基于伽罗华域高斯列消元法的RS码盲识别

为解决高码率RS（Reed Solomon）码盲识别问题,提出了一种基于伽罗华域高斯列消元法的RS码盲识别方法。先利用矩阵秩的差值函数识别符号数及码长;再遍历此时符号数对应的本原多项式,对矩阵进行伽罗华域高斯列消元,并引入熵函数差值来识别本原多项式;最后求码字多项式的根,其中连续根即为生成多项式的根。该方法可以较好地识别RS码码长、生成多项式及本原多项式,并且避免了遍历符号数时多次进行伽罗华域傅里叶变换的繁琐过程。仿真结果表明,在误码率为3×10－3的情况下,对RS码的识别概率高于90%。     

Simulation modeling and optimization for ambulance allocation considering spatiotemporal stochastic demand

To ensure a timely response to emergencies, governments are obliged to implement effective ambulance allocation plans. In practice, an emergency medical service (EMS) system works in an uncertain environment, with stochastic demand, response-times, and travel-times. This uncertainty significantly affects ambulance allocation planning. However, few studies in this field adequately consider the effect of spatiotemporal uncertainty in demand, because it is difficult to measure it quantitatively. As a result, few analytic models capture the dynamic nature of an EMS system and, thus, the allocation plans they generate are not efficient in practice. Therefore, this study proposes a simulation-based optimization method for ambulance allocation. A simulation model is constructed to mimic the operational processes of an EMS system, and to evaluate the performance of an ambulance allocation plan in an uncertain environment. Gaussian mixture model clustering is used to derive the uncertain spatial demand. Then, the simulation generates emergency demand based on the obtained spatial distribution. A Gaussian-process-based search algorithm is used together with the simulation model to identify optimal solutions. To validate the proposed method, a case study is conducted using data on emergency patients in the Shanghai Songjiang District. Compared with the current plan adopted in Songjiang, the experimental results demonstrate that the delay time and frequency of the EMS system can be reduced significantly by employing the proposed methods. Furthermore, nearly 41% of the allocation cost can be saved.     

Curvature and the elasticity of substitution: Straightening it out

This paper demonstrates that, contrary to deeply rooted beliefs, there is no link between curvature and the elasticity of substitution. We show that there is no direct, nor inverse, relationship between the two concepts, if curvature of the isoquant is defined either in its strict mathematical sense or as the isoquant's second derivative. We finally suggest to view the elasticity of substitution as an efficiency parameter.     

CHANGE DETECTION OF ORDERS IN STOCK MARKETS USING A GAUSSIAN MIXTURE MODEL

We propose a method for detecting changes in the order balance in stock markets by applying a stochastic model to the feature vectors extracted from the order‐book data of stocks. First, the data are divided into training and test periods. Next, a Gaussian mixture model is estimated from the feature vectors extracted from the order‐book data in the training period. Finally, the goodness of fit of the feature vectors in the test period over this model is calculated. Using the proposed method, we found that the order balances of stocks for which insider trading was reported were unusual. Copyright © 2014 John Wiley & Sons, Ltd.     

Probabilistic recalibration of forecasts

We present a scheme by which a probabilistic forecasting system whose predictions have a poor probabilistic calibration may be recalibrated through the incorporation of past performance information in order to produce a new forecasting system that is demonstrably superior to the original, inasmuch as one may use it to win wagers consistently against someone who is using the original system. The scheme utilizes Gaussian process (GP) modeling to estimate a probability distribution over the probability integral transform (PIT) of a scalar predictand. The GP density estimate gives closed-form access to information entropy measures that are associated with the estimated distribution, which allows the prediction of winnings in wagers against the base forecasting system. A separate consequence of the procedure is that the recalibrated forecast has a uniform expected PIT distribution. One distinguishing feature of the procedure is that it is appropriate even if the PIT values are not i.i.d. The recalibration scheme is formulated in a framework that exploits the deep connections among information theory, forecasting, and betting. We demonstrate the effectiveness of the scheme in two case studies: a laboratory experiment with a nonlinear circuit and seasonal forecasts of the intensity of the El Niño-Southern Oscillation phenomenon.     

A Computational Perspective on Projection Pursuit in High Dimensions: Feasible or Infeasible Feature Extraction

Finding a suitable representation of multivariate data is fundamental in many scientific disciplines. Projection pursuit ( $\text{PP}$) aims to extract interesting ‘non-Gaussian’ features from multivariate data, and tends to be computationally intensive even when applied to data of low dimension. In high-dimensional settings, a recent work (Bickel et al., 2018) on $\text{PP}$ addresses asymptotic characterization and conjectures of the feasible projections as the dimension grows with sample size. To gain practical utility of and learn theoretical insights into $\text{PP}$ in an integral way, data analytic tools needed to evaluate the behaviour of $\text{PP}$ in high dimensions become increasingly desirable but are less explored in the literature. This paper focuses on developing computationally fast and effective approaches central to finite sample studies for (i) visualizing the feasibility of $\text{PP}$ in extracting features from high-dimensional data, as compared with alternative methods like $\text{PCA}$ and $\text{ICA}$, and (ii) assessing the plausibility of $\text{PP}$ in cases where asymptotic studies are lacking or unavailable, with the goal of better understanding the practicality, limitation and challenge of $\text{PP}$ in the analysis of large data sets.     

结合多源特征与高斯过程模型的SAR图像目标识别

针对合成孔径雷达（Synthetic Aperture Radar，SAR）图像目标识别问题，提出结合多源特征和高斯过程模型的方法。分别利用主成分分析（Principal Component Analysis，PCA）、非负矩阵分解（Non-negative Matrix Factorization，NMF）以及单演信号提取SAR图像的特征矢量，并将它们串接为单一矢量。三类特征从不同角度描述SAR图像目标特性，从而为目标识别提供更为有效的信息。决策分类过程采用高斯过程模型进行多元分类，基于融合特征矢量获得概率意义上的最佳决策。实验中，采用MSTAR数据集设置3类目标、10类目标、型号差异以及俯仰角差异识别问题，结果验证了提出方法的优越性能。