Asymptotic behavior of stochastic Cohen-Grossberg neural networks with variable delays
Using Chebyshev inequality and nonnegative semi-martingale convergence theorem, the paper investigates asymptotic behavior of stochastic Cohen-Grossberg neural networks with delay by constructing suitable Lyapunov functional. Algebraic criteria are given for stochastic ultimate bounded and almost exponential stability. The result in the paper extend the main conclusion. In the end, examples are given to verify the effective of our results.

title: A Modified Projection Neural Network for Linear Variational Inequalities and Quadratic Optimization Problems
abstract: Variational inequalities provide us with a tool to study a wide class of optimization arising in pure and applied sciences. In the paper,we present a neural network for solving linear variational inequalities and quadratic optimization by using a projection techniques. We also consider the global uniqueness of the solution of the neural network as well as the convergence of the modified projection neural network. Our results present a significant improvement of previously known projection methods for solving variational inequalities and related optimization problems. Two simulation examples are provided to show the effectiveness of the approach and applicability of the proposed criteria.

title: Stochastic Lotka-Volterra Competitive Systems with Variable Delay
abstract: In this paper we reveal that the environmental noise will not only suppress a potential population explosion in the stochastic Lotka-Volterra competitive systems with variable delay, but also make the solutions to be stochastically ultimately bounded. To reveal these interesting facts, we stochastically perturb the Lotka-Volterra competitive systems with variable delay ẋ(t)=diag(x1(t),...,xn(t))[b+Ax(t−δ(t))] into the Ito form dx(t)=diag(x1(t),...,xn(t))[b+Ax(t−δ(t))]dt+σx(t)dw(t) and show that although the solution to the original delay systems may explode to infinity in a finite time, with probability one that of the associated stochastic delay systems do not. We also show that the stochastic systems will be stochastically ultimately bounded without any additional conditions on the matrix A.

title: Global Exponential Stability of Cohen-Grossberg Neural Networks with Time-Varying Delays and Continuously Distributed Delays
abstract: In this paper, we investigate the global exponential stability for a class of Cohen-Grossberg neural networks (CGNN) with time-varying delays and continuously distributed delays. CGNN herein is a general neural networks model which includes some well-known neural networks as its special cases. Firstly, applying the homeomorphism theory, we establish the new sufficient condition of existence and uniqueness of the equilibrium point to CGNN. Then, the sufficient criteria of global exponential stability of CGNN, which are easy to verify, are given by M-matrix. Our results imply and generalize some existed ones in previous literature. Furthermore, it is convenient to estimate the exponential convergence rates of the neural networks by using the criteria. Compared with the previous methods, our method does not resort to any Lyapunov functions or functionals. Finally, a example is given to illustrate the effective of our results.

title: An LMI-Based Approach to the Global Stability of Bidirectional Associative Memory Neural Networks with Variable Delay
abstract: Based on the linear matrix inequality (LMI), new sufficient conditions on the global exponential stability and asymptotic stability of bidirectional associative memory neural networks with variable delay are presented, and exponential converging velocity index is estimated. Furthermore, the results in this paper are less conservative than the ones reported so far in the literature. One example is given to illustrate the feasibility of our main results.

title: A Generalized LMI-Based Approach to the Global Exponential Stability of Recurrent Neural Networks with Delay
abstract: A new theoretical result on the global exponential stability of recurrent neural networks with delay is presented. It should be noted that the activation functions of recurrent neural network do not require to be bounded. The presented criterion, which has the attractive feature of possessing the structure of linear matrix inequality (LMI), is a generalization and improvement over some previous criteria. A example is given to illustrate our results.

title: Feature Mining Paradigms for Scientific Data
abstract: Numerical simulation is replacing experimentation as a means to gain insight into complex physical phenomena. Analyzing the data produced by such simulations is extremely challenging, given the enormous sizes of the datasets involved. In order to make efficient progress, analyzing such data must advance from current techniques that only visualize static images of the data, to novel techniques that can mine, track, and visualize the important features in the data. In this paper, we present our research on a unified framework that addresses this critical challenge in two science domains: computational fluid dynamics and molecular dynamics. We offer a systematic approach to detect the significant features in both domains, characterize and track them, and formulate hypotheses with regard to their complex evolution. Our framework includes two paradigms for feature mining, and the choice of one over the other, for a given application, can be determined based on local or global influence of relevant features in the data.

