{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:HK7XRVYJRDPT5MPMJVPCCLY2GP","short_pith_number":"pith:HK7XRVYJ","schema_version":"1.0","canonical_sha256":"3abf78d70988df3eb1ec4d5e212f1a33d2b9e8b788df936cfd161cc338b6a468","source":{"kind":"arxiv","id":"1803.10354","version":3},"attestation_state":"computed","paper":{"title":"An optimization parameter for seriation of noisy data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.CO","authors_text":"Jeannette Janssen, Mahya Ghandehari","submitted_at":"2018-03-27T22:53:08Z","abstract_excerpt":"A square symmetric matrix is a Robinson similarity matrix if entries in its rows and columns are non-decreasing when moving towards the diagonal. A Robinson similarity matrix can be viewed as the affinity matrix between objects arranged in linear order, where objects closer together have higher affinity. We define a new parameter, $\\Gamma_\\max$, which measures how badly a given matrix fails to be Robinson similarity. Namely, a matrix is Robinson similarity precisely when its $\\Gamma_\\max$ attains zero, and a matrix with small $\\Gamma_\\max$ is close (in the normalized $\\ell^1$-norm) to a Robins"},"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":"1803.10354","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.CO","submitted_at":"2018-03-27T22:53:08Z","cross_cats_sorted":[],"title_canon_sha256":"8ef2d71aa3a4efe520e09851d881b48c043dd7ae3f85ede884b148fc20fe44d1","abstract_canon_sha256":"2388cbf8a21ced849566cefe72d0cc556bc8c9376ca7cb2d5912272288cd43dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:36:04.871507Z","signature_b64":"AMwfJUZRnZbhrz2EhisCpE/dmdr3K0aUx1q5m+dPLLNz18iwvKeCNQ4q00bc8IKcVzkjp3jJm4ehY1Dk+17zAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3abf78d70988df3eb1ec4d5e212f1a33d2b9e8b788df936cfd161cc338b6a468","last_reissued_at":"2026-07-05T08:36:04.871050Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:36:04.871050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An optimization parameter for seriation of noisy data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.CO","authors_text":"Jeannette Janssen, Mahya Ghandehari","submitted_at":"2018-03-27T22:53:08Z","abstract_excerpt":"A square symmetric matrix is a Robinson similarity matrix if entries in its rows and columns are non-decreasing when moving towards the diagonal. A Robinson similarity matrix can be viewed as the affinity matrix between objects arranged in linear order, where objects closer together have higher affinity. We define a new parameter, $\\Gamma_\\max$, which measures how badly a given matrix fails to be Robinson similarity. Namely, a matrix is Robinson similarity precisely when its $\\Gamma_\\max$ attains zero, and a matrix with small $\\Gamma_\\max$ is close (in the normalized $\\ell^1$-norm) to a Robins"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.10354","kind":"arxiv","version":3},"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/1803.10354/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":"1803.10354","created_at":"2026-07-05T08:36:04.871107+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.10354v3","created_at":"2026-07-05T08:36:04.871107+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.10354","created_at":"2026-07-05T08:36:04.871107+00:00"},{"alias_kind":"pith_short_12","alias_value":"HK7XRVYJRDPT","created_at":"2026-07-05T08:36:04.871107+00:00"},{"alias_kind":"pith_short_16","alias_value":"HK7XRVYJRDPT5MPM","created_at":"2026-07-05T08:36:04.871107+00:00"},{"alias_kind":"pith_short_8","alias_value":"HK7XRVYJ","created_at":"2026-07-05T08:36:04.871107+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/HK7XRVYJRDPT5MPMJVPCCLY2GP","json":"https://pith.science/pith/HK7XRVYJRDPT5MPMJVPCCLY2GP.json","graph_json":"https://pith.science/api/pith-number/HK7XRVYJRDPT5MPMJVPCCLY2GP/graph.json","events_json":"https://pith.science/api/pith-number/HK7XRVYJRDPT5MPMJVPCCLY2GP/events.json","paper":"https://pith.science/paper/HK7XRVYJ"},"agent_actions":{"view_html":"https://pith.science/pith/HK7XRVYJRDPT5MPMJVPCCLY2GP","download_json":"https://pith.science/pith/HK7XRVYJRDPT5MPMJVPCCLY2GP.json","view_paper":"https://pith.science/paper/HK7XRVYJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.10354&json=true","fetch_graph":"https://pith.science/api/pith-number/HK7XRVYJRDPT5MPMJVPCCLY2GP/graph.json","fetch_events":"https://pith.science/api/pith-number/HK7XRVYJRDPT5MPMJVPCCLY2GP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HK7XRVYJRDPT5MPMJVPCCLY2GP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HK7XRVYJRDPT5MPMJVPCCLY2GP/action/storage_attestation","attest_author":"https://pith.science/pith/HK7XRVYJRDPT5MPMJVPCCLY2GP/action/author_attestation","sign_citation":"https://pith.science/pith/HK7XRVYJRDPT5MPMJVPCCLY2GP/action/citation_signature","submit_replication":"https://pith.science/pith/HK7XRVYJRDPT5MPMJVPCCLY2GP/action/replication_record"}},"created_at":"2026-07-05T08:36:04.871107+00:00","updated_at":"2026-07-05T08:36:04.871107+00:00"}