Gene Selection Through Sensitivity Analysis of Support Vector Machines
We present a novel approach to gene selection for microarry data through the sensitivity analysis of support vector machines (SVMs). A new measurement (sensitivity) is defined to quantify the saliencies of individual features (genes) by analyzing the discriminative function in SVMs. Our feature selection strategy is first to select the features with higher sensitivities but meanwhile keep the remaining ones, and then refine the selected subset by tentatively substituting some part with fragments of the previously rejected features. The accuracy of our method is validated experimentally on the benchmark microarray datasets.

title: Support Vector Clustering for Brain Activation Detection
abstract: In this paper, we propose a new approach to detect activated time series in functional MRI using support vector clustering (SVC). We extract Fourier coefficients as the features of fMRI time series and cluster these features by SVC. In SVC, these features are mapped from their original feature space to a very high dimensional kernel space. By finding a compact sphere that encloses the mapped features in the kernel space, one achieves a set of cluster boundaries in the feature space. The SVC is an effective and robust fMRI activation detection method because of its advantages in (1) better discovery of real data structure since there is no cluster shape restriction, (2) high quality detection results without explicitly specifying the number of clusters, (3) the stronger robustness due to the mechanism in outlier elimination. Experimental results on simulated and real fMRI data demonstrate the effectiveness of SVC.

title: Synchronization in Relaxation Oscillator Networks with Conduction Delays
abstract: We study locally coupled networks of relaxation oscillators with excitatory connections and conduction delays and propose a mechanism for achieving zero phase-lag synchrony. Our mechanism is based on the observation that different rates of motion along different nullclines of the system can lead to synchrony in the presence of conduction delays. We analyze the system of two coupled oscillators and derive phase compression rates. This analysis indicates how to choose nullclines for individual relaxation oscillators in order to induce rapid synchrony. The numerical simulations demonstrate that our analytical results extend to locally coupled networks with conduction delays and that these networks can attain rapid synchrony with appropriately chosen nullclines and initial conditions. The robustness of the proposed mechanism is verified with respect to different nullclines, variations in parameter values, and initial conditions.

title: SAT Based Predicate Abstraction for Hardware Verification
abstract: Predicate abstraction is an important technique for extracting compact finite state models from large or infinite state systems. Predicate abstraction uses decision procedures to compute a model which is amenable to model checking, and has been used successfully for software verification. Little work however has been done on applying predicate abstraction to large scale finite state systems, most notably, hardware, where the decision procedures are SAT solvers. We consider predicate abstraction for hardware in the framework of Counterexample-Guided Abstraction Refinement where in the course of verification, the abstract model has to be repeatedly refined. The goal of the refinement is to eliminate spurious behavior in the abstract model which is not present in the original model, and gives rise to false negatives (spurious counterexamples).

title: A neuro-fuzzy approach for diagnosis of antibody deficiency syndrome
abstract: This paper presents a neuro-fuzzy approach for diagnosis of antibody deficiency syndrome, where a new neuro-fuzzy network with fuzzy activation functions (FAFs) at hidden layer is used. The FAFs capturing some essential information on pattern distributions, can be adaptively constructed using training examples. To improve the generalization capability and reduce the model complexity, a heuristic method for feature selection is proposed by measuring the size of non-overlapped areas of the FAFs. The effectiveness of our proposed techniques is investigated by an immunology clinical data set collected from the University of California, Irvine (UCI) immunology laboratory.

title: E-Coli Promoter Recognition Using Neural Networks with Feature Selection
abstract: This paper investigates the effects on neural classification performance of biological data by features selection. Where the Relief-F and Symmetrical Tau feature selection algorithms were employed on a set of high level features of DNA and structural profiles. It was observed that even with a small percentage of the features used in neural classifiers, the recognition rate of E.coli promoters was not degraded significantly.

title: Computing Triangular Systems and Regular Systems
abstract: A previous algorithm of computing simple systems is modified and extended to compute triangular systems and regular systems from any given polynomial system. The resulting algorithms, based on the computation of subresultant regular subchains, have a simple structure and are efficient in practice. Preliminary experiments indicate that they perform at least as well as some of the known algorithms. Several properties about regular systems are also proved.