{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:MHGLXTUFRXDGUBMUIM7IIJRBGS","short_pith_number":"pith:MHGLXTUF","canonical_record":{"source":{"id":"1605.02766","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-09T20:33:30Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"72718932f4dc73de30a8254fc055e72c6b060586438297b30f74900f073198c1","abstract_canon_sha256":"5a96e4b288ab37c214fa551d0c1500e010650aa8701a8b0b1111a318ab006cb0"},"schema_version":"1.0"},"canonical_sha256":"61ccbbce858dc66a0594433e84262134a6b94f20ff6b65fa9261aeba82c46d8f","source":{"kind":"arxiv","id":"1605.02766","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1605.02766","created_at":"2026-05-18T01:10:04Z"},{"alias_kind":"arxiv_version","alias_value":"1605.02766v3","created_at":"2026-05-18T01:10:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.02766","created_at":"2026-05-18T01:10:04Z"},{"alias_kind":"pith_short_12","alias_value":"MHGLXTUFRXDG","created_at":"2026-05-18T12:30:32Z"},{"alias_kind":"pith_short_16","alias_value":"MHGLXTUFRXDGUBMU","created_at":"2026-05-18T12:30:32Z"},{"alias_kind":"pith_short_8","alias_value":"MHGLXTUF","created_at":"2026-05-18T12:30:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:MHGLXTUFRXDGUBMUIM7IIJRBGS","target":"record","payload":{"canonical_record":{"source":{"id":"1605.02766","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-09T20:33:30Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"72718932f4dc73de30a8254fc055e72c6b060586438297b30f74900f073198c1","abstract_canon_sha256":"5a96e4b288ab37c214fa551d0c1500e010650aa8701a8b0b1111a318ab006cb0"},"schema_version":"1.0"},"canonical_sha256":"61ccbbce858dc66a0594433e84262134a6b94f20ff6b65fa9261aeba82c46d8f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:10:04.909282Z","signature_b64":"x26Jf77X0lvGiROBeyFvBrECDbLhYUuNW4tiAjMK549ikr1wu9ChMFPaVlnRl29hNBo34XUp4SpoQ50W1/gcAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61ccbbce858dc66a0594433e84262134a6b94f20ff6b65fa9261aeba82c46d8f","last_reissued_at":"2026-05-18T01:10:04.908395Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:10:04.908395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1605.02766","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:10:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YEykCOJ/zplp5OBkufbxfkAXqmafz82my1tSWjmwECBlAv/HmFISwf1KdEwIzmqqY6VHyRDMuP4bv//2xA2xAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T23:38:01.586094Z"},"content_sha256":"27f1d31710934811094ca664f9eb67632b2f752a6c11863eaf9e3049a98901ab","schema_version":"1.0","event_id":"sha256:27f1d31710934811094ca664f9eb67632b2f752a6c11863eaf9e3049a98901ab"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:MHGLXTUFRXDGUBMUIM7IIJRBGS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Chengxi Ye, Chen Zhao, Cornelia Fermuller, Yezhou Yang, Yiannis Aloimonos","submitted_at":"2016-05-09T20:33:30Z","abstract_excerpt":"LightNet is a lightweight, versatile and purely Matlab-based deep learning framework. The idea underlying its design is to provide an easy-to-understand, easy-to-use and efficient computational platform for deep learning research. The implemented framework supports major deep learning architectures such as Multilayer Perceptron Networks (MLP), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The framework also supports both CPU and GPU computation, and the switch between them is straightforward. Different applications in computer vision, natural language processing and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.02766","kind":"arxiv","version":3},"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":"2026-05-18T01:10:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DTCsPNm2BJNQ2hSNDlabFzOy6z56rrXIAlhTy9qqvVpNsqeVzxkJm7/jYiNqbM+A41jhh+SLI6QjcfVfUxeTDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T23:38:01.586811Z"},"content_sha256":"11f237562ad451cf94fb91416cd0eb8299c847b073e933b8fd3f8d917f8257e8","schema_version":"1.0","event_id":"sha256:11f237562ad451cf94fb91416cd0eb8299c847b073e933b8fd3f8d917f8257e8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MHGLXTUFRXDGUBMUIM7IIJRBGS/bundle.json","state_url":"https://pith.science/pith/MHGLXTUFRXDGUBMUIM7IIJRBGS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