{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:BRN227FHJBG6ALYOWRAKYGGWTS","short_pith_number":"pith:BRN227FH","canonical_record":{"source":{"id":"1808.04456","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-13T20:36:08Z","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"title_canon_sha256":"0b804fcbd07ae4458cf56a6127f148b2ddcdd528dc3d22f0286f9c4e52e5f4db","abstract_canon_sha256":"6fed2ec902ed628d50c2965a446a28415869d37095d83cbba22ec735caeabca9"},"schema_version":"1.0"},"canonical_sha256":"0c5bad7ca7484de02f0eb440ac18d69c99ef499ad6613f7c3fcc71c0a4930f5b","source":{"kind":"arxiv","id":"1808.04456","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.04456","created_at":"2026-05-18T00:05:45Z"},{"alias_kind":"arxiv_version","alias_value":"1808.04456v2","created_at":"2026-05-18T00:05:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04456","created_at":"2026-05-18T00:05:45Z"},{"alias_kind":"pith_short_12","alias_value":"BRN227FHJBG6","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"BRN227FHJBG6ALYO","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"BRN227FH","created_at":"2026-05-18T12:32:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:BRN227FHJBG6ALYOWRAKYGGWTS","target":"record","payload":{"canonical_record":{"source":{"id":"1808.04456","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-13T20:36:08Z","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"title_canon_sha256":"0b804fcbd07ae4458cf56a6127f148b2ddcdd528dc3d22f0286f9c4e52e5f4db","abstract_canon_sha256":"6fed2ec902ed628d50c2965a446a28415869d37095d83cbba22ec735caeabca9"},"schema_version":"1.0"},"canonical_sha256":"0c5bad7ca7484de02f0eb440ac18d69c99ef499ad6613f7c3fcc71c0a4930f5b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:45.112796Z","signature_b64":"ZSDBPOyZn7iH/MSgR5z4/rhz+YF06EWoq4YAVcWrTZfZOKKz0i0Ru/Uw+Hocgo9QgKHANCZl38AeE6AFLic4AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c5bad7ca7484de02f0eb440ac18d69c99ef499ad6613f7c3fcc71c0a4930f5b","last_reissued_at":"2026-05-18T00:05:45.112308Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:45.112308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.04456","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":"2026-05-18T00:05:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vJMDwtn32IByksSfuK17+Mp0AqNgF7iEQExZB391OfUKfZZLereqBb88RM7Qe6O/nm+VHoQ/vEK6gYg5sW5QDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T10:27:43.110255Z"},"content_sha256":"82c013440e4011cf8a4a566aa7df1447c46f95966294b4ae1e1f302785d9d4fc","schema_version":"1.0","event_id":"sha256:82c013440e4011cf8a4a566aa7df1447c46f95966294b4ae1e1f302785d9d4fc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:BRN227FHJBG6ALYOWRAKYGGWTS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Abhinav Vishnu, Charles Siegel, Garrett B. Goh, Jim Pfaendtner, Khushmeen Sakloth","submitted_at":"2018-08-13T20:36:08Z","abstract_excerpt":"Deep learning algorithms excel at extracting patterns from raw data, and with large datasets, they have been very successful in computer vision and natural language applications. However, in other domains, large datasets on which to learn representations from may not exist. In this work, we develop a novel multimodal CNN-MLP neural network architecture that utilizes both domain-specific feature engineering as well as learned representations from raw data. We illustrate the effectiveness of such network designs in the chemical sciences, for predicting biodegradability. DeepBioD, a multimodal CN"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04456","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":"2026-05-18T00:05:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lM++EvZLOnBAmj2KNoPjZgpU9UypqUnpWrfzq1vLeyoVTFJFMfjuCOywYByW9vHbyk25eXDjAsuoNb/4jynRDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T10:27:43.110630Z"},"content_sha256":"7adbd0711776999be455cff3c55e0616f905e651c625f305515641ed26591c09","schema_version":"1.0","event_id":"sha256:7adbd0711776999be455cff3c55e0616f905e651c625f305515641ed26591c09"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BRN227FHJBG6ALYOWRAKYGGWTS/bundle.json","state_url":"https://pith.science/pith/BRN227FHJBG6ALYOWRAKYGGWTS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BRN227FHJBG6ALYOWRAKYGGWTS/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":"2026-06-04T10:27:43Z","links":{"resolver":"https://pith.science/pith/BRN227FHJBG6ALYOWRAKYGGWTS","bundle":"https://pith.science/pith/BRN227FHJBG6ALYOWRAKYGGWTS/bundle.json","state":"https://pith.science/pith/BRN227FHJBG6ALYOWRAKYGGWTS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BRN227FHJBG6ALYOWRAKYGGWTS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:BRN227FHJBG6ALYOWRAKYGGWTS","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":"6fed2ec902ed628d50c2965a446a28415869d37095d83cbba22ec735caeabca9","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-13T20:36:08Z","title_canon_sha256":"0b804fcbd07ae4458cf56a6127f148b2ddcdd528dc3d22f0286f9c4e52e5f4db"},"schema_version":"1.0","source":{"id":"1808.04456","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.04456","created_at":"2026-05-18T00:05:45Z"},{"alias_kind":"arxiv_version","alias_value":"1808.04456v2","created_at":"2026-05-18T00:05:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04456","created_at":"2026-05-18T00:05:45Z"},{"alias_kind":"pith_short_12","alias_value":"BRN227FHJBG6","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"BRN227FHJBG6ALYO","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"BRN227FH","created_at":"2026-05-18T12:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:7adbd0711776999be455cff3c55e0616f905e651c625f305515641ed26591c09","target":"graph","created_at":"2026-05-18T00:05:45Z","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":"Deep learning algorithms excel at extracting patterns from raw data, and with large datasets, they have been very successful in computer vision and natural language applications. However, in other domains, large datasets on which to learn representations from may not exist. In this work, we develop a novel multimodal CNN-MLP neural network architecture that utilizes both domain-specific feature engineering as well as learned representations from raw data. We illustrate the effectiveness of such network designs in the chemical sciences, for predicting biodegradability. DeepBioD, a multimodal CN","authors_text":"Abhinav Vishnu, Charles Siegel, Garrett B. Goh, Jim Pfaendtner, Khushmeen Sakloth","cross_cats":["cs.AI","cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-13T20:36:08Z","title":"Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04456","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":"sha256:82c013440e4011cf8a4a566aa7df1447c46f95966294b4ae1e1f302785d9d4fc","target":"record","created_at":"2026-05-18T00:05:45Z","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":"6fed2ec902ed628d50c2965a446a28415869d37095d83cbba22ec735caeabca9","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-13T20:36:08Z","title_canon_sha256":"0b804fcbd07ae4458cf56a6127f148b2ddcdd528dc3d22f0286f9c4e52e5f4db"},"schema_version":"1.0","source":{"id":"1808.04456","kind":"arxiv","version":2}},"canonical_sha256":"0c5bad7ca7484de02f0eb440ac18d69c99ef499ad6613f7c3fcc71c0a4930f5b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0c5bad7ca7484de02f0eb440ac18d69c99ef499ad6613f7c3fcc71c0a4930f5b","first_computed_at":"2026-05-18T00:05:45.112308Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:45.112308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZSDBPOyZn7iH/MSgR5z4/rhz+YF06EWoq4YAVcWrTZfZOKKz0i0Ru/Uw+Hocgo9QgKHANCZl38AeE6AFLic4AA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:45.112796Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.04456","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:82c013440e4011cf8a4a566aa7df1447c46f95966294b4ae1e1f302785d9d4fc","sha256:7adbd0711776999be455cff3c55e0616f905e651c625f305515641ed26591c09"],"state_sha256":"4d0c7b2dae521fc437c92e778c84a44fe29f752f99502d1d4e6df78e1d969d71"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BnETmI39zKt8mqFUM2U1MHNrgoZhtbBCkZHSI/JMAT2gtiRKZyo9/mEekHS4BrhzOc2f70V33s1pLt/JoeSTBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T10:27:43.112601Z","bundle_sha256":"b2565dc40200dfc6abfa9b1d183df3b8d65fb77711603e1a491ce6e08d9db234"}}