{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:SXOQXDCTJ26NCFOKIXR4H4G6GK","short_pith_number":"pith:SXOQXDCT","schema_version":"1.0","canonical_sha256":"95dd0b8c534ebcd115ca45e3c3f0de329e5e0ee53d1a84e37d05cf1a278f2e55","source":{"kind":"arxiv","id":"2101.08763","version":1},"attestation_state":"computed","paper":{"title":"GPU-Accelerated Optimizer-Aware Evaluation of Submodular Exemplar Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DC","authors_text":"Katharina Morik, Philipp-Jan Honysz, Sebastian Buschj\\\"ager","submitted_at":"2021-01-21T18:23:44Z","abstract_excerpt":"The optimization of submodular functions constitutes a viable way to perform clustering. Strong approximation guarantees and feasible optimization w.r.t. streaming data make this clustering approach favorable. Technically, submodular functions map subsets of data to real values, which indicate how \"representative\" a specific subset is. Optimal sets might then be used to partition the data space and to infer clusters. Exemplar-based clustering is one of the possible submodular functions, but suffers from high computational complexity. However, for practical applications, the particular real-tim"},"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":"2101.08763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2021-01-21T18:23:44Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6d3faf3f75a9fde2e216804341f9941faaecc08e0ac1b7e11ce3facf48c1c279","abstract_canon_sha256":"0615e1f7bc61ff996cb7ef5696e8eb277cc78daf777fa1718a9ee189e6bc9ee1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:08:39.061119Z","signature_b64":"t6KrSd9Mid3DouzzRY2N3eHRgVk0wXg2JFGGX33bVhQ+HoiiUOScROe4OARPRaR2BY65wXDoHhwZl3rXHQhIDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"95dd0b8c534ebcd115ca45e3c3f0de329e5e0ee53d1a84e37d05cf1a278f2e55","last_reissued_at":"2026-07-05T02:08:39.060770Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:08:39.060770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GPU-Accelerated Optimizer-Aware Evaluation of Submodular Exemplar Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DC","authors_text":"Katharina Morik, Philipp-Jan Honysz, Sebastian Buschj\\\"ager","submitted_at":"2021-01-21T18:23:44Z","abstract_excerpt":"The optimization of submodular functions constitutes a viable way to perform clustering. Strong approximation guarantees and feasible optimization w.r.t. streaming data make this clustering approach favorable. Technically, submodular functions map subsets of data to real values, which indicate how \"representative\" a specific subset is. Optimal sets might then be used to partition the data space and to infer clusters. Exemplar-based clustering is one of the possible submodular functions, but suffers from high computational complexity. However, for practical applications, the particular real-tim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.08763","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2101.08763/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":"2101.08763","created_at":"2026-07-05T02:08:39.060826+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.08763v1","created_at":"2026-07-05T02:08:39.060826+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.08763","created_at":"2026-07-05T02:08:39.060826+00:00"},{"alias_kind":"pith_short_12","alias_value":"SXOQXDCTJ26N","created_at":"2026-07-05T02:08:39.060826+00:00"},{"alias_kind":"pith_short_16","alias_value":"SXOQXDCTJ26NCFOK","created_at":"2026-07-05T02:08:39.060826+00:00"},{"alias_kind":"pith_short_8","alias_value":"SXOQXDCT","created_at":"2026-07-05T02:08:39.060826+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.13834","citing_title":"Scalable Submodular Policy Optimization via Pruned Submodularity Graph","ref_index":11,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SXOQXDCTJ26NCFOKIXR4H4G6GK","json":"https://pith.science/pith/SXOQXDCTJ26NCFOKIXR4H4G6GK.json","graph_json":"https://pith.science/api/pith-number/SXOQXDCTJ26NCFOKIXR4H4G6GK/graph.json","events_json":"https://pith.science/api/pith-number/SXOQXDCTJ26NCFOKIXR4H4G6GK/events.json","paper":"https://pith.science/paper/SXOQXDCT"},"agent_actions":{"view_html":"https://pith.science/pith/SXOQXDCTJ26NCFOKIXR4H4G6GK","download_json":"https://pith.science/pith/SXOQXDCTJ26NCFOKIXR4H4G6GK.json","view_paper":"https://pith.science/paper/SXOQXDCT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.08763&json=true","fetch_graph":"https://pith.science/api/pith-number/SXOQXDCTJ26NCFOKIXR4H4G6GK/graph.json","fetch_events":"https://pith.science/api/pith-number/SXOQXDCTJ26NCFOKIXR4H4G6GK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SXOQXDCTJ26NCFOKIXR4H4G6GK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SXOQXDCTJ26NCFOKIXR4H4G6GK/action/storage_attestation","attest_author":"https://pith.science/pith/SXOQXDCTJ26NCFOKIXR4H4G6GK/action/author_attestation","sign_citation":"https://pith.science/pith/SXOQXDCTJ26NCFOKIXR4H4G6GK/action/citation_signature","submit_replication":"https://pith.science/pith/SXOQXDCTJ26NCFOKIXR4H4G6GK/action/replication_record"}},"created_at":"2026-07-05T02:08:39.060826+00:00","updated_at":"2026-07-05T02:08:39.060826+00:00"}