{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:6GQYOMRD54Z7PHSGQZS4O7GU22","short_pith_number":"pith:6GQYOMRD","canonical_record":{"source":{"id":"1901.02565","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-01-09T00:26:00Z","cross_cats_sorted":[],"title_canon_sha256":"037a64f04d749a23f92e43852e767c16901074d328fb26594655b1b3f0e529b4","abstract_canon_sha256":"75ec5046e967d986be92818f09eaa9de4b12e171797c9a952d929acd94e1e0ba"},"schema_version":"1.0"},"canonical_sha256":"f1a1873223ef33f79e468665c77cd4d68dc65ba90ac031ea23e38ef27ea8268b","source":{"kind":"arxiv","id":"1901.02565","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.02565","created_at":"2026-05-17T23:56:22Z"},{"alias_kind":"arxiv_version","alias_value":"1901.02565v2","created_at":"2026-05-17T23:56:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.02565","created_at":"2026-05-17T23:56:22Z"},{"alias_kind":"pith_short_12","alias_value":"6GQYOMRD54Z7","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"6GQYOMRD54Z7PHSG","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"6GQYOMRD","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:6GQYOMRD54Z7PHSGQZS4O7GU22","target":"record","payload":{"canonical_record":{"source":{"id":"1901.02565","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-01-09T00:26:00Z","cross_cats_sorted":[],"title_canon_sha256":"037a64f04d749a23f92e43852e767c16901074d328fb26594655b1b3f0e529b4","abstract_canon_sha256":"75ec5046e967d986be92818f09eaa9de4b12e171797c9a952d929acd94e1e0ba"},"schema_version":"1.0"},"canonical_sha256":"f1a1873223ef33f79e468665c77cd4d68dc65ba90ac031ea23e38ef27ea8268b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:22.702984Z","signature_b64":"tv8e8W70WmgBj/veQAxi6K2dD+kuk2vRF0UL3FD4FRQ9EKAKiMCF4fuypeAKudbwCmQwx1eZ28ZJkT4OQysPDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f1a1873223ef33f79e468665c77cd4d68dc65ba90ac031ea23e38ef27ea8268b","last_reissued_at":"2026-05-17T23:56:22.702522Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:22.702522Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.02565","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":"2026-05-17T23:56:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fc6G91g1m69uAYhxkJzcDF9VQlBmBm7oTnsN8pl+zHdB61E+U1QEE5hgfrKz+hJqumAK+BzoLVoKbIXZ/jARBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T18:35:53.502161Z"},"content_sha256":"bb8fd2aa37c5956ed52a2040f3410218f9761fd8f3577ac3c3f89fc4c6d6c403","schema_version":"1.0","event_id":"sha256:bb8fd2aa37c5956ed52a2040f3410218f9761fd8f3577ac3c3f89fc4c6d6c403"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:6GQYOMRD54Z7PHSGQZS4O7GU22","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"High-Fidelity Vector Space Models of Structured Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Achille Fokoue, Constantine Nakos, Kenneth Forbus, Lingfei Wu, Maria Chang, Maxwell Crouse, Michael Witbrock, Pavan Kapanipathi, Ryan Musa","submitted_at":"2019-01-09T00:26:00Z","abstract_excerpt":"Machine learning systems regularly deal with structured data in real-world applications. Unfortunately, such data has been difficult to faithfully represent in a way that most machine learning techniques would expect, i.e. as a real-valued vector of a fixed, pre-specified size. In this work, we introduce a novel approach that compiles structured data into a satisfiability problem which has in its set of solutions at least (and often only) the input data. The satisfiability problem is constructed from constraints which are generated automatically a priori from a given signature, thus trivially "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.02565","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":""},"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":"2026-05-17T23:56:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T+980geXD4AFHeO1392Vo7NduxoNfhkkQyb9Gg0w9/J5lswvXZdL/Rsiwm07o8MrmyFWgHOh1LCCiXSJt4hVCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T18:35:53.502554Z"},"content_sha256":"3594e5f7a7b31aea389a02368773e90028f38f75621502aa955b08e3555c98d8","schema_version":"1.0","event_id":"sha256:3594e5f7a7b31aea389a02368773e90028f38f75621502aa955b08e3555c98d8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6GQYOMRD54Z7PHSGQZS4O7GU22/bundle.json","state