{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:NINJIZW22KENFM537N4SKWSURK","short_pith_number":"pith:NINJIZW2","schema_version":"1.0","canonical_sha256":"6a1a9466dad288d2b3bbfb79255a548ab481be7f234fc133dc348ad8bfb1f0ac","source":{"kind":"arxiv","id":"2112.00290","version":1},"attestation_state":"computed","paper":{"title":"Unsupervised Statistical Learning for Die Analysis in Ancient Numismatics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhinav Natarajan, Andreas Heinecke, Emanuel Mayer, Yoonju Jung","submitted_at":"2021-12-01T06:02:07Z","abstract_excerpt":"Die analysis is an essential numismatic method, and an important tool of ancient economic history. Yet, manual die studies are too labor-intensive to comprehensively study large coinages such as those of the Roman Empire. We address this problem by proposing a model for unsupervised computational die analysis, which can reduce the time investment necessary for large-scale die studies by several orders of magnitude, in many cases from years to weeks. From a computer vision viewpoint, die studies present a challenging unsupervised clustering problem, because they involve an unknown and large num"},"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":"2112.00290","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-12-01T06:02:07Z","cross_cats_sorted":[],"title_canon_sha256":"644a1829d242da9ffcbf5d80f31a3fe9f4a9181b8f7e917823db191de08e0344","abstract_canon_sha256":"a3e368f6357b5f00685d35531440a5dbbb3bd5c1edbbc3d4d2e6388d55cc05ed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:36:44.720477Z","signature_b64":"9B2Zx0d2CXlNetdEd/vS0VSKozebTvef7j1hPgJFVDdmgownQ81MPhX7EWHY5/3HAl3e96SXgHv3rigThNOuCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a1a9466dad288d2b3bbfb79255a548ab481be7f234fc133dc348ad8bfb1f0ac","last_reissued_at":"2026-07-05T03:36:44.720075Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:36:44.720075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Statistical Learning for Die Analysis in Ancient Numismatics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhinav Natarajan, Andreas Heinecke, Emanuel Mayer, Yoonju Jung","submitted_at":"2021-12-01T06:02:07Z","abstract_excerpt":"Die analysis is an essential numismatic method, and an important tool of ancient economic history. Yet, manual die studies are too labor-intensive to comprehensively study large coinages such as those of the Roman Empire. We address this problem by proposing a model for unsupervised computational die analysis, which can reduce the time investment necessary for large-scale die studies by several orders of magnitude, in many cases from years to weeks. From a computer vision viewpoint, die studies present a challenging unsupervised clustering problem, because they involve an unknown and large num"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.00290","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/2112.00290/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":"2112.00290","created_at":"2026-07-05T03:36:44.720132+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.00290v1","created_at":"2026-07-05T03:36:44.720132+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.00290","created_at":"2026-07-05T03:36:44.720132+00:00"},{"alias_kind":"pith_short_12","alias_value":"NINJIZW22KEN","created_at":"2026-07-05T03:36:44.720132+00:00"},{"alias_kind":"pith_short_16","alias_value":"NINJIZW22KENFM53","created_at":"2026-07-05T03:36:44.720132+00:00"},{"alias_kind":"pith_short_8","alias_value":"NINJIZW2","created_at":"2026-07-05T03:36:44.720132+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.01186","citing_title":"A High-Accuracy SSIM-based Scoring System for Coin Die Link Identification","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NINJIZW22KENFM537N4SKWSURK","json":"https://pith.science/pith/NINJIZW22KENFM537N4SKWSURK.json","graph_json":"https://pith.science/api/pith-number/NINJIZW22KENFM537N4SKWSURK/graph.json","events_json":"https://pith.science/api/pith-number/NINJIZW22KENFM537N4SKWSURK/events.json","paper":"https://pith.science/paper/NINJIZW2"},"agent_actions":{"view_html":"https://pith.science/pith/NINJIZW22KENFM537N4SKWSURK","download_json":"https://pith.science/pith/NINJIZW22KENFM537N4SKWSURK.json","view_paper":"https://pith.science/paper/NINJIZW2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.00290&json=true","fetch_graph":"https://pith.science/api/pith-number/NINJIZW22KENFM537N4SKWSURK/graph.json","fetch_events":"https://pith.science/api/pith-number/NINJIZW22KENFM537N4SKWSURK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NINJIZW22KENFM537N4SKWSURK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NINJIZW22KENFM537N4SKWSURK/action/storage_attestation","attest_author":"https://pith.science/pith/NINJIZW22KENFM537N4SKWSURK/action/author_attestation","sign_citation":"https://pith.science/pith/NINJIZW22KENFM537N4SKWSURK/action/citation_signature","submit_replication":"https://pith.science/pith/NINJIZW22KENFM537N4SKWSURK/action/replication_record"}},"created_at":"2026-07-05T03:36:44.720132+00:00","updated_at":"2026-07-05T03:36:44.720132+00:00"}