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arXiv 2022年

作者： Zhang, Justin Lee, Deborah Jungles, Kylie Shaltis, Diane Najarian, Kayvan Ravikumar, Rajan Sanders, Georgiana Gryak, Jonathan Department of Electrical and Computer Engineering University of Michigan Ann ArborMI United States Department of Internal Medicine University of Michigan Ann ArborMI United States Department of Pediatrics University of Michigan Ann ArborMI United States Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Michigan Institute for Data Science University of Michigan Ann ArborMI United States Department of Emergency Medicine University of Michigan Ann ArborMI United States Department of Computer Science and Engineering University of Michigan Ann ArborMI United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI United States Mary H. Weiser Food Allergy Center University of Michigan Ann ArborMI United States Department of Computer Science Queens College City University of New York New YorkNY United States

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values ≥ 89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Diagnosing colorectal polyps in the wild with capsule networks

arXiv

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arXiv 2020年

作者： LaLonde, Rodney Kandel, Pujan Spampinato, Concetto Wallace, Michael B. Bagci, Ulas Center for Research in Computer Vision University of Central Florida United States Division of Gastroenterology and Hepatology Mayo Clinic JacksonvilleFL United States Department of Electrical Electronics and Computer Engineering University of Catania Italy

Colorectal cancer, largely arising from precursor lesions called polyps, remains one of the leading causes of cancer-related death worldwide. Current clinical standards require the resection and histopathological analysis of polyps due to test accuracy and sensitivity of optical biopsy methods falling substantially below recommended levels. In this study, we design a novel capsule network architecture (D-Caps) to improve the viability of optical biopsy of colorectal polyps. Our proposed method introduces several technical novelties including a novel capsule architecture with a capsule-average pooling (CAP) method to improve efficiency in large-scale image classification. We demonstrate improved results over the previous state-of-the-art convolutional neural network (CNN) approach by as much as 43%. This work provides an important benchmark on the new Mayo Polyp dataset, a significantly more challenging and larger dataset than previous polyp studies, with results stratified across all available categories, imaging devices and modalities, and focus modes to promote future direction into AI-driven colorectal cancer screening systems. Code is publicly available at https://***/lalonderodney/D-Caps. Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

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Quantum computer Systems for Scientific Discovery

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PRX Quantum 2021年第1期2卷 017001-017001页

作者： Yuri Alexeev Dave Bacon Kenneth R. Brown Robert Calderbank Lincoln D. Carr Frederic T. Chong Brian DeMarco Dirk Englund Edward Farhi Bill Fefferman Alexey V. Gorshkov Andrew Houck Jungsang Kim Shelby Kimmel Michael Lange Seth Lloyd Mikhail D. Lukin Dmitri Maslov Peter Maunz Christopher Monroe John Preskill Martin Roetteler Martin J. Savage Jeff Thompson Argonne National Laboratory Lemont Illinois USA Google Inc. Seattle Washington USA Department of Electrical and Computer Engineering Duke University Durham North Carolina USA Department of Chemistry Duke University Durham North Carolina USA Department of Physics Duke University Durham North Carolina USA Department of Computer Science and Department of Mathematics Duke University Durham North Carolina USA Department of Physics Colorado School of Mines Golden Colorado USA Department of Computer Science University of Chicago Chicago Illinois USA Department of Physics and IQUIST University of Illinois Urbana-Champaign Illinois USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts USA Google Inc. Venice California USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts USA Joint Quantum Institute Joint Center for Quantum Information and Computer Science and Department of Physics University of Maryland College Park Maryland USA National Institute of Standards and Technology Gaithersburg Maryland USA Department of Electrical Engineering Princeton University Princeton New Jersey USA IonQ Inc. College Park Maryland USA Department of Computer Science Middlebury College Middlebury Vermont USA L3Harris Technologies Melborune Florida USA Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA Department of Physics Harvard University Cambridge Massachusetts USA IBM T.J. Watson Research Center Yorktown Heights New York USA Sandia National Laboratories Albuquerque New Mexico USA Institute for Quantum Information and Matter and Walter Burke Institute for Theoretical Physics California Institute of Technology Pasadena California USA Microsoft Quantum Redmond Washington USA Institute for Nuclear Theory and Department of Physics University of Washington Seattle Washington USA

The great promise of quantum computers comes with the dual challenges of building them and finding their useful applications. We argue that these two challenges should be considered together, by codesigning full-stack quantum computer systems along with their applications in order to hasten their development and potential for scientific discovery. In this context, we identify scientific and community needs, opportunities, a sampling of a few use case studies, and significant challenges for the development of quantum computers for science over the next 2–10 years. This document is written by a community of university, national laboratory, and industrial researchers in the field of Quantum Information Science and Technology, and is based on a summary from a U.S. National Science Foundation workshop on Quantum Computing held on October 21–22, 2019 in Alexandria, VA.

