非局部群稀疏表示的图像去噪模型

针对图像的非局部稀疏表示忽略图像中结构相似信息的缺点,将群稀疏表示引入到图像的最优滤波中,提出了一种有效去除图像高斯噪声的非局部群稀疏表示模型。该模型首先选择图像非局部相似块构建相似矩阵,在群稀疏限制下对相似矩阵进行正交分解得到正交矩阵;在已知噪声服从高斯分布的情况下,再通过求得的正交矩阵结合贝叶斯最小均方误差准则实现对特征矩阵的最优估计;最后通过正交矩阵与特征矩阵重构去噪后的图像。实验对比证明,所提的非局部群稀疏表示的图像去噪模型在去除噪声的同时更好地保留了图像的结构信息,获得了更好的主客观评价指标,去噪的峰值信噪比提高1 dB以上。     

CONTINUOUS PREFERENCE ORDERINGS REPRESENTABLE BY UTILITY FUNCTIONS

This paper surveys the conditions under which it is possible to represent a continuous preference ordering using utility functions. We start with a historical perspective on the notions of utility and preferences, continue by defining the mathematical concepts employed in this literature, and then list several key contributions to the topic of representability. These contributions concern both the preference orderings and the spaces where they are defined. For any continuous preference ordering, we show the need for separability and the sufficiency of connectedness and separability, or second countability, of the space where it is defined. We emphasize the need for separability by showing that in any nonseparable metric space, there are continuous preference orderings without utility representation. However, by reinforcing connectedness, we show that countably boundedness of the preference ordering is a necessary and sufficient condition for the existence of a (continuous) utility representation. Finally, we discuss the special case of strictly monotonic preferences.     

Error analysis of finite difference and Markov chain approximations for option pricing

Mijatovi? and Pistorius proposed an efficient Markov chain approximation method for pricing European and barrier options in general one‐dimensional Markovian models. However, sharp convergence rates of this method for realistic financial payoffs, which are nonsmooth, are rarely available. In this paper, we solve this problem for general one‐dimensional diffusion models, which play a fundamental role in financial applications. For such models, the Markov chain approximation method is equivalent to the method of lines using the central difference. Our analysis is based on the spectral representation of the exact solution and the approximate solution. By establishing the convergence rate for the eigenvalues and the eigenfunctions, we obtain sharp convergence rates for the transition density and the price of options with nonsmooth payoffs. In particular, we show that for call‐/put‐type payoffs, convergence is second order, while for digital‐type payoffs, convergence is generally only first order. Furthermore, we provide theoretical justification for two well‐known smoothing techniques that can restore second‐order convergence for digital‐type payoffs and explain oscillations observed in the convergence for options with nonsmooth payoffs. As an extension, we also establish sharp convergence rates for European options for a rich class of Markovian jump models constructed from diffusions via subordination. The theoretical estimates are confirmed using numerical examples.     

Functional Itô calculus†

We extend some results of the Itô calculus to functionals of the current path of a process to reflect the fact that often the impact of randomness is cumulative and depends on the history of the process, not merely on its current value. We express the differential of the functional in terms of adequately defined partial derivatives to obtain an Itô formula. We develop an extension of the Feynman-Kac formula to the functional case and an explicit expression of the integrand in the Martingale Representation Theorem. We establish that under certain conditions, even path dependent options prices satisfy a partial differential equation in a local sense. We exploit this fact to find an expression of the price difference between two models and compute variational derivatives with respect to the volatility surface.     

The effect of diplomatic representation on trade: A panel data analysis

This study addresses current gaps in the empirical literature regarding the effect of diplomatic representation on trade using a panel data set for 100 countries with 5‐year interval data from 1985 to 2005 and four‐digit level industry data. The results indicate that the effect of diplomatic representation on exports in differentiated goods is positive and significant and larger than on exports in homogeneous goods on average, but not statistically different from it. Furthermore, diplomatic representation only increases trade along the extensive margin and not along the intensive margin. The results indicate that diplomatic representation is effective in performing its function as a network search intermediary and that it is a useful policy tool to alleviate market failure.     

Recovering Sociology from the Trash Heap: Of Waste Politics and the Spatialization of Local Representation in Lagos

This article excavates the debate that unfolded during the 1980s between Jacques Rancière and Pierre Bourdieu to shed light on the theoretical divide that still cuts across the field of urban studies today. Looking at contemporary Rancièrian scholarship through a Bourdieusian lens, it points to their main theoretical shortcomings and reasserts the value of relational, field‐based and empirically grounded approaches to urban politics. At the same time, this article engages seriously Rancière's critique of Bourdieu's failure to account for space in order to question the territoriality inherent to the notion of field. We put this theoretical discussion to the test of Lagos's garbage. Drawing on an ethnographic study conducted in Lagos from 2015 to 2016, we propose analyzing the process of spatialization of the field of local representation by looking at the ways solid waste—here conceived as a political opportunity—is mobilized by different actors. We argue that the deployment of a waste infrastructure in Lagos is congruent with a relative disinvestment in practices of territorial control that reshapes the structure of local representation, reconfiguring the ‘stage’ on which politics is played out in the Nigerian metropolis.     

基于信息辅助块稀疏贝叶斯学习的直扩通信窄带干扰抑制

现有基于Nyquist-Shannon采样定理的窄带干扰(Narrowband Interference,NBI)抑制方法存在应用受限于采样率较高的问题。应用压缩感知(Compressive Sensing,CS)理论解决上述问题，利用NBI在频域表现出的块稀疏特性以及直接序列扩频(Direct Sequence Spread Spectrum,DSSS)信号的类噪声特性，提出了基于块稀疏贝叶斯学习(Block Sparse Bayesian Learning,BSBL)框架的DSSS通信NBI抑制模型。实现干扰抑制后，利用传统的CS重构算法实现DSSS信号的压缩域解调。为进一步提高算法性能，将NBI稀疏分块的块内自相关矩阵建模为单位矩阵，提出了信息辅助BSBL(Aid BSBL,ABSBL)算法，设计了基于ABSBL的DSSS通信NBI抑制算法。该算法在保持较好NBI抑制性能的条件下，提高了运算效率并且不依赖NBI的稀疏结构。仿真验证和对比分析结果表明，所提方法能够有效抑制DSSS通信中的NBI，在干扰强度相同的条件下，NBI带宽越小、压缩率越大，算法对NBI的抑制性能越好。     

基于PSO-SAS的联合侦察任务规划

针对联合侦察筹划中任务规划阶段机动侦察平台阵位与路线确定困难的问题，提出了一种基于粒子群优化-稀疏A星(Particle Swarm Optimization—Sparse A-star,PSO-SAS)算法的规划方法。该方法综合考虑侦察装备机动性能以及敌火力威胁、地形等因素，在侦察阵位规划上，建立了阵位综合评估模型，并利用粒子群算法进行阵位寻优；在路线规划上，采用稀疏A*算法进行航迹规划，通过将机动性能、安全距离、路程等约束引入搜索过程，缩短最优路线的计算时间。仿真试验验证了所提方法生成的侦察阵位和路线能够满足侦察任务要求。     

基于云模型的动态物流过程知识表示

以提高物流过程管理实时优化调度的智能性和科学性为突破口,引入云模型和知识工程的知识表示理论,提出一种基于云模型的物流过程的知识表示法。通过云模型的期望值、熵和超熵三个数字特征有效地表达定性概念,并实现定性和定量的不确定转换,从而描述具有动态特点和一定非确定性的物流过程知识。