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arXiv 2019年

作者： Zhang, Mingrui Chen, Lin Mokhtari, Aryan Hassani, Hamed Karbasi, Amin Department of Statistics and Data Science Yale University New HavenCT06511 United States Yale Institute for Network Science Department of Electrical Engineering Yale University New HavenCT06511 United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical Engineering and Computer Science Yale University New HavenCT06511 United States

How can we efficiently mitigate the overhead of gradient communications in distributed optimization? This problem is at the heart of training scalable machine learning models and has been mainly studied in the unconstrained setting. In this paper, we propose Quantized Frank-Wolfe (QFW), the first projection-free and communication-efficient algorithm for solving constrained optimization problems at scale. We consider both convex and non-convex objective functions, expressed as a finite-sum or more generally a stochastic optimization problem, and provide strong theoretical guarantees on the convergence rate of QFW. This is accomplished by proposing novel quantization schemes that efficiently compress gradients while controlling the noise variance introduced during this process. Finally, we empirically validate the efficiency of QFW in terms of communication and the quality of returned solution against natural baselines. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

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四川生理科学杂志 2022年第7期44卷 1299-1299页

作者： Jonathan E Pekar Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA 92093 USA Department of Biomedical Informatics University of California San Diego La Jolla CA 92093 USA Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA 92037 USA Department of Computer Science and Engineering University of California San Diego La Jolla CA 92093 USA Department of Mathematics University of California San Diego La Jolla CA 92093 USA Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ 85721 USA W. Harry Feinstone Department of Molecular Microbiology and Immunology Johns Hopkins Bloomberg School of Public Health Baltimore MD 21205 USA New York City Public Health Laboratory New York City Department of Health and Mental Hygiene New York NY 11101 USA Department of Microbiology Institute for Viral Diseases Biosafety Center College of Medicine Korea University Seoul South Korea BK21 Graduate Program Department of Biomedical Sciences Korea University College of Medicine Seoul 02841 Republic of Korea National Public Health Laboratory National Centre for Infectious Diseases Singapore Malaysia Genome and Vaccine Institute Jalan Bangi 43000 Kajang Selangor Malaysia Department of Medicine University of California San Diego La Jolla CA 92093 USA Department of Microbiology and Immunology School of Medicine Tulane University New Orleans LA 70112 USA Zalgen Labs Frederick MD 21703 USA Global Virus Network (GVN) Baltimore MD 21201 USA Sydney Institute for Infectious Diseases School of Life and Environmental Sciences and School of Medical Sciences The University of Sydney Sydney NSW 2006 Australia Institute of Evolutionary Biology University of Edinburgh King’s Buildings Edinburgh EH9 3FL UK Department of Biomathematics David Geffen School of Medicine Un

Understanding the circumstances that lead to pandemics is important for their prevention. Here, we analyze the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted A and B. Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October-8 December), while the separate introduction of lineage A likely occurred within weeks of this event.

关键词： epidemiology likely acute

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A chaotic artificial bee colony algorithm based on lévy search

A chaotic artificial bee colony algorithm based on lévy sea...

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作者： Lin, Shijie Dong, Chen Wang, Zhiqiang Guo, Wenzhong Chen, Zhenyi Ye, Yin College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Key Laboratory of Information Security of Network Systems Fuzhou350116 China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China Department of Electrical Engineering University of South Florida TampaFL33620 United States

A Lévy search strategy based chaotic artificial bee colony algorithm (LABC) is proposed in this paper. The chaotic sequence, global optimal mechanism and Lévy flight mechanism were introduced respectively into the initialization, the employed bee search and the onlooker bee search. The experiments show that the proposed algorithm performed better in convergence speed, global search ability and optimization accuracy than other improved ABC. Copyright © 2018 The Institute of Electronics, Information and Communication Engineers.

