{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:S3X7J4C4WNKZZDDAFHUBXWXWMG","short_pith_number":"pith:S3X7J4C4","canonical_record":{"source":{"id":"1612.02141","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-07T08:07:05Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3e841fee3dec15805c020df46d8db21c84bfed417c94e247546570bcb5384c42","abstract_canon_sha256":"d9d744453ebdf72abe1744557436fdc7b7b980dd23da4618b9f8f3235e880ae1"},"schema_version":"1.0"},"canonical_sha256":"96eff4f05cb3559c8c6029e81bdaf661a0d3757ec72c725be93a736e9bdb61e8","source":{"kind":"arxiv","id":"1612.02141","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.02141","created_at":"2026-05-18T00:40:13Z"},{"alias_kind":"arxiv_version","alias_value":"1612.02141v2","created_at":"2026-05-18T00:40:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.02141","created_at":"2026-05-18T00:40:13Z"},{"alias_kind":"pith_short_12","alias_value":"S3X7J4C4WNKZ","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"S3X7J4C4WNKZZDDA","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"S3X7J4C4","created_at":"2026-05-18T12:30:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:S3X7J4C4WNKZZDDAFHUBXWXWMG","target":"record","payload":{"canonical_record":{"source":{"id":"1612.02141","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-07T08:07:05Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3e841fee3dec15805c020df46d8db21c84bfed417c94e247546570bcb5384c42","abstract_canon_sha256":"d9d744453ebdf72abe1744557436fdc7b7b980dd23da4618b9f8f3235e880ae1"},"schema_version":"1.0"},"canonical_sha256":"96eff4f05cb3559c8c6029e81bdaf661a0d3757ec72c725be93a736e9bdb61e8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:13.532237Z","signature_b64":"i6O6P2/9azm7Iy8+I8o9O8BY+WQ8NNQ/AWMoMbUXpYVn0cgAwESRLFMMasmCyzEi0Jf1bAQYqtQVyDJqmh0JAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"96eff4f05cb3559c8c6029e81bdaf661a0d3757ec72c725be93a736e9bdb61e8","last_reissued_at":"2026-05-18T00:40:13.531662Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:13.531662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.02141","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":"2026-05-18T00:40:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t+49k/+n+Cpjj6Mp/tdvzpkR8Joh9a4J+3nfs522kVDbdAOj3r0pjBxiV35qdTbXeBwHbYySWSoteLFv6BcADg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T16:56:37.077780Z"},"content_sha256":"aa3ab7f96b710c3b2ea6c2e8770537959d6111e980630e70145b3d6709151f9a","schema_version":"1.0","event_id":"sha256:aa3ab7f96b710c3b2ea6c2e8770537959d6111e980630e70145b3d6709151f9a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:S3X7J4C4WNKZZDDAFHUBXWXWMG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CV","authors_text":"Adarsh Krishnamurthy, Aditya Balu, Gavin Young, Kin Gwn Lore, Sambit Ghadai, Soumik Sarkar","submitted_at":"2016-12-07T08:07:05Z","abstract_excerpt":"3D convolutional neural networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. In this paper, we present a 3D-CNN based method to learn distinct local geometric features of interest within an object. In this context, the voxelized representation may not be sufficient to capture the distinguishing information about such local features. To enable efficient learning, we augment the voxel data with surface normals of the object boundary. We then train a 3D-CNN with this augmented data and identify the local features critical for decision-making using 3D "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.02141","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":"2026-05-18T00:40:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TEXAaZQCbb35tZD2xnMmmOUAoCKbBHYVz9eSUJU06yPbRa0tw7N4sQUHSdxWteuHaoZiy+meQ3YQ93FqkjmjDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T16:56:37.078430Z"},"content_sha256":"6ce4eedc5077d4e8c9f18bb173a74d607320dc2a28b0dfa8d81869c6f4e51a8e","schema_version":"1.0","event_id":"sha256:6ce4eedc5077d4e8c9f18bb173a74d607320dc2a28b0dfa8d81869c6f4e51a8e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S3X7J4C4WNKZZDDAFHUBXWXWMG/bundle.json","state_url":"https://pith.science/pith/