KBO Orientability

This article presents three new approaches to prove termination of rewrite systems with the Knuth–Bendix order efficiently. The constraints for the weight function and for the precedence are encoded in (pseudo-)propositional logic or linear arithmetic and the resulting formula is tested for satisfiability using dedicated solvers. Any satisfying assignment represents a weight function and a precedence such that the induced Knuth–Bendix order orients the rules of the encoded rewrite system from left to right. This means that in contrast to the dedicated methods our approach does not directly solve the problem but transforms it to equivalent formulations for which sophisticated back-ends exist. In order to make all approaches complete we present a method to compute upper bounds on the weights. Furthermore, our encodings take dependency pairs into account to increase the applicability of the order. The first and third authors are supported by FWF (Austrian Science Fund) project P18763 and the second author is supported by the Grant-in-Aid for Young Scientists 20800022 of the Ministry of Education, Culture, Sports, Science and Technology of Japan.     

An Extreme Processor for an Extreme Experiment

Particle physics experiments stretch processing requirements to the limit, requiring the selection of extremely rare events at tens of terabytes per second. A network of 283,392 mixed-signal MIMD processors operating in parallel at 17 Tbytes/s will help physicists interpret data from the world's largest particle accelerator.     

双阈值法在图像边缘检测中的应用

在多尺度小波变换局部模极大值图的基础上,分别以图像边缘链的长度和按边缘链长自适应分块的块灰度作为双重阈值检测图像的边缘。实验表明,采用这一方法,可获得单像素宽、定位精准、抗噪声效果好的边缘图像。     

Estimation of Detection Thresholds for Redirected Walking Techniques

In immersive virtual environments (IVEs), users can control their virtual viewpoint by moving their tracked head and walking through the real world. Usually, movements in the real world are mapped one-to-one to virtual camera motions. With redirection techniques, the virtual camera is manipulated by applying gains to user motion so that the virtual world moves differently than the real world. Thus, users can walk through large-scale IVEs while physically remaining in a reasonably small workspace. In psychophysical experiments with a two-alternative forced-choice task, we have quantified how much humans can unknowingly be redirected on physical paths that are different from the visually perceived paths. We tested 12 subjects in three different experiments: (E1) discrimination between virtual and physical rotations, (E2) discrimination between virtual and physical straightforward movements, and (E3) discrimination of path curvature. In experiment E1, subjects performed rotations with different gains, and then had to choose whether the visually perceived rotation was smaller or greater than the physical rotation. In experiment E2, subjects chose whether the physical walk was shorter or longer than the visually perceived scaled travel distance. In experiment E3, subjects estimate the path curvature when walking a curved path in the real world while the visual display shows a straight path in the virtual world. Our results show that users can be turned physically about 49 percent more or 20 percent less than the perceived virtual rotation, distances can be downscaled by 14 percent and upscaled by 26 percent, and users can be redirected on a circular arc with a radius greater than 22 m while they believe that they are walking straight.     

双阈值法在图像边缘检测中的应用

在多尺度小波变换局部模极大值图的基础上,分别以图像边缘链的长度和按边缘链长自适应分块的块灰度作为双重阈值检测图像的边缘.实验表明,采用这一方法,可获得单像素宽、定位精准、抗噪声效果好的边缘图像.     

Thresholds for error probability measures of business process models

The quality of conceptual business process models is highly relevant for the design of corresponding information systems. In particular, a precise measurement of model characteristics can be beneficial from a business perspective, helping to save costs thanks to early error detection. This is just as true from a software engineering point of view. In this latter case, models facilitate stakeholder communication and software system design. Research has investigated several proposals as regards measures for business process models, from a rather correlational perspective. This is helpful for understanding, for example size and complexity as general driving forces of error probability. Yet, design decisions usually have to build on thresholds, which can reliably indicate that a certain counter-action has to be taken. This cannot be achieved only by providing measures; it requires a systematic identification of effective and meaningful thresholds. In this paper, we derive thresholds for a set of structural measures for predicting errors in conceptual process models. To this end, we use a collection of 2000 business process models from practice as a means of determining thresholds, applying an adaptation of the ROC curve method. Furthermore, an extensive validation of the derived thresholds was conducted by using 429 EPC models from an Australian financial institution. Finally, significant thresholds were adapted to refine existing modeling guidelines in a quantitative way.     

抢占阈值调度的功耗优化

DVS(Dynamic Voltage Scaling)技术的应用使得任务执行时间延长进而使得处理器的静态功耗(由CMOS电路的泄露电流引起)迅速增加.延迟调度(Procrastination Scheduling)算法是近年提出用于减少静态功耗的有效方法,它通过推迟任务的正常执行来尽可能长时间地让处理器处于睡眠或关闭状态,从而避免过多的静态功耗泄露.文中针对可变电压处理器上运用抢占阈值调度策略的周期性任务集合,将节能调度和延迟调度结合起来,提出一种两阶段节能调度算法,先使用离线算法来计算每个任务的最优处理器执行速度,而后使用在线模拟调度算法来计算每个任务的延迟时间,从而动态判定处理器开启/关闭时刻.实例研究和仿真实验表明,作者的方法能够进一步降低抢占阈值任务调度算法的功耗.     

