{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:U4L3BUKIHSSQJX2Q7BEEDRWM7E","short_pith_number":"pith:U4L3BUKI","canonical_record":{"source":{"id":"2406.02601","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-02T01:13:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"66e1086bdfef5dab23893a9ea29392803805ef4f2cd894863712bbf0dc54b4d6","abstract_canon_sha256":"605f4c552e2d7843c048851adc5f9cefccd597cce1c5b0232a8b901f5f366f30"},"schema_version":"1.0"},"canonical_sha256":"a717b0d1483ca504df50f84841c6ccf90c8bf1038181588c8b00939bb8a5178c","source":{"kind":"arxiv","id":"2406.02601","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.02601","created_at":"2026-07-05T08:27:27Z"},{"alias_kind":"arxiv_version","alias_value":"2406.02601v1","created_at":"2026-07-05T08:27:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.02601","created_at":"2026-07-05T08:27:27Z"},{"alias_kind":"pith_short_12","alias_value":"U4L3BUKIHSSQ","created_at":"2026-07-05T08:27:27Z"},{"alias_kind":"pith_short_16","alias_value":"U4L3BUKIHSSQJX2Q","created_at":"2026-07-05T08:27:27Z"},{"alias_kind":"pith_short_8","alias_value":"U4L3BUKI","created_at":"2026-07-05T08:27:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:U4L3BUKIHSSQJX2Q7BEEDRWM7E","target":"record","payload":{"canonical_record":{"source":{"id":"2406.02601","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-02T01:13:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"66e1086bdfef5dab23893a9ea29392803805ef4f2cd894863712bbf0dc54b4d6","abstract_canon_sha256":"605f4c552e2d7843c048851adc5f9cefccd597cce1c5b0232a8b901f5f366f30"},"schema_version":"1.0"},"canonical_sha256":"a717b0d1483ca504df50f84841c6ccf90c8bf1038181588c8b00939bb8a5178c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:27:27.714781Z","signature_b64":"px0fWW1mi6YsAr8oGK+72yDP/TwLJ0+OW1pltCtENekoBRKVbyyWT4ZF1Q6845/Cp8g+LFMGlhMcfrsD0dEjBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a717b0d1483ca504df50f84841c6ccf90c8bf1038181588c8b00939bb8a5178c","last_reissued_at":"2026-07-05T08:27:27.714306Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:27:27.714306Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.02601","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":"2026-07-05T08:27:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vwV4mj5dX6LGbVMTk1jrwgrqJ+MUTAutfahPV8+w66ggQgt5bZAkIdwTQSIleuGl/EV7A5WjxW8GDk9lkCu/DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:10:38.514288Z"},"content_sha256":"513103de92c66a27dd24eb142da7160eb2fa09fabc42ebd930b0ac6d4b57fecd","schema_version":"1.0","event_id":"sha256:513103de92c66a27dd24eb142da7160eb2fa09fabc42ebd930b0ac6d4b57fecd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:U4L3BUKIHSSQJX2Q7BEEDRWM7E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chenwei Wu, David Restrepo, Diego M L\\'opez, Leo Anthony Celi, Luis Filipe Nakayama, Sebasti\\'an Andr\\'es Cajas","submitted_at":"2024-06-02T01:13:01Z","abstract_excerpt":"Large-scale multi-modal deep learning models have revolutionized domains such as healthcare, highlighting the importance of computational power. However, in resource-constrained regions like Low and Middle-Income Countries (LMICs), limited access to GPUs and data poses significant challenges, often leaving CPUs as the sole resource. To address this, we advocate for leveraging vector embeddings to enable flexible and efficient computational methodologies, democratizing multimodal deep learning across diverse contexts.\n  Our paper investigates the efficiency and effectiveness of using vector emb"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.02601","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2406.02601/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T08:27:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZJq2Klw52Np7UyvmF4LOzj2U7KUfYUpOC84pCUD86hanemU1YupDEnTn6QEycWxvVHDIL3561ZJtnz+KkezoAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:10:38.514816Z"},"content_sha256":"f18cdf7907a19f46bd2303694c5ebd0407e7e49b78df587daa5901238e59b3d9","schema_version":"1.0","event_id":"sha256:f18cdf7907a19f46bd2303694c5ebd0407e7e49b78df587daa5901238e59b3d9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U4L3BUKIHSSQJX2Q7BEEDRWM7E/bundle.json","state_url":"https://pith.science/pith/U4L3BUKIHSSQJX2Q7BEEDRWM7E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U4L3BUKIHSSQJX2Q7BEEDRWM7E/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":"2026-08-07T17:10:38Z","links":{"resolver":"https://pith.science/pith/U4L3BUKIHSSQJX2Q7BEEDRWM7E","bundle":"https://pith.science/pith/U4L3BUKIHSSQJX2Q7BEEDRWM7E/bundle.json","state":"https://pith.science/pith/U4L3BUKIHSSQJX2Q7BEEDRWM7E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U4L3BUKIHSSQJX2Q7BEEDRWM7E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:U4L3BUKIHSSQJX2Q7BEEDRWM7E","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":"605f4c552e2d7843c048851adc5f9cefccd597cce1c5b0232a8b901f5f366f30","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-02T01:13:01Z","title_canon_sha256":"66e1086bdfef5dab23893a9ea29392803805ef4f2cd894863712bbf0dc54b4d6"},"schema_version":"1.0","source":{"id":"2406.02601","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.02601","created_at":"2026-07-05T08:27:27Z"},{"alias_kind":"arxiv_version","alias_value":"2406.02601v1","created_at":"2026-07-05T08:27:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.02601","created_at":"2026-07-05T08:27:27Z"},{"alias_kind":"pith_short_12","alias_value":"U4L3BUKIHSSQ","created_at":"2026-07-05T08:27:27Z"},{"alias_kind":"pith_short_16","alias_value":"U4L3BUKIHSSQJX2Q","created_at":"2026-07-05T08:27:27Z"},{"alias_kind":"pith_short_8","alias_value":"U4L3BUKI","created_at":"2026-07-05T08:27:27Z"}],"graph_snapshots":[{"event_id":"sha256:f18cdf7907a19f46bd2303694c5ebd0407e7e49b78df587daa5901238e59b3d9","target":"graph","created_at":"2026-07-05T08:27:27Z","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/2406.02601/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale multi-modal deep learning models have revolutionized domains such as healthcare, highlighting the importance of computational power. However, in resource-constrained regions like Low and Middle-Income Countries (LMICs), limited access to GPUs and data poses significant challenges, often leaving CPUs as the sole resource. To address this, we advocate for leveraging vector embeddings to enable flexible and efficient computational methodologies, democratizing multimodal deep learning across diverse contexts.\n  Our paper investigates the efficiency and effectiveness of using vector emb","authors_text":"Chenwei Wu, David Restrepo, Diego M L\\'opez, Leo Anthony Celi, Luis Filipe Nakayama, Sebasti\\'an Andr\\'es Cajas","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-02T01:13:01Z","title":"Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.02601","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":"sha256:513103de92c66a27dd24eb142da7160eb2fa09fabc42ebd930b0ac6d4b57fecd","target":"record","created_at":"2026-07-05T08:27:27Z","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":"605f4c552e2d7843c048851adc5f9cefccd597cce1c5b0232a8b901f5f366f30","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-02T01:13:01Z","title_canon_sha256":"66e1086bdfef5dab23893a9ea29392803805ef4f2cd894863712bbf0dc54b4d6"},"schema_version":"1.0","source":{"id":"2406.02601","kind":"arxiv","version":1}},"canonical_sha256":"a717b0d1483ca504df50f84841c6ccf90c8bf1038181588c8b00939bb8a5178c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a717b0d1483ca504df50f84841c6ccf90c8bf1038181588c8b00939bb8a5178c","first_computed_at":"2026-07-05T08:27:27.714306Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:27:27.714306Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"px0fWW1mi6YsAr8oGK+72yDP/TwLJ0+OW1pltCtENekoBRKVbyyWT4ZF1Q6845/Cp8g+LFMGlhMcfrsD0dEjBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:27:27.714781Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.02601","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:513103de92c66a27dd24eb142da7160eb2fa09fabc42ebd930b0ac6d4b57fecd","sha256:f18cdf7907a19f46bd2303694c5ebd0407e7e49b78df587daa5901238e59b3d9"],"state_sha256":"43b4be6ce5c9980bc65fd55522ea476d9f829ea5b7d5f8980e6867d41eb03a99"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LmjT1VE+9TnCamq374djD4sADvjEKASw52Diqm2MEQGUnAroGTckf51fnICa23gsLV6zpfqVmk8kPxMpAjf2Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T17:10:38.518699Z","bundle_sha256":"873837fdcff868e43f631dd5b25a618dec313923aace4b50b98d1169bf3983eb"}}