Pairwise马尔科夫模型下的势均衡多目标多伯努利滤波器

由于在实际应用中目标模型不一定满足隐马尔科夫模型（Hidden Markov model,HMM）隐含的马尔科夫假设和独立性假设条件,一种更为一般化的Pairwise马尔科夫模型（Pairwise Markov model,PMM）被提出.它放宽了HMM的结构性限制,可以有效地处理更为复杂的目标跟踪场景.本文针对杂波环境下的多目标跟踪问题,提出一种在PMM框架下的势均衡多目标多伯努利（Cardinality balanced multi-target multi-Bernoulli,CBMeMBer）滤波器,并给出它在线性高斯PMM条件下的高斯混合（Gaussian mixture,GM）实现.最后,采用一种满足HMM局部物理特性的线性高斯PMM,将本文所提算法与概率假设密度（Probability hypothesis density,PHD）滤波器进行比较.实验结果表明本文所提算法的跟踪性能优于PHD滤波器.     

Partitioning functions for stateful data parallelism in stream processing

In this paper, we study partitioning functions for stream processing systems that employ stateful data parallelism to improve application throughput. In particular, we develop partitioning functions that are effective under workloads where the domain of the partitioning key is large and its value distribution is skewed. We define various desirable properties for partitioning functions, ranging from balance properties such as memory, processing, and communication balance, structural properties such as compactness and fast lookup, and adaptation properties such as fast computation and minimal migration. We introduce a partitioning function structure that is compact and develop several associated heuristic construction techniques that exhibit good balance and low migration cost under skewed workloads. We provide experimental results that compare our partitioning functions to more traditional approaches such as uniform and consistent hashing, under different workload and application characteristics, and show superior performance.     

Continuous mixture modeling via goodness-of-fit ridges

We present a novel method for representing “extruded” distributions. An extruded distribution is an M-dimensional manifold in the parameter space of the component distribution. Representations of that manifold are “continuous mixture models”. We present a method for forming one-dimensional continuous Gaussian mixture models of sampled extruded Gaussian distributions via ridges of goodness-of-fit. Using Monte Carlo simulations and ROC analysis, we explore the utility of a variety of binning techniques and goodness-of-fit functions. We demonstrate that extruded Gaussian distributions are more accurately and consistently represented by continuous Gaussian mixture models than by finite Gaussian mixture models formed via maximum likelihood expectation maximization.     

Comparison between Karhunen–Loève expansion and translation-based simulation of non-Gaussian processes

The Karhunen–Loève (K–L) expansion has been successfully applied to the simulation of highly skewed non-Gaussian processes based on the prescribed covariance and marginal distribution functions. When the stationary random process is indexed over a domain that is much larger than the correlation distance, the K–L expansion will approach the spectral representation. The non-Gaussian K–L technique is applied in the popular spectral representation as a special case to facilitate comparison with translation-based spectral representation. Processes with both incompatible and compatible spectral density and marginal distribution functions are simulated numerically. It is demonstrated that K–L expansion can be used to address the situation with incompatible target functions where the commonly used translation approach may not be applicable. It is therefore a more robust method for simulation of non-Gaussian processes because it can generate different processes satisfying the same target spectral density function and the same target marginal distribution function regardless of their compatibility.     

Firing variability is higher than deduced from the empirical coefficient of variation

A convenient and often used summary measure to quantify the firing variability in neurons is the coefficient of variation (CV), defined as the standard deviation divided by the mean. It is therefore important to find an estimator that gives reliable results from experimental data, that is, the estimator should be unbiased and have low estimation variance. When the CV is evaluated in the standard way (empirical standard deviation of interspike intervals divided by their average), then the estimator is biased, underestimating the true CV, especially if the distribution of the interspike intervals is positively skewed. Moreover, the estimator has a large variance for commonly used distributions. The aim of this letter is to quantify the bias and propose alternative estimation methods. If the distribution is assumed known or can be determined from data, parametric estimators are proposed, which not only remove the bias but also decrease the estimation errors. If no distribution is assumed and the data are very positively skewed, we propose to correct the standard estimator. When defining the corrected estimator, we simply use that it is more stable to work on the log scale for positively skewed distributions. The estimators are evaluated through simulations and applied to experimental data from olfactory receptor neurons in rats.     

