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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.18602">arXiv:2406.18602</a> <span> [<a href="https://arxiv.org/pdf/2406.18602">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation">stat.CO</span> </div> </div> <p class="title is-5 mathjax"> Multi-level Phenotypic Models of Cardiovascular Disease and Obstructive Sleep Apnea Comorbidities: A Longitudinal Wisconsin Sleep Cohort Study </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nguyen%2C+D">Duy Nguyen</a>, <a href="/search/cs?searchtype=author&query=Hoang%2C+C">Ca Hoang</a>, <a href="/search/cs?searchtype=author&query=Huynh%2C+P+K">Phat K. Huynh</a>, <a href="/search/cs?searchtype=author&query=Truong%2C+T">Tien Truong</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+D">Dang Nguyen</a>, <a href="/search/cs?searchtype=author&query=Sharma%2C+A">Abhay Sharma</a>, <a href="/search/cs?searchtype=author&query=Le%2C+T+Q">Trung Q. Le</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.18602v1-abstract-short" style="display: inline;"> Cardiovascular diseases (CVDs) are notably prevalent among patients with obstructive sleep apnea (OSA), posing unique challenges in predicting CVD progression due to the intricate interactions of comorbidities. Traditional models typically lack the necessary dynamic and longitudinal scope to accurately forecast CVD trajectories in OSA patients. This study introduces a novel multi-level phenotypic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.18602v1-abstract-full').style.display = 'inline'; document.getElementById('2406.18602v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.18602v1-abstract-full" style="display: none;"> Cardiovascular diseases (CVDs) are notably prevalent among patients with obstructive sleep apnea (OSA), posing unique challenges in predicting CVD progression due to the intricate interactions of comorbidities. Traditional models typically lack the necessary dynamic and longitudinal scope to accurately forecast CVD trajectories in OSA patients. This study introduces a novel multi-level phenotypic model to analyze the progression and interplay of these conditions over time, utilizing data from the Wisconsin Sleep Cohort, which includes 1,123 participants followed for decades. Our methodology comprises three advanced steps: (1) Conducting feature importance analysis through tree-based models to underscore critical predictive variables like total cholesterol, low-density lipoprotein (LDL), and diabetes. (2) Developing a logistic mixed-effects model (LGMM) to track longitudinal transitions and pinpoint significant factors, which displayed a diagnostic accuracy of 0.9556. (3) Implementing t-distributed Stochastic Neighbor Embedding (t-SNE) alongside Gaussian Mixture Models (GMM) to segment patient data into distinct phenotypic clusters that reflect varied risk profiles and disease progression pathways. This phenotypic clustering revealed two main groups, with one showing a markedly increased risk of major adverse cardiovascular events (MACEs), underscored by the significant predictive role of nocturnal hypoxia and sympathetic nervous system activity from sleep data. Analysis of transitions and trajectories with t-SNE and GMM highlighted different progression rates within the cohort, with one cluster progressing more slowly towards severe CVD states than the other. This study offers a comprehensive understanding of the dynamic relationship between CVD and OSA, providing valuable tools for predicting disease onset and tailoring treatment approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.18602v1-abstract-full').style.display = 'none'; document.getElementById('2406.18602v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">30 pages, 5 figure, 5 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.00737">arXiv:2311.00737</a> <span> [<a href="https://arxiv.org/pdf/2311.00737">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> </div> </div> <p class="title is-5 mathjax"> Real-Time Magnetic Tracking and Diagnosis of COVID-19 via Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nguyen%2C+D">Dang Nguyen</a>, <a href="/search/cs?searchtype=author&query=Huynh%2C+P+K">Phat K. Huynh</a>, <a href="/search/cs?searchtype=author&query=Bui%2C+V+D+A">Vinh Duc An Bui</a>, <a href="/search/cs?searchtype=author&query=Hwang%2C+K+Y">Kee Young Hwang</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+N">Nityanand Jain</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+C">Chau Nguyen</a>, <a href="/search/cs?searchtype=author&query=Minh%2C+L+H+N">Le Huu Nhat Minh</a>, <a href="/search/cs?searchtype=author&query=Van+Truong%2C+L">Le Van Truong</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+X+T">Xuan Thanh Nguyen</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+D+H">Dinh Hoang Nguyen</a>, <a href="/search/cs?searchtype=author&query=Dung%2C+L+T">Le Tien Dung</a>, <a href="/search/cs?searchtype=author&query=Le%2C+T+Q">Trung