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30th International Symposium on Semiconductor Manufacturing, ISSM 2024

作者： Uedono, Akira Kawakami, Kazuya Kuribara, Tatsunori Hasunuma, Ryu Harashima, Yosuke Shigeta, Yasuteru Ni, Zeyuan Yan, Ruolin Nakamura, Hideo Notake, Akira Moriya, Tsuyoshi Michishio, Koji Ishibashi, Shoji University of Tsukuba Faculty of Pure and Applied Science Ibaraki Tsukuba305-8573 Japan Nara Institute of Science and Technology Graduate School of Science and Technology Nara Ikoma630-0192 Japan Tokyo Electron Technology Solutions Ltd. S-Technology Development Center Yamanashi Nirasaki407-0192 Japan Tokyo Electron Technology Solutions Ltd. Development Department 1 Yamanashi Nirasaki407-0192 Japan Tokyo Electron Ltd. Digital Design Center Hokkaido Sapporo060-0003 Japan Tokyo Electron Technology Solutions Ltd. Software and Data Engineering Division Yamanashi Nirasaki407-0192 Japan National Institute of Advanced Industrial Science and Technology Research Institute for Measurement and Analytical Instrumentation Ibaraki Tsukuba305-8568 Japan National Institute of Advanced Industrial Science and Technology Research Center for Computational Design of Advanced Functional Materials Ibaraki Tsukuba305-8568 Japan

ISBN: (纸本)9798331510589

Behaviors of vacancies during the amorphous-to-crystalline transition of thin HfO2 layers were studied by monoenergetic positron beams. The HfO2 layers with 430 nm thickness were fabricated on TiN/Si templates by atomic layer deposition technique. The major vacancy-type defects in the layers were identified as (i) Hf vacancies (VHf) coupled with oxygen vacancies (VOs) and (ii) vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters increased, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to crystalline phases. The phase transition started near the interface between the HfO2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased. © 2024 IEEE.

关键词： Positron annihilation

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Estimating geographic variation of infection fatality ratios during epidemics

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Infectious Disease Modelling 2024年第2期9卷 634-643页

作者： Joshua Ladau Eoin L.Brodie Nicola Falco Ishan Bansal Elijah B.Hoffman Marcin P.Joachimiak Ana M.Mora Angelica M.Walker Haruko M.Wainwright Yulun Wu Mirko Pavicic Daniel Jacobson Matthias Hess James B.Brown Katrina Abuabara Departments of Computational Precision Health and Dermatology University of CaliforniaSan FranciscoCA94115USA Arva Intelligence Inc.Salt Lake CityUT84101USA Computational Biosciences Group Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Earth and Environmental Sciences Area Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Graduate Group in Biostatistics University of CaliforniaBerkeleyCA94720USA Biosystems Data Science Environmental Genomics and Systems BiologyLawrence Berkeley National LaboratoryBerkeleyCA94720USA Center for Environmental Research and Community Health(CERCH) School of Public HealthUniversity of CaliforniaBerkeleyCA94720USA Bredesen Center for Interdisciplinary Research and Graduate Education University of TennesseeKnoxvilleTN37996USA Department of Nuclear Science and Engineering Massachusetts Institute of TechnologyBostonMA02139USA Biosciences Oak Ridge National LaboratoryOak RidgeTN37830USA University of California DavisCA95616USA Statistics Department University of CaliforniaBerkeleyCA94720USA Division of Epidemiology and Biostatistics University of California Berkeley School of Public Health2121 Berkeley WayBerkeleyCA94720USA

Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and quality of data on disease burden are limited during an *** We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek *** demonstrate the robustness,accuracy,and precision of this framework,and apply it to the United States(U.S.)COVID-19 pandemic to estimate county-level SARS-CoV-2 *** The estimators for the numbers of infections and IFRs showed high accuracy and precision;for instance,when applied to simulated validation data sets,across counties,Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928,respectively,and they showed strong robustness to model *** the county-level estimators to the real,unsimulated COVID-19 data spanning April 1,2020 to September 30,2020 from across the U.S.,we found that IFRs varied from 0 to 44.69,with a standard deviation of 3.55 and a median of *** The proposed estimation framework can be used to identify geographic variation in IFRs across settings.

