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Prospective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers (vol 2, pg 1, 2022)

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INFECTION CONTROL & HOSPITAL EPIDEMIOLOGY 2023年第10期44卷 1705-1705页

作者： Kamineni, Meghana Otles, Erkin Oh, Jeeheh Rao, Krishna Young, Vincent B. Li, Benjamin Y. West, Lauren R. Hooper, David C. Shenoy, Erica S. Guttag, John G. Wiens, Jenna Makar, Maggie Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge Massachusetts Medical Scientist Training Program University of Michigan Medical School Ann Arbor Michigan Department of Industrial and Operations Engineering University of Michigan College of Engineering Ann Arbor Michigan Division of Computer Science and Engineering University of Michigan College of Engineering Ann Arbor Michigan Department of Internal Medicine Division of Infectious Diseases University of Michigan Medical School Ann Arbor Michigan Infection Control Unit Massachusetts General Hospital Boston Massachusetts Division of Infectious Diseases Massachusetts General Hospital Boston Massachusetts Harvard Medical School Boston Massachusetts

Many data-driven patient risk stratification models have not been evaluated prospectively. We performed and compared the prospective and retrospective evaluations of 2 Clostridioides difficile infection (CDI) risk-prediction models at 2 large academic health centers, and we discuss the models’ robustness to data-set shifts.

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Supersymmetric Microring Laser Arrays in Higher-Dimensional Space

Supersymmetric Microring Laser Arrays in Higher-Dimensional ...

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CLEO: science and Innovations, S and I 2022

作者： Qiao, Xingdu Midya, Bikashkali Gao, Zihe Zhang, Zhifeng Zhao, Haoqi Wu, Tianwei Yim, Jieun Agarwal, Ritesh Litchinitser, Natalia M. Feng, Liang Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Materials Science and Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States

To achieve single fundamental supermode operation in coupled arrays, a two-dimensional supersymmetry formalism for mode control is developed and experimentally demonstrated with microring laser arrays. © Optica P...

ISBN: (纸本)9781557528209

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Machine learning-based classification of rock discontinuity trace:SMOTE oversampling integrated with GBT ensemble learning

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International Journal of Mining science and Technology 2022年第2期32卷 309-322页

作者： Jiayao Chen Hongwei Huang Anthony G.Cohn Dongming Zhang Mingliang Zhou Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Department of Geotechnical EngineeringTongji UniversityShanghai 200092China School of Computing University of LeedsLS29JT LeedsUnited Kingdom Department of Computer Science and Technology Tongji UniversityShanghai 211985China School of Civil Engineering Shandong UniversityJinan 250061China Luzhong Institute of Safety Environmental Protection Engineering and MaterialsQingdao University of Science and TechnologyZibo 255000China School of Mechanical and Electrical Engineering Qingdao University of Science and TechnologyQingdao 260061China

This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique(SMOTE),random search(RS)hyper-parameters optimization algorithm and gradient boosting tree(GBT)to achieve efficient and accurate rock trace identification.A thirteen-dimensional database consisting of basic,vector,and discontinuity features is established from image *** data points are classified as either‘‘trace”or‘‘non-trace”to divide the ultimate results into candidate trace *** is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:***,sixteen classifiers generated from four basic machine learning(ML)models are applied for performance *** results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and nontrace ***,discussions on feature importance,generalization ability and classification error are conducted for the proposed *** experimental results indicate that more critical features affecting the trace classification are primarily from the discontinuity ***,cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall classification *** proposed method provides a new alternative approach for the identification of 3D rock trace.

关键词： Tunnel face Rock discontinuity trace Machine learning Gradient boosting tree Generalization ability

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Thermal Emission Properties of a Hot Electron Laser Assisted Cathode

Thermal Emission Properties of a Hot Electron Laser Assisted...

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International Conference on Vacuum Electronics

作者： Brandon E.J. Cortez Ragib Ahsan Ryan Jacobs Anika Priyoti Juan Sanchez Vazquez John H. Booske Nader Behdad Rehan Kapadia Department of Electrical & Computer Engineering University of Wisconsin – Madison Madison WI USA Department of Electrical & Computer Engineering University of Southern California Los Angeles CA USA Department of Materials Science & Engineering University of Wisconsin – Madison Madison WI USA

ISBN: (数字)9798350348705

ISBN: (纸本)9798350348712

We experimentally investigated the impact on electron emission of adding thermal energy to two types of Hot Electron Laser Assisted Cathodes (HELACs). We tested both types for evidence of theoretically predicted thermal-enhancement of photo-gated emission. We observed no thermal enhancement of photoemission on the first type due to lack of stability at elevated temperatures. Surprisingly, for the first type, we observed Richardson-Dushman-like thermionic emission consistent with a low work function material. This thermionic emission was weak or absent from the second type of HELAC. Our current investigations are focused on understanding the effective-low-work-function thermionic emission of the first HELAC type, while pursuing further experiments to confirm theoretical predictions that heating should enhance photo-gated emission for the second (more thermally stable) type of HELAC.

