2.9 段寄存器 我们前面讲到. 8086CPU 在访问内存时要由相关部件提供内存单元的段地址和偏移地 址，送入地址加法器合成物理地址。 这里，我们要看一下，是什么部件提供段地址。 段地 址在 8086CPU 的段寄存器中存放。 8086CPU 有 4 个段寄存器: CS , DS , SS 、 ES。当 8086CPU 要访问内存时由这 4 个段寄存器提供内存单元的段地址。 我们本课中只介绍 CS o 2.10 CS 和 IP CS 和 IP 是 8086CPU 中两个最关键的寄存器，它们指示了 CPU 当前要读取指令的地

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.