Residual-based specification of a hidden random field included in a hierarchical spatial model

A method for specifying a hidden random field (HRF) included in a hierarchical spatial model is proposed. In hierarchical models of interest the first stage describes, conditional on a realization of the HRF, a response variable which is observable on a continuous spatial domain; the second stage models the HRF which reflects unobserved spatial heterogeneity. The question which is investigated is how can the HRF be modeled, i.e. specified. The method developed to address this question is based on residuals obtained when the base model, i.e. the hierarchical model in which the HRF is assumed constant, is fitted to data. It is shown that the residuals are linked with the HRF, and the link is used to specify the HRF. The method is applied to simulated data in order to assess its performance, and then to real data on radionuclide concentrations on Rongelap Island.     

Cross-validation as a means of investigating DEM interpolation error

Studies of the detailed characteristics of DEM error have been hampered by the difficulty in obtaining a large sample of error values for a DEM. The approach proposed in this paper is to resample a DEM to a lower resolution and then reinterpolate back to the original resolution which produces a large sample of error values well distributed across the DEM. This method is applied to a sample area from Scotland, which contains a variety of terrain types. The results show that the standard measure of error, the root mean square error (RMSE) of elevation, shows only moderate correlation with a visual assessment of the quality of DEMs produced by a range of interpolation methods. The frequency distribution and strength of spatial autocorrelation are shown to vary with the initial data density and interpolation method. When the source data density is low, the error has strong spatial autocorrelation and a distribution that is close to being Gaussian. However, as the data density increases, levels of spatial autocorrelation drop and the distribution becomes leptokurtic with values very strongly clustered around zero. At the level of the individual DEM point, elevation error is shown to be a poor predictor of error in slope derivatives which depend on the spatial pattern of elevation errors around the point and are also sensitive to differences in terrain. At the level of a whole DEM, however, RMSE of elevation is a good predictor of RMSE in gradient and aspect but not of curvature.     

A note on a random walk model for texture analysis

Wechsler and Citron recently published a random walk model with applications in texture analysis. It is shown in the current note that their chi-square test of homogeneity based on this model is misleading.     

从BPMN模型导出组合服务的代数规约

针对应用规约自动测试BPEL表示组合服务时需要解决BPEL服务的规约生成问题,提出了一种从BPMN模型导出BPEL规范定义的组合Web服务的由代数规约语言CASOCC-WS表示的代数规约方法。首先,定义从BPMN模型转换成基调的规则和从BPMN结构转换成正则表达式的规则,设计由正则表达式导出构成公理的项的算法;然后,提出根据所得的项人工书写公理的启发式规则;最后,实现一个从BPMN模型导出组合服务基调的工具原型。案例研究表明,该方法可以解决BPEL服务的代数规约生成问题。     

A wavelet-based Markov random field segmentation model in segmenting microarray experiments

In the present study, an adaptation of the Markov Random Field (MRF) segmentation model, by means of the stationary wavelet transform (SWT), applied to complementary DNA (cDNA) microarray images is proposed (WMRF). A 3-level decomposition scheme of the initial microarray image was performed, followed by a soft thresholding filtering technique. With the inverse process, a Denoised image was created. In addition, by using the Amplitudes of the filtered wavelet Horizontal and Vertical images at each level, three different Magnitudes were formed. These images were combined with the Denoised one to create the proposed SMRF segmentation model. For numerical evaluation of the segmentation accuracy, the segmentation matching factor (SMF), the Coefficient of Determination (r²), and the concordance correlation (p_c) were calculated on the simulated images. In addition, the SMRF performance was contrasted to the Fuzzy C Means (FCM), Gaussian Mixture Models (GMM), Fuzzy GMM (FGMM), and the conventional MRF techniques. Indirect accuracy performances were also tested on the experimental images by means of the Mean Absolute Error (MAE) and the Coefficient of Variation (CV). In the latter case, SPOT and SCANALYZE software results were also tested. In the former case, SMRF attained the best SMF, r², and p_c (92.66%, 0.923, and 0.88, respectively) scores, whereas, in the latter case scored MAE and CV, 497 and 0.88, respectively. The results and support the performance superiority of the SMRF algorithm in segmenting cDNA images.     

Identifying and comparing changes in the spatial concentrations of urban poverty and affluence: a case study of inner London

There is increasing interest, both academically and politically, in the spatial concentrations of poverty and affluence within cities. Although there has been a long history of research in this area, the majority of work has been concerned with poverty rather than affluence and has relied upon methods and measurement techniques that do not take into account the spatial concentrations of poverty. This research attempts to address this problem by measuring poverty and affluence using GIS and spatial statistical methods. By utilising contemporary census data and data collected by Charles Booth one hundred years ago, the research attempts to reveal the long-term changes in the spatial concentrations of poverty and affluence within inner London.     