A class of adaptive directional image smoothing filters

The gray level distribution around a pixel of an image usually tends to be more coherent in some directions compared to other directions. The idea of adaptive directional filtering is to estimate the direction of higher coherence around each pixel location and then to employ a window which approximates a line segment in that direction. Hence, the details of the image may be preserved while maintaining a satisfactory level of noise suppression performance. In this paper we describe a class of adaptive directional image smoothing filters based on generalized Gaussian distributions. We propose a measure of spread for the pixel values based on the maximum likelihood estimate of a scale parameter involved in the generalized Gaussian distribution. Several experimental results indicate a significant improvement compared to some standard filters.     

Focused multi-task learning in a Gaussian process framework

Multi-task learning, learning of a set of tasks together, can improve performance in the individual learning tasks. Gaussian process models have been applied to learning a set of tasks on different data sets, by constructing joint priors for functions underlying the tasks. In these previous Gaussian process models, the setting has been symmetric in the sense that all the tasks have been assumed to be equally important, whereas in settings such as transfer learning the goal is asymmetric, to enhance performance in a target task given the other tasks. We propose a focused Gaussian process model which introduces an ??explaining away?? model for each of the additional tasks to model their non-related variation, in order to focus the transfer to the task-of-interest. This focusing helps reduce the key problem of negative transfer, which may cause performance to even decrease if the tasks are not related closely enough. In experiments, our model improves performance compared to single-task learning, symmetric multi-task learning using hierarchical Dirichlet processes, transfer learning based on predictive structure learning, and symmetric multi-task learning with Gaussian processes.     

Bounded generalized Gaussian mixture model

The generalized Gaussian mixture model (GGMM) provides a flexible and suitable tool for many computer vision and pattern recognition problems. However, generalized Gaussian distribution is unbounded. In many applications, the observed data are digitalized and have bounded support. A new bounded generalized Gaussian mixture model (BGGMM), which includes the Gaussian mixture model (GMM), Laplace mixture model (LMM), and GGMM as special cases, is presented in this paper. We propose an extension of the generalized Gaussian distribution in this paper. This new distribution has a flexibility to fit different shapes of observed data such as non-Gaussian and bounded support data. In order to estimate the model parameters, we propose an alternate approach to minimize the higher bound on the data negative log-likelihood function. We quantify the performance of the BGGMM with simulations and real data.     

Predicting a distribution of implied volatilities for option pricing

In this paper, we propose a method that predicts a distribution of the implied volatility functions and that provides confidence intervals for the option prices from it. The proposed method, based on a Bayesian approach, employs a Bayesian kernel machine, so-called Gaussian process regression. To verify the performance of the proposed method, we conducted simulations on some model-generated option prices data and real option market data. The simulation results show that the proposed method performs well with practically meaningful option ranges as well as overcomes the problem of containing negative prices in their predicted confidence intervals by the previous works.     

Plurigau: a computer program for simulating spatial facies using the truncated plurigaussian method

Truncated plurigaussian simulation is a useful method for simulating spatial categorical variables, such as facies, in a geological context. The method is an extension of the truncated Gaussian method that retains the main advantages of the latter (mainly that it produces permissible sets of indicator semi-variograms and cross-semi-variograms) but overcomes its limitations (the truncated Gaussian method only reproduces sequentially ranked categories). The method is based on the truncation of two Gaussian random functions that may, or may not, be correlated. PLURIGAU is an ANSI Fortran-77 computer program for performing conditional or unconditional truncated plurigaussian simulations of spatial categories. The number of facies, spatial relations between the facies, proportions of each facies, indicator semi-variograms and indicator cross-semi-variograms must be known or estimated from experimental data. The program calculates the four thresholds for each of the facies (two for each of the Gaussian random functions) and the covariance models for the two Gaussian random functions.The simulation of the Gaussian random functions may be done using any of the methods available. Conditioning has been implemented by a simple acceptance–rejection technique embedded within sequential Gaussian simulation algorithm. A case study is provided so that the implementation of the programs can be checked and the results are discussed.     

