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Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension

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Journal of Ambient Intelligence and Humanized Computing 2017年 1-11页

作者： Wang, Shui-Hua Muhammad, Khan Phillips, Preetha Dong, Zhengchao Zhang, Yu-Dong Jiangsu Key Laboratory of Advanced Manufacturing Technology HuaiyinJiangsu223003 China School of Computer Science and Technology Henan Polytechnic University JiaozuoHenan454000 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing China College of Software Convergence Sejong University Seoul Korea Republic of Department of Informatics University of Leicester LeicesterLE1 7RH United Kingdom West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Translational Imaging Division and MRI Unit Columbia University and New York State Psychiatric Institute New YorkNY10032 United States

Ductal carcinoma in situ (DCIS) is a severe breast disease. It generates little symptom and may be neglected in prodromal stage. In this study, we developed a novel DCIS detection method based on breast thermography, which can provide earlier alert than other exams. We created a 40 breast-thermogram dataset. We used six statistical measures, and we used fractal dimension to describe the texture measure. The extreme learning machine was used as the classifier. Our developed system yielded a sensitivity of 93.0 ± 2.6%, a specificity of 92.5 ± 2.6%, and an accuracy of 92.8 ± 1.8%. The extreme learning machine was better than support vector machine, artificial neural network, decision tree, and weighted k-nearest neighbors. Besides, our developed system was superior to six state-of-the-art approaches. © 2017 Springer-Verlag GmbH Germany, part of Springer Nature

关键词： Fractal dimension

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Flexible multiple base station association and activation for downlink heterogeneous networks

arXiv

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arXiv 2017年

作者： Shen, Kaiming Liu, Ya-Feng Ding, David Yiwei Yu, Wei Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada 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

This letter shows that the flexible association of possibly multiple base stations (BSs) to each user over multiple frequency bands, along with the joint optimization of BS transmit power that encourages the turning-off of the BSs at off-peak time, can significantly improve the performance of a downlink heterogeneous wireless cellular network. We propose a gradient projection algorithm for optimizing BS association and an iteratively reweighting scheme together with a novel proximal gradient method for optimizing power in order to find the optimal tradeoff between network utility and power consumption. Simulation results reveal significant performance improvement as compared to the conventional single-BS association. Copyright © 2017, The Authors. All rights reserved.

关键词： Chemical activation

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Allele-defined genome of the autopolyploid sugarcane Saccharum spontaneum L (vol 50, pg 1565, 2018)

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NATURE GENETICS 2018年第12期50卷 1754-1754页