title: Convergence Studies on Iterative Algorithms for Image Reconstruction
abstract: We introduce a general iterative scheme for image reconstruction based on Landweber's method. In our configuration, a sequential block-iterative (SeqBI) version can be readily formulated from a simultaneous block-iterative (SimBI) version, and vice versa. This provides a mechanism to derive new algorithms from known ones. It is shown that some widely used iterative algorithms, such as the algebraic reconstruction technique (ART), simultaneous ART (SART), Cimmino's, and the recently designed diagonal weighting and component averaging algorithms, are special examples of the general scheme. We prove convergence of the general scheme under conditions more general than assumed in earlier studies, for its SeqBI and SimBI versions in the consistent and inconsistent cases, respectively. Our results suggest automatic relaxation strategies for the SeqBI and SimBI versions and characterize the dependence of the limit image on the initial guess. It is found that in all cases the limit is the sum of the minimum norm solution of a weighted least-squares problem and an oblique projection of the initial image onto the null space of the system matrix.

title: Cone-beam reconstruction for Micro-CT
abstract: X-ray micro-CT has been a focus in the CT/Micro-CT Lab, the University of Iowa. In this paper, some recent progress we made on cone-beam micro-CT is described. First, we address approximation reconstruction using Feldkamp-type algorithms with multiple X-ray sources and displaced detector configurations. Then, we explain three types of artifacts associated with exact reconstruction in the Grangeat framework. Finally, we report the convergence of a generalized block-iterative Landweber scheme. Representative images from simulation and experiments are also given.

title: Quantitative steganalysis of binary images
abstract: We propose a quantitative steganalysis method to detect hidden information embedded by flipping pixels along boundaries in binary images. We model steganographic embedding as an additive noise process and use compression rate as a distinguishing statistic that aids in discriminating between stego-images and cover-images. We specifically use the JBIG 2 binary image compression algorithm to derive a quantitative relation between compression rate and embedding rate. Based on this relationship, a practical steganalysis technique is proposed by examining the change of compression rate as embedding rate increases. Experiments conducted show that the proposed technique can reliably detect a steganographic embedding process that flips boundary pixels. Furthermore, it can estimate embedding rate with reasonable accuracy.

title: An Optimized Two-Step Solution for Updating XML Views
abstract: View updating is a long standing difficult problem. Given a view defined over base data sources and a view update, there are several different updates over the base data sources, called translations, that perform the update. A translation is said to be correct if it performs the update and at the same time does not update any portion of the view not specified in the update (no view side-effects). The view update problem finds a correct translation for a given view update if one exists. In the relational scenario, previous research has attempted to study the view update problem either by utilizing only the schema knowledge, or by directly examining the base data. While utilizing only the schema knowledge is very efficient, we are not guaranteed to find a correct translation even if one exists. On the other hand, examining the base data is guaranteed to find a correct translation if one exists, but is very time-consuming. The view update problem is even more complex in the XML context due to the nested hierarchical structure of XML and the restructuring capabilities of the XQUERY view specification. In this paper we propose a schema-centric framework, named HUX, for efficiently updating XML views specified over relational databases. HUX is complete (always finds a correct translation if one exists) and is efficient. The efficiency of HUX is achieved as follows. Given a view update, HUX first exploits the schema to determine whether there will never be a correct translation, or there will always be a correct translation. Only if the update cannot be classified using the schema, HUX will examine the base data to determine if there is a correct translation. This data-level checking is further optimized in HUX, by exploiting the schema knowledge extracted in the first step to significantly prune the space of potential translations that is explored. Experiments illustrate the performance benefits of HUX over previous solutions.