Gaussian Processes for Object Categorization

Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gaussian Processes (GPs) provide a framework for deriving regression techniques with explicit uncertainty models; we show here how Gaussian Processes with covariance functions defined based on a Pyramid Match Kernel (PMK) can be used for probabilistic object category recognition. Our probabilistic formulation provides a principled way to learn hyperparameters, which we utilize to learn an optimal combination of multiple covariance functions. It also offers confidence estimates at test points, and naturally allows for an active learning paradigm in which points are optimally selected for interactive labeling. We show that with an appropriate combination of kernels a significant boost in classification performance is possible. Further, our experiments indicate the utility of active learning with probabilistic predictive models, especially when the amount of training data labels that may be sought for a category is ultimately very small.     

Balancing reducer workload for skewed data using sampling-based partitioning

MapReduce has emerged as a popular tool for distributed processing of massive data. However, it is not efficient when handling skewed data and it often leads to reducer load imbalance. In this paper, we address the problem of how to efficiently partition intermediate keys to balance the workload of all reducers when processing skewed data. We present a sampling scheme to compute the approximate distribution of key frequency, estimate the overall distribution and then make a partition scheme in advance. Then, we apply it to map phase of the executing MapReduce job. This work not only provides a load-balanced partition strategy, but also keeps a high performance of synchronous mode of MapReduce. We also propose two partition methods based on sampling results: cluster combination and cluster split combination. The experimental results show that our methods achieve a better time and load balancing results.     

多径环境中宽带信号的时延-尺度性能分析

该文根据宽带目标回波模型，建立了多径宽带回波模型，分别推导出了多径环境下矩形、高斯包络线性调频信号和双曲调频信号宽带模糊度函数。分析比较了不同的发射波形在多径环境中的时间分辨率。并通过对多径矩形包络线性调频信号和高斯包络双曲调频信号回波的仿真，分别给出了两条和三条路径时，两种不同发射信号的多径回波信号的宽带模糊度函数图。仿真结果表明在多径环境中高斯包络的双曲调频信号多普勒容限优于矩形包络线性调频信号，这样我们就可以忽略尺度对模糊度函数的幅值的影响。     

Regularized continuous estimation of distribution algorithms

Regularization is a well-known technique in statistics for model estimation which is used to improve the generalization ability of the estimated model. Some of the regularization methods can also be used for variable selection that is especially useful in high-dimensional problems. This paper studies the use of regularized model learning in estimation of distribution algorithms (EDAs) for continuous optimization based on Gaussian distributions. We introduce two approaches to the regularized model estimation and analyze their effect on the accuracy and computational complexity of model learning in EDAs. We then apply the proposed algorithms to a number of continuous optimization functions and compare their results with other Gaussian distribution-based EDAs. The results show that the optimization performance of the proposed RegEDAs is less affected by the increase in the problem size than other EDAs, and they are able to obtain significantly better optimization values for many of the functions in high-dimensional settings.     

利用高斯混合模型的SAR图像目标CFAR检测新方法

SAR（合成孔径雷达）图像杂波分布模型种类繁多且对实际地物的建模能力有限。在使用基于杂波统计模型的CFAR(恒虚警率)算法对SAR图像进行目标检测时,杂波统计模型的失配会导致检测结果产生较大的CFAR损失,算法精度不高。提出了一种基于高斯混合模型的CFAR检测新方法。该方法以理论上可以拟合任意形状概率密度分布的高斯混合模型对实际SAR图像的背景杂波进行拟合,利用拟合后得到的分布模型,根据CFAR检测的原理推导出目标检测阈值的计算公式完成目标的检测。新方法对服从不同分布模型的背景杂波,使用形式上统一的模型进行描述,克服了CFAR检测高度依赖背景杂波分布的缺点,提高了CFAR的通用性。实验结果表明,即使在背景杂波类型未知的情况下,新方法依然得到了良好的目标检测效果。     

