{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:2GFBELKNDWOYMZGOL4V74NBYXI","short_pith_number":"pith:2GFBELKN","canonical_record":{"source":{"id":"1301.4291","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"stat.ME","submitted_at":"2013-01-18T04:06:38Z","cross_cats_sorted":[],"title_canon_sha256":"184216120ce70152c7e53b4913a81f50c9e5ff35b3e3ce5058c80c2b41569acb","abstract_canon_sha256":"d2058821ce2e80dab0a3a0430571637c50d0edfd7b8dfa6249f44ec99936623d"},"schema_version":"1.0"},"canonical_sha256":"d18a122d4d1d9d8664ce5f2bfe3438ba0bb6c82e7bed9d621d9d733fe682505e","source":{"kind":"arxiv","id":"1301.4291","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1301.4291","created_at":"2026-05-18T03:34:37Z"},{"alias_kind":"arxiv_version","alias_value":"1301.4291v4","created_at":"2026-05-18T03:34:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1301.4291","created_at":"2026-05-18T03:34:37Z"},{"alias_kind":"pith_short_12","alias_value":"2GFBELKNDWOY","created_at":"2026-05-18T12:27:30Z"},{"alias_kind":"pith_short_16","alias_value":"2GFBELKNDWOYMZGO","created_at":"2026-05-18T12:27:30Z"},{"alias_kind":"pith_short_8","alias_value":"2GFBELKN","created_at":"2026-05-18T12:27:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:2GFBELKNDWOYMZGOL4V74NBYXI","target":"record","payload":{"canonical_record":{"source":{"id":"1301.4291","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"stat.ME","submitted_at":"2013-01-18T04:06:38Z","cross_cats_sorted":[],"title_canon_sha256":"184216120ce70152c7e53b4913a81f50c9e5ff35b3e3ce5058c80c2b41569acb","abstract_canon_sha256":"d2058821ce2e80dab0a3a0430571637c50d0edfd7b8dfa6249f44ec99936623d"},"schema_version":"1.0"},"canonical_sha256":"d18a122d4d1d9d8664ce5f2bfe3438ba0bb6c82e7bed9d621d9d733fe682505e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:34:37.662702Z","signature_b64":"MLH2SlP/DRPdPi7Tb+B4qiGEeDRhUUbb6vdCHBbzzgYqkpF3Wvy864XKTEt9QOMuJK1bpv+hg2G9Biz9BE0RDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d18a122d4d1d9d8664ce5f2bfe3438ba0bb6c82e7bed9d621d9d733fe682505e","last_reissued_at":"2026-05-18T03:34:37.662143Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:34:37.662143Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1301.4291","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:34:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CDzeKdnVdrypfVdXaWb5Y00lcOXaSjkytNBwEIf2OeUtOo3ZyAXgkaJjRjeFf6WhtDdr8lz365xHB1uSZJMvCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T17:40:12.799260Z"},"content_sha256":"405370a559927a5b03af7536b227063420108e6c9ff4a10ef91068ca52da73b7","schema_version":"1.0","event_id":"sha256:405370a559927a5b03af7536b227063420108e6c9ff4a10ef91068ca52da73b7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:2GFBELKNDWOYMZGOL4V74NBYXI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A measure of association between vectors based on \"similarity covariance\"","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Dietrich Lehmann, Kieko Kochi, Naoto Yamada, Roberto D. Pascual-Marqui, Toshihiko Kinoshita","submitted_at":"2013-01-18T04:06:38Z","abstract_excerpt":"The \"maximum similarity correlation\" definition introduced in this study is motivated by the seminal work of Szekely et al on \"distance covariance\" (Ann. Statist. 2007, 35: 2769-2794; Ann. Appl. Stat. 2009, 3: 1236-1265). Instead of using Euclidean distances \"d\" as in Szekely et al, we use \"similarity\", which can be defined as \"exp(-d/s)\", where the scaling parameter s>0 controls how rapidly the similarity falls off with distance. Scale parameters are chosen by maximizing the similarity correlation. The motivation for using \"similarity\" originates in spectral clustering theory (see e.g. Ng et "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.4291","kind":"arxiv","version":4},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:34:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/TjNpkDqA7Rgz5dx8cmE4DUN0NlaQWhCSWQ4aaJ7wre9OEQQtdtfInfGjMRgR64aIZvPxkmkhKuL1aRBunIWAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T17:40:12.799685Z"},"content_sha256":"91e86f0e72180b20f1d4670e198d5fedf357e006c882c5ce7dbe32152b740dea","schema_version":"1.0","event_id":"sha256:91e86f0e72180b20f1d4670e198d5fedf357e006c882c5ce7dbe32152b740dea"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2GFBELKNDWOYMZGOL4V74NBYXI/bundle.json","state_url":"https://pith.science