{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:T5LYZXMH7WNOMESTZ444NC3IRL","short_pith_number":"pith:T5LYZXMH","canonical_record":{"source":{"id":"1506.07452","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-06-24T16:26:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ab86480b83c6c6312299feb5f6d288c1823d49e7835a70255b8e7edf483139b0","abstract_canon_sha256":"b9409f7278fa1ad357d2267212018abee73595c33ef43ff735b4c8bbe5f41721"},"schema_version":"1.0"},"canonical_sha256":"9f578cdd87fd9ae61253cf39c68b688afacc7da31df4701e24c093064499427c","source":{"kind":"arxiv","id":"1506.07452","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.07452","created_at":"2026-05-18T01:37:03Z"},{"alias_kind":"arxiv_version","alias_value":"1506.07452v1","created_at":"2026-05-18T01:37:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.07452","created_at":"2026-05-18T01:37:03Z"},{"alias_kind":"pith_short_12","alias_value":"T5LYZXMH7WNO","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_16","alias_value":"T5LYZXMH7WNOMEST","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_8","alias_value":"T5LYZXMH","created_at":"2026-05-18T12:29:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:T5LYZXMH7WNOMESTZ444NC3IRL","target":"record","payload":{"canonical_record":{"source":{"id":"1506.07452","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-06-24T16:26:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ab86480b83c6c6312299feb5f6d288c1823d49e7835a70255b8e7edf483139b0","abstract_canon_sha256":"b9409f7278fa1ad357d2267212018abee73595c33ef43ff735b4c8bbe5f41721"},"schema_version":"1.0"},"canonical_sha256":"9f578cdd87fd9ae61253cf39c68b688afacc7da31df4701e24c093064499427c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:37:03.508540Z","signature_b64":"yexljMCfbCrk0B6opEkstc6zGXwJQ3YdBswvdknvcZQNwGhHHNq4X3YrSc5+BGz9gdj+LwkyYddKs/+hMKkFAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f578cdd87fd9ae61253cf39c68b688afacc7da31df4701e24c093064499427c","last_reissued_at":"2026-05-18T01:37:03.508108Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:37:03.508108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1506.07452","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":"2026-05-18T01:37:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H9IK06nngyrTJ3+VAlunCNTQEo3WXd8Umj7Q520OPpGbPOc+Fwuz6ZK4jUsz1UhxiuoC2NXUcPWQ1LvuX5ARCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T01:40:29.876449Z"},"content_sha256":"9c9166ff8d0e879f35577f92214e86a0b6cd459253619f03c69d965da855e360","schema_version":"1.0","event_id":"sha256:9c9166ff8d0e879f35577f92214e86a0b6cd459253619f03c69d965da855e360"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:T5LYZXMH7WNOMESTZ444NC3IRL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Juergen Schmidhuber, Marcus Liwicki, Marijn F. Stollenga, Wonmin Byeon","submitted_at":"2015-06-24T16:26:51Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D videos to segment them. They have a fixed input size and typically perceive only small local contexts of the pixels to be classified as foreground or background. In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive the entire spatio-temporal context of each pixel in a few sweeps through all pixels, especially when the RNN is a Long Short-Term Memory (LSTM). Despite these theoretical advantages, however, unlike CNNs, previous MD-LSTM variants were hard to parallelize on GPUs. Here we re-arrange the traditi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.07452","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":""},"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":"2026-05-18T01:37:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HSSzaeTeZqneYAKYngLReWKdixYtBlGesD0nKskSc+L9mN0Ya6FyWdyff0Vis0lFy0wSkUvdGjjQK614R4xECA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T01:40:29.876785Z"},"content_sha256":"5c919b80f1be794ec13a37ed3d40f3406129810237e458279fdd9a4c1faee26d","schema_version":"1.0","event_id":"sha256:5c919b80f1be794ec13a37ed3d40f3406129810237e458279fdd9a4c1faee26d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T5LYZXMH7WNOMESTZ444NC3IRL/bundle.json","state_url":"https://pith.science/pith/T