关键词： Adiabatic quantum optimization Machine learning Measurement-based quantum computing Quantum algorithms Quantum benchmarking Quantum computation Quantum control Quantum gates Quantum simulation Surface code quantum computing Topological quantum computing

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Publisher Correction: Droplet Formation, Growth and Detachment at the Interface of a Coupled Free‑FLow–Porous Medium System: A New Model Development and Comparison

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Transport in Porous Media 2023年第2期149卷 421-421页

作者： Veyskarami, Maziar Michalkowski, Cynthia Bringedal, Carina Helmig, Rainer Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Stuttgart Germany Robert Bosch GmbH Center for Research and Development Renningen Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

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Robust end-to-end focal liver lesion detection using unregistered multiphase computed tomography images

arXiv

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arXiv 2021年

作者： Lee, Sang-Gil Kim, Eunji Bae, Jae Seok Kim, Jung Hoon Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Radiology Seoul National University College of Medicine Seoul Korea Republic of Institute of Radiation Medicine Seoul National University Medical Research Center Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses;FLL detection is the first step in such a computer-aided diagnosis. Despite the recent success of deep-learning-based approaches in detecting FLLs, current methods are not sufficiently robust for assessing misaligned multiphase data. By introducing an attention-guided multiphase alignment in feature space, this study presents a fully automated, end-to-end learning framework for detecting FLLs from multiphase computed tomography (CT) images. Our method is robust to misaligned multiphase images owing to its complete learning-based approach, which reduces the sensitivity of the model’s performance to the quality of registration and enables a standalone deployment of the model in clinical practice. Evaluation on a large-scale dataset with 280 patients confirmed that our method outperformed previous state-of-the-art methods and significantly reduced the performance degradation for detecting FLLs using misaligned multiphase CT images. The robustness of the proposed method can enhance the clinical adoption of the deep-learning-based computer-aided detection system. Copyright © 2021, The Authors. All rights reserved.

关键词： Object detection

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1002 Heart Rate Variability During Sleep and Future Risk of Parkinson’s Disease in Community-dwelling Older Men

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Sleep 2024年第SUPPLEMENT_1期47卷 A429–A430页

作者： Milton, Sasha Sun, Haoqi Kaffashi, Farhad Loparo, Kenneth Stone, Katie Yaffe, Kristine Westover, M Brandon Leng, Yue Department of Psychiatry and Behavioral Sciences University of California San Francisco Department of Neurology Beth Israel Deaconess Medical center Department of Electrical Engineering and Computer Science Case Western Reserve University Research Institute California Pacific Medical Center Departments of Psychiatry and Behavioral Sciences Neurology and Epidemiology University of California San Francisco Department of Neurology Beth Israel Deaconess Medical School Department of Psychiatry and Behavioral Sciences University of California San Francisco

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Age-optimal service and decision scheduling in internet of things

arXiv

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arXiv 2020年

作者： Bao, Zhiwei Dong, Yunquan Chen, Zhengchuan Fan, Pingyi Letaief, Khaled Ben School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China College of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering HKUST Clear Water Bay Kowloon Hong Kong

We consider an Internet of Things (IoT) system in which a sensor observes a phenomena of interest with exponentially distributed intervals and delivers the updates to a monitor with the First-come-First-served (FCFS) policy. At the monitor, the received updates are used to make decisions with deterministic or random intervals. For this system, we investigate the freshness of the updates at these decision epochs using the age upon decisions (AuD) metric. Theoretical results show that 1) when the decisions are made with exponentially distributed intervals, the average AuD of the system is smaller if the service time (e.g., transmission time) is uniformly distributed than when it is exponentially distributed, and would be the smallest if it is deterministic;2)when the decisions are made periodically, the average AuD of the system is larger than, and decreases with decision rate to, the average AuD of the corresponding system with Poisson decision intervals;3)the probability of missing to use a received update for any decisions is decreasing with the decision rate, and is the smallest if the service time is deterministic. For IoT monitoring systems, therefore, it is suggested to use deterministic monitoring schemes, deterministic transmitting schemes, and Poisson decision schemes, so that the received updates are as fresh as possible at the time they are used to make decisions. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

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Content Distribution based on Joint V2I and V2V Scheduling in mmWave Vehicular Networks

arXiv

引用

arXiv 2022年

作者： Su, Lan Niu, Yong Han, Zhu Ai, Bo He, Ruisi Wang, Yibing Wang, Ning Su, Xiang The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The Norwegian University of Science and Technology Norway and University of Oulu Finland

