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2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)最新文献

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Parallel Acceleration of Subgraph Enumeration in the Process of Network Motif Detection 网络基序检测过程中子图枚举的并行加速
B. Mursa, A. Andreica, L. Dioşan
Network motifs bring a great interest to many fields, because they are a perfect candidate to speed up the applied research in the understanding of complex networks dynamics. One of the biggest problems raised in the process of finding these motifs is the performance. Starting from splitting the initial network into subgraphs to the subgraphs clusterization, each algorithm used addresses an NP problem. Hence there are many algorithms that are trying to solve network motifs discovery, however there is no perfect approach talking from a time performance perspective, but only solutions that are faster than others. This paper presents a redesign of one of the most competitive algorithms for subgraphs enumeration found in the literature called ESU. In the proposed approach a given network can be processed by using parallel programming, either with a process-driven model, or a hybrid one (process-driven and thread-driven). Finally, the existing and proposed models are compared using benchmark graphs, revealing competitive results.
网络基序是加速复杂网络动力学理解应用研究的理想候选者,引起了许多领域的极大兴趣。在寻找这些主题的过程中出现的最大问题之一是性能。从将初始网络划分为子图到子图聚类,所使用的每种算法都解决一个NP问题。因此,有许多算法试图解决网络主题发现问题,但是从时间性能的角度来看,没有完美的方法,只有比其他方法更快的解决方案。本文提出了一个重新设计的最具竞争力的算法之一的子图枚举称为ESU在文献中发现。在建议的方法中,可以使用并行编程来处理给定的网络,可以使用进程驱动模型,也可以使用混合模型(进程驱动和线程驱动)。最后,使用基准图对现有模型和提出的模型进行比较,揭示竞争结果。
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引用次数: 1
Parallelizations of an Inpainting Algorithm Based on Convex Feasibility 一种基于凸可行性的绘图算法并行化
Liviu Octavian Mafteiu-Scai, Irina Maria Artinescu, Vlad Mafteiu-Scai
Image restoration is a very common process in present and mainly consist in attempts to recover an image that has been degraded, by altering some areas or data missing and as a result a lot of algorithms have been proposed over time. In this work, parallel algorithms based on convex feasibility, for inpainting a scratch of an image are proposed.
图像恢复是目前一个非常常见的过程,主要包括通过改变某些区域或丢失的数据来恢复已经退化的图像,因此随着时间的推移,已经提出了许多算法。在这项工作中,提出了基于凸可行性的并行算法,用于图像划痕的修复。
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引用次数: 0
Deep Learning for Metal Corrosion Control: Can Convolutional Neural Networks Measure Inhibitor Efficiency? 深度学习用于金属腐蚀控制:卷积神经网络可以测量抑制剂的效率吗?
R. Stoean, C. Stoean, A. Samide
The inhibition of corrosion is an important aspect not only from the theoretical viewpoint of physical and material sciences but also from the practical aspect of the frequent exposure and use of metals in our lives. The traditional investigation of this process is done through electrochemical measurements with local and selective inspection of some optical microscopy slides. This paper proposes a more objective and automatic way of examining the effectiveness of the employed inhibitors through convolutional neural networks. In spite of the limitation of the number of samples to few hundreds, as they can be provided from the electrochemical laboratory, the deep learner manages to offer valuable information regarding the entire surface of a metal plate and to distinguish between the states under observation.
腐蚀的抑制不仅是物理和材料科学的理论观点,也是我们生活中频繁接触和使用金属的实际方面的一个重要方面。对这一过程的传统研究是通过局部和选择性检查某些光学显微镜载玻片的电化学测量来完成的。本文提出了一种更客观、更自动的方法,通过卷积神经网络来检测所采用的抑制剂的有效性。尽管样品的数量限制在几百个,因为它们可以从电化学实验室提供,但深度学习器设法提供有关金属板整个表面的有价值的信息,并区分观察到的状态。
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引用次数: 1
Dual Criteria Determination of the Number of Clusters in Data 数据中聚类数的双准则确定
Kaixun Hua, D. Simovici
We present a method for determining the number of clusters existent in a data set involving a bi-criteria optimization that makes use of the entropy and the cohesion of a partition. The results are promising and may be applicable in dealing with clusterings of imbalanced data.
我们提出了一种确定数据集中存在的簇数的方法,该方法涉及双准则优化,该优化利用了分区的熵和内聚性。该结果具有一定的应用前景,可用于处理不平衡数据的聚类。
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引用次数: 1
Improving ACSTL Iris Segmentation Method 改进ACSTL虹膜分割方法
Cristina M. Noaica
ACSTL is a segmentation method that was initially developed for iris images that are captured with an LG2200-iris sensor. ACSTL provided a 0.326 segmentation error on the entire LG2200 dataset, but a much higher error for images from other infrared sensors, such as a close-up camera (CASIA Interval dataset). This paper shows a method to improve ACSTL in terms of both segmentation performance and the average execution time per image. The improvements are brought to the image processing and to the iris boundary selection algorithm of ACSTL.
