Influence of the Graphical Levels of Detail of a Virtual Thrower on the Perception of the Movement

Virtual reality has a number of advantages for analyzing sports interactions such as the standardization of experimental conditions, stereoscopic vision, and complete control of animated humanoid movement. Nevertheless, in order to be useful for sports applications, accurate perception of simulated movement in the virtual sports environment is essential. This perception depends on parameters of the synthetic character such as the number of degrees of freedom of its skeleton or the levels of detail (LOD) of its graphical representation. This study focuses on the influence of this latter parameter on the perception of the movement. In order to evaluate it, this study analyzes the judgments of immersed handball goalkeepers that play against a graphically modified virtual thrower. Five graphical representations of the throwing action were defined: a textured reference level (L0), a nontextured level (L1), a wire-frame level (L2), a moving point light display (MLD) level with a normal-sized ball (L3), and a MLD level where the ball is represented by a point of light (L4). The results show that judgments made by goalkeepers in the L4 condition are significantly less accurate than in all the other conditions (p

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.