关键词： Infection fatality ratio Infection fatality rate Noncentral hypergeometric distribution COVID-19 SARS-CoV-2

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Deep Convolutional Neural Network Framework for Brain Tumor Classification using MRI Images

Deep Convolutional Neural Network Framework for Brain Tumor ...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： U. Sakthi K. Thangaraj A Tamizhselvi M.K. Kirubakaran Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai TN India Department of Information Technology Sona College of Technology Junction Main Road Salem TN India Department of Information Technology St. Joseph’s College of Engineering Chennai TN India Department of Artificial Intelligence and Data Science St. Joseph’s Institute of Technology Chennai TN India

Brain cancer is one of the high-risk diseases and increases the death rate in all countries, affecting both men and women. The early diagnosis and severity of brain cancer leads to better medical treatment and save people’s lives. The machine learning procedure has been applied for early detection and treatment of brain cancer in the biomedical field by classifying them into low-risk and high-risk groups. In cancer research, the predictive and classification model has been developed using Deep Convolutional Neural Network (DCNN) algorithms for accurate decision making. The Magnetic Resonance Image (MRI) classification technique DCNN is advanced to detect and match feature points of training and test images. The DCNN classifier based on the outcome of feature points then classifies images. The key notion of this proposed research effort is to implement and execute the proposed DCNN algorithm on cancer patient datasets for risk level classification. The brain cancer affected patient details are collected from UCI machine learning data repository for experimental analysis. In this research study, the DCNN algorithm is proposed and it gives better accuracy and faster than the KNN, CNN and SVM.

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A Survey of Recent Advances in Optimization Methods for Wireless Communications

arXiv

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arXiv 2024年

作者： Liu, Ya-Feng Chang, Tsung-Hui Hong, Mingyi Wu, Zheyu So, Anthony Man-Cho Jorswieck, Eduard A. Yu, Wei The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Research Institute of Big Data China The Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States The Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong The Institute for Communication Technology Technische Universitat Braunschweig Germany The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada

Mathematical optimization is now widely regarded as an indispensable modeling and solution tool for the design of wireless communications systems. While optimization has played a significant role in the revolutionary progress in wireless communication and networking technologies from 1G to 5G and onto the future 6G, the innovations in wireless technologies have also substantially transformed the nature of the underlying mathematical optimization problems upon which the system designs are based and have sparked significant innovations in the development of methodologies to understand, to analyze, and to solve those problems. In this paper, we provide a comprehensive survey of recent advances in mathematical optimization theory and algorithms for wireless communication system design. We begin by illustrating common features of mathematical optimization problems arising in wireless communication system design. We discuss various scenarios and use cases and their associated mathematical structures from an optimization perspective. We then provide an overview of recently developed optimization techniques in areas ranging from nonconvex optimization, global optimization, and integer programming, to distributed optimization and learning-based optimization. The key to successful solution of mathematical optimization problems is in carefully choosing or developing suitable algorithms (or neural network architectures) that can exploit the underlying problem structure. We conclude the paper by identifying several open research challenges and outlining future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition

arXiv

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arXiv 2022年

作者： Dichristofano, Alex Shuster, Henry Chandra, Shefali Patwari, Neal Division of Computational & Data Sciences Washington University 1 Brookings Drive St. LouisMO63130 United States Department of Computer Science & Engineering Washington University 1 Brookings Drive St. LouisMO63130 United States Department of Women Gender and Sexuality Studies Department of History Washington University 1 Brookings Drive St. LouisMO63130 United States Department of Electrical & Systems Engineering Department of Computer Science & Engineering Division of Computational & Data Sciences Washington University 1 Brookings Drive St. LouisMO63130 United States

Past research has identified discriminatory automatic speech recognition (ASR) performance as a function of the racial group and nationality of the speaker. In this paper, we expand the discussion beyond bias as a function of the individual national origin of the speaker to look for bias as a function of the geopolitical orientation of their nation of origin. We audit some of the most popular English language ASR services using a large and global data set of speech from The Speech Accent Archive, which includes over 2,700 speakers of English born in 171 different countries. We show that, even when controlling for multiple linguistic covariates, ASR service performance has a statistically significant relationship to the political alignment of the speaker’s birth country with respect to the United States’ geopolitical power. This holds for all ASR services tested. We discuss this bias in the context of the historical use of language to maintain global and political power. © 2022, CC BY-NC-SA.

