{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:RIX54ZLJ4YYJZ7EX5BF2JUEI6E","short_pith_number":"pith:RIX54ZLJ","canonical_record":{"source":{"id":"2507.18450","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-05-16T22:22:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6e369c8d80a72f558370f28823df8eb423130916cdd0f1658e528b254217f8a1","abstract_canon_sha256":"fe2d0fc7e3b1725f34d63dba0afc2873c002f84eceebfe02b0612a98e961cff4"},"schema_version":"1.0"},"canonical_sha256":"8a2fde6569e6309cfc97e84ba4d088f12482f16194d42220c9cb61af7b7515f3","source":{"kind":"arxiv","id":"2507.18450","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.18450","created_at":"2026-07-05T11:42:48Z"},{"alias_kind":"arxiv_version","alias_value":"2507.18450v1","created_at":"2026-07-05T11:42:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.18450","created_at":"2026-07-05T11:42:48Z"},{"alias_kind":"pith_short_12","alias_value":"RIX54ZLJ4YYJ","created_at":"2026-07-05T11:42:48Z"},{"alias_kind":"pith_short_16","alias_value":"RIX54ZLJ4YYJZ7EX","created_at":"2026-07-05T11:42:48Z"},{"alias_kind":"pith_short_8","alias_value":"RIX54ZLJ","created_at":"2026-07-05T11:42:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:RIX54ZLJ4YYJZ7EX5BF2JUEI6E","target":"record","payload":{"canonical_record":{"source":{"id":"2507.18450","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-05-16T22:22:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6e369c8d80a72f558370f28823df8eb423130916cdd0f1658e528b254217f8a1","abstract_canon_sha256":"fe2d0fc7e3b1725f34d63dba0afc2873c002f84eceebfe02b0612a98e961cff4"},"schema_version":"1.0"},"canonical_sha256":"8a2fde6569e6309cfc97e84ba4d088f12482f16194d42220c9cb61af7b7515f3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:42:48.175990Z","signature_b64":"FiUycm81mVXcM96hdO34X/04wkctTVti583lhtuIUJfLWbMw2OeznSydyTQkOwf6nzosVv8D4MHvyu26dOoKBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a2fde6569e6309cfc97e84ba4d088f12482f16194d42220c9cb61af7b7515f3","last_reissued_at":"2026-07-05T11:42:48.175579Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:42:48.175579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.18450","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:42:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xnTRFVrSVuXPa72GlqWhpr6aRoXKfu2JdOIlAlD2z4Jycq8IJqRaFivFmY9HG+lwq8GuQeEZ0WyYhK24x9rtDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:22:53.735139Z"},"content_sha256":"f0a75e5106bf536f69b3b990bd5d999841d5439d4cd26f283ade0def7fe02cc9","schema_version":"1.0","event_id":"sha256:f0a75e5106bf536f69b3b990bd5d999841d5439d4cd26f283ade0def7fe02cc9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:RIX54ZLJ4YYJZ7EX5BF2JUEI6E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"High-Dimensional Data Classification in Concentric Coordinates","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.HC","authors_text":"Alice Williams, Boris Kovalerchuk","submitted_at":"2025-05-16T22:22:05Z","abstract_excerpt":"The visualization of multi-dimensional data with interpretable methods remains limited by capabilities for both high-dimensional lossless visualizations that do not suffer from occlusion and that are computationally capable by parameterized visualization. This paper proposes a low to high dimensional data supporting framework using lossless Concentric Coordinates that are a more compact generalization of Parallel Coordinates along with former Circular Coordinates. These are forms of the General Line Coordinate visualizations that can directly support machine learning algorithm visualization an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.18450","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/2507.18450/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:42:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DJpXW0WwXzzHxfEwuKPgk49JbCiwdMbtwn7DsUZsrdVLiJUtDKJ0IYsSlhBZ1aErAYR0aYZpL3LNP4R1xucSDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:22:53.735540Z"},"content_sha256":"c4c2c18aca4d1c8a1117008e551104ecfd2a2df0e27491e415d5e747cd65dfbe","schema_version":"1.0","event_id":"sha256:c4c2c18aca4d1c8a1117008e551104ecfd2a2df0e27491e415d5e747cd65dfbe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RIX54ZLJ4YYJZ7EX5BF2JUEI6E/bundle.json","state_url":"https://pith.science/pith/RIX