{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:KBE2ENV3HXMICOABMK5E4DYTLP","short_pith_number":"pith:KBE2ENV3","canonical_record":{"source":{"id":"2502.03439","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-02-05T18:34:38Z","cross_cats_sorted":["cs.LG","cs.MS","stat.CO"],"title_canon_sha256":"6e706a6212188170b3100004c55daeadd1ae3e40fded7b0eccf37fa0a73b202b","abstract_canon_sha256":"5117a38e8573f76dd237244f3d2ef1ab3d6cf00495091dbfeee8237ba240a686"},"schema_version":"1.0"},"canonical_sha256":"5049a236bb3dd881380162ba4e0f135beafb8da3291a3397ec6e81129b497b09","source":{"kind":"arxiv","id":"2502.03439","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.03439","created_at":"2026-07-05T10:10:04Z"},{"alias_kind":"arxiv_version","alias_value":"2502.03439v1","created_at":"2026-07-05T10:10:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.03439","created_at":"2026-07-05T10:10:04Z"},{"alias_kind":"pith_short_12","alias_value":"KBE2ENV3HXMI","created_at":"2026-07-05T10:10:04Z"},{"alias_kind":"pith_short_16","alias_value":"KBE2ENV3HXMICOAB","created_at":"2026-07-05T10:10:04Z"},{"alias_kind":"pith_short_8","alias_value":"KBE2ENV3","created_at":"2026-07-05T10:10:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:KBE2ENV3HXMICOABMK5E4DYTLP","target":"record","payload":{"canonical_record":{"source":{"id":"2502.03439","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-02-05T18:34:38Z","cross_cats_sorted":["cs.LG","cs.MS","stat.CO"],"title_canon_sha256":"6e706a6212188170b3100004c55daeadd1ae3e40fded7b0eccf37fa0a73b202b","abstract_canon_sha256":"5117a38e8573f76dd237244f3d2ef1ab3d6cf00495091dbfeee8237ba240a686"},"schema_version":"1.0"},"canonical_sha256":"5049a236bb3dd881380162ba4e0f135beafb8da3291a3397ec6e81129b497b09","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:10:04.861667Z","signature_b64":"eHkqBK0Dkll16f6W8axOmeFXtyTVGnq8GUXTf6uJGp4uH6MpiAyesSp3MgJUAPlRZYKrtwAmDyNET7G6Wc/sBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5049a236bb3dd881380162ba4e0f135beafb8da3291a3397ec6e81129b497b09","last_reissued_at":"2026-07-05T10:10:04.860963Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:10:04.860963Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.03439","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":"2026-07-05T10:10:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wihn1LkXqOsNTU95lVAMgJFaSkjMrBjTlIWVg1mjXVUCQf0/tOLmCmnpF8K+cs8nD7/55p6b2Vktj4x/LAEkBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T05:52:53.179007Z"},"content_sha256":"5a26fa9095f8f56fb547826a8db5f50dbb2060b8c0924f7f4c9097f7e8722706","schema_version":"1.0","event_id":"sha256:5a26fa9095f8f56fb547826a8db5f50dbb2060b8c0924f7f4c9097f7e8722706"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:KBE2ENV3HXMICOABMK5E4DYTLP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Linearized Optimal Transport pyLOT Library: A Toolkit for Machine Learning on Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.MS","stat.CO"],"primary_cat":"stat.ML","authors_text":"Alexander Cloninger, Jun Linwu, Nicholas Karris, Varun Khurana","submitted_at":"2025-02-05T18:34:38Z","abstract_excerpt":"The pyLOT library offers a Python implementation of linearized optimal transport (LOT) techniques and methods to use in downstream tasks. The pipeline embeds probability distributions into a Hilbert space via the Optimal Transport maps from a fixed reference distribution, and this linearization allows downstream tasks to be completed using off the shelf (linear) machine learning algorithms. We provide a case study of performing ML on 3D scans of lemur teeth, where the original questions of classification, clustering, dimension reduction, and data generation reduce to simple linear operations p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.03439","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/2502.03439/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":"2026-07-05T10:10:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ewDZqtyPgCUrfs9Dhf71jdwOik3rpo6KPTivmnGyFjGVNwWVxZV3FW129UXsQRVCywn1Ty0ZC9wDoOrerP/BAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T05:52:53.180039Z"},"content_sha256":"a4fdcb009019d328f5b213c83c7d7f35b051951d674c3b3bfa4d9cdf8c63acb1","schema_version":"1.0","event_id":"sha256:a4fdcb009019d328f5b213c83c7d7f35b051951d674c3b3bfa4d9cdf8c63acb1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KBE2ENV3HXMICOABMK5E4DYTLP/bundle.json","state_url":"https://pith.science/pith