{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:KJDL7APROJEV5XTHEPZC6LFJCS","short_pith_number":"pith:KJDL7APR","schema_version":"1.0","canonical_sha256":"5246bf81f172495ede6723f22f2ca91497e9cad9473e0135700cc4ff56236503","source":{"kind":"arxiv","id":"2005.12692","version":2},"attestation_state":"computed","paper":{"title":"Cubical Ripser: Software for computing persistent homology of image and volume data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.AT"],"primary_cat":"cs.CV","authors_text":"Kazushi Ahara, Shizuo Kaji, Takeki Sudo","submitted_at":"2020-05-23T08:25:49Z","abstract_excerpt":"We introduce Cubical Ripser for computing persistent homology of image and volume data (more precisely, weighted cubical complexes). To our best knowledge, Cubical Ripser is currently the fastest and the most memory-efficient program for computing persistent homology of weighted cubical complexes. We demonstrate our software with an example of image analysis in which persistent homology and convolutional neural networks are successfully combined. Our open-source implementation is available online."},"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":"2005.12692","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-23T08:25:49Z","cross_cats_sorted":["math.AT"],"title_canon_sha256":"4a49de0ea11beaba3587a20b49c779e92284462308fb3c73bea779addafdab31","abstract_canon_sha256":"46e738da7b56523aadcab4477c0cf3e26a8f6d4189111c871233eeb407b3ae8f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:09:44.876760Z","signature_b64":"YiXiTDQcNFTh666UJmzp4cCzMIdyA3AUb/mdk3WwT7qbnr0/fzMZnLyqNsjurf7GzDfu60N2KXkyEwENyHEtBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5246bf81f172495ede6723f22f2ca91497e9cad9473e0135700cc4ff56236503","last_reissued_at":"2026-07-05T01:09:44.876379Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:09:44.876379Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cubical Ripser: Software for computing persistent homology of image and volume data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.AT"],"primary_cat":"cs.CV","authors_text":"Kazushi Ahara, Shizuo Kaji, Takeki Sudo","submitted_at":"2020-05-23T08:25:49Z","abstract_excerpt":"We introduce Cubical Ripser for computing persistent homology of image and volume data (more precisely, weighted cubical complexes). To our best knowledge, Cubical Ripser is currently the fastest and the most memory-efficient program for computing persistent homology of weighted cubical complexes. We demonstrate our software with an example of image analysis in which persistent homology and convolutional neural networks are successfully combined. Our open-source implementation is available online."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.12692","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2005.12692/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2005.12692","created_at":"2026-07-05T01:09:44.876437+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.12692v2","created_at":"2026-07-05T01:09:44.876437+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.12692","created_at":"2026-07-05T01:09:44.876437+00:00"},{"alias_kind":"pith_short_12","alias_value":"KJDL7APROJEV","created_at":"2026-07-05T01:09:44.876437+00:00"},{"alias_kind":"pith_short_16","alias_value":"KJDL7APROJEV5XTH","created_at":"2026-07-05T01:09:44.876437+00:00"},{"alias_kind":"pith_short_8","alias_value":"KJDL7APR","created_at":"2026-07-05T01:09:44.876437+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26204","citing_title":"Topology-Informed Neural Networks for Flood Detection in Optical and Synthetic Aperture Radar Imagery","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2606.26204","citing_title":"Topology-Informed Neural Networks for Flood Detection in Optical and Synthetic Aperture Radar Imagery","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24342","citing_title":"The 3D Structure and Kinematics of the Local Disk-Halo Interface: Intermediate-velocity Clouds are the Minority of High-altitude Clouds in the Solar Neighborhood","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30490","citing_title":"From Frames to Features: Scalable Zigzag Persistence for Binary Video","ref_index":49,"is_internal_anchor":false},{"citing_arxiv_id":"2512.10753","citing_title":"Quantifying displacement: an urban expansion consequence via persistent homology","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KJDL7APROJEV5XTHEPZC6LFJCS","json":"https://pith.science/pith/KJDL7APROJEV5XTHEPZC6LFJCS.json","graph_json":"https://pith.science/api/pith-number/KJDL7APROJEV5XTHEPZC6LFJCS/graph.json","events_json":"https://pith.science/api/pith-number/KJDL7APROJEV5XTHEPZC6LFJCS/events.json","paper":"https://pith.science/paper/KJDL7APR"},"agent_actions":{"view_html":"https://pith.science/pith/KJDL7APROJEV5XTHEPZC6LFJCS","download_json":"https://pith.science/pith/KJDL7APROJEV5XTHEPZC6LFJCS.json","view_paper":"https://pith.science/paper/KJDL7APR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.12692&json=true","fetch_graph":"https://pith.science/api/pith-number/KJDL7APROJEV5XTHEPZC6LFJCS/graph.json","fetch_events":"https://pith.science/api/pith-number/KJDL7APROJEV5XTHEPZC6LFJCS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KJDL7APROJEV5XTHEPZC6LFJCS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KJDL7APROJEV5XTHEPZC6LFJCS/action/storage_attestation","attest_author":"https://pith.science/pith/KJDL7APROJEV5XTHEPZC6LFJCS/action/author_attestation","sign_citation":"https://pith.science/pith/KJDL7APROJEV5XTHEPZC6LFJCS/action/citation_signature","submit_replication":"https://pith.science/pith/KJDL7APROJEV5XTHEPZC6LFJCS/action/replication_record"}},"created_at":"2026-07-05T01:09:44.876437+00:00","updated_at":"2026-07-05T01:09:44.876437+00:00"}