检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Martin, Hector Garcia Radivojevic, Tijana Zucker, Jeremy Bouchard, Kristofer Sustarich, Jess Peisert, Sean Arnold, Dan Hillson, Nathan Babnigg, Gyorgy Marti, Jose Manuel Mungall, Christopher J. Beckham, Gregg T. Waldburger, Lucas Carothers, James Sundaram, ShivShankar Agarwal, Deb Simmons, Blake A. Backman, Tyler Banerjee, Deepanwita Tanjore, Deepti Ramakrishnan, Lavanya Singh, Anup Lawrence Berkeley National Laboratory Biological Systems and Engineering Division BerkeleyCA United States Department of Energy Agile BioFoundry EmeryvilleCA United States Joint BioEnergy Institute EmeryvilleCA United States BCAM Basque Center for Applied Mathematics Bilbao Spain Lawrence Berkeley National Laboratory Scientific Data Division BerkeleyCA United States Helen Wills Neuroscience Institute Redwood Center for Theoretical Neuroscience BerkeleyCA United States Lawrence Berkeley National Laboratory Energy Storage and Distributed Resources Division BerkeleyCA United States University of California Davis Department of Computer Science DavisCA United States Global Security Computing Applications Division Lawrence Livermore National Laboratory LivermoreCA United States Engineering Directorate Lawrence Livermore National Laboratory LivermoreCA United States Center for Bioengineering Lawrence Livermore National Laboratory LivermoreCA United States Department of Chemical Engineering Molecular Engineering & Sciences Institute Center for Synthetic Biology University of Washington SeattleWA United States Biosciences Division Argonne National Laboratory ArgonneIL United States Advanced Biofuels and Bioproducts Process Development Unit Lawrence Berkeley National Laboratory BerkeleyCA United States Biomaterials and Biomanufacturing Division Sandia National Laboratories LivermoreCA United States Earth and Biological Sciences Division Pacific Northwest National Laboratories RichlandWA United States Resources and Enabling Sciences Center National Renewable Energy Laboratory GoldenCO80401 United States Department of Bioengineering University of California BerkeleyCA United States

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology. © 2022, CC BY.

关键词： Synthetic biology

来源：评论

学校读者我要写书评

暂无评论

Thermosim: Deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments

arXiv

引用

arXiv 2020年

作者： Gill, Sukhpal Singh Tuli, Shreshth Toosi, Adel Nadjaran Cuadrado, Felix Garraghan, Peter Bahsoon, Rami Lutfiyya, Hanan Sakellariou, Rizos Rana, Omer Dustdar, Schahram Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Delhi India Faculty of Information Technology Monash University ClaytonVIC Australia Spain School of Computing and Communications Lancaster University United Kingdom School of Computer Science University of Birmingham Birmingham United Kingdom Department of Computer Science University of Western Ontario LondonON Canada School of Computer Science University of Manchester Oxford Road ManchesterUK United Kingdom School of Computer Science and Informatics Cardiff University CardiffUK United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Laboratory School of Computing and Information Systems University of Melbourne Australia

Current cloud computing frameworks host millions of physical servers that utilize cloud computing resources in the form of different virtual machines. Cloud Data Center (CDC) infrastructures require significant amounts of energy to deliver large scale computational services. Moreover, computing nodes generate large volumes of heat, requiring cooling units in turn to eliminate the effect of this heat. Thus, overall energy consumption of the CDC increases tremendously for servers as well as for cooling units. However, current workload allocation policies do not take into account effect on temperature and it is challenging to simulate the thermal behaviour of CDCs. There is a need for a thermal-aware framework to simulate and model the behaviour of nodes and measure the important performance parameters which can be affected by its temperature. In this paper, we propose a lightweight framework, ThermoSim, for modelling and simulation of thermal-aware resource management for cloud computing environments. This work presents a Recurrent Neural Network based deep learning temperature predictor for CDCs which is utilized by ThermoSim for lightweight resource management in constrained cloud environments. ThermoSim extends the CloudSim toolkit helping to analyse the performance of various key parameters such as energy consumption, service level agreement violation rate, number of virtual machine migrations and temperature during the management of cloud resources for execution of workloads. Further, different energy-aware and thermal-aware resource management techniques are tested using the proposed ThermoSim framework in order to validate it against the existing framework (Thas). The experimental results demonstrate the proposed framework is capable of modelling and simulating the thermal behaviour of a CDC and ThermoSim framework is better than Thas in terms of energy consumption, cost, time, memory usage and prediction accuracy. Copyright © 2020, The Authors. All rights rese

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

On the Transcriptomic Signature and General Stress State Associated with Aneuploidy

arXiv

引用

arXiv 2020年

作者： Tsai, Hung-Ji Nelliat, Anjali R. Kucharavy, Andrei Choudhury, Mohammad Ikbal Sun, Sean X. Schatz, Michael C. Li, Rong Institute of Microbiology and Infections School of Biosciences University of Birmingham BirminghamB15 2TT United Kingdom Department of Systems Biology Harvard Medical School BostonMA02115 United States Distributed Computing Laboratory School of Computerand communication Sciences Ecole Polytechnique Federale de Lausanne Lausanne Switzerland Department of Mechanical Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Center for Cell Dynamics Department of Cell Biology Johns Hopkins University School of Medicine BaltimoreMD21205 United States Mechanobiology Institute Department of Biological Science National University of Singapore Singapore117411 Singapore

