美国Mitaim S学者关于随机共振的基础研究，具有重要意义，IEEE收录文章。

IE598NH-lecture-17-Stochastic Approximation for MSP.pdfI

IE598NH-lecture-5-Two stage stochastic linear programming.pdfIE598NH-lecture-5-Two stage stochastic linear programming.pdf

We study nonzero-sum stochastic switching games. Two players compete for market dominance through controlling (via timing options) the discrete-state market regime . Switching decisions are driven by a continuous stochastic factor that modulates instantaneous revenue rates and switching costs. This generates a competitive feedback between the short-term fluctuations due to and the medium-term advantages based on . We construct threshold-type Feedback Nash Equilibria which characterize stationa

3rd Edition - Probability, Random Variables and Stochastic

随机微积分处理函数相对于随机性的变化率。 它已应用于金融数学领域。 当我们看股票价格的图表时，有很多不规则的价格波动。 模拟小波动的主要工具是布朗运动。 本文将主要介绍布朗运动的概念、随机游走的概念，以及随机微积分的主要风味，伊藤微积分。 我还将讨论各种应用，尤其是在金融领域。

Third Edition Sung Nok Chiu Department of Mathematics, Hong Kong Baptist University, Hong Kong Dietrich Stoyan Institut f¨ur Stochastik, TU Bergakademie Freiberg, Germany Wilfrid S. Kendall Department of Statistics, University of Warwick, UK Joseph Mecke Institut f¨ur Stochastik Friedrich-Schiller-Universit¨at Jena, Germany

This exploratory paper quests for a stochastic and context sensitive grammar of images. The grammar should achieve the following four objectives and thus serves as a unified framework of representation, learning, and recognition for a large number of object categories. (i) The grammar represents both the hierarchical decompositions from scenes, to objects, parts, primitives and pixels by terminal and non-terminal nodes and the contexts for spatial and functional relations by horizontal links between the nodes. It formulates each object category as the set of all possible valid configurations produced by the grammar. (ii) The grammar is embodied in a simple And–Or graph representation where each Or-node points to alternative sub-configurations and an And-node is decomposed into a number of components. This representation supports recursive top-down/bottom-up procedures for image parsing under the Bayesian framework and make it convenient to scale up in complexity. Given an input image, the image parsing task constructs a most probable parse graph on-the-fly as the output interpretation and this parse graph is a subgraph of the And–Or graph after * Song-Chun Zhu is also affiliated with the Lotus Hill Research Institute, China. making choice on the Or-nodes. (iii) A probabilistic model is defined on this And–Or graph representation to account for the natural occurrence frequency of objects and parts as well as their relations. This model is learned from a relatively small training set per category and then sampled to synthesize a large number of configurations to cover novel object instances in the test set. This generalization capability is mostly missing in discriminative machine learning methods and can largely improve recognition performance in experiments. (iv) To fill the well-known semantic gap between symbols and raw signals, the grammar includes a series of visual dictionaries and organizes them through graph composition. At the bottom-level the dictionary is a set of image primitives each having a number of anchor points with open bonds to link with other primitives. These primitives can be combined to form larger and larger graph structures for parts and objects. The ambiguities in inferring local primitives shall be resolved through top-down computation using larger structures. Finally these primitives forms a primal sketch representation which will generate the input image with every pixels explained. The proposal grammar integrates three prominent representations in the literature: stochastic grammars for composition, Markov (or graphical) models for contexts, and sparse coding with primitives (wavelets). It also combines the structure-based and appearance based methods in the vision literature. Finally the paper presents three case studies to illustrate the proposed grammar.

MARTIN HAENGGI is a Professor of Electrical Engineering, and a Concurrent Professor of Applied and Computational Mathematics and Statistics, at the University of Notre Dame, Indiana.

动态随机共振（DSR）是一种用于增强暗和低对比度图像的独特技术。 噪声对于基于DSR的图像增强来说是必需的，并且噪声水平会与亮度同时增大，这会大大降低增强图像的感知质量，并且还会增加后续降噪的难度，因为去除高水平的噪声通常会导致严重的噪声损失。图片细节。 本文提出在增强过程中逐步消除噪声，而不是在增强过程完成后消除噪声。我们首先在变分框架中重写了基于传统偏微分方程（PDE）的DSR模型，然后提出一种用于图像增强的新颖的总变化正则化（TV）DSR方法。 从理论上证明了TV正则化DSR模型解的存在性和唯一性。 此外，我们分别在变体框架和PDE框架中推广了电视正则化DSR模型，因此我们可以将更多现有的去噪方法纳入我们的方法中。 数值比较表明，所提出的技术在对比度和亮度增强以及噪声抑制方面具有显着的性能，因此可以获得具有良好感知质量的增强图像。