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Proteins: Structure, Function, and Bioinformatics 2016年第7期84卷

作者： Wei Wang Juan Liu Lin Sun Department of Computer Science and Technology College of Computer and Information Engineering Henan Normal University Xinxiang 453007 China Laboratory of Computation Intelligence and Information Processing Engineering Technology Research Center for Computing Intelligence and Data Mining Henan Province China Institute of Computer Software School of Computer Wuhan University Wuhan 430072 China

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PLoSONE：研究揭示去甲肾上腺素改善视力机制

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现代生物医学进展 2017年第14期17卷 I0001-I0001页

作者： Ryo Mizuyama, Satoshi Shimegi Laboratory of Cognitive and Behavioral Neuroscience Graduate School of Frontier Bioscience Osaka University Suita Osaka Japan Laboratory of Cognitive and Behavioral Neuroscience Graduate School of Medicine Osaka University Toyonaka Osaka Japan Universidad de Salamanca SPAINCompeting Interests: The authors have declared that no competing interests exist.Conceptualization: RM S. Shimegi.Data curation: RM.Formal analysis: RM.Funding acquisition: S. Shimegi.Investigation: RM S. Shimegi.Methodology: RM S. Soma NS.Project administration: S. Shimegi.Resources: S. Shimegi.Software: RM NS S. Soma.Supervision: S. Shimegi.Visualization: RM.Writing - original draft: RM S. Shimegi.Writing - review & editing: RM S. Shimegi. [¤a]Current address: Brain Science Institute Tamagawa University Tamagawa Tokyo Japan[¤b]Current address: Graduate School of Engineering Osaka University Suita Osaka Japan* E-mail: pj.ca.u-akaso.ssh.noisiv@igemihs

去甲肾上腺素是依赖于动物的行为环境和生理状态的大脑分泌出的一种神经调节物（neuromodulator，也被称作神经调质）。并且通过调控大脑活动来影响许多各种不同的生理功能。它可能最好被称作为一种调节心率和血液流量的激素。而且很多... 详细信息

去甲肾上腺素是依赖于动物的行为环境和生理状态的大脑分泌出的一种神经调节物（neuromodulator，也被称作神经调质）。并且通过调控大脑活动来影响许多各种不同的生理功能。它可能最好被称作为一种调节心率和血液流量的激素。而且很多药物，如广为人知的θ-阻断剂，靶向它的效应。它也调节视觉系统。

关键词：去甲肾上腺素机制视力神经调节大脑活动生理状态行为环境生理功能

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Brain tumor segmentation from multispectral MRIs using sparse representation classification and markov random field regularization

International Journal of Signal Processing, Image Processing...

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International Journal of Signal Processing, Image Processing and Pattern Recognition 2015年第9期8卷 229-238页

作者： Zhan, Tianming Gu, Shenghua Feng, Can Zhan, Yongzhao Wang, Jin School of Computer Science & communications Engineering Jiangsu University Zhenjiang China Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing China Center of software China North Industries Group Corporation Co. Ltd Nanjing China

Automatic brain tumor segmentation from multispectral magnetic resonance imaging (MRI) data is an important but a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. In this paper, we propose a fully automatic technique for brain tumor segmentation from multispectral human brain MRIs. We first use the intensities of different patches in multispectral MRIs to represent the features of both normal and abnormal tissues and generate a dictionary for following tissue classification. Then, the sparse representation classification (SRC) is applied to classify the brain tumor and normal brain tissue in the whole image. At last, the Markov random field (MRF) regularization introduces spatial constraints to the SRC to take into account the pair-wise homogeneity in terms of classification labels and multispectral voxel intensities. Our method was evaluated on 20 multi-modality patient datasets with competitive segmentation results. ©, 2015 SERSC.

关键词： Magnetic resonance imaging

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Characterizing Multi-media Retrieval Applications

Characterizing Multi-media Retrieval Applications

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International Conference on Parallel Processing (ICPP)

作者： Yunping Lu Xin Wang Weihua Zhang Yi Li Wenyun Zhao Shanghai Key Laboratory of Data Science Fudan University Shanghai China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi China Software School Fudan University Shanghai China Parallel Processing Institute Fudan University Shanghai China

