{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JRYWNIVVANWTII6KQDL2PNYRFS","short_pith_number":"pith:JRYWNIVV","schema_version":"1.0","canonical_sha256":"4c7166a2b5036d3423ca80d7a7b7112cabef62fd0ec19a9806ede0e116d6658e","source":{"kind":"arxiv","id":"2502.18152","version":1},"attestation_state":"computed","paper":{"title":"Edge Training and Inference with Analog ReRAM Technology for Hand Gesture Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Andr\\'as Horv\\'ath, Anirvan Dutta, Bert Jan Offrein, Donato Francesco Falcone, Matteo Galetta, Mohsen Kaboli, Shokoofeh Varzandeh, Tommaso Stecconi, Valeria Bragaglia, Victoria Clerico, Wooseok Choi","submitted_at":"2025-02-25T12:33:31Z","abstract_excerpt":"Tactile hand gesture recognition is a crucial task for user control in the automotive sector, where Human-Machine Interactions (HMI) demand low latency and high energy efficiency. This study addresses the challenges of power-constrained edge training and inference by utilizing analog Resistive Random Access Memory (ReRAM) technology in conjunction with a real tactile hand gesture dataset. By optimizing the input space through a feature engineering strategy, we avoid relying on large-scale crossbar arrays, making the system more suitable for edge deployment. Through realistic hardware-aware sim"},"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":"2502.18152","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2025-02-25T12:33:31Z","cross_cats_sorted":[],"title_canon_sha256":"8eaa15e8890732b3ac6d4b11a4b6c7736b31de080bd5ea7b39ad9d9929886d9c","abstract_canon_sha256":"5c7f4a96e111134902d5b0de572dfe12799a7e616def4f8a3fb47a12ecc30cc3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:19:49.731059Z","signature_b64":"lgjh48rbvqTvPAdNL7D6hARr2sQtROTg+oKRJjUGkI0JFIWBE5OE1RdqS4DIRDmPaQymYgQMjaCDen7iUkLNDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4c7166a2b5036d3423ca80d7a7b7112cabef62fd0ec19a9806ede0e116d6658e","last_reissued_at":"2026-07-05T10:19:49.730564Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:19:49.730564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Edge Training and Inference with Analog ReRAM Technology for Hand Gesture Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Andr\\'as Horv\\'ath, Anirvan Dutta, Bert Jan Offrein, Donato Francesco Falcone, Matteo Galetta, Mohsen Kaboli, Shokoofeh Varzandeh, Tommaso Stecconi, Valeria Bragaglia, Victoria Clerico, Wooseok Choi","submitted_at":"2025-02-25T12:33:31Z","abstract_excerpt":"Tactile hand gesture recognition is a crucial task for user control in the automotive sector, where Human-Machine Interactions (HMI) demand low latency and high energy efficiency. This study addresses the challenges of power-constrained edge training and inference by utilizing analog Resistive Random Access Memory (ReRAM) technology in conjunction with a real tactile hand gesture dataset. By optimizing the input space through a feature engineering strategy, we avoid relying on large-scale crossbar arrays, making the system more suitable for edge deployment. Through realistic hardware-aware sim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.18152","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/2502.18152/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":"2502.18152","created_at":"2026-07-05T10:19:49.730622+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.18152v1","created_at":"2026-07-05T10:19:49.730622+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.18152","created_at":"2026-07-05T10:19:49.730622+00:00"},{"alias_kind":"pith_short_12","alias_value":"JRYWNIVVANWT","created_at":"2026-07-05T10:19:49.730622+00:00"},{"alias_kind":"pith_short_16","alias_value":"JRYWNIVVANWTII6K","created_at":"2026-07-05T10:19:49.730622+00:00"},{"alias_kind":"pith_short_8","alias_value":"JRYWNIVV","created_at":"2026-07-05T10:19:49.730622+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/JRYWNIVVANWTII6KQDL2PNYRFS","json":"https://pith.science/pith/JRYWNIVVANWTII6KQDL2PNYRFS.json","graph_json":"https://pith.science/api/pith-number/JRYWNIVVANWTII6KQDL2PNYRFS/graph.json","events_json":"https://pith.science/api/pith-number/JRYWNIVVANWTII6KQDL2PNYRFS/events.json","paper":"https://pith.science/paper/JRYWNIVV"},"agent_actions":{"view_html":"https://pith.science/pith/JRYWNIVVANWTII6KQDL2PNYRFS","download_json":"https://pith.science/pith/JRYWNIVVANWTII6KQDL2PNYRFS.json","view_paper":"https://pith.science/paper/JRYWNIVV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.18152&json=true","fetch_graph":"https://pith.science/api/pith-number/JRYWNIVVANWTII6KQDL2PNYRFS/graph.json","fetch_events":"https://pith.science/api/pith-number/JRYWNIVVANWTII6KQDL2PNYRFS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JRYWNIVVANWTII6KQDL2PNYRFS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JRYWNIVVANWTII6KQDL2PNYRFS/action/storage_attestation","attest_author":"https://pith.science/pith/JRYWNIVVANWTII6KQDL2PNYRFS/action/author_attestation","sign_citation":"https://pith.science/pith/JRYWNIVVANWTII6KQDL2PNYRFS/action/citation_signature","submit_replication":"https://pith.science/pith/JRYWNIVVANWTII6KQDL2PNYRFS/action/replication_record"}},"created_at":"2026-07-05T10:19:49.730622+00:00","updated_at":"2026-07-05T10:19:49.730622+00:00"}