现代优化计算方法在蛋白质结构预测中的应用

现代优化计算方法在蛋白质结构预测中占有重要地位.简要地介绍了模拟退火算法,遗传算法,人工神经网络和图论算法在蛋白质结构预测中的应用.对国内外近年来应用这些算法,特别是在蛋白质构象搜索问题中,解决蛋白质结构预测的研究作了回顾,并分析、比较了这几种算法的效果和特点.     

Non-Parametric Regression Methods

The nature of the financial time series is complex, continuous interchange of stochastic and deterministic regimes. Therefore, it is difficult to forecast with parametric techniques. Instead of parametric models, we propose three techniques and compare with each other. Neural networks and support vector regression (SVR) are two universally approximators. They are data-driven non parametric models. ARCH/GARCH models are also investigated. Our assumption is that the future value of Istanbul Stock Exchange 100 index daily return depends on the financial indicators although there is no known parametric model to explain this relationship. This relationship comes from the technical analysis. Comparison shows that the multi layer perceptron networks overperform the SVR and time series model (GARCH).     

Quantum Dot and Antidot Infrared Photodetectors: Iterative Methods for Solving the Laplace Equation in Domains with Involved Geometry

We propose iteration methods for solving the Dirichlet problem in domains with involved geometry. Such problems arise in relation to the problem of optimizing quantum dot and antidot infrared detectors. We estimate the deviation of an approximate solution from the exact solution.     

Weak-mixing implies sensitive dependence

Let X be a metric space, B the σ-algebra of Borel subsets of X, and μ a probability measure on (X,B). In this note, for a measure-preserving map T (respectively a measure-preserving semi-flow φ) on (X,B,μ), we prove that if suppμ=X, and T (respectively φ) is weak-mixing, then T (respectively φ) has sensitive dependence.     

面积比较与上调和函数及其应用

本文研究了无穷远处具非负截曲率的完备非紧流形的几何和解析性质,并将 它应用于调和映照.     

Widrow-cellular neural network and optoelectronic implementation

A new type of optoelectronic cellular neural network has been developed by providing the capability of coefficients adjusment of cellular neural network (CNN) using Widrow based perceptron learning algorithm. The new supervised cellular neural network is called Widrow-CNN. Despite the unsupervised CNN, the proposed learning algorithm allows to use the Widrow-CNN for various image processing applications easily. Also, the capability of CNN for image processing and feature extraction has been improved using basic joint transform correlation architecture. This hardware application presents high speed processing capability compared to digital applications. The optoelectronic Widrow-CNN has been tested for classic CNN feature extraction problems. It yields the best results even in case of hard feature extraction problems such as diagonal line detection and vertical line determination.     

用数值方法分析微孔激光器出射光的近场特性

微孔激光器作为应用于近场光信息存储系统中的一种新型光源，它的出射光斑的近场特性对于近场光存储是十分重要的。针对纳米孔径运用角谱进行Fox-Li数值迭代，得到不同孔径微孔激光器的基模光强分布，然后运用二维非线性时域有限差分法分析微孔激光器出射端即微孔金属膜的近场光学性质，模拟计算了不同孔径和厚度的微孔金属膜的光强近场分布，从应用于近场光存储的角度，给出反映其近场光学特性的相关数据。发现由于TM模式下金属存在局域表面等离子增强效应，使得其出射强度比TE模式高一个数量级，从而更适于作为实际中近场光存储系统和原理试验的光学头。     

Benson真有效意义下集值优化的广义最优性条件

本文引入了关于集值映射的α-阶Clarke切导数、α-阶邻接切导数及α-阶 伴随切导数的概念,借此建立了约束向量集值优化Benson真有效解导数型的Kuhn- Tucker条件.     

强流束在二极磁铁中的非线性传输—Lie代数分析

 用Lie代数方法分析了带电粒子束在二极磁铁中的非线性传输，包含了空间电荷效应。粒子在相空间中的分布采用K V分布。轨迹分析的结果做到二级近似，根据需要，还可以扩展到三级或更高级近似。在分析中，将磁铁沿中心轨道分成若干小段。在每个小段上利用所得公式进行轨迹计算，得到自洽的解。然后重复此过程，作下一小段的计算，直到最后一个小段为止。     

Compact Kähler Surfaces with Trivial Canonical Bundle

The classical conjectures of Weil on K3 surfaces – that the set of suchsurfaces is connected; that a version of the Torelli theorem holds; thateach such surface is Kähler; and that the period map issurjective – are reconsidered in the light of a generalisation of theNakai–Moishezon criterion, and short proofs of all the conjectures aregiven. Most of the proofs apply equally or with minor variation tocomplex 2-tori, the only other compact Kähler surfaces with trivialcanonical bundle.