Multimedia data, especially image and video data, have become one of the most overwhelming data types on the Internet recently. Considering the user experience and real application requirements, multimedia data always demand a real-time processing speed. As a result, the huge amount of such data make retrieving useful information from them not only data-intensive, but also computation-intensive, which poses significant challenges to current system and architecture designs. Unfortunately, most prior studies focus only on text based retrieval systems or traditional multimedia processing applications. As far as we know, there is no systematic study on analyzing the characteristics of multimedia retrieval applications and how they might impact system and architecture designs. In this paper, we make the first attempt to construct a multimedia retrieval benchmark suite (called MMR Bench) to evaluate the corresponding system and architecture designs. To embody diverse multimedia retrieval applications, we collect eight state-of-the-art multimedia retrieval algorithms which cover the whole retrieval stages, including feature extraction, feature matching, and spatial verification. To satisfy diverse evaluation purposes, we implement multiple versions for each algorithm, including sequential version, pthread version for multi-core evaluation and data-parallel (i.e., Map-reduce) version for data-center evaluation. Moreover, MMR Bench provides flexible interfaces through retrieval stages, as well as a tool to adjust parameters and regenerating different scales of reasonable input. With such a flexible design, the algorithms in MMR Bench may be not only suitable for individual kernel-level evaluation, but also capable to be integrated into a complete infrastructure for system-level evaluation. Based on MMR Bench, we further analyze the inherent architectural characteristics, such as input size sensitivity and workload balance, which provides some insights into system and archite

关键词： Multimedia communication Feature extraction Benchmark testing Streaming media Algorithm design and analysis Computer architecture

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Composable and efficient functional big data processing framework

Composable and efficient functional big data processing fram...

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IEEE International Conference on Big data

作者： Dongyao Wu Sherif Sakr Liming Zhu Qinghua Lu Data61 Eveleigh NSW AU King Saud bin Abdulaziz University for Health Sciences Saudi Arabia Software Systems Research Group NICTA Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia College of Computer and Communication Engineering University of Petroleum Qingdao China

Over the past years, frameworks such as MapReduce and Spark have been introduced to ease the task of developing big data programs and applications. However, the jobs in these frameworks are roughly defined and packaged as executable jars without any functionality being exposed or described. This means that deployed jobs are not natively composable and reusable for subsequent development. Besides, it also hampers the ability for applying optimizations on the data flow of job sequences and pipelines. In this paper, we present the Hierarchically Distributed data Matrix (HDM) which is a functional, strongly-typed data representation for writing composable big data applications. Along with HDM, a runtime framework is provided to support the execution of HDM applications on distributed infrastructures. Based on the functional data dependency graph of HDM, multiple optimizations are applied to improve the performance of executing HDM jobs. The experimental results show that our optimizations can achieve improvements of between 10% to 60% of the Job-Completion-Time for different types of operation sequences when compared with the current state of art, Apache Spark.

关键词： Semantics Optimization Big data Distributed databases Sparks Programming Writing

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Unbiased feature selection in learning random forests for high-dimensional data

Scientific World Journal

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Scientific World Journal 2015年第1期2015卷 471371页

作者： Nguyen, Thanh-Tung Huang, Joshua Zhexue Nguyen, Thuy Thi Shenzhen Key Laboratory of High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China University of Chinese Academy of Sciences Beijing 100049 China School of Computer Science and Engineering Water Resources University Hanoi 10000 Viet Nam College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Faculty of Information Technology Vietnam National University of Agriculture Hanoi 10000 Viet Nam

Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data. Besides that, RFs have bias in the feature selection process where multivalued features are favored. Aiming at debiasing feature selection in RFs, we propose a new RF algorithm, called xRF, to select good features in learning RFs for high-dimensional data. We first remove the uninformative features using p-value assessment, and the subset of unbiased features is then selected based on some statistical measures. This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. This approach enables one to generate more accurate trees, while allowing one to reduce dimensionality and the amount of data needed for learning RFs. An extensive set of experiments has been conducted on 47 high-dimensional real-world datasets including image datasets. The experimental results have shown that RFs with the proposed approach outperformed the existing random forests in increasing the accuracy and the AUC measures. © 2015 Thanh-Tung Nguyen et al.

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Parallelized Race Detection Based on GPU Architecture

Parallelized Race Detection Based on GPU Architecture

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10th Annual Conference of Advanced Computer Architecture, ACA 2014

作者： Dai, Zhuofang Zhang, Zheng Wang, Haojun Li, Yi Zhang, Weihua Software School Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Parallel Processing Institute Fudan University Shanghai 201203 China State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou 450001 China