Performability analysis of computer systems: from model specification to solution

Computer systems reliability/availability modeling deals with the representation of changes in the structure of the system being modeled, which are generally due to faults, and how such changes affect the availability of the system. On the other hand, performance modeling involves representing the probabilistic nature of user demands and predicting the system capacity to perform useful work, under the assumption that the system structure remains constant. With the advent of degradable systems, the system may be restructured in response to faults and may continue to perform useful work, even though operating at lower capacity. Performability modeling considers the effect of structural changes and their impact on the overall performance of the system. The complexity of current computer systems and the variety of different problems to be analyzed, including the simultaneous evaluation of performance and availability, demonstrate the need for sophisticated tools that allow the specification of general classes of problems while incorporating powerful analytic and/or simulation techniques. Concerning model specification, a recently proposed object oriented modeling paradigm that accommodates a wide variety of applications is discussed and compared with other approaches. With respect to solution methods, a brief overview of past work on performability evaluation of Markov models is presented. Then it is shown that many performability related measures can be calculated using the uniformization or randomization technique by coloring distinguished states and/or transitions of the Markov model of the system being studied. Finally, the state space explosion problem is addressed and several techniques for dealing with the problem are discussed.     

A Bayesian mixture model to quantify parameters of spatial clustering

A new Bayesian approach for quantifying spatial clustering is proposed that employs a mixture of gamma distributions to model the squared distance of points to their second nearest neighbors. The method is designed to answer questions arising in biophysical research on nanoclusters of Ras proteins. It takes into account the presence of disturbing metacluster structures as well as non-clustering objects, both common among Ras clusters. Its focus lies on estimating the proportion of points lying in clusters, the mean cluster size and the mean cluster radius without depending on prior knowledge of the parameters. The performance of the model compared to other cluster methods is demonstrated in a comprehensive simulation study, employing a specific new class of spatial point processes, the double Matérn cluster process. Further results and arguments as well as data and code are available as supplementary material.     

Formal analysis of the Shlaer-Mellor method: Towards a toolkit of formal and informal requirements specification techniques

In this paper, we define a number of tools that we think belong to the core of any toolkit for requirements engineers. The tools are conceptual and hence, they need precise definitions that lay down as exactly as possible what their meaning and possible use is. We argue that this definition can best be achieved by a formal specification of the tool. This means that for each semi-formal requirements engineering tool we should provide a formal specification that precisely specifies its meaning. We argue that this mutually enhances the formal and semi-formal technique: it makes formal techniques more usable and, as we will argue, at the same time simplifies the diagram-based notations.At the same time, we believe that the tools of the requirements engineer should, where possible, resemble the familiar semi-formal specification techniques used in practice today. In order to achieve this, we should search existing requirements specification techniques to look for a common kernel of familiar semi-formal techniques and try to provide a formalisation for these.In this paper we illustrate this approach by a formal analysis of the Shlaer-Mellor method for object-oriented requirements specification. The formal specification language used in this analysis is LCM, a language based on dynamic logic, but similar results would have been achieved by means of another language. We analyse the techniques used in the information model, state model, process model and communication model of the Shlaer-Mellor method, identify ambiguities and redundancies, indicate how these can be eliminated and propose a formalisation of the result. We conclude with a listing of the tools extracted from the Shlaer-Mellor method that we can add to a toolkit that in addition contains LCM as formal specification technique.     

合作连锁营销下随机需求的分销系统双层补货优化控制模型

研究在每一补货间隔内需求为服从有限区间上渐变对称分布的随机变量的各零售商集体与一个地区分销中心组成的地区分销系统.分销系统中各零售商可独立决定所经营卖场的补到水平,补货周期是固定值;分销中心给各零售商送货,送货循环周期固定,各零售商补货周期等于分销中心送货循环周期.假定零售商顾客需求是均匀发生的,给出了零售商补到水平优化控制策略和分销中心进货控制策略.最后,通过一个算例验证了该模型的有效性.     

