{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:MJOUGBBKSWYLFAHGGELEZY7ULD","short_pith_number":"pith:MJOUGBBK","schema_version":"1.0","canonical_sha256":"625d43042a95b0b280e631164ce3f458e1dd32c2c8845d38215d6527ab979736","source":{"kind":"arxiv","id":"2602.08916","version":2},"attestation_state":"computed","paper":{"title":"AMS-HD: Hyperdimensional Computing for Real-Time and Energy-Efficient Acute Mountain Sickness Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.ET","cs.LG"],"primary_cat":"cs.SC","authors_text":"Abu Masum, Beth A. Beidleman, Bige Unluturk, Mehran Moghadam, M. Hassan Najafi, Sercan Aygun, Ulkuhan Guler","submitted_at":"2026-02-09T17:16:13Z","abstract_excerpt":"Objective: Acute mountain sickness (AMS) is the most prevalent altitude illness, affecting unacclimatized individuals ascending above 2,500 m and potentially escalating to life threatening cerebral or pulmonary edema. Conventional machine learning (ML) methods for AMS detection from wearable physiological signals often fail to meet real-time hardware efficiency requirements of continuous monitoring. Methods: We present AMS-HD, the first hyperdimensional computing (HDC)-based framework for real-time AMS detection, spanning high- level bipolar (-1/+1) computing for mobile platforms and low-level"},"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":"2602.08916","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SC","submitted_at":"2026-02-09T17:16:13Z","cross_cats_sorted":["cs.ET","cs.LG"],"title_canon_sha256":"29697efbbde727c6e3887c357b2f8bd8e5d09be23cf9a01d88d13f28c4026f0e","abstract_canon_sha256":"97cb7967b224471bbd75d12402640860287da2dc02c28264be10e59f0d161cb5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:05.411228Z","signature_b64":"H2DGcT8ec/zzPZ1PBwrIGWnO9zqMALR7aIyL0+Btpppyn2yYKJ/lY/qF8y/d5cinw3FpV/r8WUBsq5XrhyjJBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"625d43042a95b0b280e631164ce3f458e1dd32c2c8845d38215d6527ab979736","last_reissued_at":"2026-05-20T00:03:05.410351Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:05.410351Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AMS-HD: Hyperdimensional Computing for Real-Time and Energy-Efficient Acute Mountain Sickness Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.ET","cs.LG"],"primary_cat":"cs.SC","authors_text":"Abu Masum, Beth A. Beidleman, Bige Unluturk, Mehran Moghadam, M. Hassan Najafi, Sercan Aygun, Ulkuhan Guler","submitted_at":"2026-02-09T17:16:13Z","abstract_excerpt":"Objective: Acute mountain sickness (AMS) is the most prevalent altitude illness, affecting unacclimatized individuals ascending above 2,500 m and potentially escalating to life threatening cerebral or pulmonary edema. Conventional machine learning (ML) methods for AMS detection from wearable physiological signals often fail to meet real-time hardware efficiency requirements of continuous monitoring. Methods: We present AMS-HD, the first hyperdimensional computing (HDC)-based framework for real-time AMS detection, spanning high- level bipolar (-1/+1) computing for mobile platforms and low-level"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.08916","kind":"arxiv","version":2},"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/2602.08916/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":"2602.08916","created_at":"2026-05-20T00:03:05.410494+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.08916v2","created_at":"2026-05-20T00:03:05.410494+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.08916","created_at":"2026-05-20T00:03:05.410494+00:00"},{"alias_kind":"pith_short_12","alias_value":"MJOUGBBKSWYL","created_at":"2026-05-20T00:03:05.410494+00:00"},{"alias_kind":"pith_short_16","alias_value":"MJOUGBBKSWYLFAHG","created_at":"2026-05-20T00:03:05.410494+00:00"},{"alias_kind":"pith_short_8","alias_value":"MJOUGBBK","created_at":"2026-05-20T00:03:05.410494+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/MJOUGBBKSWYLFAHGGELEZY7ULD","json":"https://pith.science/pith/MJOUGBBKSWYLFAHGGELEZY7ULD.json","graph_json":"https://pith.science/api/pith-number/MJOUGBBKSWYLFAHGGELEZY7ULD/graph.json","events_json":"https://pith.science/api/pith-number/MJOUGBBKSWYLFAHGGELEZY7ULD/events.json","paper":"https://pith.science/paper/MJOUGBBK"},"agent_actions":{"view_html":"https://pith.science/pith/MJOUGBBKSWYLFAHGGELEZY7ULD","download_json":"https://pith.science/pith/MJOUGBBKSWYLFAHGGELEZY7ULD.json","view_paper":"https://pith.science/paper/MJOUGBBK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.08916&json=true","fetch_graph":"https://pith.science/api/pith-number/MJOUGBBKSWYLFAHGGELEZY7ULD/graph.json","fetch_events":"https://pith.science/api/pith-number/MJOUGBBKSWYLFAHGGELEZY7ULD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MJOUGBBKSWYLFAHGGELEZY7ULD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MJOUGBBKSWYLFAHGGELEZY7ULD/action/storage_attestation","attest_author":"https://pith.science/pith/MJOUGBBKSWYLFAHGGELEZY7ULD/action/author_attestation","sign_citation":"https://pith.science/pith/MJOUGBBKSWYLFAHGGELEZY7ULD/action/citation_signature","submit_replication":"https://pith.science/pith/MJOUGBBKSWYLFAHGGELEZY7ULD/action/replication_record"}},"created_at":"2026-05-20T00:03:05.410494+00:00","updated_at":"2026-05-20T00:03:05.410494+00:00"}