/KBE2ENV3HXMICOABMK5E4DYTLP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KBE2ENV3HXMICOABMK5E4DYTLP/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":"2026-08-10T05:52:53Z","links":{"resolver":"https://pith.science/pith/KBE2ENV3HXMICOABMK5E4DYTLP","bundle":"https://pith.science/pith/KBE2ENV3HXMICOABMK5E4DYTLP/bundle.json","state":"https://pith.science/pith/KBE2ENV3HXMICOABMK5E4DYTLP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KBE2ENV3HXMICOABMK5E4DYTLP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KBE2ENV3HXMICOABMK5E4DYTLP","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":"5117a38e8573f76dd237244f3d2ef1ab3d6cf00495091dbfeee8237ba240a686","cross_cats_sorted":["cs.LG","cs.MS","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-02-05T18:34:38Z","title_canon_sha256":"6e706a6212188170b3100004c55daeadd1ae3e40fded7b0eccf37fa0a73b202b"},"schema_version":"1.0","source":{"id":"2502.03439","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.03439","created_at":"2026-07-05T10:10:04Z"},{"alias_kind":"arxiv_version","alias_value":"2502.03439v1","created_at":"2026-07-05T10:10:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.03439","created_at":"2026-07-05T10:10:04Z"},{"alias_kind":"pith_short_12","alias_value":"KBE2ENV3HXMI","created_at":"2026-07-05T10:10:04Z"},{"alias_kind":"pith_short_16","alias_value":"KBE2ENV3HXMICOAB","created_at":"2026-07-05T10:10:04Z"},{"alias_kind":"pith_short_8","alias_value":"KBE2ENV3","created_at":"2026-07-05T10:10:04Z"}],"graph_snapshots":[{"event_id":"sha256:a4fdcb009019d328f5b213c83c7d7f35b051951d674c3b3bfa4d9cdf8c63acb1","target":"graph","created_at":"2026-07-05T10:10:04Z","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/2502.03439/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The pyLOT library offers a Python implementation of linearized optimal transport (LOT) techniques and methods to use in downstream tasks. The pipeline embeds probability distributions into a Hilbert space via the Optimal Transport maps from a fixed reference distribution, and this linearization allows downstream tasks to be completed using off the shelf (linear) machine learning algorithms. We provide a case study of performing ML on 3D scans of lemur teeth, where the original questions of classification, clustering, dimension reduction, and data generation reduce to simple linear operations p","authors_text":"Alexander Cloninger, Jun Linwu, Nicholas Karris, Varun Khurana","cross_cats":["cs.LG","cs.MS","stat.CO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-02-05T18:34:38Z","title":"Linearized Optimal Transport pyLOT Library: A Toolkit for Machine Learning on Point Clouds"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.03439","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":"sha256:5a26fa9095f8f56fb547826a8db5f50dbb2060b8c0924f7f4c9097f7e8722706","target":"record","created_at":"2026-07-05T10:10:04Z","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":"5117a38e8573f76dd237244f3d2ef1ab3d6cf00495091dbfeee8237ba240a686","cross_cats_sorted":["cs.LG","cs.MS","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-02-05T18:34:38Z","title_canon_sha256":"6e706a6212188170b3100004c55daeadd1ae3e40fded7b0eccf37fa0a73b202b"},"schema_version":"1.0","source":{"id":"2502.03439","kind":"arxiv","version":1}},"canonical_sha256":"5049a236bb3dd881380162ba4e0f135beafb8da3291a3397ec6e81129b497b09","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5049a236bb3dd881380162ba4e0f135beafb8da3291a3397ec6e81129b497b09","first_computed_at":"2026-07-05T10:10:04.860963Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:10:04.860963Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eHkqBK0Dkll16f6W8axOmeFXtyTVGnq8GUXTf6uJGp4uH6MpiAyesSp3MgJUAPlRZYKrtwAmDyNET7G6Wc/sBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:10:04.861667Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.03439","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5a26fa9095f8f56fb547826a8db5f50dbb2060b8c0924f7f4c9097f7e8722706","sha256:a4fdcb009019d328f5b213c83c7d7f35b051951d674c3b3bfa4d9cdf8c63acb1"],"state_sha256":"43e1f36dde881b11e18f1149cc32b1dd70eb55f44f107f040056c984d88a3bdd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RJH10DoO2jTp50+h0WeuKrYmks6qq5YgwM5YVVzT8pk+G/4ygqk/gyELAvcCjbmBqnajDceSiQWWxaqag193Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T05:52:53.185452Z","bundle_sha256":"1b3a08710d958ca380c669c76af8e3083972e6f3ec19922fc87947cd0493b9b2"}}