A methodology for transparent knowledge specification in a dynamic tuning environment

The increasing use of parallel/distributed applications demands a continuous support to take significant advantages from parallel power. This includes the evolution of performance analysis and tuning tools which automatically allows for obtaining a better behavior of the applications. Different approaches and tools have been proposed and they are continuously evolving to cover the requirements and expectations of users. One such tool is MATE (Monitoring Analysis and Tuning Environment), which provides automatic and dynamic tuning for parallel/distributed applications. The knowledge used by MATE to analyze and take decisions is based on performance models which include a set of performance parameters and a set of mathematical expressions modeling the solution of the performance problem. These elements are used by the tuning environment to conduct the monitoring and analysis steps, respectively. The tuning phase depends on the results of the performance analysis. This paper presents a methodology to specify performance models. Each performance model specification can be automatically and transparently translated into a piece of software code encapsulating the knowledge to be straightforwardly included in MATE. Applying this methodology, the user does not have to be involved in the implementation details of MATE, which makes the usage of the tool more transparent. Copyright © 2011 John Wiley & Sons, Ltd.     

Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models

Fitting of non-Gaussian hierarchical random effects models by approximate maximum likelihood can be made automatic to the same extent that Bayesian model fitting can be automated by the program BUGS. The word “automatic” means that the technical details of computation are made transparent to the user. This is achieved by combining a technique from computer science known as “automatic differentiation” with the Laplace approximation for calculating the marginal likelihood. Automatic differentiation, which should not be confused with symbolic differentiation, is mostly unknown to statisticians, and hence basic ideas and results are reviewed. The computational performance of the approach is compared to that of existing mixed-model software on a suite of datasets selected from the mixed-model literature.     

结合多空间特征的多尺度马尔可夫随机场彩色图像分割

提出了一种结合多空间特征的多尺度马尔可夫随机场(MRF)模型——MS-MRMRF。针对RGB单空间对彩色图像描述不足的问题,模型首先将图像转化为HSV空间并与RGB空间结合形成多空间特征;然后根据多空间特征的形式,提出了一种模糊化估参的多尺度MRF模型对其进行分割。彩色图像的分割实验表明：相比现有的单空间特征的多尺度MRF算法,结合多空间特征的多尺度MRF可以有效地提高分割精度。     

A hierarchical model of a linguistic variable

In this work a theoretical hierarchical model of dichotomous linguistic variables is presented. The model incorporates certain features of the approximate reasoning approach and others of the Fuzzy Control approach to Fuzzy Linguistic Variables. It allows sharing of the same hierarchical structure between the syntactic definition of a linguistic variable and its semantic definition given by fuzzy sets. This fact makes it easier to build symbolic operations between linguistic terms with a better grounded semantic interpretation. Moreover, the family of fuzzy sets which gives the semantics of each linguistic term constitutes a multiresolution system, and thanks to that any fuzzy set can be represented in terms of the set of linguistic terms. The model can also be considered a general framework for building more interpretable fuzzy linguistic variables with a high capacity of accuracy, which could be used to build more interpretable Fuzzy Rule Based Systems (FRBS).     

Multi-scale spatial sensitivity analysis of a model for economic appraisal of flood risk management policies

We demonstrate the use of sensitivity analysis to rank sources of uncertainty in models for economic appraisal of flood risk management policies, taking into account spatial scale issues. A methodology of multi-scale variance-based global sensitivity analysis is developed, and illustrated on the NOE model on the Orb River, France. The variability of the amount of expected annual flood avoided damages, and the associated sensitivity indices, are estimated over different spatial supports, ranging from small cells to the entire floodplain. Both uncertainty maps and sensitivity maps are produced to identify the key input variables in the NOE model at different spatial scales. Our results show that on small spatial supports, variance of the output indicator is mainly due to the water depth maps and the assets map (spatially distributed model inputs), while on large spatial supports, it is mainly due to the flood frequencies and depth–damage curves (non spatial inputs).     

Sensitivity analysis of a hierarchical model of mobile cloud computing

Mobile cloud computing is a new paradigm that uses cloud computing resources to overcome the limitations of mobile computing. Due to its complexity, dependability and performance studies of mobile clouds may require composite modeling techniques, using distinct models for each subsystem and combining state-based and non-state-based formalisms. This paper uses hierarchical modeling and four different sensitivity analysis techniques to determine the parameters that cause the greatest impact on the availability of a mobile cloud. The results show that distinct approaches provide similar results regarding the sensitivity ranking, with specific exceptions. A combined evaluation indicates that system availability may be improved effectively by focusing on a reduced set of factors that produce large variation on the measure of interest. The time needed to replace a fully discharged battery in the mobile device is a parameter with high impact on steady-state availability, as well as the coverage factor for the failures of some cloud servers. This paper also shows that a sensitivity analysis through partial derivatives may not capture the real level of impact for some parameters in a discrete domain, such as the number of active servers. The analysis through percentage differences, or the factorial design of experiments, fulfills such a gap.