关键词： Speech recognition

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Effect of spatial heterogeneity on level of rejuvenation in Ni80P20 metallic glass

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Chinese Physics B 2022年第9期31卷 435-443页

作者： Tzu-Chia Chen Mahyuddin KM Nasution Abdullah Hasan Jabbar Sarah Jawad Shoja Waluyo Adi Siswanto Sigiet Haryo Pranoto Dmitry Bokov Rustem Magizov Yasser Fakri Mustafa A.Surendar Rustem Zalilov Alexandr Sviderskiy Alla Vorobeva Dmitry Vorobyev Ahmed Alkhayyat Dhurakij Pundit University Bangkok 10210Thailand Data Science and Computational Intelligence Research Group Universitas Sumatera UtaraMedanIndonesia Optical Department College of Health and Medical TechnologySawa UniversityMinistry of Higher Education and Scientific ResearchAl-MuthanaaSamawahIraq College of Health&Medical Technology Al-Ayen UniversityIraq Faculty of Engineering Universitas Muhammadiyah SurakartaJawa Tengah 57102Indonesia Department of Mechanical Engineering Faculty of Science and TechnologyUniversitas Muhammadiyah Kalimantan TimurSamarinda 75124Indonesia Institute of Pharmacy Sechenov First Moscow State Medical University8 Trubetskaya St.Bldg.2Moscow 119991Russia Kazan Federal University KazanRussia Department of Pharmaceutical Chemistry College of PharmacyUniversity of MosulMosul-41001Iraq Department of Pharmacology Saveetha Dental College and HospitalSaveetha Institute of Medical and Technical SciencesChennaiIndia Nosov Magnitogorsk State Technical University MagnitogorskRussia Innovative University of Eurasia PavlodarRepublic of Kazakhstan K.G.Razumovsky Moscow State University of Technologies and Management(The First Cossack University) Moscow 109004Russia College of Technical Engineering The Islamic UniversityNajafIraq

Understanding the relation between spatial heterogeneity and structural rejuvenation is one of the hottest topics in the field of metallic glasses(MGs).In this work,molecular dynamics(MD)simulation is implemented to discover the effects of initial spatial heterogeneity on the level of rejuvenation in the Ni_(80)P_(20)*** this purpose,the samples are prepared with cooling rates of 10^(10) K/s-10^(12) K/s to make glassy alloys with different atomic ***,it is found that the increase in the cooling rate leads the Gaussian-type shear modulus distribution to widen,indicating the aggregations in both elastically soft and hard *** the primary evaluations,the elastostatic loading is also used to transform structural rejuvenation into the atomic *** results indicate that the sample with intermediate structural heterogeneity prepared with 10^(11) K/s exhibits the maximum structural rejuvenation which is due to the fact that the atomic configuration in an intermediate structure contains more potential sites for generating the maximum atomic rearrangement and loosely packed regions under an external *** features of atomic rearrangement and structural changes under the rejuvenation process are discussed in detail.

关键词： metalic glasses mechanical properties molecular dynamics simulation disordered solids

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Study of City Crimes with Smart Solutions Integrating into Smart Cities, Internet of Things (IoT), and Cybersecurity Systems

Study of City Crimes with Smart Solutions Integrating into S...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Al-Siyam Rahman Md. Sadik Tasrif Anubhove Mohammad Zeyad S.M. Masum Ahmed Md. Abul Ala Walid Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh (ABCD Laboratory Bangladesh) Chattogram Bangladesh Erasmus+ Joint MSC in Cybersecurity Faculty of Sciences Université libre de Bruxelles (ULB) Brussels Belgium Avenues Department of Urban Engineering Universite de Technologie de Compiègne (UTC) Compiègne France Centre for Research on Geography Resources Environment Energy & Networks (Green) Bocconi University Milano Italy Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Bangladesh

The development of smart cities enhances the quality of life for many people by promoting digital interconnection, which encourages dependability and accessibility in cities. Cities have multiple security issues to deal with, as well as kidnapping, rape, child abuse, and violence, completely of which stand on the rise globally. This kind of crime constantly disturbs the victim not only physically and mentally but also forces them to attempt suicide, which can ruin the happiness of many families in a city. Conventional methods are insufficient to safeguard a city from crime and provide a secure way of life for everyone, which is depraved for society and their day-to-day activities. However, this paper, first analyzes the different types of criminal activities in Bangladesh, such as kidnapping, rape, murder, and child abuse, and here offer various technological solutions, to protect cities against the worst crimes, using technologies like cybersecurity and the Internet of Things (IoT) based technologies. With the help of various technological solutions, a city becomes safer while also achieving Sustainable Development Goals (SDG's) number 11, which promotes sustainable cities and communities. This paper clarifies how to reduce criminal activities by making use of a range of technologies, which benefit the country and improve people's quality of life.