BIAWGN信道中改进的LT码优化方法

在二进制输入加性高斯白噪声信道中传输LT码时,采用高斯近似方法预测置信传播译码算法的误比特率性能不够准确。为此,提出一种改进的高斯近似方法,其中,输入节点度分布采用泊松分布,相应的软信息为高斯混合物,在此基础上给出一种LT码度分布优化方法。仿真结果证明,该方法相比同类方法性能更优越。     

Performance surfaces of a single-layer perceptron

A perceptron learning algorithm may be viewed as a steepest-descent method whereby an instantaneous performance function is iteratively minimized. An appropriate performance function for the most widely used perceptron algorithm is described and it is shown that the update term of the algorithm is the gradient of this function. An example is given of the corresponding performance surface based on Gaussian assumptions and it is shown that there is an infinity of stationary points. The performance surfaces of two related performance functions are examined. Computer simulations that demonstrate the convergence properties of the adaptive algorithms are given.     

Hidden messages in heavy-tails: DCT-domain watermark detection using alpha-stable models

This paper addresses issues that arise in copyright protection systems of digital images, which employ blind watermark verification structures in the discrete cosine transform (DCT) domain. First, we observe that statistical distributions with heavy algebraic tails, such as the alpha-stable family, are in many cases more accurate modeling tools for the DCT coefficients of JPEG-analyzed images than families with exponential tails such as the generalized Gaussian. Motivated by our modeling results, we then design a new processor for blind watermark detection using the Cauchy member of the alpha-stable family. The Cauchy distribution is chosen because it is the only non-Gaussian symmetric alpha-stable distribution that exists in closed form and also because it leads to the design of a nearly optimum detector with robust detection performance. We analyze the performance of the new detector in terms of the associated probabilities of detection and false alarm and we compare it to the performance of the generalized Gaussian detector by performing experiments with various test images.     

Distance regression by Gauss–Newton-type methods and iteratively re-weighted least-squares

We discuss the problem of fitting a curve or surface to given measurement data. In many situations, the usual least-squares approach (minimization of the sum of squared norms of residual vectors) is not suitable, as it implicitly assumes a Gaussian distribution of the measurement errors. In those cases, it is more appropriate to minimize other functions (which we will call norm-like functions) of the residual vectors. This is well understood in the case of scalar residuals, where the technique of iteratively re-weighted least-squares, which originated in statistics (Huber in Robust statistics, 1981) is known to be a Gauss–Newton-type method for minimizing a sum of norm-like functions of the residuals. We extend this result to the case of vector-valued residuals. It is shown that simply treating the norms of the vector-valued residuals as scalar ones does not work. In order to illustrate the difference we provide a geometric interpretation of the iterative minimization procedures as evolution processes.     

Preference representation using Gaussian functions on a hyperplane in evolutionary multi-objective optimization

Many-objective optimization has attracted much attention in evolutionary multi-objective optimization (EMO). This is because EMO algorithms developed so far often degrade their search ability for optimization problems with four or more objectives, which are frequently referred to as many-objective problems. One of promising approaches to handle many objectives is to incorporate the preference of a decision maker (DM) into EMO algorithms. With the preference, EMO algorithms can focus the search on regions preferred by the DM, resulting in solutions close to the Pareto front around the preferred regions. Although a number of preference-based EMO algorithms have been proposed, it is not trivial for the DM to reflect his/her actual preference in the search. We previously proposed to represent the preference of the DM using Gaussian functions on a hyperplane. The DM specifies the center and spread vectors of the Gaussian functions so as to represent his/her preference. The preference handling is integrated into the framework of NSGA-II. This paper extends our previous work so that obtained solutions follow the distribution of Gaussian functions specified. The performance of our proposed method is demonstrated mainly for benchmark problems and real-world applications with a few objectives in this paper. We also show the applicability of our method to many-objective problems.