ACSTL是一种分割方法,最初是为使用lg2200 -虹膜传感器捕获的虹膜图像而开发的。ACSTL在整个LG2200数据集上提供了0.326的分割误差,但对于来自其他红外传感器的图像,如特写相机(CASIA间隔数据集)的分割误差要高得多。本文给出了一种从分割性能和平均图像执行时间两方面改进ACSTL的方法。对ACSTL的图像处理和虹膜边界选择算法进行了改进。
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引用次数: 0
Rational Householder Transformations 理性户主转换
Ana C. Camargos Couto, D. J. Jeffrey
This paper describes a method for generating integer matrices which, when QR-decomposed through Householder transformations, require only rational computations and give rational results. The motivation is pedagogical: we want to avoid the unnecessary complications that arise from heavy arithmetic manipulations of expressions containing square-roots in linear algebra problem sets and exams.
本文描述了一种生成整数矩阵的方法,当用Householder变换进行qr分解时,它只需要合理的计算并给出合理的结果。这样做的动机是出于教学目的:我们希望避免在线性代数问题集和考试中由于对包含平方根的表达式进行大量算术操作而产生的不必要的复杂性。
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引用次数: 1
Application of Meaningful Text Analytics to Online Product Reviews 有意义文本分析在在线产品评论中的应用
C. Bǎdicǎ, Georgian Vladutu
Online reviews have a significant impact on opinion sharing, thus playing an important role in the purchase process. In this paper we examine the relations between the online review rating score and helpfulness on one hand and attributes measured by the readability tests on the other hand. Furthermore we investigated the interaction between users and, based on our data sets, we found that most of these are between customers and product owners. Lastly we proposed a sentiment analysis algorithm of reviews and we experimentally evaluated it using the review rating scores.
在线评论对意见分享有显著影响,因此在购买过程中起着重要作用。本文研究了在线评论评分分数和有用性与可读性测试所测量的属性之间的关系。此外,我们调查了用户之间的交互,根据我们的数据集,我们发现大多数交互是在客户和产品所有者之间进行的。最后,我们提出了一种评论情感分析算法,并利用评论评级分数对其进行了实验评估。
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引用次数: 3
A Terminating and Confluent Term Rewriting System for the Pure Equational Theory of Quandles 纯量子方程理论的终止和合流项改写系统
Robert W. McGrail, Thuy Trang Nguyen, Thanh Thuy Trang Tran, A. Tripathi
This article presents a term rewriting system for the first-order equational theory of quandles that is both terminating and confluent. As a consequence, it has unique normal forms and so encodes a decision procedure for quandle identities. However, the problem of computing a normal form for this term rewriting system is, in worst case, EXP hard.
本文给出了一阶量子方程理论的终止性和合流性的项改写系统。因此,它具有唯一的范式,并因此编码了一个双重身份的决策过程。然而,在最坏的情况下,计算这个术语重写系统的标准形式的问题是EXP困难。
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引用次数: 2
On the Abstractness of Continuation Semantics 论延续语义的抽象性
Gabriel Ciobanu, E. Todoran
We present a denotational semantics for a simple concurrent language based on Milner's CCS extended with multiparty synchronous interactions. We show that our denotational model is weakly abstract with respect to a corresponding operational semantics. The denotational semantics is designed with metric spaces and continuation semantics for concurrency. The significance of the results presented in this paper is given also by the flexibility provided by the continuations technique.
我们提出了一个简单并发语言的指称语义,该语义基于Milner的CCS,扩展了多方同步交互。我们证明了我们的指称模型相对于相应的操作语义是弱抽象的。指意语义采用度量空间和连续语义进行并发性设计。本文结果的意义还在于延拓技术所提供的灵活性。
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引用次数: 1
Scalable Task Deployment System Inspired from Virus Propagation Models for Large Distributed Workflow Based Systems 基于大型分布式工作流系统的病毒传播模型启发的可扩展任务部署系统
Mihai Bica, D. Gorgan
Deploying, executing and managing large task based workflows on cloud or distributed systems can be challenging. This paper proposes a solution for deploying and launching task based applications on large scale distributed systems. Scaling the existing systems to hundreds of thousands or million of nodes add significant overhead and in some cases will slow down the scalable systems below Amdahl's law because of the cost of managing so many nodes. The proposed solution for task deployment is inspired from computer virus models and realizes the deployment in an exponential fashion by starting with a single container that self divides until it populates all nodes in the datacenter. The architectural model is having the structural shape of binary tree, task metadata messages are routed on the tree model, longest message travel distance is at most log2(n). We compare two solutions for large task deployment and execution. A classical solution of using a linear method for deployment is compared with the computer virus inspired propagation model. Experimental results confirm that this solution is suitable for task based applications that can scale to few millions of worker nodes. Sending messages from the master node to worker nodes should not be a problem according to our simulation.
在云或分布式系统上部署、执行和管理基于大型任务的工作流可能具有挑战性。本文提出了一种在大规模分布式系统上部署和启动基于任务的应用程序的解决方案。将现有系统扩展到数十万或数百万个节点会增加显著的开销,并且在某些情况下,由于管理如此多节点的成本,会降低低于Amdahl定律的可扩展系统的速度。提出的任务部署解决方案的灵感来自计算机病毒模型,并通过从一个自分裂的容器开始,直到它填充数据中心的所有节点,以指数方式实现部署。该体系结构模型具有二叉树的结构形状,任务元数据消息在树模型上路由,最长消息传播距离不超过log2(n)。我们比较了两种用于大型任务部署和执行的解决方案。将采用线性方法进行部署的经典解与计算机病毒传播模型进行了比较。实验结果证实,该解决方案适用于可扩展到数百万工作节点的基于任务的应用程序。根据我们的模拟,从主节点向工作节点发送消息应该不是问题。
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引用次数: 0
期刊
2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)
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