The exponentiated convex variable step-size (ECVSS) algorithm

For some time there is a large interest in variable step-size methods for adaptive filtering. Recently, a few stochastic gradient algorithms have been proposed, which are based on cost functions that have exponential dependence on the chosen error. However, we have experienced that the cost function based on exponential of the squared error does not always satisfactorily converge. In this paper we modify this cost function in order to improve the convergence of exponentiated cost function and the novel ECVSS (exponentiated convex variable step-size) stochastic gradient algorithm is obtained. The proposed technique has attractive properties in both stationary and abrupt-change situations. (C) 2010 Elsevier B.V. All rights reserved.

Homodimerization Is Essential for the Receptor for Advanced Glycation End Products (RAGE)-mediated Signal Transduction

The receptor for advanced glycation end products (RAGE) is a pattern-recognition receptor that binds to diverse ligands and initiates a downstream proinflammatory signaling cascade. RAGE activation has been linked to diabetic complications, Alzheimer disease, infections, and cancers. RAGE is known to mediate cell signaling and downstream proinflammatory gene transcription activation, although the precise mechanism surrounding receptor-ligand interactions is still being elucidated. Recent fluorescence resonance energy transfer evidence indicates that RAGE may form oligomers on the cell surface and that this could be related to signal transduction. To investigate whether RAGE forms oligomers, protein-protein interaction assays were carried out. Here, we demonstrate the interaction between RAGE molecules via their N-terminal V domain, which is an important region involved in ligand recognition. By protein cross-linking using water-soluble and membrane-impermeable cross-linker bis(sulfosuccinimidyl) suberate and nondenaturing gels, we show that RAGE forms homodimers at the plasma membrane, a process potentiated by S100B and advanced glycation end products. Soluble RAGE, the RAGE inhibitor, is also capable of binding to RAGE, similar to V peptide, as shown by surface plasmon resonance. Incubation of cells with soluble RAGE or RAGE V domain peptide inhibits RAGE dimerization, subsequent phosphorylation of intracellular MAPK proteins, and activation of NF-kappa B pathways. Thus, the data indicate that dimerization of RAGE represents an important component of RAGE-mediated cell signaling.

Image segmentation based on multi-scan constraint satisfaction neural network

A novel image segmentation method based on a constraint satisfaction neural network (CSNN) is presented. The new method uses CSNN-based relaxation but with a modified scanning scheme of the image. The pixels are visited with more distant intervals and wider neighborhoods in the first level of the algorithm. The intervals between pixels and their neighborhoods are reduced in the following stages of the algorithm. This method contributes to the formation of more regular segments rapidly and consistently. A cluster validity index to determine the number of segments is also added to complete the proposed method into a fully automatic unsupervised segmentation scheme. The results are compared quantitatively by means of a novel segmentation evaluation criterion. The results are promising.

Predictions of large events on a spring-block model

We study the predictability of a theoretical model for earthquakes, using a pattern recognition algorithm similar to the CN and M8 algorithms known in seismology. The model, which is a stochastic spring-block model with both global correlation and local interaction, becomes more predictable as the strength of the global correlation or the local interaction is increased.

Improved conjugate gradient implementation for least squares support vector machines

As a promising method for pattern recognition and function estimation, least squares support vector machines (LS-SVM) express the training in terms of solving a linear system instead of a quadratic programming problem as for conventional support vector machines (SVM). In this paper, by using the information provided by the equality constraint, we transform the minimization problem with a single equality constraint in LS-SVM into an unconstrained minimization problem, then propose reduced formulations for LS-SVM. By introducing this transformation, the times of using conjugate gradient (CG) method, which is a greatly time-consuming step in obtaining the numerical solution, are reduced to one instead of two as proposed by Suykens et al. (1999). The comparison on computational speed of our method with the CG method proposed by Suykens et al. and the first order and second order SMO methods on several benchmark data sets shows a reduction of training time by up to 44%. (C) 2011 Elsevier B.V. All rights reserved.