54ZLJ4YYJZ7EX5BF2JUEI6E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RIX54ZLJ4YYJZ7EX5BF2JUEI6E/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":"2026-07-07T07:22:53Z","links":{"resolver":"https://pith.science/pith/RIX54ZLJ4YYJZ7EX5BF2JUEI6E","bundle":"https://pith.science/pith/RIX54ZLJ4YYJZ7EX5BF2JUEI6E/bundle.json","state":"https://pith.science/pith/RIX54ZLJ4YYJZ7EX5BF2JUEI6E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RIX54ZLJ4YYJZ7EX5BF2JUEI6E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:RIX54ZLJ4YYJZ7EX5BF2JUEI6E","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":"fe2d0fc7e3b1725f34d63dba0afc2873c002f84eceebfe02b0612a98e961cff4","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-05-16T22:22:05Z","title_canon_sha256":"6e369c8d80a72f558370f28823df8eb423130916cdd0f1658e528b254217f8a1"},"schema_version":"1.0","source":{"id":"2507.18450","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.18450","created_at":"2026-07-05T11:42:48Z"},{"alias_kind":"arxiv_version","alias_value":"2507.18450v1","created_at":"2026-07-05T11:42:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.18450","created_at":"2026-07-05T11:42:48Z"},{"alias_kind":"pith_short_12","alias_value":"RIX54ZLJ4YYJ","created_at":"2026-07-05T11:42:48Z"},{"alias_kind":"pith_short_16","alias_value":"RIX54ZLJ4YYJZ7EX","created_at":"2026-07-05T11:42:48Z"},{"alias_kind":"pith_short_8","alias_value":"RIX54ZLJ","created_at":"2026-07-05T11:42:48Z"}],"graph_snapshots":[{"event_id":"sha256:c4c2c18aca4d1c8a1117008e551104ecfd2a2df0e27491e415d5e747cd65dfbe","target":"graph","created_at":"2026-07-05T11:42:48Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2507.18450/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The visualization of multi-dimensional data with interpretable methods remains limited by capabilities for both high-dimensional lossless visualizations that do not suffer from occlusion and that are computationally capable by parameterized visualization. This paper proposes a low to high dimensional data supporting framework using lossless Concentric Coordinates that are a more compact generalization of Parallel Coordinates along with former Circular Coordinates. These are forms of the General Line Coordinate visualizations that can directly support machine learning algorithm visualization an","authors_text":"Alice Williams, Boris Kovalerchuk","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-05-16T22:22:05Z","title":"High-Dimensional Data Classification in Concentric Coordinates"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.18450","kind":"arxiv","version":1},"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:f0a75e5106bf536f69b3b990bd5d999841d5439d4cd26f283ade0def7fe02cc9","target":"record","created_at":"2026-07-05T11:42:48Z","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":"fe2d0fc7e3b1725f34d63dba0afc2873c002f84eceebfe02b0612a98e961cff4","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-05-16T22:22:05Z","title_canon_sha256":"6e369c8d80a72f558370f28823df8eb423130916cdd0f1658e528b254217f8a1"},"schema_version":"1.0","source":{"id":"2507.18450","kind":"arxiv","version":1}},"canonical_sha256":"8a2fde6569e6309cfc97e84ba4d088f12482f16194d42220c9cb61af7b7515f3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8a2fde6569e6309cfc97e84ba4d088f12482f16194d42220c9cb61af7b7515f3","first_computed_at":"2026-07-05T11:42:48.175579Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:42:48.175579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FiUycm81mVXcM96hdO34X/04wkctTVti583lhtuIUJfLWbMw2OeznSydyTQkOwf6nzosVv8D4MHvyu26dOoKBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:42:48.175990Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.18450","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f0a75e5106bf536f69b3b990bd5d999841d5439d4cd26f283ade0def7fe02cc9","sha256:c4c2c18aca4d1c8a1117008e551104ecfd2a2df0e27491e415d5e747cd65dfbe"],"state_sha256":"e0610a27663f582631299d7880809b3521f7e42cb4c8877e9571bbff65708b89"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T0BCtcGJcKLx6oOJTBtADLLAh81j9S0+4j1D+mVbaW7AsY+yXO+jDzZCGOJIy4CgWxwDWUxQI7zS6KfhmlM7Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T07:22:53.738004Z","bundle_sha256":"0b82f4269f967fded575788018c89a3c38c528abe445c8db246bea75c28ca7d7"}}