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Disk2Planet: A Robust and Automated Machine Learning Tool for Parameter Inference in Disk-Planet Systems

arXiv

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arXiv 2024年

作者： Mao, Shunyuan Dong, Ruobing Yi, Kwang Moo Lu, Lu Wang, Sifan Perdikaris, Paris Department of Physics and Astronomy University of Victoria VictoriaBCV8P 5C2 Canada Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Department of Computer Science University of British Columbia VancouverBCV6T 1Z4 Canada Department of Statistics and Data Science Yale University New HavenCT06511 United States Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA19104 United States Institute for Foundations of Data Science Yale University New HavenCT06520 United States Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA19104 United States

We introduce Disk2Planet, a machine learning-based tool to infer key parameters in disk-planet systems from observed protoplanetary disk structures. Disk2Planet takes as input the disk structures in the form of two-dimensional density and velocity maps, and outputs disk and planet properties, that is, the Shakura-Sunyaev viscosity, the disk aspect ratio, the planet-star mass ratio, and the planet's radius and azimuth. We integrate the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an evolutionary algorithm tailored for complex optimization problems, and the Protoplanetary Disk Operator Network (PPDONet), a neural network designed to predict solutions of disk-planet interactions. Our tool is fully automated and can retrieve parameters in one system in three minutes on an Nvidia A100 graphics processing unit. We empirically demonstrate that our tool achieves percent-level or higher accuracy, and is able to handle missing data and unknown levels of noise. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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Identification of Clinical Features Associated with Mortality in COVID-19 Patients

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Operations Research Forum 2023年第1期4卷 16页

作者： Eskandarian, Rahimeh Alizadehsani, Roohallah Behjati, Mohaddeseh Zahmatkesh, Mehrdad Sani, Zahra Alizadeh Haddadi, Azadeh Kakhi, Kourosh Roshanzamir, Mohamad Shoeibi, Afshin Hussain, Sadiq Khozeimeh, Fahime Darbandy, Mohammad Tayarani Joloudari, Javad Hassannataj Lashgari, Reza Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Internal Medicine Research Center Semnan University of Medical Sciences Semnan Iran Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia Rajaei Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Department of Biology Faculty of Basic Sciences Shahrekord Branch Islamic Azad University Shahrekord Iran Department of Computer Engineering Faculty of Engineering Fasa University Fasa 74617-81189 Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain System Administrator Dibrugarh University Assam 786004 India School of Architecture Islamic Azad University Taft Taft *** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Department of Computer Engineering Amol Institute of Higher Education Amol Iran Institute of Medical Science and Technology Shahid Beheshti University Tehran Iran School of Engineering and Applied Sciences Harvard Paulson Harvard University Allston 02134 MA United States Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University Geelong 3220 VIC Australia Cardiovascular Division The George Institute for Global Health Newtown Australia Sydney Medical School University of Sydney Camperdown Australia

Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings. © 2023, The Author(s).

关键词： COVID‐19 Machine learning Mortality Risk factors Symptoms

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An explainable COVID-19 detection system based on human sounds

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Smart Health 2022年 26卷 100332页

作者： Li, Huining Chen, Xingyu Qian, Xiaoye Chen, Huan Li, Zhengxiong Bhattacharjee, Soumyadeep Zhang, Hanbin Huang, Ming-Chun Xu, Wenyao Department of Computer Science and Engineering University at Buffalo United States Department of Electrical Computer and Systems Engineering Case Western Reserve University United States Department of Computer Science and Engineering University of Colorado Denver United States Department of Data and Computational Science Duke Kunshan University China Suzhou Huanmu Intelligence Technology Co. Ltd. China

Acoustic signals generated by the human body have often been used as biomarkers to diagnose and monitor diseases. As the pathogenesis of COVID-19 indicates impairments in the respiratory system, digital acoustic biomarkers of COVID-19 are under investigation. In this paper, we explore an accurate and explainable COVID-19 diagnosis approach based on human speech, cough, and breath data using the power of machine learning. We first analyze our design space considerations from the data aspect and model aspect. Then, we perform data augmentation, Mel-spectrogram transformation, and develop a deep residual architecture-based model for prediction. Experimental results show that our system outperforms the baseline, with the ROC-AUC result increased by 5.47%. Finally, we perform an interpretation analysis based on the visualization of the activation map to further validate the model. © 2022

关键词： Accurate Acoustic COVID-19 Explainable

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