_url":"https://pith.science/pith/6GQYOMRD54Z7PHSGQZS4O7GU22/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6GQYOMRD54Z7PHSGQZS4O7GU22/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":"2026-05-28T18:35:53Z","links":{"resolver":"https://pith.science/pith/6GQYOMRD54Z7PHSGQZS4O7GU22","bundle":"https://pith.science/pith/6GQYOMRD54Z7PHSGQZS4O7GU22/bundle.json","state":"https://pith.science/pith/6GQYOMRD54Z7PHSGQZS4O7GU22/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6GQYOMRD54Z7PHSGQZS4O7GU22/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:6GQYOMRD54Z7PHSGQZS4O7GU22","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":"75ec5046e967d986be92818f09eaa9de4b12e171797c9a952d929acd94e1e0ba","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-01-09T00:26:00Z","title_canon_sha256":"037a64f04d749a23f92e43852e767c16901074d328fb26594655b1b3f0e529b4"},"schema_version":"1.0","source":{"id":"1901.02565","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.02565","created_at":"2026-05-17T23:56:22Z"},{"alias_kind":"arxiv_version","alias_value":"1901.02565v2","created_at":"2026-05-17T23:56:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.02565","created_at":"2026-05-17T23:56:22Z"},{"alias_kind":"pith_short_12","alias_value":"6GQYOMRD54Z7","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"6GQYOMRD54Z7PHSG","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"6GQYOMRD","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:3594e5f7a7b31aea389a02368773e90028f38f75621502aa955b08e3555c98d8","target":"graph","created_at":"2026-05-17T23:56:22Z","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":"Machine learning systems regularly deal with structured data in real-world applications. Unfortunately, such data has been difficult to faithfully represent in a way that most machine learning techniques would expect, i.e. as a real-valued vector of a fixed, pre-specified size. In this work, we introduce a novel approach that compiles structured data into a satisfiability problem which has in its set of solutions at least (and often only) the input data. The satisfiability problem is constructed from constraints which are generated automatically a priori from a given signature, thus trivially ","authors_text":"Achille Fokoue, Constantine Nakos, Kenneth Forbus, Lingfei Wu, Maria Chang, Maxwell Crouse, Michael Witbrock, Pavan Kapanipathi, Ryan Musa","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-01-09T00:26:00Z","title":"High-Fidelity Vector Space Models of Structured Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.02565","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:bb8fd2aa37c5956ed52a2040f3410218f9761fd8f3577ac3c3f89fc4c6d6c403","target":"record","created_at":"2026-05-17T23:56:22Z","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":"75ec5046e967d986be92818f09eaa9de4b12e171797c9a952d929acd94e1e0ba","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-01-09T00:26:00Z","title_canon_sha256":"037a64f04d749a23f92e43852e767c16901074d328fb26594655b1b3f0e529b4"},"schema_version":"1.0","source":{"id":"1901.02565","kind":"arxiv","version":2}},"canonical_sha256":"f1a1873223ef33f79e468665c77cd4d68dc65ba90ac031ea23e38ef27ea8268b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f1a1873223ef33f79e468665c77cd4d68dc65ba90ac031ea23e38ef27ea8268b","first_computed_at":"2026-05-17T23:56:22.702522Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:56:22.702522Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tv8e8W70WmgBj/veQAxi6K2dD+kuk2vRF0UL3FD4FRQ9EKAKiMCF4fuypeAKudbwCmQwx1eZ28ZJkT4OQysPDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:56:22.702984Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.02565","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bb8fd2aa37c5956ed52a2040f3410218f9761fd8f3577ac3c3f89fc4c6d6c403","sha256:3594e5f7a7b31aea389a02368773e90028f38f75621502aa955b08e3555c98d8"],"state_sha256":"e56ec9283de5b4920f2d47b54d83d5119bb39cb769f96915f4640774d76a6825"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5QrDcARz4kMvTvV+NPPGcHv1REzFr8D1wPyZ7d4uV6CnwIzEqTMbf4fLF6wH6mgejQYLDwzq/5jyGLM1REa3Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T18:35:53.506045Z","bundle_sha256":"50a469833d8f16f214505979e497c194a1636dc39768c286458add2daf74c5b6"}}