Developing Systems for Real-Time Streaming Analysis

Sources of streaming data are proliferating and so are the demands to analyze and mine such data in real time. Statistical methods frequently form the core of real-time analysis, and therefore, statisticians increasingly encounter the challenges and implicit requirements of real-time systems. This work recommends a comprehensive strategy for development and implementation of streaming algorithms, beginning with exploratory data analysis in a flexible computing environment, leading to specification of a computational algorithm for the streaming setting and its initial implementation, and culminating in successive improvements to computational efficiency and throughput. This sequential development relies on a collaboration between statisticians, domain scientists, and the computer engineers developing the real-time system. This article illustrates the process in the context of a radio astronomy challenge to mitigate adverse impacts of radio frequency interference (noise) in searches for high-energy impulses from distant sources. The radio astronomy application motivates discussion of system design, code optimization, and the use of hardware accelerators such as graphics processing units, field-programmable gate arrays, and IBM Cell processors. Supplementary materials, available online, detail the computing systems typically used for streaming systems with real-time constraints and the process of optimizing code for high efficiency and throughput.     

Multidimensional Scaling With Very Large Datasets

Multidimensional scaling has a wide range of applications when observations are not continuous but it is possible to define a distance (or dissimilarity) among them. However, standard implementations are limited when analyzing very large datasets because they rely on eigendecomposition of the full distance matrix and require very long computing times and large quantities of memory. Here, a new approach is developed based on projection of the observations in a space defined by a subset of the full dataset. The method is easily implemented. A simulation study showed that its performance are satisfactory in different situations and can be run in a short time when the standard method takes a very long time or cannot be run because of memory requirements.     

与 Cauchy 微分中值定理相关的几个问题

根据Cauchy微分中值定理表达式的结构引入辅助函数 F（x ，c）＝ f （x）- f （c）g（x）- g（c）（a＜ c＜ b），通过讨论其可导性，得到相关的几个不等式，由此得出Cauchy微分中值定理存在唯一“中值点”的一个条件，并给出其逆定理的一个较弱表述。     

Combining GPS carrier phase and Doppler observations for precise velocity determination

The truncation error and propagation error are analyzed for velocity determination through differential GPS carrier phase observations,and an approach for the choice of the best number of points for the central difference method is developed.In order to overcome the disadvantages of existing GPS velocity determination methods,a new velocity determination algorithm is presented,based on combining carrier phase and Doppler observations.The basic idea is that two types of observation are combined by adding the...     

Real‐Time Measurement of the Size Distribution of Diesel Exhaust Particles using a Portable 4‐stage Electrical Low Pressure Impactor

For this study, a 4 stage electrical low pressure impactor was designed to measure the real‐time size distribution of diesel particulate matter (DPM). For the performance evaluation, sodium chloride (NaCl) particles and dioctyl sebacate (DOS) particles were used. After evaluating the collection efficiency of each stage of the impactor, the size distributions of test particles were estimated using electrical current data and their inversion algorithm, and this was found to agree with the results obtained by a scanning mobility particle sizer (SMPS). For measurement of DPM, a common‐rail direct injection (CRDI) diesel engine, for engine speeds of 1,200 rpm and 1,500 rpm at 2.7 kgf·m, was used. Therefore, it was found that the size distribution of the DPM could be easily obtained, with the currents measured by the impactor and the data inversion algorithm, in less than 5 seconds. Furthermore, the effective density of the DPM could be obtained using the calculated results and the SMPS data.     

Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data

Cost-sensitive classification is based on a set of weights defining the expected cost of misclassifying an object. In this paper, a Genetic Fuzzy Classifier, which is able to extract fuzzy rules from interval or fuzzy valued data, is extended to this type of classification. This extension consists in enclosing the estimation of the expected misclassification risk of a classifier, when assessed on low quality data, in an interval or a fuzzy number. A cooperative-competitive genetic algorithm searches for the knowledge base whose fitness is primal with respect to a precedence relation between the values of this interval or fuzzy valued risk. In addition to this, the numerical estimation of this risk depends on the entrywise product of cost and confusion matrices. These have been, in turn, generalized to vague data. The flexible assignment of values to the cost function is also tackled, owing to the fact that the use of linguistic terms in the definition of the misclassification cost is allowed.     

Optimal boundary discretization by variational data assimilation

A variational data assimilation technique applied to the identification of the optimal discretization of interpolation operators and derivatives in the nodes adjacent to the boundary of the domain is discussed in frames of the linear shallow water model. The advantage of controlling the discretization of operators near the boundary rather than boundary conditions is shown. Assimilating data that have been produced by the same model on a finer grid, in a model on a coarse grid, we have shown that optimal discretization allows us to correct such errors of the numerical scheme as under‐resolved boundary layer and wrong wave velocity. Copyright © 2010 John Wiley & Sons, Ltd.     

Cauchy-type integral formula and Plemelj formula of hypermonogenic functions on unbounded domains

In this paper, we discuss the Cauchy-type integral formula of hypermonogenic functions on unbounded domains in real Clifford analysis, then we extend the Plemelj formula and Cauchy–Pompeiu formula of hypermonogenic functions on bounded domains to unbounded domains. We also deal with the Green-type formula on unbounded domains and get several important corollaries.