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arXiv 2021年

作者： Niyazi, Lama B. Kammoun, Abla Dahrouj, Hayssam Alouini, Mohamed-Slim Al-Naffouri, Tareq Y. Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia Center of Excellence for NEOM Research at KAUST King Abdullah University of Science and Technology Thuwal Saudi Arabia

Unlike its intercept, a linear classifier’s weight vector cannot be tuned by a simple grid search. Hence, this paper proposes weight vector tuning of a generic binary linear classifier through the parameterization of a decomposition of the discriminant by a scalar which controls the trade-off between conflicting informative and noisy terms. By varying this parameter, the original weight vector is modified in a meaningful way. Applying this method to a number of linear classifiers under a variety of data dimensionality and sample size settings reveals that the classification performance loss due to non-optimal native hyperparameters can be compensated for by weight vector tuning. This yields computational savings as the proposed tuning method reduces to tuning a scalar compared to tuning the native hyperparameter, which may involve repeated weight vector generation along with its burden of optimization, dimensionality reduction, etc., depending on the classifier. It is also found that weight vector tuning significantly improves the performance of Linear Discriminant Analysis (LDA) under high estimation noise. Proceeding from this second finding, an asymptotic study of the misclassification probability of the parameterized LDA classifier in the growth regime where the data dimensionality and sample size are comparable is conducted. Using random matrix theory, the misclassification probability is shown to converge to a quantity that is a function of the true statistics of the data. Additionally, an estimator of the misclassification probability is derived. Finally, computationally efficient tuning of the parameter using this estimator is demonstrated on real data. Copyright © 2021, The Authors. All rights reserved.

关键词： Random variables

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Detecting QT prolongation From a Single-lead ECG With Deep Learning

arXiv

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arXiv 2023年

作者： Alam, Ridwan Aguirre, Aaron D. Stultz, Collin M. Research Laboratory of Electronics MIT CambridgeMA United States Computer Science & Artificial Intelligence Laboratory MIT CambridgeMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Wellman Center for Photomedicine Massachusetts General Hospital BostonMA United States Division of Cardiology Massachusetts General Hospital BostonMA United States Harvard Medical School BostonMA United States Harvard-MIT Program in Health Sciences and Technology CambridgeMA United States Institute for Medical Engineering and Science MIT CambridgeMA United States

Background and Aims For a number of antiarrhythmics, drug loading requires a 3-day hospitalization with monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would facilitate out-of-hospital care. We aim to develop a deep learning model that infers QT intervals from ECG lead-I - the lead most often acquired from ambulatory ECG monitors - and we use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading. Methods Using 4.22 million 12-lead ECG recordings from 903.6 thousand patients at the Massachusetts General Hospital, we develop a deep learning model, QTNet, that infers QT intervals from lead-I. Over 3 million ECGs from 653 thousand patients are used to train the model and an internal-test set containing 633 thousand ECGs from 135 thousand patients was used for testing. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another institution. QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs. Results QTNet achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). For the ECGRDVQ-dataset, QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%. Conclusions Drug-induced QT prolongation risk can be tracked from ECG lead-I using deep learning. This research leads the path toward out-of-hospital care using wearable ECG devices for antiarrhythmic therapies. © 2023, CC BY-NC-ND.

关键词： Electrocardiograms

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Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records

arXiv

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arXiv 2023年

作者： Sivarajkumar, Sonish Huang, Yufei Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States

Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Ultrastrong Spin-Photon Coupling in Gadolinium Gallium Garnet

Ultrastrong Spin-Photon Coupling in Gadolinium Gallium Garne...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Kritzell, T. Elijah Bao, Junzhe Doumani, Jacques Lee, Jae Joon Xu, Hongjing Tay, Fuyang Nojiri, Hiroyuki Bamba, Motoaki Baydin, Andrey Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Institute for Materials Research Tohoku University Sendai980-8577 Japan The Hakubi Center for Advanced Research Kyoto University Kyoto606-8502 Japan Department of Physics I Kyoto University Kyoto606-8502 Japan Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States

We examine ultrastrong coupling of an ensemble of paramagnetic spins in Gd3Ga5O12 with terahertz photons in the presence of a high external magnetic field both in the bulk and thin-film limits. © 2023 The Author(s)

ISBN: (纸本)9781957171258

关键词： Photons

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Effective suppression of leakage current in SnO x thin films enabled by low-concentration Al doping

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Optical Materials 2025年 167卷

作者： Junzhe Zhang Zhong Pan Yumeng Guo Yunhui Jang Mengmeng Chu Jangkun Song Yongsang Kim Muhammad Quddamah Khokhar Junsin Yi Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Gyeonggi-do Republic of Korea Department of Display Engineering Sungkyunkwan University Suwon 16419 Republic of Korea Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Suwon 16419 Gyeonggi-do Republic of Korea

SnO 2 inherently exhibits a high carrier concentration, which, while beneficial for conductivity, poses reliability challenges in electronic devices, including leakage current, threshold voltage drift, and overall performance degradation. To address these issues and enhance device stability, this study demonstrates that low-concentration Al doping via atomic layer deposition (ALD) effectively modulates the carrier density of SnO x thin films, leading to improved electrical properties. Optimized 2 % Al-doped SnO x thin films, annealed at 600 °C in oxygen for 1 h, exhibit enhanced device performance, achieving a mobility of 2.87 cm 2 /V·s, a leakage current reduction from 10 −8 A to 10 −10 A, and an on-off ratio of 10 6 . X-ray photoelectron spectroscopy (XPS) analysis reveals a significant reduction in oxygen vacancies after Al doping, which plays a crucial role in suppressing leakage pathways and stabilizing the threshold voltage. These improvements contribute to enhanced device reliability and electrical stability. Furthermore, the tailored Al doping strategy not only effectively controls the carrier concentration but also preserves the intrinsic conductivity of SnO x , making it a promising approach for high-performance thin-film transistors (TFTs) and optoelectronic applications. This work highlights the effectiveness of precise dopant engineering in optimizing metal oxide semiconductors, providing a scalable strategy to enhance device longevity and functionality.