关键词： Laser theory Thermionic emission Laser stability Electron emission Photoelectricity Vacuum electronics Electron sources Thermal stability Thermal energy Cathodes

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Normality Addition via Normality Detection in Industrial Image Anomaly Detection Models

arXiv

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arXiv 2024年

作者： Yi, Jihun Jung, Dahuin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of School of Computer Science and Engineering Soongsil University Seoul Korea Republic of Department of Electrical and Computer Engineering and Interdisciplinary Program in AI Seoul National University Seoul Korea Republic of

The task of image anomaly detection (IAD) aims to identify deviations from normality in image data. These anomalies are patterns that deviate significantly from what the IAD model has learned from the data during training. However, in real-world scenarios, the criteria for what constitutes normality often change, necessitating the reclassification of previously anomalous instances as normal. To address this challenge, we propose a new scenario termed "normality addition," involving the post-training adjustment of decision boundaries to incorporate new normalities. To address this challenge, we propose a method called Normality Addition via Normality Detection (NAND), leveraging a vision-language model. NAND performs normality detection which detect patterns related to the intended normality within images based on textual descriptions. We then modify the results of a pre-trained IAD model to implement this normality addition. Using the benchmark dataset in IAD, MVTec AD, we establish an evaluation protocol for the normality addition task and empirically demonstrate the effectiveness of the NAND method. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Hybrid Integration of Hook-shaped Traveling-wave Semiconductor Optical Amplifier and Taiji Ring Resonator for Unidirectional Lasing

Hybrid Integration of Hook-shaped Traveling-wave Semiconduct...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Siwei Zeng Xiaolei Zhao Lance Sweatt Chas Porter Lin Zhu Department of Electrical and Computer Engineering Center for Optical Materials Science and Engineering Technologies Clemson University Clemson SC

ISBN: (纸本)9781957171258

We demonstrate a unidirectional ring hybrid diode laser based on integration of a hook-shaped traveling-wave semiconductor optical amplifier (SOA) and Taiji ring resonator. The additional crossover bending waveguide inside the silicon nitride Taiji ring introduces a nonreciprocal loss in the laser cavity while the gain is provided by a multiple depth etched hook-shaped SOA.

关键词： Hybrid lasers Ring lasers Ring resonators Semiconductor optical amplifiers Silicon nitride Tunable lasers

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Optimizing 3D Clothing Models for VR: A Deep Learning Approach to Triangle Count Reduction

Optimizing 3D Clothing Models for VR: A Deep Learning Approa...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Surasachai Doungtap Varinya Phanichraksaphong Jenq-Haur Wang International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This study presents a deep learning framework optimizing 3D clothing models for VR, using a CNN to significantly reduce the triangle count of models from DeepFashion3D and CAP-UDF datasets. Achieving a balance between efficiency and detail, it cuts triangle count from over 160,000 to below 4,000, maintaining high DPI. The approach automates optimization, promising scalability and efficiency in VR fashion, setting a foundation for future 3D content development, enhancing virtual garment realism and interactivity.

关键词： Deep learning Solid modeling Three-dimensional displays Scalability Clothing Optimization Consumer electronics

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Quantum-secured covert sensing for the Doppler effect

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Physical Review Research 2023年第4期5卷 043206-043206页

作者： Shuhong Hao Zheshen Zhang Department of Materials Science and Engineering University of Arizona Tucson Arizona 85721 USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan 48109 USA

Quantum mechanics has paved the way for establishing shared privacy among communicating parties through a secure regime known as quantum-secured communication. Recent advancements in both theory and experimentation have unveiled the potential of quantum resources in enhancing the performance and security of estimating unknown parameters. This has led to the emergence of a novel paradigm known as quantum-secured covert sensing, wherein the sensing operation is concealed from an adversary monitoring the environment by embedding the probe signal in a bright noise background. The performance and security of such protocols are quantified using quantum measurement theory. While previous investigations into quantum-secured covert sensing primarily focused on proof-of-concept phase estimation problems, this paper presents a pioneering quantum-secured covert sensing system designed for the Doppler effect—a versatile estimation problem with broad applications. Our research uncovers an inherent trade-off among measurement precision, security, and range within the system. This work establishes a new avenue for incorporating physical-layer security into sensing systems, thereby opening up exciting possibilities for future research in this field.

关键词： Quantum Fisher information Quantum cryptography Quantum engineering Quantum information processing with continuous variables Quantum measurements Quantum metrology Quantum parameter estimation Quantum sensing

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Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study

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BMC Medical Imaging 2024年第1期24卷 92页

作者： Chang, Joseph Lin, Bo-Ru Wang, Ti-Hao Chen, Chung-Ming Department of Biomedical Engineering College of Medicine and College of Engineering National Taiwan University No. 1 Sec. 1 Jen-Ai Road Taipei 100 Taipei 100 Taiwan EverFortune.AI Co. Ltd Taichung Taiwan The Data Science Degree Program College of Electrical Engineering and Computer Science National Taiwan University and Academia Sinica Taipei Taiwan Department of Radiation Oncology China Medical University Hospital Taichung Taiwan Department of Medicine China Medical University Taichung Taiwan

Background: The study aimed to develop and validate a deep learning-based computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States. Methods: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists. Results: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong’s CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings. Conclusion: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings. © The Author(s) 2024.

关键词： Active learning Chest radiographs Deep learning Pleural effusion X-rays

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A Low Complexity ML-Based Methods for Malware Classification

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computers, materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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