作者： Zhang, Jisen Zhang, Xingtan Tang, Haibao Zhang, Qing Hua, Xiuting Ma, Xiaokai Zhu, Fan Jones, Tyler Zhu, Xinguang Bowers, John Wai, Ching Man Zheng, Chunfang Shi, Yan Chen, Shuai Xu, Xiuming Yue, Jingjing Nelson, David R. Huang, Lixian Li, Zhen Xu, Huimin Zhou, Dong Wang, Yongjun Hu, Weichang Lin, Jishan Deng, Youjin Pandey, Neha Mancini, Melina Zerpa, Dessiree Nguyen, Julie K. Wang, Liming Yu, Liang Xin, Yinghui Ge, Liangfa Arro, Jie Han, Jennifer O. Chakrabarty, Setu Pushko, Marija Zhang, Wenping Ma, Yanhong Ma, Panpan Lv, Mingju Chen, Faming Zheng, Guangyong Xu, Jingsheng Yang, Zhenhui Deng, Fang Chen, Xuequn Liao, Zhenyang Zhang, Xunxiao Lin, Zhicong Lin, Hai Yan, Hansong Kuang, Zheng Zhong, Weimin Liang, Pingping Wang, Guofeng Yuan, Yuan Shi, Jiaxian Hou, Jinxiang Lin, Jingxian Jin, Jingjing Cao, Peijian Shen, Qiaochu Jiang, Qing Zhou, Ping Ma, Yaying Zhang, Xiaodan Xu, Rongrong Liu, Juan Zhou, Yongmei Jia, Haifeng Ma, Qing Qi, Rui Zhang, Zhiliang Fang, Jingping Fang, Hongkun Song, Jinjin Wang, Mengjuan Dong, Guangrui Wang, Gang Chen, Zheng Ma, Teng Liu, Hong Dhungana, Singha R. Huss, Sarah E. Yang, Xiping Sharma, Anupma Trujillo, Jhon H. Martinez, Maria C. Hudson, Matthew Riascos, John J. Schuler, Mary Chen, Li-Qing Braun, David M. Li, Lei Yu, Qingyi Wang, Jianping Wang, Kai Schatz, Michael C. Heckerman, David Van Sluys, Marie-Anne Souza, Glaucia Mendes Moore, Paul H. Sankoff, David VanBuren, Robert Paterson, Andrew H. Nagai, Chifumi Ming, Ray Fujian Agriculture and Forestry University and University of Illinois at Urbana-Champaign School of Integrative Biology Joint Center for Genomics and Biotechnology National Sugarcane Engineering Technology Research Center Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology Key Laboratory of Genetics Breeding and Multiple Utilization of Corps Ministry of Education Fujian Agriculture and Forestry University Fuzhou China Department of Plant Biology University of Illinois at Urbana-Champaign Urbana IL USA Hawaii Agriculture Research Center Kunia HI USA Institute for Plant Physiology and Ecology Chinese Academy of Sciences Shanghai China Department of Plant Biology University of Georgia Athens GA USA Department of Horticulture Michigan State University East Lansing MI USA Department of Mathematics and Statistics University of Ottawa Ottawa Ontario Canada Department of Microbiology Immunology and Biochemistry University of Tennessee HSC Memphis TN USA Chinese Academy of Sciences-Max-Planck-Gesellschaft Partner Institute for Computational Biology Chinese Academy of Sciences Shanghai China China Tobacco Gene Research Center Zhengzhou Tobacco Research Institute of CNTC Zhengzhou China Division of Biological Sciences University of Missouri Columbia MO USA Department of Agronomy University of Florida Gainesville FL USA Texas A&M AgriLife Research Texas A&M University System Dallas TX USA Centro de Investigación de la Caña de Azúcar de Colombia (Cenicaña) Cali Colombia Department of Crop Sciences University of Illinois at Urbana-Champaign Urbana IL USA Departments of Computer Science and Biology Johns Hopkins University Baltimore MD USA Microsoft Research Redmond WA USA Departamento de Botânica Instituto de Biociências Universidade de São Paulo São Paulo Brazil Departamento de Bioquímica Instituto de Química Universidade de São Paulo São Paulo Brazil

Modern sugarcanes are polyploid interspecific hybrids, combining high sugar content from Saccharum officinarum with hardiness, disease resistance and ratooning of Saccharum spontaneum. Sequencing of a haploid S. spontaneum, AP85-441, facilitated the assembly of 32 pseudo-chromosomes comprising 8 homologous groups of 4 members each, bearing 35,525 genes with alleles defined. The reduction of basic chromosome number from 10 to 8 in S. spontaneum was caused by fissions of 2 ancestral chromosomes followed by translocations to 4 chromosomes. Surprisingly, 80% of nucleotide binding site-encoding genes associated with disease resistance are located in 4 rearranged chromosomes and 51% of those in rearranged regions. Resequencing of 64 S. spontaneum genomes identified balancing selection in rearranged regions, maintaining their diversity. Introgressed S. spontaneum chromosomes in modern sugarcanes are randomly distributed in AP85-441 genome, indicating random recombination among homologs in different S. spontaneum accessions. The allele-defined Saccharum genome offers new knowledge and resources to accelerate sugarcane improvement.

关键词： Genomics Plant genetics

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Design of endurable networks in the presence of aging

arXiv

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arXiv 2017年

作者： Lin, Yuansheng Patron, Amikam Guo, Shu Kang, Rui Li, Daqing Havlin, Shlomo Cohen, Reuven School of Reliability and Systems Engineering Beihang University Beijing100191 China Science and Technology on Reliability and Environmental Engineering Laboratory Beijing100191 China Department of Computer Science University of California DavisCA95616 United States Department of Physics Bar-Ilan University ramat-gan5290002 Israel Department of Mathematics Bar-Ilan University ramat-gan5290002 Israel