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Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

SSRN

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SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

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Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety

arXiv

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arXiv 2023年

作者： Ardabili, Babak Rahimi Pazho, Armin Danesh Noghre, Ghazal Alinezhad Neff, Christopher Bhaskararayuni, Sai Datta Ravindran, Arun Reid, Shannon Tabkhi, Hamed Public Policy Program University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States Electrical Engineering and Computer Systems University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States Criminal Justice University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States

Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of personally identifiable information (PII) commonly pose an increased risk to personal privacy. To address these issues, this paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance. Further, we propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications. Finally, we propose quantitative and qualitative metrics to evaluate intelligent video surveillance systems. The system shows the 17.8 frame-per-second (FPS) processing in extreme video scenes. However, considering privacy in designing such a system results in preferring the pose-based algorithm to the pixel-based one. This choice resulted in dropping accuracy in both action and anomaly detection tasks. The results drop from 97.48 to 73.72 in anomaly detection and 96 to 83.07 in the action detection task. On average, the latency of the end-to-end system is 36.1 seconds. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Can Personalized Medicine Coexist with Health Equity? Examining the Cost Barrier and Ethical Implications

arXiv

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arXiv 2024年

作者： Francisco, Kishi Kobe Yee Apuhin, Andrane Estelle Carnicer Tan, Myles Joshua Toledo Byers, Mickael Cavanaugh Maravilla, Nicholle Mae Amor Tan Karim, Hezerul Abdul AlDahoul, Nouar Biology Program College of Arts and Science University of St. La Salle Negros Occidental Bacolod City Philippines Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health & Health Professions College of Medicine University of Florida GainesvilleFL United States Department of Natural Sciences College of Arts and Science University of St. La Salle Negros Occidental Bacolod City Philippines Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City Philippines Yo-Vivo Corporation Negros Occidental Bacolod City Philippines Department of Civil and Coastal Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Faculty of Engineering Multimedia University Selangor Cyberjaya Malaysia Department of Computer Science Division of Science New York University Abu Dhabi Saadiyat Marina District Abu Dhabi United Arab Emirates

Personalized medicine (PM) promises to transform healthcare by providingtreatments tailored to individual genetic, environmental, and lifestylefactors. However, its high costs and infrastructure demands raise concernsabout exacerbating health disparities, especially between high-income countries(HICs) and low- and middle-income countries (LMICs). While HICs benefit fromadvanced PM applications through AI and genomics, LMICs often lack theresources necessary to adopt these innovations, leading to a wideninghealthcare divide. This paper explores the financial and ethical challenges ofPM implementation, with a focus on ensuring equitable access. It proposesstrategies for global collaboration, infrastructure development, and ethicalframeworks to support LMICs in adopting PM, aiming to prevent furtherdisparities in healthcare accessibility and outcomes. © 2024, CC BY.

关键词： Personalized medicine

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Sequential Markov Chain Monte Carlo for Lagrangian Data Assimilation with Applications to Unknown Data Locations

arXiv

引用

arXiv 2023年

作者： Ruzayqat, Hamza Beskos, Alexandros Crisan, Dan Jasra, Ajay Kantas, Nikolas Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Statistical Science University College London LondonWC1E 6BT United Kingdom Department of Mathematics Imperial College London LondonSW7 2AZ United Kingdom

We consider a class of high-dimensional spatial filtering problems, where the spatial locations of observations are unknown and driven by the partially observed hidden signal. This problem is exceptionally challenging as not only is high-dimensional, but the model for the signal yields longer-range time dependencies through the observation locations. Motivated by this model we revisit a lesser-known and provably convergent computational methodology from [4, 11, 28] that uses sequential Markov Chain Monte Carlo (MCMC) chains. We extend this methodology for data filtering problems with unknown observation locations. We benchmark our algorithms on Linear Gaussian state space models against competing ensemble methods and demonstrate a significant improvement in both execution speed and accuracy. Finally, we implement a realistic case study on a high-dimensional rotating shallow water model (of about 104 - 105 dimensions) with real and synthetic data. The data is provided by the National Oceanic and Atmospheric Administration (NOAA) and contains observations from ocean drifters in a domain of the Atlantic Ocean restricted to the longitude and latitude intervals [-51◦,-41◦], [17◦,27◦] *** Codes 62M20, 60G35, 60J20, 94A12, 93E11, 65C40 © 2023, CC BY.

关键词： Location

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Meta-optic Accelerators for Object Classifiers

arXiv

引用

arXiv 2022年

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision making when computation resources are limited. Here, we demonstrate a meta-optic based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems resulting in a robust classifier that achieves 95% accurate classification of handwriting digits and 94% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine-vision and artificial intelligence. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy utilization

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