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Optimization and simulation methods in addressing COVID-19 physical distancing policy: A bibliometric analysis

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AIP Conference Proceedings 2022年第1期2472卷 040010页

作者： Fazillah Mohmad Kamal Mohd Kamal Mohd Nawawi Ruzelan Khalid Antoni Wibowo Amalia Zahra School of Quantitative Sciences UUM College of Arts and Sciences Universiti Utara Malaysia 06010 UUM Sintok Kedah Malaysia Institute of Strategic Industrial Decision Modeling School of Quantitative Sciences Universiti Utara Malaysia 06010 UUM Sintok Kedah Malaysia Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

COVID-19 has received tremendous attention from scholars worldwide and even being labelled as a black swan event that has disrupted every aspect of human life. Within a short time span of the pandemic, a large volume of research pertaining to COVID-19 has been published in diverse research fields. This paper adopts a bibliometric analysis to systematically evaluate the research development in the application of optimization and simulation methods to address COVID-19 physical distancing policy (OSPhyD) using Bibliometrix R package. A textual query on Scopus database using the combination of four classes of keywords; covid-19, optimization, simulation, and physical distance has returned a total of 299 original research articles and reviews published in English. Appropriate visualizations were generated to describe the collaborations between different authors, countries, and institutions, whilst co-word analysis that uses text mining technique has produced a conceptual cluster via co-occurrence network map to underscore the emerging themes in the current research interest. The main findings pinpoint that: 1) OSPhyD as a scientific research field is an emerging multidisciplinary research topic that is growing progressively and steadily in the fields of medicine, engineering, social sciences, mathematics and decision science, 2) The field has attracted the attention of scholars from all over the world particularly from United States of America, United Kingdom, European countries including authors from Asia, and 3) Three dominant themes or research front emerged from the publications including COVID-19 mainstream, medical education and undergraduate.

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

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NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

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2019第十一届数字图像处理国际会议

作者： Jiachen Zhong Rita Tse Gustavo Marfia Giovanni Pau Macao Polytechnic Institute Computing Program UCLA Computer Science Department Dipartimento di Informatica Scienza e Ingegneria University of Bologna

In this paper,we provide an idea about how to utilize the deep neural network with large scale social network data to judge the quality of fashion ***,our aim is to build a deep neural network based model which is able to predict the popularity of fashion-related *** Neural Network（CNN） and Multi-layer Perceptron（MLP） are the two major tools to construct the model architecture,in which the CNN is responsible for analyzing images and the MLP is responsible for analyzing other types of social network meta *** on this general idea,various tentative model structures are proposed,implemented,and compared in this *** perform experiments,we constructed a fashion-related dataset which contains over 1 million records from the online social *** no real word prediction task has been tried yet,according to the result of dataset-based tests,our models demonstrate good abilities on predicting the popularity of fashion from the online social network using the Xception ***,we also find a very interesting phenomenon,which intuitively indicates there may be limited correlation between popularity and visual design of a fashion due to the power and influence of the online social network.

关键词： Fashion Deep Learning Social Network

Ammonia: An approach for deriving project-specific bug patterns

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arXiv 2020年

作者： Higo, Yoshiki Hayashi, Shinpei Hata, Hideaki Nagappan, Meiyappan Graduate School of Information Science and Technology Osaka University 1-5 Yamadaoka Suita Osaka565-0871 Japan School of Computing Tokyo Institute of Technology Ookayama 2-12-1-W8-71 Ookayama Meguro-ku Tokyo152-8550 Japan Graduate School of Science and Technology Nara Institute of Science and Technology 8916-5 Takayama-cho Ikoma Nara630-0192 Japan Cheriton School of Computer Science University of Waterloo 200 University Avenue West WaterlooON Canada

Finding and fixing buggy code is an important and cost-intensive maintenance task, and static analysis (SA) is one of the methods developers use to perform it. SA tools warn developers about potential bugs by scanning their source code for commonly occurring bug patterns, thus giving those developers opportunities to fix the warnings (potential bugs) before they release the software. Typically, SA tools scan for general bug patterns that are common to any software project (such as null pointer dereference), and not for project specific patterns. However, past research has pointed to this lack of customizability as a severe limiting issue in SA. Accordingly, in this paper, we propose an approach called Ammonia, which is based on statically analyzing changes across the development history of a project, as a means to identify project-specific bug patterns. Furthermore, the bug patterns identified by our tool do not relate to just one developer or one specific commit, they reflect the project as a whole and compliment the warnings from other SA tools that identify general bug patterns. Herein, we report on the application of our implemented tool and approach to four Java projects: Ant, Camel, POI, and Wicket. The results obtained show that our tool could detect 19 project specific bug patterns across those four projects. Next, through manual analysis, we determined that six of those change patterns were actual bugs and submitted pull requests based on those bug patterns. As a result, five of the pull requests were merged. Copyright © 2020, The Authors. All rights reserved.