Networks are designed to satisfy given objectives under specific requirements. While the static connectivity of networks is normally analyzed and corresponding design principles for static robustness are proposed, the challenge still remains of how to design endurable networks that maintain the required level of connectivity during its whole lifespan, against component aging. We introduce network endurance as a new concept to evaluate networks overall performance during its whole lifespan, considering both network connectivity and network duration. We develop a framework for designing an endurable network by allocating the expected lifetimes of its components, given a limited budget. Based on percolation theory and simulation, we find that the maximal network endurance can be achieved with a quantitative balance between network duration and connectivity. For different endurance requirements, we find that the optimal design can be separated into two categories: strong dependence of lifetime on node’s degree leads to larger network lifetime, while weak dependence generates stronger network connectivity. Our findings could help network design, by providing a quantitative prediction of network endurance based on network topology. Copyright © 2017, The Authors. All rights reserved.

关键词： Solvents

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Deep learning for multi-messenger astrophysics a gateway for discovery in the big data era

arXiv

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arXiv 2019年

作者： Allen, Gabrielle Andreoni, Igor Bachelet, Etienne Bruce Berriman, G. Bianco, Federica B. Biswas, Rahul Kind, Matias Carrasco Chard, Kyle Cho, Minsik Cowperthwaite, Philip S. Etienne, Zachariah B. George, Daniel Gibbs, Tom Graham, Matthew Gropp, William Gupta, Anushri Haas, Roland Huerta, E.A. Jennings, Elise Katz, Daniel S. Khan, Asad Kindratenko, Volodymyr Kramer, William T.C. Liu, Xin Mahabal, Ashish McHenry, Kenton Miller, J.M. Neubauer, M.S. Oberlin, Steve Olivas, Alexander R. Rosofsky, Shawn Ruiz, Milton Saxton, Aaron Schutz, Bernard Schwing, Alex Seidel, Ed Shapiro, Stuart L. Shen, Hongyu Shen, Yue Sipocz, Brigitta M. Sun, Lunan Towns, John Tsokaros, Antonios Wei, Wei Wells, Jack Williams, Timothy J. Xiong, Jinjun Zhao, Zhizhen NCSA University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117 United States Caltech/IPAC-NExScI 1200 E California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Joseph R. Biden Jr . School of Public Policy and Administration University of Delaware NewarkDE19716 United States Data Science Institute University of Delaware NewarkDE19716 United States Center for Urban Science and Progress New York University 370 Jay St BrooklynNY11201 United States The Oskar Klein Centre for Cosmoparticle Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden University of Chicago ChicagoIL60605 United States IBM Systems AustinTX78717 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Department of Mathematics West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95050 United States Center for Data Driven Discovery Caltech PasadenaCA91125 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Argonne National Laboratory Leadership Computing Facility LemontIL60439 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 Unit

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics;(ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data;(iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups;(iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era;(v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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Heterogeneous defect prediction via exploiting correlation subspace 28

Heterogeneous defect prediction via exploiting correlation s...

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28th International Conference on software engineering and Knowledge engineering, SEKE 2016

作者： Cheng, Ming Wu, Guoqing Jiang, Min Wan, Hongyan You, Guoan Yuan, Mengting State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430072 China Department of Cognitive Science and Technology Fujian Key Laboratory of Brain-Like Intelligent Systems Xiamen University Xiamen361005 China

ISBN: (纸本)189170639X

software defect prediction generally builds models from intra-project data. Lack of training data at the early stage of software testing limits the efficiency of prediction in practice. Thereby researchers proposed cross-project defect prediction using the data from other projects. Most previous efforts assumed the cross-project defect data have the same metrics set which means the metrics used and size of metrics set are same in the data of projects. However, in real scenarios, this assumption may not hold. In addition, software defect datasets have the class imbalance problem increasing the difficulty for the learner to predict defects. In this paper, we advance canonical correlation analysis for deriving a joint feature space for associating crossproject data and propose a novel support vector machine algorithm which incorporates the correlation transfer information into classifier design for cross-project prediction. Moreover, we take different misclassification costs into consideration to make the classification inclining to classify a module as a defective one, alleviating the impact of imbalanced data. Experiments on public heterogeneous datasets from different projects show that our method is more effective, compared to state-of-the-art methods. Copyright © 2016 by KSI Research Inc. and Knowledge systems Institute Graduate School.