Q. Le</a>, <a href="/search/cs?searchtype=author&query=Phan%2C+M">Manh-Huong Phan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.00737v1-abstract-short" style="display: inline;"> The COVID-19 pandemic underscored the importance of reliable, noninvasive diagnostic tools for robust public health interventions. In this work, we fused magnetic respiratory sensing technology (MRST) with machine learning (ML) to create a diagnostic platform for real-time tracking and diagnosis of COVID-19 and other respiratory diseases. The MRST precisely captures breathing patterns through thre… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.00737v1-abstract-full').style.display = 'inline'; document.getElementById('2311.00737v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.00737v1-abstract-full" style="display: none;"> The COVID-19 pandemic underscored the importance of reliable, noninvasive diagnostic tools for robust public health interventions. In this work, we fused magnetic respiratory sensing technology (MRST) with machine learning (ML) to create a diagnostic platform for real-time tracking and diagnosis of COVID-19 and other respiratory diseases. The MRST precisely captures breathing patterns through three specific breath testing protocols: normal breath, holding breath, and deep breath. We collected breath data from both COVID-19 patients and healthy subjects in Vietnam using this platform, which then served to train and validate ML models. Our evaluation encompassed multiple ML algorithms, including support vector machines and deep learning models, assessing their ability to diagnose COVID-19. Our multi-model validation methodology ensures a thorough comparison and grants the adaptability to select the most optimal model, striking a balance between diagnostic precision with model interpretability. The findings highlight the exceptional potential of our diagnostic tool in pinpointing respiratory anomalies, achieving over 90% accuracy. This innovative sensor technology can be seamlessly integrated into healthcare settings for patient monitoring, marking a significant enhancement for the healthcare infrastructure. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.00737v1-abstract-full').style.display = 'none'; document.getElementById('2311.00737v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.08897">arXiv:2202.08897</a> <span> [<a href="https://arxiv.org/pdf/2202.08897">pdf</a>, <a href="https://arxiv.org/format/2202.08897">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huynh%2C+P+K">Phu Khanh Huynh</a>, <a href="/search/cs?searchtype=author&query=Varshika%2C+M+L">M. Lakshmi Varshika</a>, <a href="/search/cs?searchtype=author&query=Paul%2C+A">Ankita Paul</a>, <a href="/search/cs?searchtype=author&query=Isik%2C+M">Murat Isik</a>, <a href="/search/cs?searchtype=author&query=Balaji%2C+A">Adarsha Balaji</a>, <a href="/search/cs?searchtype=author&query=Das%2C+A">Anup Das</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.08897v1-abstract-short" style="display: inline;"> Recently, both industry and academia have proposed several different neuromorphic systems to execute machine learning applications that are designed using Spiking Neural Networks (SNNs). With the growing complexity on design and technology fronts, programming such systems to admit and execute a machine learning application is becoming increasingly challenging. Additionally, neuromorphic systems ar… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.08897v1-abstract-full').style.display = 'inline'; document.getElementById('2202.08897v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.08897v1-abstract-full" style="display: none;"> Recently, both industry and academia have proposed several different neuromorphic systems to execute machine learning applications that are designed using Spiking Neural Networks (SNNs). With the growing complexity on design and technology fronts, programming such systems to admit and execute a machine learning application is becoming increasingly challenging. Additionally, neuromorphic systems are required to guarantee real-time performance, consume lower energy, and provide tolerance to logic and memory failures. Consequently, there is a clear need for system software frameworks that can implement machine learning applications on current and emerging neuromorphic systems, and simultaneously address performance, energy, and reliability. Here, we provide a comprehensive overview of such frameworks proposed for both, platform-based design and hardware-software co-design. We highlight challenges and opportunities that the future holds in the area of system software technology for neuromorphic computing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.08897v1-abstract-full').style.display = 'none'; document.getElementById('2202.08897v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a 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