关键词： Static analysis

Single-Shot Secure Quantum Network Coding for General Multiple Unicast Network with Free One-Way Public Communication

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arXiv 2020年

作者： Kato, Go Owari, Masaki Hayashi, Masahito NTT Communication Science Laboratories NTT Corporation Japan Department of Computer Science Faculty of Informatics Shizuoka University Japan Graduate School of Mathematics Nagoya University Japan Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Quantum Computing Peng Cheng Laboratory Shenzhen518000 China Centre for Quantum Technologies National University of Singapore Singapore

It is natural in a quantum network system that multiple users intend to send their quantum message to their respective receivers, which is called a multiple unicast quantum network. We propose a canonical method to derive a secure quantum network code over a multiple unicast quantum network from a secure classical network code. Our code correctly transmits quantum states when there is no attack. It also guarantees the secrecy of the transmitted quantum state even with the existence of an attack when the attack satisfies a certain natural condition. In our security proof, the eavesdropper is allowed to modify wiretapped information dependently on the previously wiretapped messages. Our protocol guarantees the secrecy by utilizing one-way classical information transmission (public communication) in the same direction as the quantum network although the verification of quantum information transmission requires two-way classical communication. Our secure network code can be applied to several networks including the butterfly network. Copyright © 2020, The Authors. All rights reserved.

关键词： Network coding

Parameter Identifiability and Input-Output Equations

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arXiv 2020年

作者： Ovchinnikov, Alexey Pogudin, Gleb Thompson, Peter Department of Mathematics CUNY Queens College and Ph.D. Programs in Mathematics and Computer Science CUNY Graduate Center NY United States LIX CNRS École Polytechnique Institute Polytechnique de Paris France Department of Computer Science National Research University Higher School of Economics Moscow Russia Courant Institute of Mathematical Sciences New York University New York United States Ph.D. Program in Mathematics CUNY Graduate Center New York United States

Structural parameter identifiability is a property of a differential model with parameters that allows for the parameters to be determined from the model equations in the absence of noise. One of the standard approaches to assessing this problem is via input-output equations and, in particular, characteristic sets of differential ideals. The precise relation between identifiability and input-output identifiability is subtle. The goal of this note is to clarify this relation. The main results are: • identifiability implies input-output identifiability;• these notions coincide if the model does not have rational first integrals;• the field of input-output identifiable functions is generated by the coefficients of a "minimal" characteristic set of the corresponding differential ideal. We expect that some of these facts may be known to the experts in the area, but we are not aware of any articles in which these facts are stated precisely and rigorously proved. Copyright © 2020, The Authors. All rights reserved.

关键词： Rational functions

Modelling and Prediction of the 2019 Coronavirus Disease Spreading in China Incorporating Human Migration Data

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SSRN

SSRN 2020年

作者： Zhan, Choujun Tse, Chi K. Fu, Yuxia Lai, Zhikang Zhang, Haijun School of Computing South China Normal University Guangzhou China Department of Electronic Engineering City University of Hong Kong Hong Kong School of Computer Science and Engineering Nanfang College of Sun Yat-Sen University China Shenzhen Graduate School Harbin Institute of Technology Shenzhen China

This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China are collected from Baidu Migration, a mobile-app based human migration tracking data system. Historical data of infected, recovered and death cases from official source are used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure is used for estimation of the dynamics of epidemic spreading in the coming months. Our results show that the number of infections in most cities in China will peak between mid February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively. Funding Statement: This work was supported by National science Foundation of China Project 61703355, Guangdong Youth University Innovative Talents Project 2016KQNCX223, and City University of Hong Kong under Special Fund 9380114. Declaration of Interests: None. © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Chiral Phonons with Giant Magnetic Moments in a Topological Crystalline Insulator

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arXiv 2022年

作者： Hernandez, Felix G.G. Baydin, Andrey Chaudhary, Swati Tay, Fuyang Katayama, Ikufumi Takeda, Jun Nojiri, Hiroyuki Okazaki, Anderson K. Rappl, Paulo H.O. Abramof, Eduardo Rodriguez-Vega, Martin Fiete, Gregory A. Kono, Junichiro Instituto de Física Universidade de São Paulo SP São Paulo05508-090 Brazil Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics The University of Texas at Austin AustinTX78712 United States Department of Physics Northeastern University BostonMA02115 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics Graduate School of Engineering Science Yokohama National University Yokohama240-8501 Japan Institute for Materials Research Tohoku University Sendai980-8577 Japan Instituto Nacional de Pesquisas Espaciais SP São José dos Campos12201-970 Brazil Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States

We have studied the magnetic response of transverse optical phonons in Pb1−xSnxTe films. Polarization-dependent terahertz magnetospectroscopy measurements revealed Zeeman splittings and diamagnetic shifts, demonstrating that these phonon modes become chiral in magnetic fields. Films in the topological crystalline insulator phase (x > 0.32) exhibited magnetic moment values that are larger than those for topologically trivial films (x Copyright © 2022, The Authors. All rights reserved.

关键词： Phonons