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Nontrivial resource requirement in the early stage for containment of epidemics

Physical Review E 2019年第3期100卷 032310-032310页

作者： Xiaolong Chen Tianshou Zhou Ling Feng Junhao Liang Fredrik Liljeros Shlomo Havlin Yanqing Hu School of Data and Computer Science Sun Yat-sen University Guangzhou 510006 China Web Sciences Center University of Electronic Science and Technology of China Chengdu 611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China School of Mathematics Sun Yat-sen University Guangzhou 510006 China Institute of High Performance Computing A*STAR 138632 Singapore Department of Physics National University of Singapore 117551 Singapore Department of Sociology Stockholm University 17177 Stockholm Sweden Department of Physics Bar-Ilan University Ramat-Gan 52900 Israel

During epidemic control, containment of the disease is usually achieved through increasing a devoted resource to reduce the infectiousness. However, the impact of this resource expenditure has not been studied quantitatively. For disease spread, the recovery rate can be positively correlated with the average amount of resource devoted to infected individuals. By incorporating this relation we build a novel model and find that insufficient resource leads to an abrupt increase in the infected population size, which is in marked contrast with the continuous phase transitions believed previously. Counterintuitively, this abrupt phase transition is more pronounced in less contagious diseases. Furthermore, we find that even for a single infection source, the public resource needs to be available in a significant amount, which is proportional to the total population size, to ensure epidemic containment. Our findings provide a theoretical foundation for efficient epidemic containment strategies in the early stage.

关键词： Patterns in complex systems Scaling laws of complex systems

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Double-precision FPUs in high-performance computing: An embarrassment of riches?

arXiv

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arXiv 2018年

作者： Domke, Jens Matsumura, Kazuaki Wahib, Mohamed Zhang, Haoyu Yashima, Keita Tsuchikawa, Toshiki Tsuji, Yohei Podobas, Artur Matsuoka, Satoshi Global Scientific Information and Computing Center Tokyo Institute of Technology Department of Mathematical and Computing Science Tokyo Institute of Technology AIST-TokyoTech Real World Big-Data Computation Open Innovation Laboratory Tokyo Japan RIKEN Japan

Among the (uncontended) common wisdom in High-Performance computing (HPC) is the applications' need for large amount of double-precision support in hardware. Hardware manufacturers, the TOP500 list, and (rarely revisited) legacy software have without doubt followed and contributed to this view. In this paper, we challenge that wisdom, and we do so by exhaustively comparing a large number of HPC proxy applications on two processors: Intel's Knights Landing (KNL) and Knights Mill (KNM). Although similar, the KNL and KNM architecturally deviate at one important point: the silicon area devoted to double-precision arithmetics. This fortunate discrepancy allows us to empirically quantify the performance impact in reducing the amount of hardware double-precision arithmetic. Our analysis shows that this common wisdom might not always be right. We find that the investigated HPC proxy applications do allow for a (significant) reduction in double-precision with little-to-no performance implications. With the advent of a failing of Moore's law, our results partially reinforce the view taken by modern industry (e.g., upcoming Fujitsu ARM64FX) to integrate hybrid-precision hardware units. Copyright © 2018, The Authors. All rights reserved.

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BCStore: Bandwidth-Efficient in-memory kv-store with batch coding 33

BCStore: Bandwidth-Efficient in-memory kv-store with batch c...

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33rd International Conference on Massive Storage Systems and Technology, MSST 2017

作者： Li, Shenglong Zhang, Quanlu Yang, Zhi Dai, Yafei Peking University China Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology and Applications China