关键词： Evolutionary algorithms

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Small worlds and clustering in spatial networks

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Physical Review Research 2020年第2期2卷 023040-023040页

作者： Marián Boguñá Dmitri Krioukov Pedro Almagro M. Ángeles Serrano Departament de Física de la Matèria Condensada Universitat de Barcelona Martí i Franquès 1 E-08028 Barcelona Spain Universitat de Barcelona Institute of Complex Systems (UBICS) Universitat de Barcelona Barcelona Spain Network Science Institute Northeastern University 177 Huntington avenue Boston Massachusetts 022115 USA Department of Physics Department of Mathematics Department of Electrical & Computer Engineering Northeastern University 110 Forsyth Street 111 Dana Research Center Boston Massachusetts 02115 USA Institució Catalana de Recerca i Estudis Avançats (ICREA) Passeig Lluís Companys 23 E-08010 Barcelona Spain

networks with underlying metric spaces attract increasing research attention in network science, statistical physics, applied mathematics, computer science, sociology, and other fields. This attention is further amplified by the current surge of activity in graph embedding. In the vast realm of spatial network models, only a few reproduce even the most basic properties of real-world networks. Here, we focus on three such properties—sparsity, small worldness, and clustering—and identify the general subclass of spatial homogeneous and heterogeneous network models that are sparse small worlds and that have nonzero clustering in the thermodynamic limit. We rely on the maximum entropy approach in which network links correspond to noninteracting fermions whose energy depends on spatial distances between nodes.

关键词： Clustering Degree correlations Degree distributions network formation & growth

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On the inevitability of online echo chambers

arXiv

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arXiv 2019年

作者： Sasahara, Kazutoshi Chen, Wen Peng, Hao Ciampaglia, Giovanni Luca Flammini, Alessandro Menczer, Filippo Department of Complex Systems Science Graduate School of Informatics Nagoya University Japan JST PRESTO Japan Center for Complex Networks and Systems Research Indiana University Bloomington United States School of Informatics Computing and Engineering Indiana University Bloomington United States School of Information University of Michigan United States Network Science Institute Indiana University United States Department of Computer Science and Engineering University of South Florida United States

While social media make it easy to connect with and access information from anyone, they also facilitate basic inuence and unfriending mechanisms that may lead to segregated and polarized clusters known as "echo chambers." Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of inuence and unfriending. Users can change both their opinions and social connections based on the information to which they are exposed through sharing. The model dynamics show that even with minimal amounts of inuence and unfriending, the social network rapidly devolves into segregated, homogeneous communities. These predictions are consistent with empirical data from Twitter. Although our findings suggest that echo chambers are somewhat inevitable given the mechanisms at play in online social media, they also provide insights into possible mitigation strategies. Copyright © 2019, The Authors. All rights reserved.

关键词： Social networking (online)

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A survey of gene selection and classification techniques based on cancer microarray data analysis 4

A survey of gene selection and classification techniques bas...

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4th IEEE International Conference on computer and Communications, ICCC 2018

作者： Zhang, Xinman He, Tingting Ouyang, Liwei Xu, Xuebin Chen, Sushing School of Electronics and Information Engineering MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an China Guangdong xi'An Jiaotong University Academy Foshan China CISE Department for Computer Information Science Engineering University of Florida Systems Biology Lab Gainesville United States

ISBN: (纸本)9781538683392

A growing number of cancer deaths draws attractions in the areas of bioinformatics worldwide. It is worth noting that cancer microarray data is composed of tens of thousands of genes, but there are only a few dozens of genes that are really relative to cancers. Thus, it is particularly import to pick out the relevant genes by means of microarray data analysis. Gene selection and classification techniques capture the essence of cancer mechanism and represent a suite of the refreshing cancer cure. In the paper, we summarize 4 types of cancer microarrays which show up in reference providing their attributes and download websites. Furthermore, we also review most extensively studied gene selection and classification techniques. Meanwhile, comparison and analysis on accuracies of gene selection and classification methods in view of remarkably influential algorithms are outlined with unique and randomized algorithms. Thus, the most correlative cancer genes are selected, Leukemia and Colon cancer as typical representatives are listed in the survey. From the microcosmic perspectives, these emerging techniques are considerably conductive to solve the dimensional curse of cancer microarray data applied in the treatment of cancer. © 2018 IEEE.