来源：评论

学校读者我要写书评

暂无评论

Correction to: Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

引用

Archives of Computational Methods in engineering 2021年第7期28卷 5039-5039页

作者： Ismail, Leila Materwala, Huned Tayefi, Maryam Ngo, Phuong Karduck, Achim P. Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Norwegian Centre for E-Health Research Tromsø Norway Faculty of Informatics Furtwangen University Furtwangen Germany

来源：评论

学校读者我要写书评

暂无评论

The Future of Cloud Computing: Opportunities, Challenges and Research Trends

The Future of Cloud Computing: Opportunities, Challenges and...

引用

International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Amanpreet Kaur V.P. Singh Sukhpal Singh Gill Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala Punjab India Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia

ISBN: (纸本)9781538614433;9781538614426

In a cloud computing environment, datacenter consists of number of servers, cooling and power delivery equipment's that require enormous measure of computational energy to drive complex frameworks. Due to the rising demand of the computation power, datacenter has become the hub for significant increase in the power consumption, heat dissipation and rise in temperature of the servers. Cloud datacenter's energy consumption has increased tremendously due to increase in the computation requirements of the user workload. Thus, saving energy has become an important concern to address. Researchers proposed different techniques to optimize the energy consumption. In this paper, we focus on different aspects of cloud computing for holistic management of cloud resources in an energy-efficient, reliable and sustainable manner. We recognized different opportunities, identified research challenges and propose possible future research directions for cloud computing.

关键词： Servers Cloud computing Energy efficiency Energy consumption Data centers Processor scheduling Dynamic scheduling

来源：评论

学校读者我要写书评

暂无评论

Cloud workload prediction based on workflow execution time discrepancies

arXiv

引用

arXiv 2018年

作者： Kecskemeti, Gabor Nemeth, Zsolt Kertesz, Attila Ranjan, Rajiv Department of Computer Science Liverpool John Moores University Laboratory of Parallel and Distributed Systems MTA SZTAKI Software Engineering Department University of Szeged School of Computing Newcastle University

Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase the reliability or performance of their applications, they would need solutions to detect behavioural changes in the underlying system. Existing runtime solutions for such purposes offer limited capabilities as they are mostly restricted to revealing weekly or yearly behavioural periodicity in the infrastructure. This article proposes a technique for predicting generic background workload by means of simulations that are capable of providing additional knowledge of the underlying private cloud systems in order to support activities like cloud orchestration or workflow enactment. Our technique uses long-running scientific workflows and their behaviour discrepancies and tries to replicate these in a simulated cloud with known (trace-based) workloads. We argue that the better we can mimic the current discrepancies the better we can tell expected workloads in the near future on the real life cloud. We evaluated the proposed prediction approach with a biochemical application on both real and simulated cloud infrastructures. The proposed algorithm has shown to produce significantly (∼20%) better workload predictions for the future of simulated clouds than random workload selection. Copyright © 2018, The Authors. All rights reserved.

关键词： Infrastructure as a service (IaaS)

来源：评论

学校读者我要写书评

暂无评论

An environment for sustainable research software in Germany and beyond: Current state, open challenges, and call for action

arXiv

引用

arXiv 2020年

作者： Anzt, Hartwig Bach, Felix Druskat, Stephan Löffler, Frank Loewe, Axel Renard, Bernhard Y. Seemann, Gunnar Struck, Alexander Achhammer, Elke Aggarwal, Piush Appel, Franziska Bader, Michael Brusch, Lutz Busse, Christian Chourdakis, Gerasimos Dabrowski, Piotr W. Ebert, Peter Flemisch, Bernd Friedl, Sven Fritzsch, Bernadette Funk, Maximilian D. Gast, Volker Goth, Florian Grad, Jean-Noël Hermann, Sibylle Hohmann, Florian Janosch, Stephan Kutra, Dominik Linxweiler, Jan Muth, Thilo Peters-Kottig, Wolfgang Rack, Fabian Raters, Fabian H.C. Rave, Stephan Reina, Guido Reißig, Malte Ropinski, Timo Schaarschmidt, Joerg Seibold, Heidi Thiele, Jan P. Uekerman, Benjamin Unger, Stefan Weeber, Rudolf Germany Innovative Computing Lab University of Tennessee KnoxvilleTN United States Department of English Studies Friedrich Schiller University Jena Germany Germany Department of Computer Science Humboldt-Universität zu Berlin Germany Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany Michael Stifel Center Jena Germany Center for Computation and Technology Louisiana State University Baton Rouge United States Germany Robert Koch Institute Berlin Germany Hasso Plattner Institute Faculty of Digital Engineering University of Potsdam Germany Institute for Experimental Cardiovascular Medicine University Heart Centre Freiburg Bad Krozingen Germany Faculty of Medicine University of Freiburg Freiburg Germany Matters of Activity. Image Space Material. Cluster of Excellence at Humboldt-Universität zu Berlin Germany Technische Universität München Germany Language Technology Lab Universität Duisburg-Essen Germany Germany Department of Informatics Technical University of Munich Germany Technische Universität Dresden Germany Deutsches Krebsforschungszentrum Heidelberg Germany Scientific Computing in Computer Science Technical University Munich Germany School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Germany Center for Bioinformatics Saar Saarland Informatics Campus Germany Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Berlin Institute of Health Germany Scientific Computing Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany Max-Planck-Gesellschaft e.V. Institut für Theoretische Physik und Astrophysik Universität Würzburg Germany Institut für Computerphysik University of Stuttgart Germany University Library University of Stuttgart Germany Universität Bremen Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany European Molecular Biology Laboratory Heidelb