In-memory key-value store KV-store is a crucial building block for many systems including large-scale web services and databases. Recent research works pay close attention to data availability of in-memory KV-store. Replication is a common solution to achieve data availability, but the significant memory consumption makes it not applicable for memoryintensive applications. Integrating erasure coding into inmemory KV-store is an emerging approach to achieve memory efficiency. However, traditional in-place update mechanism of erasure coding incurs significant bandwidth cost for writeintensive workload due to frequent broadcast of data update. In this paper, we build BCStore, a bandwidth-efficient inmemory KV-store with erasure coding. We propose a novel batch coding mechanism to optimize the bandwidth cost, and verify its feasibility and efficiency through theoretical analysis. To recycle the memory space due to delete and update operations, we design an efficient move-based garbage collection (GC) mechanism with a novel data layout called virtual coding stripe. Besides, BCStore guarantees consistent data read and write under these new designs, and supports fast online data recovery. We evaluate the performance of BCStore under various workloads. Experimental results show that BCStore can save up to 41% memory compared with replication, and achieve up to 2.4x throughput improvement and 51% bandwidth saving compared to erasure coding with in-place update. © 33rd International Conference on Massive Storage Systems and Technology, MSST 2017. All rights reserved.

关键词： Bandwidth

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An Enhanced Computational Feature Selection Method for Medical Synonym Identification via Bilingualism and Multi-corpus Training 2

An Enhanced Computational Feature Selection Method for Medic...

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2017 IEEE 2nd International Conference on big data Analysis（ICBDA 2017）

作者： Kai Lei Shangchun Si Desi Wen Ying Shen Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology and Applications School of Electronic and Computer Engineering(SECE) Peking University

ISBN: (纸本)9781509036202

Medical synonym identification has been an important part of medical natural language processing(NLP).However,in the field of Chinese medical synonym identification,there are problems like low precision and low recall *** solve the problem,in this paper,we propose a method for identifying Chinese medical *** first selected 13 features including Chinese and English *** we studied the synonym identification results of each feature alone and different combinations of the *** the comparison among identification results,we present an optimal combination of features for Chinese medical synonym *** show that our selected features have achieved 97.37% precision rate,96.00% recall rate and 97.33% F1 score.

关键词： medical synonym feature selection bilingual multi-corpus

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MMHealth 2017: Workshop on Multimedia for Personal Health and Health Care

MMHealth 2017: Workshop on Multimedia for Personal Health an...

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作者： Boll, Susanne Ebrahimi, Touradj Gurrin, Cathal Jalali, Laleh Jain, Ramesh Meyer, Jochen Oconnor, Noel E. University of Oldenburg Germany École Polytechnique Fédérale de Lausanne Lausanne Switzerland School of Computing Dublin City University Insight Centre for Data Analytics Dublin Ireland Hitachi America Ltd. Big Data Lab United States Donald Bren School of Information and Computer Sciences University of California Irvine United States RandD Division Health OFFIS Institute for Information Technology Oldenburg Germany Centre for Data Analytics Dublin City University Dublin Ireland

The goal of the MMHealth 2017 Workshop on Multimedia for Personal Health and Health Care was to explore the relevance and contribution of multimedia in healthcare and personal health. After a successful debut in Amsterdam in 2016, the second workshop at ACM Multimedia 2017 in Mountain View, California, again attracted more than 30 participants, indicating the relevance that this topic has in the multimedia research community. © 2018 IEEE.

关键词： Health care

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Robust Matrix Discriminative Analysis for Feature Extraction From Hyperspectral Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2017年第5期10卷 2002-2011页

作者： Hang, Renlong Liu, Qingshan Sun, Yubao Yuan, Xiaotong Pei, Hucheng Plaza, Javier Plaza, Antonio Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing China Beijing Electro-Mechanical Engineering Institute Beijing China Hyperspectral Computing Laboratory University of Extremadura Caceres Spain

Linear discriminative analysis (LDA) is an effective feature extraction method for hyperspectral image (HSI) classification. Most of the existing LDA-related methods are based on spectral features, ignoring spatial information. Recently, a matrix discriminative analysis (MDA) model has been proposed to incorporate the spatial information into the LDA. However, due to sensor interferers, calibration errors, and other issues, HSIs can be noisy. These corrupted data easily degrade the performance of the MDA. In this paper, a robust MDA (RMDA) model is proposed to address this important issue. Specifically, based on the prior knowledge that the pixels in a small spatial neighborhood of the HSI lie in a low-rank subspace, a denoising model is first employed to recover the intrinsic components from the noisy HSI. Then, the MDA model is used to extract discriminative spatial-spectral features from the recovered components. Besides, different HSIs exhibit different spatial contextual structures, and even a single HSI may contain both large and small homogeneous regions simultaneously. To sufficiently describe these multiscale spatial structures, a multiscale RMDA model is further proposed. Experiments have been conducted using three widely used HSIs, and the obtained results show that the proposed method allows for a significant improvement in the classification performance when compared to other LDA-based methods.

