A Neural Model of Contour Integration in the Primary Visual Cortex
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: Reducing Energy Consumption of Disk Storage Using Power-Aware Cache Management
abstract: Reducing energy consumption is an important issue for data centers. Among the various components of a data center, storage is one of the biggest consumers of energy. Previous studies have shown that the average idle period for a server disk in a data center is very small compared to the time taken to spin down and spin up. This significantly limits the effectiveness of disk power management schemes. This paper proposes several power-aware storage cache management algorithms that provide more opportunities for the underlying disk power management schemes to save energy. More specifically, we present an off-line power-aware greedy algorithm that is more energy-efficient than Belady’s off-line algorithm (which minimizes cache misses only). We also propose an online power-aware cache replacement algorithm. Our trace-driven simulations show that, compared to LRU, our algorithm saves 16% more disk energy and provides 50% better average response time for OLTP I/O workloads. We have also investigated the effects of four storage cache write policies on disk energy consumption.

title: A theoretical foundation for program transformations to reduce cache thrashing due to true data sharing
abstract: Cache thrashing due to true data sharing can degrade the performance of parallel programs significantly. Our previous work showed that parallel task alignment via program transformations can be quite effective for the reduction of such cache thrashing. In this paper, we present a theoretical foundation for such program transformations. Based on linear algebra and the theory of numbers, our work analyzes the data dependences among the tasks created by a fork-join parallel program and determines at compile time how these tasks should be assigned to processors in order to reduce cache thrashing due to true data sharing. Our analysis and program transformations can be easily performed by compilers for parallel computers.

title: Reducing cache conflicts by partitioning and privatizing shared arrays
abstract: Parallelizing compilers for shared-memory multiprocessors typically generate fork-join programs in which multiple threads may access different sections of shared arrays. Each thread tends to access discontiguous addresses due to the gap between sections which belong to different arrays. Such interarray discontiguity often causes cache-set conflicts even when the private cache is large enough to hold the working set. A compiler technique, called array partition and privatization (or APP), is proposed to correct this problem. The APP technique partitions a shared array into sections, one for each computing thread, and then maps each section to the private stack of the corresponding thread. Interarray discontiguity is thus removed and the cache performance improved. This paper presents a compiler algorithm for APP and preliminary experimental results based on four benchmark programs running on an SGI Origin 2000 multiprocessor.

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: WDEE: Web Data Extraction by Example
abstract: Web data extraction systems in use today transform semi-structured Web documents and deliver structured documents to end users. Some systems provide a visual interface to users to generate the extraction rules. However, to end users, the visual effect of Web documents is lost during the transformation process. In this paper, we propose an approach that allows a user to query extracted documents without knowledge of formal query language. We bridge the gap between visual effect of Web documents and structured documents extracted by providing a QBE-like (Query by Example) interface named Wdee. The principle component of our method is the notion of a document schema. Document schemata are patterns of structures embedded in documents. Wdee generates tree skeletons based on schema information and a user may execute queries by input condition in the skeltons. By maintaining the mapping relation among schemata of Web documents and extracted documents, a visual example may be presented to end users. With the example, Wdee allows a user to construct tree skeletons in a manner that resembles the browsing of Web pages.