Hybrid Monte Carlo Methods for Matrix Computation
In this paper we consider hybrid (fast stochastic approximation and deterministic refinement) algorithms for Matrix Inversion (MI) and Solving Systems of Linear Equations (SLAE). Monte Carlo methods are used for the stochastic approximation, since it is known that they are very efficient in finding a quick rough approximation of the element or a row of the inverse matrix or finding a component of the solution vector. We show how the stochastic approximation of the MI can be combined with a deterministic refinement procedure to obtain MI with the required precision and further solve the SLAE using MI. We employ a splitting A = D - C of a given non-singular matrix A, where D is a diagonal dominant matrix and matrix C is a diagonal matrix. In our algorithm for solving SLAE and MI different choices of D can be considered in order to control the norm of matrix T = D-1C, of the resulting SLAE and to minimize the number of the Markov Chains required to reach given precision. Experimental results with dense and sparse matrices are presented.

title: Numerical Experiments with Monte Carlo Methods and SPAI Preconditioner for Solving System of Linear Equations
abstract: In this paper we present the results of experiments comparing the performance of the mixed Monte Carlo algorithms and SPAI preconditener with BICGSTAB. The experiments are carried out on a Silicon Graphics ONYX2 machine. Based on our experiments, we conclude that these techniques are comparable from the point of view of robustness and rates of convergence, with the Monte Carlo approach performing better for some general cases and SPAI approach performing better in case of very sparse matrices.

title: A weight based compact genetic algorithm
abstract: In order to improve the performance of the compact Genetic Algorithm (cGA) to solve difficult optimization problems, an improved cGA which named as the weight based compact Genetic Algorithm (wcGA) is proposed. In the wcGA, S individuals are generated from the probability vector in each generation, when the winner competing with the other S-1 individuals to update the probability vector, different weights are multiplied to each solution according to the sequence of the solution ranked in the S-1 individuals. Experimental results on three kinds of Benchmark functions show that the proposed algorithm has higher optimal precision than that of the standard cGA and the cGA simulating higher selection pressures.

title: A Novel Worm Detection Model Based on Host Packet Behavior Ranking
abstract: Traditional behavior-based worm detection can't eliminate the influence of the worm-like P2P traffic effectively, as well as detect slow worms. To try to address these problems, this paper first presents a user habit model to describe the factors which influent the generation of network traffic, then a design of HPBRWD (Host Packet Behavior Ranking Based Worm detection) and some key issues about it are introduced. This paper has three contributions to the worm detection: 1) presenting a hierarchical user habit model; 2) using normal software and time profile to eliminate the worm-like P2P traffic and accelerate the detection of worms; 3) presenting HPBRWD to effectively detect worms. Experiments results show that HPBRWD is effective to detect worms.

title: Semi-Data-Aided Nonlinear Carrier Recovery for BPSK Burst-Mode Transmissions
abstract: This paper focuses on carrier frequency recovery for BPSK burst transmissions over AWGN channel. Semi-data- aided (SDA) nonlinear frequency estimation algorithms based on maximum-likelihood (ML) principle are derived. According to the algorithms, fewer pilot symbols and some unknown data symbols can be used together for frequency estimation in order that both estimation accuracy and spectral efficiency can be satisfied simultaneously. Simulations are employed to assess the performance of the algorithms. The corresponding Cramer-Rao bounds are discussed. The theoretical analysis of the threshold effect of one estimator is also presented and compared with simulation results.

title: Theoretical study on the OH + CH3NHC(O)OCH3 reaction
abstract: The multiple-channel reactions OH + CH3NHC(O)OCH3 --> products are investigated by direct dynamics method. The optimized geometries, frequencies, and minimum energy path are all obtained at the MP2/6-311+G(d,p) level, and energetic information is further refined by the BMC-CCSD (single-point) method. The rate constants for every reaction channels, R1, R2, R3, and R4, are calculated by canonical variational transition state theory with small-curvature tunneling correction over the temperature range 200-1000 K. The total rate constants are in good agreement with the available experimental data and the two-parameter expression k(T) = 3.95 x 10(-12) exp(15.41/T) cm3 molecule(-1) s(-1) over the temperature range 200-1000 K is given. Our calculations indicate that hydrogen abstraction channels R1 and R2 are the major channels due to the smaller barrier height among four channels considered, and the other two channels to yield CH3NC(O)OCH3 + H2O and CH3NHC(O)(OH)OCH3 + H2O are minor channels over the whole temperature range.

title: Multi-Space-Mapped SVMs for Multi-class Classification
abstract: In SVMs-based multiple classification, it is not always possible to find an appropriate kernel function to map all the classes from different distribution functions into a feature space where they are linearly separable from each other. This is even worse if the number of classes is very large. As a result, the classification accuracy is not as good as expected. In order to improve the performance of SVMs-based multi-classifiers, this paper proposes a method, named multi-space-mapped SVMs, to map the classes into different feature spaces and then classify them. The proposed method reduces the requirements for the kernel function. Substantial experiments have been conducted on one-against-all, one-against-one, FSVM, DDAG algorithms and our algorithm using six UCI data sets. The statistical results show that the proposed method has a higher probability of finding appropriate kernel functions than traditional methods and outperforms others.