关键词： Hyperspectral image (HSI) classification multiscale fusion robust matrix discriminative analysis (RMDA) spatial-spectral feature extraction (FE)

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Word2Cluster: A New Multi-Label Text Clustering Algorithm with an Adaptive Clusters Number

Word2Cluster: A New Multi-Label Text Clustering Algorithm wi...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Kaili Mao Jianwei Niu Xuefeng Liu Shui Yu Longbo Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University School of Computer Science and Cyber Engineering Guangzhou University Guangdong China China aerospace science and industry corporation China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Text clustering has been widely used in many Natural Language Processing (NLP) applications such as text summarization and news recommendation. However, most of the current algorithms need to predefine a clustering number, which is difficult to obtain. Moreover, the mutli-label clustering is useful in multiple clustering tasks in many applications, but related works are rarely available. Although several studies have attempted to solve above two problems, there is a need for methods that can solve the two issues simultaneously. Therefore, we propose a new text clustering algorithm called Word2Cluster. Word2Cluster can automatically generate an adaptive number of clusters and support multi-label clustering. To test the performance of Wrod2Cluster, we build a Chinese text dataset, Hotline, according to real world applications. To evaluate the clustering results better, we propose an improved evaluation method based on basic accuracy, precision and recall for multi-label text clustering. Experimental results on a Chinese text dataset (Hotline) and a public English text dataset (Reuters) demonstrate that our algorithm can achieve better F1-measure and runs faster than the state-of- the-art baselines.

关键词： Clustering algorithms Biological system modeling Merging Task analysis Computer science Technological innovation Natural language processing

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MapReduce a Comprehensive Review

MapReduce a Comprehensive Review

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2018 International Conference on Smart computing and Electronic Enterprise, ICSCEE 2018

作者： Al-Khasawneh, Mahmoud Ahmad Shamsuddin, Siti Mariyam Hasan, Shafaatunnur Bakar, Adamu Abu Faculty of Computing Universiti Teknologi Malaysia Johor Malaysia Faculty of Computer and Information Technology Al-Madinah International University Shah Alam Malaysia UTM Big Data Centre Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia Johor Malaysia Kulliyyah of Information and Communication Technology International Islamic University Selangor Malaysia

ISBN: (纸本)9781538648360

MapReduce encompasses a framework in the processing and management of large scale datasets within a distributed cluster. The framework has been employed in several applications including search indexes generation, analysis of access log, document clustering, and other data analytics. A flexible computation model is adopted in MapReduce in addition to plain interface which comprises the functions of map and reduce. The interface is customizable based on application developers. MapReduce has captured the interest among many scholars whereby the interest has been on increasing its usability and efficiency in support to database-centric operations. Accordingly, this paper provides a complete review regarding a vast continuum of proposals and systems concentrating basically on the support of distributed data management and processing with the use of the framework of MapReduce. © 2018 IEEE.

关键词： MapReduce

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Simulating nearby disc galaxies on the main star formation sequence I. Bar formation and the building of the central gas reservoir