5LYZXMH7WNOMESTZ444NC3IRL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T5LYZXMH7WNOMESTZ444NC3IRL/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":"2026-06-12T01:40:29Z","links":{"resolver":"https://pith.science/pith/T5LYZXMH7WNOMESTZ444NC3IRL","bundle":"https://pith.science/pith/T5LYZXMH7WNOMESTZ444NC3IRL/bundle.json","state":"https://pith.science/pith/T5LYZXMH7WNOMESTZ444NC3IRL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T5LYZXMH7WNOMESTZ444NC3IRL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:T5LYZXMH7WNOMESTZ444NC3IRL","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":"b9409f7278fa1ad357d2267212018abee73595c33ef43ff735b4c8bbe5f41721","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-06-24T16:26:51Z","title_canon_sha256":"ab86480b83c6c6312299feb5f6d288c1823d49e7835a70255b8e7edf483139b0"},"schema_version":"1.0","source":{"id":"1506.07452","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.07452","created_at":"2026-05-18T01:37:03Z"},{"alias_kind":"arxiv_version","alias_value":"1506.07452v1","created_at":"2026-05-18T01:37:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.07452","created_at":"2026-05-18T01:37:03Z"},{"alias_kind":"pith_short_12","alias_value":"T5LYZXMH7WNO","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_16","alias_value":"T5LYZXMH7WNOMEST","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_8","alias_value":"T5LYZXMH","created_at":"2026-05-18T12:29:42Z"}],"graph_snapshots":[{"event_id":"sha256:5c919b80f1be794ec13a37ed3d40f3406129810237e458279fdd9a4c1faee26d","target":"graph","created_at":"2026-05-18T01:37:03Z","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":"Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D videos to segment them. They have a fixed input size and typically perceive only small local contexts of the pixels to be classified as foreground or background. In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive the entire spatio-temporal context of each pixel in a few sweeps through all pixels, especially when the RNN is a Long Short-Term Memory (LSTM). Despite these theoretical advantages, however, unlike CNNs, previous MD-LSTM variants were hard to parallelize on GPUs. Here we re-arrange the traditi","authors_text":"Juergen Schmidhuber, Marcus Liwicki, Marijn F. Stollenga, Wonmin Byeon","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-06-24T16:26:51Z","title":"Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.07452","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":"sha256:9c9166ff8d0e879f35577f92214e86a0b6cd459253619f03c69d965da855e360","target":"record","created_at":"2026-05-18T01:37:03Z","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":"b9409f7278fa1ad357d2267212018abee73595c33ef43ff735b4c8bbe5f41721","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-06-24T16:26:51Z","title_canon_sha256":"ab86480b83c6c6312299feb5f6d288c1823d49e7835a70255b8e7edf483139b0"},"schema_version":"1.0","source":{"id":"1506.07452","kind":"arxiv","version":1}},"canonical_sha256":"9f578cdd87fd9ae61253cf39c68b688afacc7da31df4701e24c093064499427c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9f578cdd87fd9ae61253cf39c68b688afacc7da31df4701e24c093064499427c","first_computed_at":"2026-05-18T01:37:03.508108Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:37:03.508108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yexljMCfbCrk0B6opEkstc6zGXwJQ3YdBswvdknvcZQNwGhHHNq4X3YrSc5+BGz9gdj+LwkyYddKs/+hMKkFAw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:37:03.508540Z","signed_message":"canonical_sha256_bytes"},"source_id":"1506.07452","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9c9166ff8d0e879f35577f92214e86a0b6cd459253619f03c69d965da855e360","sha256:5c919b80f1be794ec13a37ed3d40f3406129810237e458279fdd9a4c1faee26d"],"state_sha256":"69b8749898408b4a44eec83da66847ce6a633dc27a753e8ef0611a243198fd17"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yf0MF+jcfPfVC00B3gaoJtPBoDhjJxOMMgpF09cVbOlg1/q9kwYdVnt5ascpkZTllSDK+xtL33QoPG14op6jBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T01:40:29.878598Z","bundle_sha256":"0475993ccbb6948b42067f031d54c8f3d00567b6b75ba5c02cece9021724bae9"}}