{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YFXFYGPLNNLOXKYD3AKYHWI5MG","short_pith_number":"pith:YFXFYGPL","schema_version":"1.0","canonical_sha256":"c16e5c19eb6b56ebab03d81583d91d61acae7b3dd02cd3f1c54eb1da0101a19e","source":{"kind":"arxiv","id":"2605.28320","version":1},"attestation_state":"computed","paper":{"title":"Identifying Explicit Parsimonious Piece-wise Polynomial Relationships in Industrial time-series: Application to manipulator robots","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Mazen Alamir, Sacha Clavel","submitted_at":"2026-05-27T11:23:30Z","abstract_excerpt":"This paper addresses the problem of identifying parsimonious explicit piece-wise polynomial relationships that might involve a relatively large number of raw features. The algorithm leverages a recently proposed identification algorithm that yields parsimonious implicit relationships enabling to derive normality characterization in the context of anomaly detection and localization. The algorithm proposed in this paper goes a step further by deriving explicit piece-wise representations that are built using the set of polynomials involved in the implicit representations. The framework is illustr"},"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":"2605.28320","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-27T11:23:30Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1e62511ded68ee1cbbedc72419413048260fcb42251e29424d1768d20253eec4","abstract_canon_sha256":"2bee9a8f4ed08d391c620b1157122af36b494b5ad50948d14bf7da793aa57308"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:05:06.312334Z","signature_b64":"JVutYU4X/GIHOwKKvKlSSnsIQuyqtAh4N+PqfoL/cWunDnc4cCXqNIKmXeF31NvGQEl5HDFP8jtCaD0LBg5rCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c16e5c19eb6b56ebab03d81583d91d61acae7b3dd02cd3f1c54eb1da0101a19e","last_reissued_at":"2026-05-28T01:05:06.311113Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:05:06.311113Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Identifying Explicit Parsimonious Piece-wise Polynomial Relationships in Industrial time-series: Application to manipulator robots","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Mazen Alamir, Sacha Clavel","submitted_at":"2026-05-27T11:23:30Z","abstract_excerpt":"This paper addresses the problem of identifying parsimonious explicit piece-wise polynomial relationships that might involve a relatively large number of raw features. The algorithm leverages a recently proposed identification algorithm that yields parsimonious implicit relationships enabling to derive normality characterization in the context of anomaly detection and localization. The algorithm proposed in this paper goes a step further by deriving explicit piece-wise representations that are built using the set of polynomials involved in the implicit representations. The framework is illustr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28320","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/2605.28320/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":"2605.28320","created_at":"2026-05-28T01:05:06.311909+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.28320v1","created_at":"2026-05-28T01:05:06.311909+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28320","created_at":"2026-05-28T01:05:06.311909+00:00"},{"alias_kind":"pith_short_12","alias_value":"YFXFYGPLNNLO","created_at":"2026-05-28T01:05:06.311909+00:00"},{"alias_kind":"pith_short_16","alias_value":"YFXFYGPLNNLOXKYD","created_at":"2026-05-28T01:05:06.311909+00:00"},{"alias_kind":"pith_short_8","alias_value":"YFXFYGPL","created_at":"2026-05-28T01:05:06.311909+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/YFXFYGPLNNLOXKYD3AKYHWI5MG","json":"https://pith.science/pith/YFXFYGPLNNLOXKYD3AKYHWI5MG.json","graph_json":"https://pith.science/api/pith-number/YFXFYGPLNNLOXKYD3AKYHWI5MG/graph.json","events_json":"https://pith.science/api/pith-number/YFXFYGPLNNLOXKYD3AKYHWI5MG/events.json","paper":"https://pith.science/paper/YFXFYGPL"},"agent_actions":{"view_html":"https://pith.science/pith/YFXFYGPLNNLOXKYD3AKYHWI5MG","download_json":"https://pith.science/pith/YFXFYGPLNNLOXKYD3AKYHWI5MG.json","view_paper":"https://pith.science/paper/YFXFYGPL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.28320&json=true","fetch_graph":"https://pith.science/api/pith-number/YFXFYGPLNNLOXKYD3AKYHWI5MG/graph.json","fetch_events":"https://pith.science/api/pith-number/YFXFYGPLNNLOXKYD3AKYHWI5MG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YFXFYGPLNNLOXKYD3AKYHWI5MG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YFXFYGPLNNLOXKYD3AKYHWI5MG/action/storage_attestation","attest_author":"https://pith.science/pith/YFXFYGPLNNLOXKYD3AKYHWI5MG/action/author_attestation","sign_citation":"https://pith.science/pith/YFXFYGPLNNLOXKYD3AKYHWI5MG/action/citation_signature","submit_replication":"https://pith.science/pith/YFXFYGPLNNLOXKYD3AKYHWI5MG/action/replication_record"}},"created_at":"2026-05-28T01:05:06.311909+00:00","updated_at":"2026-05-28T01:05:06.311909+00:00"}