{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:GAVV3NHUE7MAN2VSKXSKDDPWIW","short_pith_number":"pith:GAVV3NHU","canonical_record":{"source":{"id":"2403.07536","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-12T11:19:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"39e177896dfb6c0c5856fea72e43c8fc451709686a1c0d65e860a7628a5e480c","abstract_canon_sha256":"be78ff2d9cf867d3b33e4ab03029982ef271e7d138dfbe04ec9f4ddcd5c89d73"},"schema_version":"1.0"},"canonical_sha256":"302b5db4f427d806eab255e4a18df64584e6ecda65fce74af47051c69d8ace4b","source":{"kind":"arxiv","id":"2403.07536","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.07536","created_at":"2026-07-05T09:30:09Z"},{"alias_kind":"arxiv_version","alias_value":"2403.07536v2","created_at":"2026-07-05T09:30:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.07536","created_at":"2026-07-05T09:30:09Z"},{"alias_kind":"pith_short_12","alias_value":"GAVV3NHUE7MA","created_at":"2026-07-05T09:30:09Z"},{"alias_kind":"pith_short_16","alias_value":"GAVV3NHUE7MAN2VS","created_at":"2026-07-05T09:30:09Z"},{"alias_kind":"pith_short_8","alias_value":"GAVV3NHU","created_at":"2026-07-05T09:30:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:GAVV3NHUE7MAN2VSKXSKDDPWIW","target":"record","payload":{"canonical_record":{"source":{"id":"2403.07536","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-12T11:19:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"39e177896dfb6c0c5856fea72e43c8fc451709686a1c0d65e860a7628a5e480c","abstract_canon_sha256":"be78ff2d9cf867d3b33e4ab03029982ef271e7d138dfbe04ec9f4ddcd5c89d73"},"schema_version":"1.0"},"canonical_sha256":"302b5db4f427d806eab255e4a18df64584e6ecda65fce74af47051c69d8ace4b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:30:09.462409Z","signature_b64":"WVmB5Ipxzl/wG3oQsx5OIsUS1ApFD+gKmXUIJ/I73qVlGmuzj6WzOwU0cIkuW9swEsZdMVZ9HXEnBcpsPswgAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"302b5db4f427d806eab255e4a18df64584e6ecda65fce74af47051c69d8ace4b","last_reissued_at":"2026-07-05T09:30:09.461882Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:30:09.461882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.07536","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":"2026-07-05T09:30:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cjWNAO/WcqcUbCSWp92Z6kvbt5l/IxdPW7RkdW/UnyB0NWE6SBwxn/TtLQEJ4ktA9HLDn98f5qnA08vXycAYBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T17:54:19.796961Z"},"content_sha256":"bec2deaceed7c65bbc2d818c1be146893a2ff7ed76dccff1cc9d8cb8d7a573f5","schema_version":"1.0","event_id":"sha256:bec2deaceed7c65bbc2d818c1be146893a2ff7ed76dccff1cc9d8cb8d7a573f5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:GAVV3NHUE7MAN2VSKXSKDDPWIW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LaB-GATr: geometric algebra transformers for large biomedical surface and volume meshes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Baris Imre, Jelmer M. Wolterink, Julian Suk","submitted_at":"2024-03-12T11:19:46Z","abstract_excerpt":"Many anatomical structures can be described by surface or volume meshes. Machine learning is a promising tool to extract information from these 3D models. However, high-fidelity meshes often contain hundreds of thousands of vertices, which creates unique challenges in building deep neural network architectures. Furthermore, patient-specific meshes may not be canonically aligned which limits the generalisation of machine learning algorithms. We propose LaB-GATr, a transfomer neural network with geometric tokenisation that can effectively learn with large-scale (bio-)medical surface and volume m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.07536","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.07536/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":"2026-07-05T09:30:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PgRSX6vulFueDIPfF/lYF3inb5I9SbbAaF3o/QC9qneMW8rlLjnuhJv4p5cZcnrQwatopmfOUZrJekUDN53zCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T17:54:19.797860Z"},"content_sha256":"950f8c4ce1bc8ef26d5bde85ddc41f053849fa9b2ad51482a011704956ddb09f","schema_version":"1.0","event_id":"sha256:950f8c4ce1bc8ef26d5bde85ddc41f053849fa9b2ad51482a011704956ddb09f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GAVV3NHUE7MAN2VSKXSKDDPWIW/bundle.json","state_url":"https://pith.science/pith/GAVV3NHUE7MAN2VSKXSKDDPWIW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GAVV3NHUE7MAN2VSKXSKDDPWIW/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":"2026-08-04T17:54:19Z","links":{"resolver":"https://pith.science/pith/GAVV3NHUE7MAN2VSKXSKDDPWIW","bundle":"https://pith.science/pith/GAVV3NHUE7MAN2VSKXSKDDPWIW/bundle.json","state":"https://pith.science/pith/GAVV3NHUE7MAN2VSKXSKDDPWIW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GAVV3NHUE7MAN2VSKXSKDDPWIW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GAVV3NHUE7MAN2VSKXSKDDPWIW","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":"be78ff2d9cf867d3b33e4ab03029982ef271e7d138dfbe04ec9f4ddcd5c89d73","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-12T11:19:46Z","title_canon_sha256":"39e177896dfb6c0c5856fea72e43c8fc451709686a1c0d65e860a7628a5e480c"},"schema_version":"1.0","source":{"id":"2403.07536","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.07536","created_at":"2026-07-05T09:30:09Z"},{"alias_kind":"arxiv_version","alias_value":"2403.07536v2","created_at":"2026-07-05T09:30:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.07536","created_at":"2026-07-05T09:30:09Z"},{"alias_kind":"pith_short_12","alias_value":"GAVV3NHUE7MA","created_at":"2026-07-05T09:30:09Z"},{"alias_kind":"pith_short_16","alias_value":"GAVV3NHUE7MAN2VS","created_at":"2026-07-05T09:30:09Z"},{"alias_kind":"pith_short_8","alias_value":"GAVV3NHU","created_at":"2026-07-05T09:30:09Z"}],"graph_snapshots":[{"event_id":"sha256:950f8c4ce1bc8ef26d5bde85ddc41f053849fa9b2ad51482a011704956ddb09f","target":"graph","created_at":"2026-07-05T09:30:09Z","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/2403.07536/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many anatomical structures can be described by surface or volume meshes. Machine learning is a promising tool to extract information from these 3D models. However, high-fidelity meshes often contain hundreds of thousands of vertices, which creates unique challenges in building deep neural network architectures. Furthermore, patient-specific meshes may not be canonically aligned which limits the generalisation of machine learning algorithms. We propose LaB-GATr, a transfomer neural network with geometric tokenisation that can effectively learn with large-scale (bio-)medical surface and volume m","authors_text":"Baris Imre, Jelmer M. Wolterink, Julian Suk","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-12T11:19:46Z","title":"LaB-GATr: geometric algebra transformers for large biomedical surface and volume meshes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.07536","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:bec2deaceed7c65bbc2d818c1be146893a2ff7ed76dccff1cc9d8cb8d7a573f5","target":"record","created_at":"2026-07-05T09:30:09Z","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":"be78ff2d9cf867d3b33e4ab03029982ef271e7d138dfbe04ec9f4ddcd5c89d73","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-12T11:19:46Z","title_canon_sha256":"39e177896dfb6c0c5856fea72e43c8fc451709686a1c0d65e860a7628a5e480c"},"schema_version":"1.0","source":{"id":"2403.07536","kind":"arxiv","version":2}},"canonical_sha256":"302b5db4f427d806eab255e4a18df64584e6ecda65fce74af47051c69d8ace4b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"302b5db4f427d806eab255e4a18df64584e6ecda65fce74af47051c69d8ace4b","first_computed_at":"2026-07-05T09:30:09.461882Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:30:09.461882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WVmB5Ipxzl/wG3oQsx5OIsUS1ApFD+gKmXUIJ/I73qVlGmuzj6WzOwU0cIkuW9swEsZdMVZ9HXEnBcpsPswgAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:30:09.462409Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.07536","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bec2deaceed7c65bbc2d818c1be146893a2ff7ed76dccff1cc9d8cb8d7a573f5","sha256:950f8c4ce1bc8ef26d5bde85ddc41f053849fa9b2ad51482a011704956ddb09f"],"state_sha256":"9c14d6d947e0cc19ea82dc93fd234488a41b84bcd9c1bd7a81c010ccad498cf7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qrXlL7hkr1E7N2tY2McECZPTwXk4QUGuLJq2l0wmZbkGP3AUFibg5DSOJEqqY0EBSFOcvYx77rFSlvKiQCMVBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T17:54:19.809619Z","bundle_sha256":"9ef46272b46ba5dbe94dc63d82444244634d2e8d457e313a37737b2e0136dbf3"}}