With the explosive growth of vehicle applications, vehicular networks based on millimeter wave (mmWave) bands have attracted interests from both academia and industry. mmWave communications are able to utilize the huge available bandwidth to provide multiple Gbps transmission rates among vehicles. In this paper, we address the content distribution scheduling problem in mmWave vehicular networks. It has been challenging for all vehicles in the same network to complete content downloading due to the limited communication resources of roadside units (RSUs) and the high mobility of vehicles. We propose a joint vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) scheduling scheme to minimize the total number of content distribution time slots from a global optimization perspective. In the V2I phase, the RSU serially transmits integrity content to vehicles, which are selected according to the vehicular network topology and transmission scheduling scheme. In the V2V phase, full-duplex communications and concurrent transmissions are exploited to achieve content sharing between vehicles and improve transmission efficiency. Performance evaluations demonstrate that our proposed scheme reduces the number of time slots and significantly improves system throughput when compared with other schemes, especially under large-size file transfers and a large number of vehicles. Copyright © 2022, The Authors. All rights reserved.

关键词： Millimeter waves

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Nesting-structured nuclear norm minimization for spatially correlated matrix variate

引用

Pattern Recognition 2019年 91卷 147-161页

作者： Luo, Lei Yang, Jian Zhang, Yigong Xu, Yong Huang, Heng School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Bio-Computing Research Center Shenzhen Graduate School Harbin Institute of Technology Shenzhen 518055 China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA United States

Integrating the structure prior in modeling has achieved considerable attention in pattern recognition and computer vision. Most current state-of-the-art methods (such as low rank representation and structured sparsity) search for a structured metric to fit the structure of the estimated variate, which either bear high time complexity (e.g., compute singular value decomposition for large-scale matrices), or cannot effectively exploit structure information of a matrix variate. In this work, we introduce a nesting-structured nuclear norm to characterize the matrix variate with structure prior and provide a unified framework for solving nesting-structured nuclear norm minimization (NSNM) problem by resorting to an improved sub-gradient method. This not only takes local and global structures of the matrix variate into joint consideration, but also enjoys the lower time complexity than traditional nuclear norm minimization. The revealed statistical meaning explains the rationality of the proposed method. Moreover, we apply NSNM to matrix regression and completion problems, respectively. The extensive experiments for face recognition and large-scale matrix completion clearly demonstrate the superiority of NSNM over some existing methods. © 2019 Elsevier Ltd

关键词： Low rank Matrix completion Matrix regression Nesting-structured nuclear norm Structured sparsity Sub-gradient method

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metasnf: Meta Clustering with Similarity Network Fusion in R

arXiv

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arXiv 2024年

作者： Velayudhan, Prashanth S. Xu, Xiaoqiao Kallurkar, Prajkta Balbon, Ana Patricia Secara, Maria T. Taback, Adam Sabac, Denise Chan, Nicholas Ma, Shihao Wang, Bo Felsky, Daniel Ameis, Stephanie H. Cox, Brian Hawco, Colin Erdman, Lauren Wheeler, Anne L. The Hospital for Sick Children TorontoON Canada Department of Physiology The University of Toronto TorontoON Canada Centre for Computational Medicine Hospital for Sick Children TorontoON Canada Department of Obstetrics and Gynecology University of Toronto TorontoON Canada Campbell Family Mental Health Research Institute Centre for Addiction and Mental Health TorontoON Canada Institute of Medical Science University of Toronto TorontoON Canada The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoON Canada Krembil Centre for Neuroinformatics Centre for Addiction and Mental Health TorontoON Canada Division of Rheumatology Hospital for Sick Children TorontoON Canada Vector Institute TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Division of Biostatistics Dalla Lana School of Public Health University of Toronto TorontoON Canada Department of Psychiatry University of Toronto TorontoON Canada Department of Anthropology University of Toronto TorontoON Canada Department of Psychiatry Temerty Faculty of Medicine University of Toronto TorontoON Canada Department of Psychiatry The Hospital for Sick Children TorontoON Canada Division of Gastroenterology Department of Pediatrics Cincinnati Children’s Hospital Medical Center University of Cincinnati College of Medicine CincinnatiOH United States James M. Anderson Center for Health Systems Excellence Cincinnati Children’s Hospital Medical Center CincinnatiOH United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH United States

metasnf is an R package that enables users to apply meta clustering, a method for efficiently searching a broad space of cluster solutions by clustering the solutions themselves, to clustering workflows based on similarity network fusion (SNF). SNF is a multi-modal data integration algorithm commonly used for biomedical subtype discovery. The package also contains functions to assist with cluster visualization, characterization, and validation. This package can help researchers identify SNF-derived cluster solutions that are guided by context-specific utility over context-agnostic measures of quality. Copyright © 2024, The Authors. All rights reserved.

关键词： Cluster analysis

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