ISBN: (纸本)9783662444900

In order to harness abundant hardware resources, parallel programming has become a necessity in multicore era. However, parallel programs are prone to concurrency bugs, especially data races. Even worse, current software tools always suffer from both large runtime overheads and poor scalability, while most of hardware supports for race detection are not available in parallel programming. Therefore, it has been a challenge that how to introduce a practical and fast race detection tools. Nowadays, GPUs with massive parallel computation resources have become one of the most popular hardware platforms. Hence, the prevalence of GPU architectures has opened an opportunity of accelerating data race detection. In this paper, we first have a deeply analysis on data race detection algorithms like happens-before and observe that these algorithms have very good computation and data parallelism. Based on the observation, we propose Grace, a software approach that leverages massive parallelism computation units of GPU architectures to accelerate data race detection. Grace deploys detection, the most computation intensive workload, on GPU to fully utilize the computation resource in GPU. Moreover, Grace leverages coarse-grained pipeline parallelism and data parallelism through exploiting the computation resource in multi-core CPUs to further improve performance. Experimental results show that Grace is fast and scalable. It achieves over 80x speedup compared to the sequential version even under a 128-thread configuration. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Graphics processing unit

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Cost-based join algorithm selection in Hadoop

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8787卷 246-261页

作者： Gu, Jun Peng, Shu Wang, X. Sean Rao, Weixiong Yang, Min Cao, Yu School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China School of Software Engineering Tongji University Shanghai China EMC Labs Tsinghua Science Park Beijing China

In recent years, MapReduce has become a popular computing framework for big data analysis. Join is a major query type for data analysis and various algorithms have been designed to process join queries on top of Hadoop. Since the efficiency of different algorithms differs on the join tasks on hand, to achieve a good performance, users need to select an appropriate algorithm and use the algorithm with a proper configuration, which is rather difficult for many end users. This paper proposes a cost model to estimate the cost of four popular join algorithms. Based on the cost model, the system may automatically choose the join algorithm with the least cost, and then give the reasonable configuration values for the chosen algorithm. Experimental results with the TPC-H benchmark verify that the proposed method can correctly choose the best join algorithm, and the chosen algorithm can achieve a speedup of around 1.25 times over the default join algorithm. © Springer International Publishing Switzerland 2014.

关键词： data handling

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Implementation of physics simulation in serious game

Implementation of physics simulation in serious game

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International Conference on ICT For Smart Society (ICISS)

作者： Rizal Broer Bahaweres Angga Pratama Benhard Sitohang Data & Software Engineering Research Division-School of Electrical Engineering & Informatics Institute of Technology Bandung Faculty of Science and Technology State Islamic University Syarif Hidayatullah Jakarta Indonesia

Three dimension computer graphics has become important to present virtual objects to be more realistic and physically correct. One way to enhancing realism is by representing the physics simulation. We tried to implement physics simulation into a serious game. It is because of ability to increase realism in synthesis on virtual object motion. We design a serious game called “Traffic Drive”. Traffic Drive is a vehicle simulation game that has a goal to drive a vehicle, especially cars, with emphasis on safety and presented in a fun way. In physics simulation and game that are vehicle's physics a physics of gear and physics of rollover. It was expected that physics simulation can make the behavior of vehicles in the game resembles the behavior of the vehicle in terms of more realistics. And it is used as the object of research to compare the results of the data simulation with data from manual calculations. Comparison of results from the simulation output data and manual calculations were analyzed using T test hypotheses. From that T test was concluded that the output of data simulation with formula calculation almost the same.

关键词： Physics Games Gears Solid modeling Manuals Mathematical model Vehicles

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data driven quantitative trust model for the Internet of Agricultural Things

Data driven quantitative trust model for the Internet of Agr...

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Internet of Things (IoT)

作者： Weili Han Yun Gu Yin Zhang Lirong Zheng Shanghai Key Laboratory of Data Science Fudan University Shanghai DNSLAB China Internet Network Information Center Beijing Software School Fudan University Shanghai School of Information Science and Engineering Fudan University Shanghai

ISBN: (纸本)9781479951550

With frequent food safety incidents, it becomes urgent and important to design an intuitively quantitative trust model to describe the trustworthiness of foods delivered in supply chains. However, current existing models are usually too subjective, because they heavily depend on experts' experiences to model the trust and set relevant parameters. Fortunately, the Internet of Agricultural Things may offer a big volume of business data, including product information and delivery information etc., via its pervasive sensing. These data motivate us to design a data driven quantitative model to evaluate the trust of sensed products in supply chains. The proposed trust model leverages a Bayesian network, where almost all parameters are set by the data rather than experts' experiences, to evaluate the trust value of a target product. Finally, a case of pork product is used to show the effectiveness of our trust model. Based on the comparison with other models, our model is promising to reduce the subjectivity and time-delay of the trust evaluation.

关键词： data models Accidents Supply chains Internet of things Companies Bayes methods Safety

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