{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:YQN4CNZV6U73DZ4JXF62MCX7TH","short_pith_number":"pith:YQN4CNZV","canonical_record":{"source":{"id":"1908.03690","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2019-08-10T05:03:52Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"2821b5113dba5254278190a579eb26bba1a2e26ee1cb59b8b18adb488e8f65a7","abstract_canon_sha256":"4c11f013514984d6c3953682414fdd627c2705dceef085aabc3649e72fa9b3dc"},"schema_version":"1.0"},"canonical_sha256":"c41bc13735f53fb1e789b97da60aff99e2e28f6a11f0578cc0180d0e1685dded","source":{"kind":"arxiv","id":"1908.03690","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.03690","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"arxiv_version","alias_value":"1908.03690v1","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.03690","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"pith_short_12","alias_value":"YQN4CNZV6U73","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"pith_short_16","alias_value":"YQN4CNZV6U73DZ4J","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"pith_short_8","alias_value":"YQN4CNZV","created_at":"2026-07-05T00:42:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:YQN4CNZV6U73DZ4JXF62MCX7TH","target":"record","payload":{"canonical_record":{"source":{"id":"1908.03690","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2019-08-10T05:03:52Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"2821b5113dba5254278190a579eb26bba1a2e26ee1cb59b8b18adb488e8f65a7","abstract_canon_sha256":"4c11f013514984d6c3953682414fdd627c2705dceef085aabc3649e72fa9b3dc"},"schema_version":"1.0"},"canonical_sha256":"c41bc13735f53fb1e789b97da60aff99e2e28f6a11f0578cc0180d0e1685dded","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:42:32.189563Z","signature_b64":"UzZh2oDDP4C3yosnXZK7TNK8bo4dKQqV8WtLk0/jhE/IYm2PFPP5F2mY3+e9bSD3VCe670bNJoEqQ4IKztFJBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c41bc13735f53fb1e789b97da60aff99e2e28f6a11f0578cc0180d0e1685dded","last_reissued_at":"2026-07-05T00:42:32.189142Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:42:32.189142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.03690","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":"2026-07-05T00:42:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IfSKNww0aQzk/3oq4Hoq6cibPu6pB1KjC4KgVhtvvDjFssHjdHln39uNwHcG8RxOiya/I/1z1hXdCVDnTSoEBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T01:13:18.051542Z"},"content_sha256":"26bfc9f99cad6eab72af7be58061808dad5658173afee8f631e4031e0474609f","schema_version":"1.0","event_id":"sha256:26bfc9f99cad6eab72af7be58061808dad5658173afee8f631e4031e0474609f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:YQN4CNZV6U73DZ4JXF62MCX7TH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive RBF Interpolation for Estimating Missing Values in Geographical Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Francesco Piccialli, Gang Mei, Kaifeng Gao, Nengxiong Xu, Salvatore Cuomo","submitted_at":"2019-08-10T05:03:52Z","abstract_excerpt":"The quality of datasets is a critical issue in big data mining. More interesting things could be mined from datasets with higher quality. The existence of missing values in geographical data would worsen the quality of big datasets. To improve the data quality, the missing values are generally needed to be estimated using various machine learning algorithms or mathematical methods such as approximations and interpolations. In this paper, we propose an adaptive Radial Basis Function (RBF) interpolation algorithm for estimating missing values in geographical data. In the proposed method, the sam"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.03690","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/1908.03690/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":"2026-07-05T00:42:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qRThG18HbiBR3Fjr8Kz3UOn5s656N5EbBgyVuXfIN0TJJnJZ5FLKd0G8TYnW/9kMfj3DgRhGGCL05Kc7ocKTBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T01:13:18.052050Z"},"content_sha256":"74dd69241604be6165266e542acb37d63d4bb2cbb856ec58df147c40c675e045","schema_version":"1.0","event_id":"sha256:74dd69241604be6165266e542acb37d63d4bb2cbb856ec58df147c40c675e045"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YQN4CNZV6U73DZ4JXF62MCX7TH/bundle.json","state_url":"https://pith.science/pith/YQN4CNZV6U73DZ4JXF62MCX7TH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YQN4CNZV6U73DZ4JXF62MCX7TH/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":"2026-08-20T01:13:18Z","links":{"resolver":"https://pith.science/pith/YQN4CNZV6U73DZ4JXF62MCX7TH","bundle":"https://pith.science/pith/YQN4CNZV6U73DZ4JXF62MCX7TH/bundle.json","state":"https://pith.science/pith/YQN4CNZV6U73DZ4JXF62MCX7TH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YQN4CNZV6U73DZ4JXF62MCX7TH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:YQN4CNZV6U73DZ4JXF62MCX7TH","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":"4c11f013514984d6c3953682414fdd627c2705dceef085aabc3649e72fa9b3dc","cross_cats_sorted":["cs.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2019-08-10T05:03:52Z","title_canon_sha256":"2821b5113dba5254278190a579eb26bba1a2e26ee1cb59b8b18adb488e8f65a7"},"schema_version":"1.0","source":{"id":"1908.03690","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.03690","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"arxiv_version","alias_value":"1908.03690v1","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.03690","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"pith_short_12","alias_value":"YQN4CNZV6U73","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"pith_short_16","alias_value":"YQN4CNZV6U73DZ4J","created_at":"2026-07-05T00:42:32Z"},{"alias_kind":"pith_short_8","alias_value":"YQN4CNZV","created_at":"2026-07-05T00:42:32Z"}],"graph_snapshots":[{"event_id":"sha256:74dd69241604be6165266e542acb37d63d4bb2cbb856ec58df147c40c675e045","target":"graph","created_at":"2026-07-05T00:42:32Z","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/1908.03690/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The quality of datasets is a critical issue in big data mining. More interesting things could be mined from datasets with higher quality. The existence of missing values in geographical data would worsen the quality of big datasets. To improve the data quality, the missing values are generally needed to be estimated using various machine learning algorithms or mathematical methods such as approximations and interpolations. In this paper, we propose an adaptive Radial Basis Function (RBF) interpolation algorithm for estimating missing values in geographical data. In the proposed method, the sam","authors_text":"Francesco Piccialli, Gang Mei, Kaifeng Gao, Nengxiong Xu, Salvatore Cuomo","cross_cats":["cs.NA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2019-08-10T05:03:52Z","title":"Adaptive RBF Interpolation for Estimating Missing Values in Geographical Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.03690","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":"sha256:26bfc9f99cad6eab72af7be58061808dad5658173afee8f631e4031e0474609f","target":"record","created_at":"2026-07-05T00:42:32Z","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":"4c11f013514984d6c3953682414fdd627c2705dceef085aabc3649e72fa9b3dc","cross_cats_sorted":["cs.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2019-08-10T05:03:52Z","title_canon_sha256":"2821b5113dba5254278190a579eb26bba1a2e26ee1cb59b8b18adb488e8f65a7"},"schema_version":"1.0","source":{"id":"1908.03690","kind":"arxiv","version":1}},"canonical_sha256":"c41bc13735f53fb1e789b97da60aff99e2e28f6a11f0578cc0180d0e1685dded","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c41bc13735f53fb1e789b97da60aff99e2e28f6a11f0578cc0180d0e1685dded","first_computed_at":"2026-07-05T00:42:32.189142Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:42:32.189142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UzZh2oDDP4C3yosnXZK7TNK8bo4dKQqV8WtLk0/jhE/IYm2PFPP5F2mY3+e9bSD3VCe670bNJoEqQ4IKztFJBA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:42:32.189563Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.03690","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:26bfc9f99cad6eab72af7be58061808dad5658173afee8f631e4031e0474609f","sha256:74dd69241604be6165266e542acb37d63d4bb2cbb856ec58df147c40c675e045"],"state_sha256":"f3d6b654b0ea4a83ed71fd78749f0c43a5edc6f14533bb749380f4e4ade43997"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dBsvvdZsHz2v24Fp7IYTohC+5u65tEL0TDEx2jtuVnj88I62aOvZ+YuKHEaGzxqW2HLphm41Y2iPKRJ+zGQPCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T01:13:18.056860Z","bundle_sha256":"eae6ddd33b9b318f45cf80b5405e519ed41ecdbf9643c92cea3021a431ce1564"}}