"content_sha256":"7930ec71cd96613b3ff32cacf2e5aaf3308dd4f37bd7663f01b1fba1d345789e","schema_version":"1.0","event_id":"sha256:7930ec71cd96613b3ff32cacf2e5aaf3308dd4f37bd7663f01b1fba1d345789e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:ZVAJ26IIDO53GNADFAS2N3XU75","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Embedding for Spatial Role Labeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","cs.NE"],"primary_cat":"cs.CL","authors_text":"Marie-Francine Moens, Oswaldo Ludwig, Parisa Kordjamshidi, Xiao Liu","submitted_at":"2016-03-28T18:38:46Z","abstract_excerpt":"This paper introduces the visually informed embedding of word (VIEW), a continuous vector representation for a word extracted from a deep neural model trained using the Microsoft COCO data set to forecast the spatial arrangements between visual objects, given a textual description. The model is composed of a deep multilayer perceptron (MLP) stacked on the top of a Long Short Term Memory (LSTM) network, the latter being preceded by an embedding layer. The VIEW is applied to transferring multimodal background knowledge to Spatial Role Labeling (SpRL) algorithms, which recognize spatial relations"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.08474","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2026-05-18T01:18:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ftZEhHa75eiTjQHn8e/Rhx/rm2mb1e7i6PHuxB7bJ9KRY/jop9ZzO5yqT7YAiXGFGDbDCSWZ6Pqgj1n4lTLXCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T20:26:06.074378Z"},"content_sha256":"44928004493ef124ad5f15bf9edfcbfb1f37ee24e3b12d50af0fbef0454f377e","schema_version":"1.0","event_id":"sha256:44928004493ef124ad5f15bf9edfcbfb1f37ee24e3b12d50af0fbef0454f377e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZVAJ26IIDO53GNADFAS2N3XU75/bundle.json","state_url":"https://pith.science/pith/ZVAJ26IIDO53GNADFAS2N3XU75/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZVAJ26IIDO53GNADFAS2N3XU75/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":"2026-05-30T20:26:06Z","links":{"resolver":"https://pith.science/pith/ZVAJ26IIDO53GNADFAS2N3XU75","bundle":"https://pith.science/pith/ZVAJ26IIDO53GNADFAS2N3XU75/bundle.json","state":"https://pith.science/pith/ZVAJ26IIDO53GNADFAS2N3XU75/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZVAJ26IIDO53GNADFAS2N3XU75/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:ZVAJ26IIDO53GNADFAS2N3XU75","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":"a93ab10c6ac76530236a16a7a942089aedd14df9ae923fca893ccab489138b82","cross_cats_sorted":["cs.CV","cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-03-28T18:38:46Z","title_canon_sha256":"3be259299ceee3709fc481937d82a630de17abe77c5fb9823ff8590b0f8d5d91"},"schema_version":"1.0","source":{"id":"1603.08474","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.08474","created_at":"2026-05-18T01:18:10Z"},{"alias_kind":"arxiv_version","alias_value":"1603.08474v1","created_at":"2026-05-18T01:18:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.08474","created_at":"2026-05-18T01:18:10Z"},{"alias_kind":"pith_short_12","alias_value":"ZVAJ26IIDO53","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"ZVAJ26IIDO53GNAD","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"ZVAJ26II","created_at":"2026-05-18T12:30:55Z"}],"graph_snapshots":[{"event_id":"sha256:44928004493ef124ad5f15bf9edfcbfb1f37ee24e3b12d50af0fbef0454f377e","target":"graph","created_at":"2026-05-18T01:18:10Z","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":"This paper introduces the visually informed embedding of word (VIEW), a continuous vector representation for a word extracted from a deep neural model trained using the Microsoft COCO data set to forecast the spatial arrangements between visual objects, given a textual description. The model is composed of a deep multilayer perceptron (MLP) stacked on the top of a Long Short Term Memory (LSTM) network, the latter being preceded by an embedding layer. The VIEW is applied to transferring multimodal background knowledge to Spatial Role Labeling (SpRL) algorithms, which recognize spatial relations","authors_text":"Marie-Francine Moens, Oswaldo Ludwig, Parisa Kordjamshidi, Xiao Liu","cross_cats":["cs.CV","cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-03-28T18:38:46Z","title":"Deep Embedding for Spatial Role Labeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.08474","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":"sha256:7930ec71cd96613b3ff32cacf2e5aaf3308dd4f37bd7663f01b1fba1d345789e","target":"record","created_at":"2026-05-18T01:18:10Z","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":"a93ab10c6ac76530236a16a7a942089aedd14df9ae923fca893ccab489138b82","cross_cats_sorted":["cs.CV","cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-03-28T18:38:46Z","title_canon_sha256":"3be259299ceee3709fc481937d82a630de17abe77c5fb9823ff8590b0f8d5d91"},"schema_version":"1.0","source":{"id":"1603.08474","kind":"arxiv","version":1}},"canonical_sha256":"cd409d79081bbbb334032825a6eef4ff5506d94d7db58994236f7d69ec8c280d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cd409d79081bbbb334032825a6eef4ff5506d94d7db58994236f7d69ec8c280d","first_computed_at":"2026-05-18T01:18:10.678901Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:18:10.678901Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iI+cLWvEnM9vdPrk9nfuSLwTdegoHmOxi6mZB/aHjP+ZzHPaIptrJtX4TbBNMNWVM61oLwnpt7/JZfNXguAgBw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:18:10.679478Z","signed_message":"canonical_sha256_bytes"},"source_id":"1603.08474","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7930ec71cd96613b3ff32cacf2e5aaf3308dd4f37bd7663f01b1fba1d345789e","sha256:44928004493ef124ad5f15bf9edfcbfb1f37ee24e3b12d50af0fbef0454f377e"],"state_sha256":"b286718e144dc5897ec7ec18cf0d7548d18a180d6b4893623ad8fb75a6695308"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PllPiscgX6VIWm8WkSPqkUrVXo1vLnSzKuw5j9DFKgiT63kqU0p3ydtA8RuQX7yybxSturwyKvvjD5DyRi57Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T20:26:06.077829Z","bundle_sha256":"718d2943d3d30a332d142cc7ddbc49f0be9a6ebebb1c322196d21e68760873cd"}}