关键词： Support vector machines

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Combined Cloud： A Mixture of Voluntary Cloud and Reserved Instance Marketplace

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Journal of computer science & Technology 2016年第6期31卷 1110-1123页

作者： Wei Shen Wan-Chun Dou Fan Wu Shaojie Tang Qiang Ni State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210046 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Information Systems University of Texas at Dallas Texas TX 75080 U.S.A. School of Computing and Communications Lancaster University Lancaster LA1 4WA U.K.

Voluntary cloud is a new paradigm of cloud computing. It provides an alternative selection along with some well-provisioned clouds. However, for the uncertain time span that participants share their computing resources in voluntary cloud, there are some challenging issues, i.e., fluctuation, under-capacity and low-benefit. In this paper, an architecture is first proposed based on Bittorrent protocol. In this architecture, resources could be reserved or requested from Reserved Instance Marketplace and could be accessed with a lower price in a short circle. Actually, these resources could replenish the inadequate resource pool and relieve the fluctuation and under-capacity issue in voluntary cloud. Then, the fault rate of each node is used to evaluate the uncertainty of its sharing time. By leveraging a linear prediction model, it is enabled by a distribution function which is used for evaluating the computing capacity of the system. Moreover, the cost optimization problem is investigated and a computational method is presented to solve the low-benefit issue in voluntary cloud. At last, the system performance is validated by two sets of simulations. And the experimental results show the effectiveness of our computational method for resource reservation optimization.

关键词： voluntary cloud system architecture resource reservation

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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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Exploiting Correlation Subspace to Predict Heterogeneous Cross-Project Defects

Exploiting Correlation Subspace to Predict Heterogeneous Cro...

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作者： Cheng, Ming Wu, Guoqing Wan, Hongyan You, Guoan Yuan, Mengting Jiang, Min State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan Hubei China Department of Cognitive Science and Technology School of Information Science and Engineering Xiamen University Xiamen Fujian China Fujian Key Laboratory of Brain-Like Intelligent Systems School of Information Science and Engineering Xiamen University Xiamen Fujian China

Cross-project defect prediction trains a prediction model using historical data from source projects and applies the model to target projects. Most previous efforts assumed the cross-project data have the same metrics set, which means the metrics used and the size of metrics set are the same. However, this assumption may not hold in practical scenarios. In addition, software defect datasets have the class-imbalance problem which increases the difficulty for the learner to predict defects. In this paper, we advance canonical correlation analysis by deriving a joint feature space for associating cross-project data. We also propose a novel support vector machine algorithm which incorporates the correlation transfer information into classifier design for cross-project prediction. Moreover, we take different misclassification costs into consideration to make the classification inclining to classify a module as a defective one, alleviating the impact of imbalanced data. The experimental results show that our method is more effective compared to state-of-the-art methods. © 2016 World Scientific Publishing Company.

关键词： Support vector machines

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GB-RC4: Effective brute force attacks on RC4 algorithm using GPU 7

GB-RC4: Effective brute force attacks on RC4 algorithm using...

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7th International Green and Sustainable Computing Conference, IGSC 2016

作者： Xue, Pei Li, Tao Dong, Han Liu, Chunbo Ma, Wenjing Pei, Songwen College of Computer and Control Engineering Nankai University Tianjin300071 China Information Security Evaluation Center Civil Aviation University of China Tianjin300300 China Laboratory of Parallel Software and Computational Science State Key Laboratory of Computing Science Institute of Software CAS Beijing China Shanghai Key Lab of Modern Optical Systems Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781509051175

Encryption algorithms are applied to a variety of fields and the security of encryption algorithms depends heavily on the computational infeasibility of exhaustive key-space search. RC4 algorithm has an extensive application for stream encryption, however, the disadvantages of traditional RC4 serial algorithm are large computational quantity and slow computation speed, which means a great challenge for throughput of computing system. Graphics processors recently have been widely used as accelerators for high throughput computing. In this paper, GPU is used to improve the efficiency and performance of attacking RC4 algorithm. First, we revised RC4 algorithm to make it easier to parallelize on a GPU. Then we take advantage of constant memory and shared memory in GPU to search the key space of RC4 algorithm efficiently. Besides, we solved the bank conflict problem in shared memory to further improve the performance. Our approach was evaluated in the whole key space of 40-bit, and the results show it can complete searching of the whole key space within 12.8 hours. © 2016 IEEE.

关键词： Graphics processing unit

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