一种工程化的双门限辐射源目标识别方法

针对辐射源数量多、运动速度差异大、信息处理时效性要求高等特点,提出一类双门限辐射源目标识别方法;该方法首先构造信号与目标的相似度度量,通过双门限检测得到辐射源信号不属于任何目标、信号属于某个目标、模糊三类判定结论,对于模糊判定结果,构造差值门限进一步减小不确定性,提高目标识别能力;仿真结果表明所提出的方法能够快速有效地实现目标识别,便于工程化应用。     

极限图形

以肾上腺素为燃料的极限运动品牌设计作品已经不再影响整个行业了,因为主流品牌借用了极限的创意美学。Mark Penfold报道。     

Extreme learning machine for interval neural networks

Interval data offer a valuable way of representing the available information in complex problems where uncertainty, inaccuracy, or variability must be taken into account. Considered in this paper is the learning of interval neural networks, of which the input and output are vectors with interval components, and the weights are real numbers. The back-propagation (BP) learning algorithm is very slow for interval neural networks, just as for usual real-valued neural networks. Extreme learning machine (ELM) has faster learning speed than the BP algorithm. In this paper, ELM is applied for learning of interval neural networks, resulting in an interval extreme learning machine (IELM). There are two steps in the ELM for usual feedforward neural networks. The first step is to randomly generate the weights connecting the input and the hidden layers, and the second step is to use the Moore–Penrose generalized inversely to determine the weights connecting the hidden and output layers. The first step can be directly applied for interval neural networks. But the second step cannot, due to the involvement of nonlinear constraint conditions for IELM. Instead, we use the same idea as that of the BP algorithm to form a nonlinear optimization problem to determine the weights connecting the hidden and output layers of IELM. Numerical experiments show that IELM is much faster than the usual BP algorithm. And the generalization performance of IELM is much better than that of BP, while the training error of IELM is a little bit worse than that of BP, implying that there might be an over-fitting for BP.     

极限学习机多目标模型选择研究

为了克服极限学习机输入权重与偏置的随机性对模型泛化能力的负面影响,提出一种基于多目标优化的极限学习机模型选择方法将极限学习机模型选择视为一个多目标全局优化问题,可将泛化误差和输出权重的模作为优化目标;为加快优化速度,引入极限学习机的快速留一法误差估计指代泛化误差,并考虑到优化目标间的互斥性,最终采用多目标综合学习粒子群算法寻找非支配解.通过5个UCI回归数据集上的仿真结果表明,与常用极限学习机模型选择方法相比,改进方法均取得更低的预测误差,同时网络结构更加紧凑.     

Extreme learning machine for structured output spaces

Recently, extreme learning machine (ELM) has attracted increasing attention due to its successful applications in classification, regression, and ranking. Normally, the desired output of the learning system using these machine learning techniques is a simple scalar output. However, there are many applications in machine learning which require more complex output rather than a simple scalar one. Therefore, structured output is used for such applications where the system is trained to predict structured output instead of simple one. Previously, support vector machine (SVM) has been introduced for structured output learning in various applications. However, from machine learning point of view, ELM is known to offer better generalization performance compared to other learning techniques. In this study, we extend ELM to more generalized framework to handle complex outputs where simple outputs are considered as special cases of it. Besides the good generalization property of ELM, the resulting model will possesses rich internal structure that reflects task-specific relations and constraints. The experimental results show that structured ELM achieves similar (for binary problems) or better (for multi-class problems) generalization performance when compared to ELM. Moreover, as verified by the simulation results, structured ELM has comparable or better precision performance with structured SVM when tested for more complex output such as object localization problem on PASCAL VOC2006. Also, the investigation on parameter selections is presented and discussed for all problems.

     

六边形网格细分法实现二阈值推算定位

推算定位技术是大规模分布交互仿真系统缓解网络传输延时及减少网络通讯量的传统方法。以往的推算定位技术在设定推算阈值时 ,总因为顾及近距离实体的精确度需求而设置较小的阈值 ,这样无可避免地增加了远距离实体的数据接收频率 ,增加了网络通讯开销。为此本文提出了多阈值推算定位技术 ,针对不同距离的接收实体对应不同的误差容许阈值进行推算 ,针对 DIS系统给出了一种基于六边形网络细分的二阈值推算定位技术的实现方法。该方法实现简单 ,可以有效减少系统的通讯量     

Thresholds: On the Need for Frame Discarding in Cell-Switched Networks

This paper introduces a novel policing mechanism, called Packet Leaky Bucket in which all cells of a frame are marked with the same level of priority. Moreover, it discusses the need for having a frame discard mechanism in cell-switched networks when frame-oriented policing mechanisms are used.     

Extreme programming. Rapid development for Web-based applications

Agile processes like extreme programming (XP), Scrum, Crystal, and adaptive software development aim to increase a software organization's responsiveness while decreasing development overhead. They focus on delivering executable code and see people as the strongest ingredient of software development. We offer an overview of the philosophy and practice behind XP, which is currently the most popular agile methodology     

Extreme learning machine model for water network management

Neural Computing and Applications - A novel failure rate prediction model is developed by the extreme learning machine (ELM) to provide key information needed for optimum ongoing...