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. Copyright © 2020, The Authors. All rights reserved.

关键词： Software design

来源：评论

学校读者我要写书评

暂无评论

Rapid mobile development with ARC: Application framework for robust communications for disaster risk reduction and management 12

Rapid mobile development with ARC: Application framework for...

引用

12th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2016

作者： Santos, Jan Vincent Torres, Danah Veronica Villanoy, Monica Kathrina Festin, Cedric Angelo Doctor, John Norman Ocampo, Roel Networks and Distributed Systems Group Department of Computer Science University of the Philippines Philippines Computer Networks Laboratory Electrical and Electronics Engineering Institute University of the Philippines Philippines

ISBN: (纸本)9781509007240

In disaster situations communication and power infrastructure could be damaged, disrupting their associated services. This could delay relief efforts and cost more lives. In situations where connections are damaged and unreliable, peer-to-peer (P2P) and delay-tolerant networking (DTN) technologies can help provide alternative communication modes particularly in support of rescue and relief operations. Combining the benefits of the store-and-forward capability of DTN and the data propagation techniques of Wi-Fi Direct, we have created a flexible framework called ARC which allows users to create applications that could meet their specific requirements, particularly those in support of emergency communications. To demonstrate our framework, we created a simple application using ARC, and used this application to evaluate its DTN and Wi-Fi Direct group recovery capabilities. © 2016 IEEE.

关键词： Wireless local area networks (WLAN)

来源：评论

学校读者我要写书评

暂无评论

Streaming 4K/UHD video to the rest of us: Can bandwidth sharing help? 16

Streaming 4K/UHD video to the rest of us: Can bandwidth shar...

引用

16th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2015

作者： Montes, Isabel Chua, Jaymari Cruz, Michelangelo Remillano, Augusto Young, Joseph Darwin Ocampo, Roel Festin, Cedric Computer Networks Laboratory Electrical and Electronics Engineering Institute University of the Philippines Diliman Philippinese United States Networks and Distributed Systems Group Department of Computer Science University of the Philippines Diliman Philippinese United States

ISBN: (纸本)9781479984619

High quality video streaming for mobile users is difficult to achieve in some areas of the world due to poor broadband capacity and sparse network coverage. We propose a bandwidth-sharing scheme to allow users with limited uplink bandwidth to borrow idle bandwidth from nearby access points and provide these users the opportunity to experience higher quality video than what their own bandwidth could normally support. We developed a Linux kernel implementation of a multipath less-than-best-effort (MP-LBE) protocol for bandwidth scavenging and demonstrated, through testbed experiments, that video quality may be improved through bandwidth sharing. This may bring us closer to the 'video everywhere' vision even as incremental upgrades are made on broadband Internet infrastructure. © 2015 IEEE.

关键词： Video streaming

来源：评论

学校读者我要写书评

暂无评论

Two-dimensional euler PCA for face recognition 21

Two-dimensional euler PCA for face recognition

引用

21st International Conference on MultiMedia Modeling, MMM 2015

作者： Tan, Huibin Zhang, Xiang Guan, Naiyang Tao, Dacheng Huang, Xuhui Luo, Zhigang Science and Technology on Parallel Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan410073 China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan410073 China Centre for Quantum Computation and Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia

ISBN: (纸本)9783319144412

Principal component analysis (PCA) projects data on the directions with maximal variances. Since PCA is quite effective in dimension reduction, it has been widely used in computer vision. However, conventional PCA suffers from following deficiencies: 1) it spends much computational costs to handle high-dimensional data, and 2) it cannot reveal the nonlinear relationship among different features of data. To overcome these deficiencies, this paper proposes an efficient two-dimensional Euler PCA (2D-ePCA) algorithm. Particularly, 2D-ePCA learns projection matrix on the 2D pixel matrix of each image without reshaping it into 1D long vector, and uncovers nonlinear relationships among features by mapping data onto complex representation. Since such 2D complex representation induces much smaller kernel matrix and principal subspaces, 2D-ePCA costs much less computational overheads than Euler PCA on large-scale dataset. Experimental results on popular face datasets show that 2D-ePCA outperforms the representative algorithms in terms of accuracy, computational overhead, and robustness. © Springer International Publishing Switzerland 2015.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：