S3X7J4C4WNKZZDDAFHUBXWXWMG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S3X7J4C4WNKZZDDAFHUBXWXWMG/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":"2026-05-28T16:56:37Z","links":{"resolver":"https://pith.science/pith/S3X7J4C4WNKZZDDAFHUBXWXWMG","bundle":"https://pith.science/pith/S3X7J4C4WNKZZDDAFHUBXWXWMG/bundle.json","state":"https://pith.science/pith/S3X7J4C4WNKZZDDAFHUBXWXWMG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S3X7J4C4WNKZZDDAFHUBXWXWMG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:S3X7J4C4WNKZZDDAFHUBXWXWMG","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":"d9d744453ebdf72abe1744557436fdc7b7b980dd23da4618b9f8f3235e880ae1","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-07T08:07:05Z","title_canon_sha256":"3e841fee3dec15805c020df46d8db21c84bfed417c94e247546570bcb5384c42"},"schema_version":"1.0","source":{"id":"1612.02141","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.02141","created_at":"2026-05-18T00:40:13Z"},{"alias_kind":"arxiv_version","alias_value":"1612.02141v2","created_at":"2026-05-18T00:40:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.02141","created_at":"2026-05-18T00:40:13Z"},{"alias_kind":"pith_short_12","alias_value":"S3X7J4C4WNKZ","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"S3X7J4C4WNKZZDDA","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"S3X7J4C4","created_at":"2026-05-18T12:30:41Z"}],"graph_snapshots":[{"event_id":"sha256:6ce4eedc5077d4e8c9f18bb173a74d607320dc2a28b0dfa8d81869c6f4e51a8e","target":"graph","created_at":"2026-05-18T00:40:13Z","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":"3D convolutional neural networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. In this paper, we present a 3D-CNN based method to learn distinct local geometric features of interest within an object. In this context, the voxelized representation may not be sufficient to capture the distinguishing information about such local features. To enable efficient learning, we augment the voxel data with surface normals of the object boundary. We then train a 3D-CNN with this augmented data and identify the local features critical for decision-making using 3D ","authors_text":"Adarsh Krishnamurthy, Aditya Balu, Gavin Young, Kin Gwn Lore, Sambit Ghadai, Soumik Sarkar","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-07T08:07:05Z","title":"Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.02141","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:aa3ab7f96b710c3b2ea6c2e8770537959d6111e980630e70145b3d6709151f9a","target":"record","created_at":"2026-05-18T00:40:13Z","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":"d9d744453ebdf72abe1744557436fdc7b7b980dd23da4618b9f8f3235e880ae1","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-07T08:07:05Z","title_canon_sha256":"3e841fee3dec15805c020df46d8db21c84bfed417c94e247546570bcb5384c42"},"schema_version":"1.0","source":{"id":"1612.02141","kind":"arxiv","version":2}},"canonical_sha256":"96eff4f05cb3559c8c6029e81bdaf661a0d3757ec72c725be93a736e9bdb61e8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"96eff4f05cb3559c8c6029e81bdaf661a0d3757ec72c725be93a736e9bdb61e8","first_computed_at":"2026-05-18T00:40:13.531662Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:40:13.531662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i6O6P2/9azm7Iy8+I8o9O8BY+WQ8NNQ/AWMoMbUXpYVn0cgAwESRLFMMasmCyzEi0Jf1bAQYqtQVyDJqmh0JAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:40:13.532237Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.02141","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aa3ab7f96b710c3b2ea6c2e8770537959d6111e980630e70145b3d6709151f9a","sha256:6ce4eedc5077d4e8c9f18bb173a74d607320dc2a28b0dfa8d81869c6f4e51a8e"],"state_sha256":"51658493e9596112b7984bf7382f49aacf245a1d67fe48e9e1b961aec99def56"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2+vH6vVc2dr9hSMojoHsKOB7FfooL9UR0aHOrihyEER68tfbIZEnpkwZP8LXA4BdfUc5FEzibbxCybpP97OPBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T16:56:37.082220Z","bundle_sha256":"b196de00874096829ab3e41b7e78a0b34a509f686cedda2b36b1bfad05571a36"}}