Different Retinal Ganglion Cells have Different Functional Goals
In mammalian retina, the Y (or M) ganglion cells are significantly more transient in response, more selective to stimuli of low spatial and high temporal frequencies and less selective to spectral information than the X (or P) cells. It is shown that these differences in cell properties can be explained by a model that assigns different functional goals to the different ganglion cell types. In this model, the goal of the Y cells is to extract as fast as possible the minimum amount of information necessary for quick responses. In contrast, the goal of the X cells is to extract as much information as possible. Temporal characteristics of the information extraction by the two cell groups are also derived.

title: A Neural Model of Contour Integration in the Primary Visual Cortex
abstract: Experimental observations suggest that contour integration may take place in V1. However, there has yet to be a model of contour integration that uses only known V1 elements, operations, and connection patterns.This article introduces such a model, using orient ation selective cells, local cortical circuits, and horizontal intracortical connections. The model is composed of recurrently connected excitatory neurons and inhibitory interneurons, receiving visual input via oriented receptive fields resembling those found in primary visual cortex. Intracortical interactions modify initial activity patterns from input, selectively amplifying the activities of edges that form smooth contours in the image. The neural activities produced by such interactions are oscillatory and edge segments within a contour oscillate in synchrony. It is shown analytically and empirically that the extent of contour enhancement and neural synchrony increases with the smoothness, length, and closure of contours, as observed in experiments on some of these phenomena. In addition, the model incorporates a feedback mechanism that allows higher visual centers selectively to enhance or suppress sensitivities to given contours, effectively segmenting one from another. The model makes the testable prediction that the horizontal cortical connections are more likely to target excitatory (or inhibitory) cells when the two linked cells have their preferred orientation aligned with (or orthogonal to) their relative receptive field center displacements.

title: Computational Differences between Asymmetrical and Symmetrical Networks
abstract: Symmetrically connected recurrent networks have recently been used as models of a host of neural computations. However, biological neural networks have asymmetrical connections, at the very least because of the separation between excitatory and inhibitory neurons in the brain. We study characteristic differences between asymmetrical networks and their symmetrical counterparts in cases for which they act as selective amplifiers for particular classes of input patterns. We show that the dramatically different dynamical behaviours to which they have access, often make the asymmetrical networks computationally superior. We illustrate our results in networks that selectively amplify oriented bars and smooth contours in visual inputs.

title: Optimal skewed tiling for cache locality enhancement
abstract: Iterative stencil loops are used in scientific programs to implement relaxation methods for numerical simulation and signal processing. Such loops iteratively modify the same array elements over different time steps. Hence, exploitation of temporal data locality can lead to significantly improved cache performance. This paper shows that, to optimally tile iterative stencil loops, the imperfectly nested inner loops must be realigned such that they can be minimally skewed across different time steps. A memory-reference cost analysis proves that the number of cache misses is minimized when the skewing is minimum. A graph-theoretical algorithm, which takes polynomial time, is presented to determine the minimum skew factors for a given nesting of iterative stencil loops.

title: Fast video shot retrieval by trace geometry matching in principal component space
abstract: Content-based video retrieval technology holds the key to the efficient management and sharing of video content from different sources, in different scales, across different platforms, and shared over different communication channels. In this work we present a fast retrieval algorithm based on matching the geometry of video sequence traces in the principal component space. Techniques to address scale (spatial and temporal) issues, as well as, noise and other possible distortions, such as frame dropping, are discussed. Experimental results demonstrate the effectiveness of the proposed approach.

title: Haplotype reconstruction from SNP fragments by minimum error correction
abstract: Haplotype reconstruction based on aligned single nucleotide polymorphism (SNP) fragments is to infer a pair of haplotypes from localized polymorphism data gathered through short genome fragment assembly. An important computational model of this problem is the minimum error correction (MEC) model, which has been mentioned in several literatures. The model retrieves a pair of haplotypes by correcting minimum number of SNPs in given genome fragments coming from an individual's DNA.



In the first part of this paper, an exact algorithm for the MEC model is presented. Owing to the NP-hardness of the MEC model, we also design a genetic algorithm (GA). The designed GA is intended to solve large size problems and has very good performance. The strength and weakness of the MEC model are shown using experimental results on real data and simulation data. In the second part of this paper, to improve the MEC model for haplotype reconstruction, a new computational model is proposed, which simultaneously employs genotype information of an individual in the process of SNP correction, and is called MEC with genotype information (shortly, MEC/GI). Computational results on extensive datasets show that the new model has much higher accuracy in haplotype reconstruction than the pure MEC model.

title: A compiler scheme for reusing intermediate computation results
abstract: Recent research has shown that programs often exhibit value locality. Such locality occurs when a code segment, although executed repeatedly in the program, takes only a small number of different values as input and, naturally, generates a small number of different outputs. It is potentially beneficial to replace such a code segment by a table, which records the computation results for the previous inputs. When the program execution re-enters the code segment with a repeated input, its computation can be simplified to a table look-up. We discuss a compiler scheme to identify code segments, which are good candidates for computation reuse. We discuss the conditions under which the table look-up costs less than repeating the execution, and we perform profiling to identify candidates, which have many repeated inputs at run time. Compared to previous work, this scheme requires no special hardware support and is therefore particularly useful for resource constrained systems such as handheld computing devices. We implement our scheme and its supporting analyses in GCC. We experiment with several multimedia benchmarks and the GNU Go game by executing them on a handheld computing device. The results show the scheme to improve the performance and to reduce the energy consumption quite substantially for these programs.

title: Factoring systems of linear PDEs with finite-dimensional solution spaces
abstract: A D-finite system is a finite set of linear homogeneous partial differential equations in several independent and dependent variables, whose solution space is of finite dimension. Let L be a D-finite system with rational function coefficients. We present an algorithm for computing all hyperexponential solutions of L, and an algorithm for computing all D-finite systems whose coefficients are also rational functions, and whose solutions are contained in the solution space of L.