An inequality connecting entropy distance,Fisher Information and large deviations

In this paper we introduce a new generalisation of the relative Fisher Information for Markov jump processes on a finite or countable state space, and prove an inequality which connects this object with the relative entropy and a large deviation rate functional. In addition to possessing various favourable properties, we show that this generalised Fisher Information converges to the classical Fisher Information in an appropriate limit. We then use this generalised Fisher Information and the aforementioned inequality to qualitatively study coarse-graining problems for jump processes on discrete spaces.     

Linearization of column responses by correlation chromatography. Separation improvement and reduction of undesired adsorption effects

Summary Experiments and simulations prove that correlation chromatography can greatly reduce the disadvantage of a non-linear response of the chromatographic column. A factor that has been accepted as being an important source of error in correlation or multiplex chromatography, has been shown not to be. Separations affected, improve dramatically when correlation chromatography is used, and a substantial amount of correlation noise only arises when there is a large difference in separation between a conventional chromatogram and a correlogram.A model has been developed for simulating these nonlinearities. It is shown that, especially for correlation chromatography, the simulation results match the practical measurements very well.     

Using activity focus networks to pressure terrorist organizations

The predominant idea for using network concepts to fight terrorists centers on disabling key parts of their communication networks. Although this counternetwork strategy is clearly a sound approach, it is vulnerable to missing, incomplete, or erroneous information about the network. This paper describes a different and complementary application of network concepts to terrorist organizations. It is based on activity focus networks (AFNs), which represent the complex activity system of an organization. An activity focus is a conceptual or physical entity around which joint activity is organized. Any organization has a number of these, which are in some cases compatible and in some cases incompatible. The set of foci and their relations of compatibility and incompatibility define the AFN. A hypothetical AFN for a terrorist organization is specified and tested in a simulation called AQAS. It shows that certain activity foci, and in particular one combination, have high potential as pressure points for the activity system. The AFN approach complements the counternetwork approach by reducing the downside risk of incomplete information about the communication network, and enhancing the effectiveness of counternetwork approaches over time. Steven R. Corman is Professor in the Hugh Downs School of Human Communication at Arizona State University and Chair of the Organizational Communication Division of the International Communication Association. His research interests include communication networks and activity systems, high-resolution text and discourse analysis, and modeling and simulation of human communication systems.     

Large deviation for the empirical eigenvalue density of truncated Haar unitary matrices

Let U_m be an m×m Haar unitary matrix and U_[m,n] be its n×n truncation. In this paper the large deviation is proven for the empirical eigenvalue density of U_[m,n] as m/n→λ and n→∞. The rate function and the limit distribution are given explicitly. U_[m,n] is the random matrix model of quq, where u is a Haar unitary in a finite von Neumann algebra, q is a certain projection and they are free. The limit distribution coincides with the Brown measure of the operator quq.     

大焦深成像系统仿真实验研究

如何增大非相干光学成像系统的焦深是应用光学研究领域的热点问题.本文对采用高次非球面光学掩模板与图像处理相结合增大成像光学系统焦深的新方法进行了深入分析,建立了大焦深成像系统仿真实验模型,并进行了大焦深成像系统仿真实验.实验结果证明了该方法在维持原相对孔径的同时使光学系统在较大的离焦范围内有好的成像质量.实际应用中还要综合考虑模板参量、信噪比等关键因素.     

Decentralized iterative learning control for a class of large scale interconnected dynamical systems

The problem of decentralized iterative learning control for a class of large scale interconnected dynamical systems is considered. In this paper, it is assumed that the considered large scale dynamical systems are linear time-varying, and the interconnections between each subsystem are unknown. For such a class of uncertain large scale interconnected dynamical systems, a method is presented whereby a class of decentralized local iterative learning control schemes is constructed. It is also shown that under some given conditions, the constructed decentralized local iterative learning controllers can guarantee the asymptotic convergence of the local output error between the given desired local output and the actual local output of each subsystem through the iterative learning process. Finally, as a numerical example, the system coupled by two inverted pendulums is given to illustrate the application of the proposed decentralized iterative learning control schemes.     

An optimal design of collateralized mortgage obligation with PAC-companion structure using dynamic cash reserve

This paper presents a model for optimally designing a collateralized mortgage obligation (CMO) with a planned amortization class (PAC)-companion structure using dynamic cash reserve. In this structure, the mortgage pool’s cash flow is allocated by rule to the two bond classes such that PAC bondholders receive substantial prepayment protection, that protection being provided by the companion bondholders. The structure we propose provides greater protection to the PAC bondholders than current structures during periods of rising interest rates when this class of bondholders faces greater extension risk. We do so by allowing a portion of the cash flow from the collateral to be reserved to meet the PAC’s scheduled cash flow in subsequent periods. The greater protection is provided by the companion bondholders exposure to interest loss. To tackle this problem, we transform the problem of designing the optimal PAC-companion structure into a standard stochastic linear programming problem which can be solved efficiently. Moreover, we present an extended model by considering the quality of the companion bond and by relaxing the PAC bondholder shortfall constraint. Based on numerical experiments through Monte Carlo simulation, we show the utility of the proposed model.     

Internal Rotation of the Methyl Group in Cations of Toluene Derivatives

The potential of the internal rotation of the methyl group was determined for o-, m-, and p-fluorotoluene cations by pulsed field ionization spectroscopy. The potential of the internal rotational motion was also surveyed for other toluene derivative cations. It was found that the barrier height generally increases by ionization. The increase in the barrier height has been discussed in connection with the reduction of the internal rotational constant B by ionization. The geometrical distortion of the methyl group during the internal rotation has been suggested.     

Anisotropic elastodynamics in a half space: An analytic method for polynomial data

An initial boundary value problem for the dynamic system of anisotropic elasticity in a half space is studied in the paper. A novel method of finding an exact solution of this problem for a special polynomial form of initial data and inhomogeneous term of the system is described. On the base of this method the simulation of elastic waves in different homogeneous anisotropic half spaces is implemented.