/pith/2GFBELKNDWOYMZGOL4V74NBYXI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2GFBELKNDWOYMZGOL4V74NBYXI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-11T17:40:12Z","links":{"resolver":"https://pith.science/pith/2GFBELKNDWOYMZGOL4V74NBYXI","bundle":"https://pith.science/pith/2GFBELKNDWOYMZGOL4V74NBYXI/bundle.json","state":"https://pith.science/pith/2GFBELKNDWOYMZGOL4V74NBYXI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2GFBELKNDWOYMZGOL4V74NBYXI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:2GFBELKNDWOYMZGOL4V74NBYXI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d2058821ce2e80dab0a3a0430571637c50d0edfd7b8dfa6249f44ec99936623d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"stat.ME","submitted_at":"2013-01-18T04:06:38Z","title_canon_sha256":"184216120ce70152c7e53b4913a81f50c9e5ff35b3e3ce5058c80c2b41569acb"},"schema_version":"1.0","source":{"id":"1301.4291","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1301.4291","created_at":"2026-05-18T03:34:37Z"},{"alias_kind":"arxiv_version","alias_value":"1301.4291v4","created_at":"2026-05-18T03:34:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1301.4291","created_at":"2026-05-18T03:34:37Z"},{"alias_kind":"pith_short_12","alias_value":"2GFBELKNDWOY","created_at":"2026-05-18T12:27:30Z"},{"alias_kind":"pith_short_16","alias_value":"2GFBELKNDWOYMZGO","created_at":"2026-05-18T12:27:30Z"},{"alias_kind":"pith_short_8","alias_value":"2GFBELKN","created_at":"2026-05-18T12:27:30Z"}],"graph_snapshots":[{"event_id":"sha256:91e86f0e72180b20f1d4670e198d5fedf357e006c882c5ce7dbe32152b740dea","target":"graph","created_at":"2026-05-18T03:34:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"The \"maximum similarity correlation\" definition introduced in this study is motivated by the seminal work of Szekely et al on \"distance covariance\" (Ann. Statist. 2007, 35: 2769-2794; Ann. Appl. Stat. 2009, 3: 1236-1265). Instead of using Euclidean distances \"d\" as in Szekely et al, we use \"similarity\", which can be defined as \"exp(-d/s)\", where the scaling parameter s>0 controls how rapidly the similarity falls off with distance. Scale parameters are chosen by maximizing the similarity correlation. The motivation for using \"similarity\" originates in spectral clustering theory (see e.g. Ng et ","authors_text":"Dietrich Lehmann, Kieko Kochi, Naoto Yamada, Roberto D. Pascual-Marqui, Toshihiko Kinoshita","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"stat.ME","submitted_at":"2013-01-18T04:06:38Z","title":"A measure of association between vectors based on \"similarity covariance\""},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.4291","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:405370a559927a5b03af7536b227063420108e6c9ff4a10ef91068ca52da73b7","target":"record","created_at":"2026-05-18T03:34:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d2058821ce2e80dab0a3a0430571637c50d0edfd7b8dfa6249f44ec99936623d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"stat.ME","submitted_at":"2013-01-18T04:06:38Z","title_canon_sha256":"184216120ce70152c7e53b4913a81f50c9e5ff35b3e3ce5058c80c2b41569acb"},"schema_version":"1.0","source":{"id":"1301.4291","kind":"arxiv","version":4}},"canonical_sha256":"d18a122d4d1d9d8664ce5f2bfe3438ba0bb6c82e7bed9d621d9d733fe682505e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d18a122d4d1d9d8664ce5f2bfe3438ba0bb6c82e7bed9d621d9d733fe682505e","first_computed_at":"2026-05-18T03:34:37.662143Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:34:37.662143Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MLH2SlP/DRPdPi7Tb+B4qiGEeDRhUUbb6vdCHBbzzgYqkpF3Wvy864XKTEt9QOMuJK1bpv+hg2G9Biz9BE0RDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:34:37.662702Z","signed_message":"canonical_sha256_bytes"},"source_id":"1301.4291","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:405370a559927a5b03af7536b227063420108e6c9ff4a10ef91068ca52da73b7","sha256:91e86f0e72180b20f1d4670e198d5fedf357e006c882c5ce7dbe32152b740dea"],"state_sha256":"aab2f80cd76f419c069e6bf4d58b5d839ccfab7983356be0341064106f945588"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GbTv6eaJpZFGKKRF+CkpJbZOe4KWc5TW+KsmYTyjd5DFhfVvulAfeibi+GXWYop3KzmBj1OtW/1PAhmy03rHCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T17:40:12.802073Z","bundle_sha256":"423cdc46844ee2bf4115baba5f4f801f4eb4ccfc47eec90f3ac9ba5c7d73b74a"}}