arXiv

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arXiv 2024年

作者： Verwilghen, Pierrick Emsellem, Eric Renaud, Florent Valentini, Milena Sun, Jiayi Jeffreson, Sarah Klessen, Ralf S. Sormani, Mattia C. Barnes, Ashley T. Dolag, Klaus Grasha, Kathryn Liang, Fu-Heng Meidt, Sharon Neumann, Justus Querejeta, Miguel Schinnerer, Eva Williams, Thomas G. Excellence Cluster ORIGINS Boltzmannstraße 2 Garching85748 Germany European Southern Observatory Karl-Schwarzschild-Straße 2 Garching85748 Germany Univ Lyon Univ Lyon1 ENS de Lyon CNRS Centre de Recherche Astrophysique de Lyon UMR5574 Saint-Genis-LavalF-69230 France Observatoire Astronomique de Strasbourg Université de Strasbourg CNRS UMR 7550 StrasbourgF-67000 France University of Strasbourg Institute for Advanced Study 5 allée du Général Rouvillois StrasbourgF-67083 France Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 TriesteI-34131 Italy INAF – Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA United States Universität Heidelberg Zentrum für Astronomie Institut für Theoretische Astrophysik Albert-Ueberle-Str. 2 HeidelbergD-69120 Germany Universität Heidelberg Interdisziplinäres Zentrum für Wissenschaftliches Rechnen Im Neuenheimer Feld 205 HeidelbergD-69120 Germany Università dell’Insubria via Valleggio 11 Como22100 Italy Department of Physics University of Surrey GuildfordGU2 7XH United Kingdom Universitäts-Sternwarte Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstr. 1 München81679 Germany Max-Planck-Institut für Astrophysik Karl-Schwarzschild-Straße 1 Garching85741 Germany Research School of Astronomy and Astrophysics Australian National University CanberraACT2611 Australia Australia Sub-Department of Astrophysics Department of Physics University of Oxford Keble Road OxfordOX1 3RH United Kingdom Sterrenkundig Observatorium Universiteit Gent Krijgslaan 281 S9 GentB-9000 Belgium Max Planck Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany C/Alfonso XII 3 Madri

Past studies have long emphasised the key role played by galactic stellar bars in the context of disc secular evolution, via the redistribution of gas and stars, the triggering of star formation, and the formation of prominent structures such as rings and central mass concentrations. However, the exact physical processes acting on those structures, as well as the timescales associated with the building and consumption of central gas reservoirs are still not well understood. We are building a suite of hydro-dynamical RAMSES simulations of isolated, low-redshift galaxies that mimic the properties of the PHANGS sample. The initial conditions of the models reproduce the observed stellar mass, disc scale length, or gas fraction, and this paper presents a first subset of these models. Most of our simulated galaxies develop a prominent bar structure, which itself triggers central gas fuelling and the building of an over-density with a typical scale of 100-1000 pc. We confirm that if the host galaxy features an ellipsoidal component, the formation of the bar and gas fuelling are delayed. We show that most of our simulations follow a common time evolution, when accounting for mass scaling and the bar formation time. In our simulations, the stellar mass of 1010 M☉ seems to mark a change in the phases describing the time evolution of the bar and its impact on the interstellar medium. In massive discs (M★ ≥ 1010 M☉), we observe the formation of a central gas reservoir with star formation mostly occurring within a restricted starburst region, leading to a gas depletion phase. Lower-mass systems (M★ 10 M☉) do not exhibit such a depletion phase, and show a more homogeneous spread of star-forming regions along the bar structure, and do not appear to host inner bar-driven discs or rings. Our results seem to be supported by observations, and we briefly discuss how this new suite of simulations can help our understanding of the secular evolution of main sequence disc galaxies. © 2024,

关键词： Gases

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An Improved Chaotic Image Encryption Algorithm

An Improved Chaotic Image Encryption Algorithm

引用

2018 International Conference on Smart computing and Electronic Enterprise, ICSCEE 2018

ISBN: (纸本)9781538648360

Chaotic-based image encryption algorithms are countless in number. Encryption techniques based on Chaos are among the most effectual algorithms for encryption of data image. In past works, chaos-based cryptosystems applied the chaotic dynamical system with the linkage to the harmonization of two chaotic systems and controls. Good performances have resulted but there were several downsides pertaining to the single rule usage by each, impacting security, privacy and dependability of the techniques mentioned. Serious problems were also documented in their usage in satellite imagery. As a possible solution, a novel chaos-based symmetric method of key cryptosystem is proposed in this paper. This method employs external secret key that Logistic, Henon and Gauss iterated maps have previously expanded. For creating the secret key matrix for image encryption, these maps are merged. Here, simple logical XOR and multiple key generation processes were applied. Assessment to the method is performed on the satellite images dataset, and security is evaluated through the experimental analysis. As evidenced, the chaos-based satellite image cryptosystem demonstrates appropriateness for satellite image encryption and decryption in the preservation of security and dependability of the storage and transmission process. © 2018 IEEE.

关键词： Cryptography

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