Top-Down Attention Control at Feature Space for Robust Pattern Recognition
In order to improve visual pattern recognition capability, this paper focuses on top-down selective attention at feature space. The baseline recognition system consists of local feature extractors and a multi-layer Perceptron (MLP) classifier. An attention layer is added just in front of the multi-layer Perceptron. Attention gains are adjusted to cope with the top-down attention process and ellucidate expected input features. After attention adaptation, the distance between original input features and expected features becomes an important measure for the confidence of the attended class. The proposed algorithms improves recognition accuracy for handwritten digit recognition tasks, and is capable of recognizing 2 superimposed patterns one by one.

title: ICA-based Clustering of Genes from Microarray Expression Data
abstract: We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model of genomic expression patterns, linear and nonlinear ICA finds components that are specific to certain biological processes. Genes that exhibit significant up-regulation or down-regulation within each component are grouped into clusters. We test the statistical significance of enrichment of gene annotations within each cluster. ICA-based clustering outperformed other leading methods in constructing functionally coherent clusters on various datasets. This result supports our model of genomic expression data as composite effect of independent biological processes. Comparison of clustering performance among various ICA algorithms including a kernel-based nonlinear ICA algorithm shows that nonlinear ICA performed the best for small datasets and natural-gradient maximization-likelihood worked well for all the datasets.

title: Feature Sensitive Out-of-Core Chartification of Large Polygonal Meshes
abstract: Mesh chartification is an important tool for processing meshes in various applications. In this paper, we present a novel feature sensitive mesh chartification technique that can handle huge meshes with limited main memory. Our technique adapts the mesh chartification approach using Lloyd-Max quantization to out-of-core processing. While the previous approach updates chartification globally at each iteration of Lloyd-Max quantization, we propose a local update algorithm where only a part of the chartification is processed at a time. By repeating the local updates, we can obtain a chartification of a huge mesh that cannot fit into the main memory. We verify the accuracy of the serialized local updates by comparing the results with the global update approach. We demonstrate that our technique can successfully process huge meshes for applications, such as mesh compression, shape approximation, and remeshing.

title: Requirements, Implementation and Applications of Hand-held Virtual Reality
abstract: While hand-held computing devices are capable of rendering advanced 3D graphics and processing of multimedia data, they are not designed to provide and induce sufficient sense of immersion and presence for virtual reality. In this paper, we propose minimal requirements for realizing VR on a hand-held device. Furthermore, based on the proposed requirements, we have designed and implemented a low cost hand-held VR platform by adding multimodal sensors and display components to a hand-held PC. The platform enables a motion based interface, an essential part of realizing VR on a small hand-held device, and provides outputs in three modalities, visual, aural and tactile/haptic for a reasonable sensory experience. We showcase our platform and demonstrate the possibilities of hand-hand VR through three VR applications: a typical virtual walkthrough, a 3D multimedia contents browser, and a motion based racing game. Index Terms—Virtual Reality, Computer interface human factors

title: Adaptive prefetching technique for shared virtual memory
abstract: Though shared virtual memory (SVM) systems promise low cost solutions for high performance computing, they suffer from long memory latencies. These latencies are usually caused by repetitive invalidation on shared data. Since shared data are accessed through synchronizations and the patterns by which threads synchronize are repetitive, a prefetching scheme based on such repetitiveness would reduce memory latencies. Based on this observation, we propose a prefetching technique which predicts future access behavior by analyzing access history per synchronization variable. Our technique was evaluated on an 8-node SVM system using the SPLASH-2 benchmark. The results show that our technique could achieve 34%-45% reduction in memory access latencies.

title: Out-of-Vocabulary Rejection based on Selective Attention Model
abstract: Based on a psychological selective attention theory a new algorithm is developed to provide reliable out-of-vocabulary (OOV) rejection for speech recognition systems in noisy environments. The developed attention model is based on Broadbent's ‘early filtering’ theory, and the attention adaptation process utilizes a gradient-descent error minimization algorithm with error backpropagation rule. The developed model is applied to isolated-word recognition tasks, and much higher in-vocabulary recognition rates are achieved with the same out-of-vocabulary rejection rates.

title: Contention free embedding of complete binary trees into 3D meshes in half and full-duplexed network models
abstract: We consider the problem of dimension-ordered embedding of complete binary trees into 3-dimensional meshes without link contention. In this paper, the embedding problems are studied under two different network models: half-duplexed networks and full-duplexed networks. Gibbons and Paterson showed that a complete binary tree T/sub p/ could be disjointedly embedded into a full-duplexed 2-dimensional mesh of optimum size without dimension-ordered routing. Using the dimension-ordered routing, the authors showed that T/sub p/ could be embedded into a 3-dimensional mesh of optimum size with link congestion two. This paper presents the dimension-ordered embedding algorithms of the complete binary trees into the wormhole routed 3-dimensional meshes without link contention achieving expansion of no larger than 1.125 of optimum in half-duplexed network model and optimum in full-duplexed network model.