{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YIREONJRFU2HPS4AGVZUA3RAJJ","short_pith_number":"pith:YIREONJR","schema_version":"1.0","canonical_sha256":"c2224735312d3477cb803573406e204a4060ced4081bac4bd6348ab7d42d2d7b","source":{"kind":"arxiv","id":"2507.13480","version":1},"attestation_state":"computed","paper":{"title":"Multiresolution local smoothness detection in non-uniformly sampled multivariate signals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","cs.NA"],"primary_cat":"math.NA","authors_text":"Gianluca Giacchi, Michael Multerer, Sara Avesani","submitted_at":"2025-07-17T18:46:01Z","abstract_excerpt":"Inspired by edge detection based on the decay behavior of wavelet coefficients, we introduce a (near) linear-time algorithm for detecting the local regularity in non-uniformly sampled multivariate signals. Our approach quantifies regularity within the framework of microlocal spaces introduced by Jaffard. The central tool in our analysis is the fast samplet transform, a distributional wavelet transform tailored to scattered data. We establish a connection between the decay of samplet coefficients and the pointwise regularity of multivariate signals. As a by product, we derive decay estimates fo"},"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":"2507.13480","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2025-07-17T18:46:01Z","cross_cats_sorted":["cs.CV","cs.LG","cs.NA"],"title_canon_sha256":"c5dfcf38ac0cd6ae9a957aafca4a4e2f56841c607d662f8600b7e77381f39fd1","abstract_canon_sha256":"e1886b6af3d88a5b7d362da2bfd888dd9fe79d035a608d5b70b755e035ddebd6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:18.045909Z","signature_b64":"fcJNcjHokKF3XwiRVFjZFiEB05hmUEE0FRpxr1++8qzIYd0PbP9nODPMCt8gY12+mKLWCk+f+VNG2ioQHwGuBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c2224735312d3477cb803573406e204a4060ced4081bac4bd6348ab7d42d2d7b","last_reissued_at":"2026-07-05T11:39:18.045420Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:18.045420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multiresolution local smoothness detection in non-uniformly sampled multivariate signals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","cs.NA"],"primary_cat":"math.NA","authors_text":"Gianluca Giacchi, Michael Multerer, Sara Avesani","submitted_at":"2025-07-17T18:46:01Z","abstract_excerpt":"Inspired by edge detection based on the decay behavior of wavelet coefficients, we introduce a (near) linear-time algorithm for detecting the local regularity in non-uniformly sampled multivariate signals. Our approach quantifies regularity within the framework of microlocal spaces introduced by Jaffard. The central tool in our analysis is the fast samplet transform, a distributional wavelet transform tailored to scattered data. We establish a connection between the decay of samplet coefficients and the pointwise regularity of multivariate signals. As a by product, we derive decay estimates fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.13480","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/2507.13480/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":"2507.13480","created_at":"2026-07-05T11:39:18.045487+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.13480v1","created_at":"2026-07-05T11:39:18.045487+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.13480","created_at":"2026-07-05T11:39:18.045487+00:00"},{"alias_kind":"pith_short_12","alias_value":"YIREONJRFU2H","created_at":"2026-07-05T11:39:18.045487+00:00"},{"alias_kind":"pith_short_16","alias_value":"YIREONJRFU2HPS4A","created_at":"2026-07-05T11:39:18.045487+00:00"},{"alias_kind":"pith_short_8","alias_value":"YIREONJR","created_at":"2026-07-05T11:39:18.045487+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.19181","citing_title":"Bespoke multiresolution analysis of graph signals","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YIREONJRFU2HPS4AGVZUA3RAJJ","json":"https://pith.science/pith/YIREONJRFU2HPS4AGVZUA3RAJJ.json","graph_json":"https://pith.science/api/pith-number/YIREONJRFU2HPS4AGVZUA3RAJJ/graph.json","events_json":"https://pith.science/api/pith-number/YIREONJRFU2HPS4AGVZUA3RAJJ/events.json","paper":"https://pith.science/paper/YIREONJR"},"agent_actions":{"view_html":"https://pith.science/pith/YIREONJRFU2HPS4AGVZUA3RAJJ","download_json":"https://pith.science/pith/YIREONJRFU2HPS4AGVZUA3RAJJ.json","view_paper":"https://pith.science/paper/YIREONJR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.13480&json=true","fetch_graph":"https://pith.science/api/pith-number/YIREONJRFU2HPS4AGVZUA3RAJJ/graph.json","fetch_events":"https://pith.science/api/pith-number/YIREONJRFU2HPS4AGVZUA3RAJJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YIREONJRFU2HPS4AGVZUA3RAJJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YIREONJRFU2HPS4AGVZUA3RAJJ/action/storage_attestation","attest_author":"https://pith.science/pith/YIREONJRFU2HPS4AGVZUA3RAJJ/action/author_attestation","sign_citation":"https://pith.science/pith/YIREONJRFU2HPS4AGVZUA3RAJJ/action/citation_signature","submit_replication":"https://pith.science/pith/YIREONJRFU2HPS4AGVZUA3RAJJ/action/replication_record"}},"created_at":"2026-07-05T11:39:18.045487+00:00","updated_at":"2026-07-05T11:39:18.045487+00:00"}