{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:CL6NHAWPTFBD2TTCHOT2U527G2","short_pith_number":"pith:CL6NHAWP","schema_version":"1.0","canonical_sha256":"12fcd382cf99423d4e623ba7aa775f3690aec45389912d49253deedd090f6c89","source":{"kind":"arxiv","id":"1410.2327","version":1},"attestation_state":"computed","paper":{"title":"Robust dissimilarity measure for Network Localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Cl\\'audia Soares, Jo\\~ao Gomes","submitted_at":"2014-10-09T01:01:57Z","abstract_excerpt":"In practice, network applications have to deal with failing nodes, malicious attacks, or, somehow, nodes facing highly corrupted data --- generally classified as outliers. This calls for robust, uncomplicated, and efficient methods. We propose a dissimilarity model for network localization which is robust to high-power noise, but also discriminative in the presence of regular gaussian noise. We capitalize on the known properties of the M-estimator Huber penalty function to obtain a robust, but nonconvex, problem, and devise a convex underestimator, tight in the function terms, that can be mini"},"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":"1410.2327","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-10-09T01:01:57Z","cross_cats_sorted":[],"title_canon_sha256":"d82cfa5507654aa13cbffbf05883c01cb1f13c0b3db964462a197456f4d77807","abstract_canon_sha256":"d6032ec539fe3f9a9c430aebd035de99285496662428986c502a324722d13e18"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:40:46.652777Z","signature_b64":"LaixSGDpAO2IMtRmZcTWOCwSpCfdZGIdgPPdXeZYHZTRP/eFI1WEoDoLpTkkZYoU8T+/0x+iln5RKs33ecB6BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12fcd382cf99423d4e623ba7aa775f3690aec45389912d49253deedd090f6c89","last_reissued_at":"2026-05-18T02:40:46.652220Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:40:46.652220Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust dissimilarity measure for Network Localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Cl\\'audia Soares, Jo\\~ao Gomes","submitted_at":"2014-10-09T01:01:57Z","abstract_excerpt":"In practice, network applications have to deal with failing nodes, malicious attacks, or, somehow, nodes facing highly corrupted data --- generally classified as outliers. This calls for robust, uncomplicated, and efficient methods. We propose a dissimilarity model for network localization which is robust to high-power noise, but also discriminative in the presence of regular gaussian noise. We capitalize on the known properties of the M-estimator Huber penalty function to obtain a robust, but nonconvex, problem, and devise a convex underestimator, tight in the function terms, that can be mini"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.2327","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":""},"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":"1410.2327","created_at":"2026-05-18T02:40:46.652311+00:00"},{"alias_kind":"arxiv_version","alias_value":"1410.2327v1","created_at":"2026-05-18T02:40:46.652311+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.2327","created_at":"2026-05-18T02:40:46.652311+00:00"},{"alias_kind":"pith_short_12","alias_value":"CL6NHAWPTFBD","created_at":"2026-05-18T12:28:22.404517+00:00"},{"alias_kind":"pith_short_16","alias_value":"CL6NHAWPTFBD2TTC","created_at":"2026-05-18T12:28:22.404517+00:00"},{"alias_kind":"pith_short_8","alias_value":"CL6NHAWP","created_at":"2026-05-18T12:28:22.404517+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/CL6NHAWPTFBD2TTCHOT2U527G2","json":"https://pith.science/pith/CL6NHAWPTFBD2TTCHOT2U527G2.json","graph_json":"https://pith.science/api/pith-number/CL6NHAWPTFBD2TTCHOT2U527G2/graph.json","events_json":"https://pith.science/api/pith-number/CL6NHAWPTFBD2TTCHOT2U527G2/events.json","paper":"https://pith.science/paper/CL6NHAWP"},"agent_actions":{"view_html":"https://pith.science/pith/CL6NHAWPTFBD2TTCHOT2U527G2","download_json":"https://pith.science/pith/CL6NHAWPTFBD2TTCHOT2U527G2.json","view_paper":"https://pith.science/paper/CL6NHAWP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1410.2327&json=true","fetch_graph":"https://pith.science/api/pith-number/CL6NHAWPTFBD2TTCHOT2U527G2/graph.json","fetch_events":"https://pith.science/api/pith-number/CL6NHAWPTFBD2TTCHOT2U527G2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CL6NHAWPTFBD2TTCHOT2U527G2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CL6NHAWPTFBD2TTCHOT2U527G2/action/storage_attestation","attest_author":"https://pith.science/pith/CL6NHAWPTFBD2TTCHOT2U527G2/action/author_attestation","sign_citation":"https://pith.science/pith/CL6NHAWPTFBD2TTCHOT2U527G2/action/citation_signature","submit_replication":"https://pith.science/pith/CL6NHAWPTFBD2TTCHOT2U527G2/action/replication_record"}},"created_at":"2026-05-18T02:40:46.652311+00:00","updated_at":"2026-05-18T02:40:46.652311+00:00"}