MHGLXTUFRXDGUBMUIM7IIJRBGS/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":"2026-05-31T23:38:01Z","links":{"resolver":"https://pith.science/pith/MHGLXTUFRXDGUBMUIM7IIJRBGS","bundle":"https://pith.science/pith/MHGLXTUFRXDGUBMUIM7IIJRBGS/bundle.json","state":"https://pith.science/pith/MHGLXTUFRXDGUBMUIM7IIJRBGS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MHGLXTUFRXDGUBMUIM7IIJRBGS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:MHGLXTUFRXDGUBMUIM7IIJRBGS","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":"5a96e4b288ab37c214fa551d0c1500e010650aa8701a8b0b1111a318ab006cb0","cross_cats_sorted":["cs.CV","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-09T20:33:30Z","title_canon_sha256":"72718932f4dc73de30a8254fc055e72c6b060586438297b30f74900f073198c1"},"schema_version":"1.0","source":{"id":"1605.02766","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1605.02766","created_at":"2026-05-18T01:10:04Z"},{"alias_kind":"arxiv_version","alias_value":"1605.02766v3","created_at":"2026-05-18T01:10:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.02766","created_at":"2026-05-18T01:10:04Z"},{"alias_kind":"pith_short_12","alias_value":"MHGLXTUFRXDG","created_at":"2026-05-18T12:30:32Z"},{"alias_kind":"pith_short_16","alias_value":"MHGLXTUFRXDGUBMU","created_at":"2026-05-18T12:30:32Z"},{"alias_kind":"pith_short_8","alias_value":"MHGLXTUF","created_at":"2026-05-18T12:30:32Z"}],"graph_snapshots":[{"event_id":"sha256:11f237562ad451cf94fb91416cd0eb8299c847b073e933b8fd3f8d917f8257e8","target":"graph","created_at":"2026-05-18T01:10:04Z","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"},"paper":{"abstract_excerpt":"LightNet is a lightweight, versatile and purely Matlab-based deep learning framework. The idea underlying its design is to provide an easy-to-understand, easy-to-use and efficient computational platform for deep learning research. The implemented framework supports major deep learning architectures such as Multilayer Perceptron Networks (MLP), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The framework also supports both CPU and GPU computation, and the switch between them is straightforward. Different applications in computer vision, natural language processing and ","authors_text":"Chengxi Ye, Chen Zhao, Cornelia Fermuller, Yezhou Yang, Yiannis Aloimonos","cross_cats":["cs.CV","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-09T20:33:30Z","title":"LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.02766","kind":"arxiv","version":3},"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":"sha256:27f1d31710934811094ca664f9eb67632b2f752a6c11863eaf9e3049a98901ab","target":"record","created_at":"2026-05-18T01:10:04Z","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":"5a96e4b288ab37c214fa551d0c1500e010650aa8701a8b0b1111a318ab006cb0","cross_cats_sorted":["cs.CV","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-09T20:33:30Z","title_canon_sha256":"72718932f4dc73de30a8254fc055e72c6b060586438297b30f74900f073198c1"},"schema_version":"1.0","source":{"id":"1605.02766","kind":"arxiv","version":3}},"canonical_sha256":"61ccbbce858dc66a0594433e84262134a6b94f20ff6b65fa9261aeba82c46d8f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"61ccbbce858dc66a0594433e84262134a6b94f20ff6b65fa9261aeba82c46d8f","first_computed_at":"2026-05-18T01:10:04.908395Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:10:04.908395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x26Jf77X0lvGiROBeyFvBrECDbLhYUuNW4tiAjMK549ikr1wu9ChMFPaVlnRl29hNBo34XUp4SpoQ50W1/gcAA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:10:04.909282Z","signed_message":"canonical_sha256_bytes"},"source_id":"1605.02766","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:27f1d31710934811094ca664f9eb67632b2f752a6c11863eaf9e3049a98901ab","sha256:11f237562ad451cf94fb91416cd0eb8299c847b073e933b8fd3f8d917f8257e8"],"state_sha256":"526f58b81d74e1896a6a4c09f330f62beba2d2b53ec08296aff7234d33414d6d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jqfz3jBoFsbDdNPpnyhCaFNXJOKZw0vZsmrXKsCr3+d34QO/YriWfStReMlWu/4dQlTMm2mgqp1Fine0WRUCCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T23:38:01.590895Z","bundle_sha256":"efaa3d84c503c7c8efca49113f769e0906434e35679f0684c59caa5be348b8d8"}}