关键词： Genes

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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Correlation-Aware Virtual Machine Placement in Data Center networks 7th

Correlation-Aware Virtual Machine Placement in Data Center N...

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7th EAI International Conference on Cloud Computing, CloudComp 2016 and 1st EAI International Conference on Security and Privacy in New Computing Environments, SPNCE 2016

作者： Chen, Tao Zhu, Yaoming Gao, Xiaofeng Kong, Linghe Chen, Guihai Wang, Yongjian Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Information Network Security of Ministry of Public Security The Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9783319696041

The resource utilization (CPU, memory) is a key performance metric in data center networks. The goal of the cloud platform supported by data center networks is achieving high average resource utilization while guaranteeing the quality of cloud services. Previous work focus on increasing the time-average resource utilization and decreasing the overload ratio of servers by designing various efficient virtual machine placement schemes. Unfortunately, most of virtual machine placement schemes did not involve the service level agreements and statistical methods. In this paper, we propose a correlation-aware virtual machine placement scheme that effectively places virtual machines on physical machines. First, we employ Neural networks model to forecast the resource utilization trend according to the historical resource utilization data. Second, we design correlation-aware placement algorithms to enhance resource utilization while meeting the user-defined service level agreements. The results show that the efficiency of our virtual machine placement algorithms outperform the previous work by about 15%. © 2018, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Virtual machine

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Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes (vol 12, pg 631, 2020)

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GENETICS IN MEDICINE 2020年第8期22卷 1427-1427页

作者： Chang, Willie H. Mashouri, Pouria Lozano, Alexander X. Johnstone, Brittney Husic, Mia Olry, Annie Maiella, Sylvie Balci, Tugce B. Sawyer, Sarah L. Robinson, Peter N. Rath, Ana Brudno, Michael Centre for Computational Medicine The Hospital For Sick Children Toronto ON Canada Department of Computer Science Princeton University Princeton NJ USA Faculty of Medicine University of Toronto Toronto ON Canada Department of Materials Science & Engineering Stanford University Stanford CA USA Sunnybrook Health Sciences Centre Toronto ON Canada Orphanet Institut national de la santé et de la recherche médicale Paris France Medical Genetics Program of Southwestern Ontario London Health Sciences Centre London ON Canada Department of Genetics Children’s Hospital of Eastern Ontario and Children’s Hospital of Eastern Ontario Research Institute University of Ottawa Ottawa ON Canada The Jackson Laboratory for Genomic Medicine Farmington CT USA Institute for Systems Genomics University of Connecticut Farmington CT USA Department of Computer Science University of Toronto Toronto ON Canada Genetics and Genome Biology Program The Hospital for Sick Children Toronto ON Canada University Health Network Toronto ON Canada

PurposeComputational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student *** developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated *** Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research *** enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.

关键词： rare diseases phenotype crowdsourcing medical education machine learning

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Preface

Studies in Big Data

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Studies in Big Data 2020年 68卷 v-viii页

作者： Dash, Sujata Acharya, Biswa Ranjan Mittal, Mamta Abraham, Ajith Kelemen, Arpad Department of Computer Science North Orissa University Odisha Takatpur India Computer Science and Engineering Department G. B. Pant Government Engineering College Delhi New Delhi India Department of Organizational Systems and Adult Health University of Maryland BaltimoreMD United States School of Computer Science and Engineering KIIT Deemed to University Odisha Bhubaneswar India Scientific Network for Innovation and Research Excellence Machine Intelligence Research Labs AuburnAL United States

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