{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:VEAILZ5N4QFUK7W343C7FST7XN","short_pith_number":"pith:VEAILZ5N","schema_version":"1.0","canonical_sha256":"a90085e7ade40b457edbe6c5f2ca7fbb431af6c2ae067e685eac024b7e87c1d9","source":{"kind":"arxiv","id":"1804.00816","version":1},"attestation_state":"computed","paper":{"title":"Classifying the Large Scale Structure of the Universe with Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Miguel A. Aragon-Calvo","submitted_at":"2018-04-03T04:07:19Z","abstract_excerpt":"We present the first application of deep neural networks to the semantic segmentation of cosmological filaments and walls in the Large Scale Structure of the Universe. Our results are based on a deep Convolutional Neural Network (CNN) with a U-Net architecture trained using an existing state-of-the-art manually-guided segmentation method. We successfully trained an tested an U-Net with a Voronoi model and an N-body simulation. The predicted segmentation masks from the Voronoi model have a Dice coefficient of 0.95 and 0.97 for filaments and mask respectively. The predicted segmentation masks fr"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1804.00816","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2018-04-03T04:07:19Z","cross_cats_sorted":[],"title_canon_sha256":"2aa73982da03261774ea6e3cd1a9011c98878c9550a3a930686bc5e13944ed47","abstract_canon_sha256":"5c647cd1291abb65c7c39d181932fcce87c74b884eb21441acaf827e566e56c7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:16.584009Z","signature_b64":"GpykEML3luGs1c6Lk8W7QoVEqsb2BB8xbPJH/pAh4JOGiZFMnpKhkvcGPWMkEzJ4iOqf+RUDxd0YgRVgCPbXDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a90085e7ade40b457edbe6c5f2ca7fbb431af6c2ae067e685eac024b7e87c1d9","last_reissued_at":"2026-05-17T23:54:16.583461Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:16.583461Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Classifying the Large Scale Structure of the Universe with Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.CO","authors_text":"Miguel A. Aragon-Calvo","submitted_at":"2018-04-03T04:07:19Z","abstract_excerpt":"We present the first application of deep neural networks to the semantic segmentation of cosmological filaments and walls in the Large Scale Structure of the Universe. Our results are based on a deep Convolutional Neural Network (CNN) with a U-Net architecture trained using an existing state-of-the-art manually-guided segmentation method. We successfully trained an tested an U-Net with a Voronoi model and an N-body simulation. The predicted segmentation masks from the Voronoi model have a Dice coefficient of 0.95 and 0.97 for filaments and mask respectively. The predicted segmentation masks fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.00816","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1804.00816","created_at":"2026-05-17T23:54:16.583535+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.00816v1","created_at":"2026-05-17T23:54:16.583535+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.00816","created_at":"2026-05-17T23:54:16.583535+00:00"},{"alias_kind":"pith_short_12","alias_value":"VEAILZ5N4QFU","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"VEAILZ5N4QFUK7W3","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"VEAILZ5N","created_at":"2026-05-18T12:32:59.047623+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VEAILZ5N4QFUK7W343C7FST7XN","json":"https://pith.science/pith/VEAILZ5N4QFUK7W343C7FST7XN.json","graph_json":"https://pith.science/api/pith-number/VEAILZ5N4QFUK7W343C7FST7XN/graph.json","events_json":"https://pith.science/api/pith-number/VEAILZ5N4QFUK7W343C7FST7XN/events.json","paper":"https://pith.science/paper/VEAILZ5N"},"agent_actions":{"view_html":"https://pith.science/pith/VEAILZ5N4QFUK7W343C7FST7XN","download_json":"https://pith.science/pith/VEAILZ5N4QFUK7W343C7FST7XN.json","view_paper":"https://pith.science/paper/VEAILZ5N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.00816&json=true","fetch_graph":"https://pith.science/api/pith-number/VEAILZ5N4QFUK7W343C7FST7XN/graph.json","fetch_events":"https://pith.science/api/pith-number/VEAILZ5N4QFUK7W343C7FST7XN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VEAILZ5N4QFUK7W343C7FST7XN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VEAILZ5N4QFUK7W343C7FST7XN/action/storage_attestation","attest_author":"https://pith.science/pith/VEAILZ5N4QFUK7W343C7FST7XN/action/author_attestation","sign_citation":"https://pith.science/pith/VEAILZ5N4QFUK7W343C7FST7XN/action/citation_signature","submit_replication":"https://pith.science/pith/VEAILZ5N4QFUK7W343C7FST7XN/action/replication_record"}},"created_at":"2026-05-17T23:54:16.583535+00:00","updated_at":"2026-05-17T23:54:16.583535+00:00"}