A grouped P2P network for scalable grid information services

Information services play a crucial role in grid environments in that the state information can be used to facilitate the discovery of resources and the services available to meet user requirements, and also to help tune the performance of a grid system. However, the large size and dynamic nature of the grid brings forth a number of challenges for information services. This paper presents PIndex, a grouped peer-to-peer network that can be used for scalable grid information services. PIndex builds on Globus MDS4, but introduces peer groups to dynamically split the large grid information search space into many small sections to enhance its scalability and resilience. PIndex is subsequently modeled with Colored Petri Nets for performance evaluation. The simulation results show that PIndex is scalable and resilient in dealing with a large number of peer nodes.

Design of highly parallel architecture with Alpha and Handel

We propose a bridge between two important parallel programming paradigms: data parallelism and communicating sequential processes (CSP). Data parallel pipelined architectures obtained with the Alpha language can be embedded in a control intensive application expressed in CSP-based Handel formalism. The interface is formally defined from the semantics of the languages Alpha and Handel. This work will ease the design of compute intensive applications on FPGAs.

SARS coronavirus unique domain: three-domain molecular architecture in solution and RNA binding

Nonstructural protein 3 of the severe acute respiratory syndrome (SARS) coronavirus includes a "SARS-unique domain" (SUD) consisting of three globular domains separated by short linker peptide segments. This work reports NMR structure determinations of the C-terminal domain (SUD-C) and a two-domain construct (SUD-MC) containing the middle domain (SUD-M) and the C-terminal domain, and NMR data on the conformational states of the N-terminal domain (SUD-N) and the SUD-NM two-domain construct. Both SUD-N and SUD-NM are monomeric and globular in solution; in SUD-NM, there is high mobility in the two-residue interdomain linking sequence, with no preferred relative orientation of the two domains. SUD-C adopts a frataxin like fold and has structural similarity to DNA-binding domains of DNA-modifying enzymes. The structures of both SUD-M (previously determined) and SUD-C (from the present study) are maintained in SUD-MC, where the two domains are flexibly linked. Gel-shift experiments showed that both SUD-C and SUD-MC bind to single-stranded RNA and recognize purine bases more strongly than pyrimidine bases, whereby SUD-MC binds to a more restricted set of purine-containing RNA sequences than SUD-M. NMR chemical shift perturbation experiments with observations of (15)N-labeled proteins further resulted in delineation of RNA binding sites (i.e., in SUD-M, a positively charged surface area with a pronounced cavity, and in SUD-C, several residues of an anti-parallel beta-sheet). Overall, the present data provide evidence for molecular mechanisms involving the concerted actions of SUD-M and SUD-C, which result in specific RNA binding that might be unique to the SUD and, thus, to the SARS coronavirus.

Space partitioning for scalable K-means

K-Means is a popular clustering algorithm which adopts an iterative refinement procedure to determine data partitions and to compute their associated centres of mass, called centroids. The straightforward implementation of the algorithm is often referred to as `brute force' since it computes a proximity measure from each data point to each centroid at every iteration of the K-Means process. Efficient implementations of the K-Means algorithm have been predominantly based on multi-dimensional binary search trees (KD-Trees). A combination of an efficient data structure and geometrical constraints allow to reduce the number of distance computations required at each iteration. In this work we present a general space partitioning approach for improving the efficiency and the scalability of the K-Means algorithm. We propose to adopt approximate hierarchical clustering methods to generate binary space partitioning trees in contrast to KD-Trees. In the experimental analysis, we have tested the performance of the proposed Binary Space Partitioning K-Means (BSP-KM) when a divisive clustering algorithm is used. We have carried out extensive experimental tests to compare the proposed approach to the one based on KD-Trees (KD-KM) in a wide range of the parameters space. BSP-KM is more scalable than KDKM, while keeping the deterministic nature of the `brute force' algorithm. In particular, the proposed space partitioning approach has shown to overcome the well-known limitation of KD-Trees in high-dimensional spaces and can also be adopted to improve the efficiency of other algorithms in which KD-Trees have been used.

Queue-based agent architecture for multimodal interfaces

This paper presents a queue-based agent architecture for multimodal interfaces. Using a novel approach to intelligently organise both agents and input data, this system has the potential to outperform current state-of-the-art multimodal systems, while at the same time allowing greater levels of interaction and flexibility. This assertion is supported by simulation test results showing that significant improvements can be obtained over normal sequential agent scheduling architectures. For real usage, this translates into faster, more comprehensive systems, without the limited application domain that restricts current implementations.