3084140
Ranking individuals by group comparisons
This paper proposes new approaches to rank individuals from their group competition results. Many real-world problems are of this type. For example, ranking players from team games is important in some sports. We propose an exponential model to solve such problems. To estimate individual rankings through the proposed model we introduce two convex minimization formulas with easy and efficient solution procedures. Experiments on real bridge records and multi-class classification demonstrate the viability of the proposed model.
7664224
Probability Estimates for Multi-class Classification by Pairwise Coupling
Pairwise coupling is a popular multi-class classification method that combines all comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both methods can be reduced to linear systems and are easy to implement. We show conceptually and experimentally that the proposed approaches are more stable than the two existing popular methods: voting and the method by Hastie and Tibshirani (1998)
52872372
Modeling and Performance Analysis of a Multiserver Multiqueue System on the Grid
Grid computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and high-performance orientation. A Grid integrates and coordinates resources and users that live within different control domains with the goal of delivering nontrivial quality of service (QoS). Consequently, task management and scheduling is of central importance for the Grid-based systems. In this paper, we model the task dispatching and selecting on the distributed Grid computing system by a multiserver multiqueue (MSMQ) model, and propose a modeling and analysis technique based on Stochastic Petri Net (SPN) methods. An approximate analysis technique is also proposed to reduce the complexity of the model.
6012193
Performability Evaluation of Resource Scheduling Algorithms for Computational Grids
Resource scheduling is a fundamental issue in achieving high performance on computational grids. In this paper, we concentrate on parallel resource scheduling problems for hierarchical resource scheduling architecture of computational grids, and propose a stochastic reward nets (SRN) based performability evaluation framework for evaluating the performability of resource scheduling algorithms for computational grid with resource unavailability. Within the proposed framework, performability model of hierarchical resource scheduling is constructed, 36 combinations of 6 global job scheduling algorithms and 6 local processor allocation algorithms are specified in the uniform performability model, and performability metrics are derived
18052662
Probabilistic analysis on mesh network fault tolerance
Mesh networks are among the most important interconnection network topologies for large multicomputer systems. Mesh networks perform poorly in tolerating faults in the view of worst-case analysis. On the other hand, such worst cases occur very rarely in realistic situations. In this paper, we study the fault tolerance of 2-D and 3-D mesh networks under a more realistic model in which each network node has an independent failure probability. We first observe that if the node failure probability is fixed, then the connectivity probability of these mesh networks can be arbitrarily small when the network size is sufficiently large. Thus, it is practically important for multicomputer system manufacture to determine the upper bound for node failure probability when the probability of network connectivity and the network size are given. We develop a novel technique to formally derive lower bounds on the connectivity probability for 2-D and 3-D mesh networks. Our study shows that these mesh networks of practical size can tolerate a large number of faulty nodes thus are reliable enough for multicomputer systems. For example, it is formally proved that as long as the node failure probability is bounded by 0.5%, a 3-D mesh network of up to a million nodes remains connected with a probability larger than 99%.
322920
Introduction to the special issue on statistical language modeling
The goal of statistical language modeling (SLM) is to estimate the likelihood (or probability) of a word string. SLM is fundamental to many natural language applications like automatic speech recognition (ASR) [Jelinek 1990], statistical machine translation (SMT) [Brown et al. 1993], and Asian language text input [Gao et al. 2002a]. The research on SLM basically involves two main tasks: modeling and estimation. Modeling is to determine the structure of a statistical model; estimation is to determine the free parameters of the model using training data. SLM usually uses a parametric model with Maximum Likelihood Estimation (MLE) and various smoothing methods to tackle data sparseness problems. Different statistical models have been proposed in the past, but n-gram models (in particular, bigram and trigram models) still dominate SLM research. SLM has recently been demonstrated as an effective framework for a few new applications, such as question answering [Berger 2001], text summarization, paraphrasing [Barzilay and Lee 2004], and information retrieval [Croft and Lafferty 2003]. However, these new applications come with new challenges. For example, in the SLM approaches to information retrieval, a language model has to be trained on a single document, an extremely small training set; while in ASR, a language model is typically trained on a million word corpus. The recent development of related techniques stimulates new modeling and estimation methods that are beyond the scope of the traditional approaches. Two representative examples of such techniques are statistical parsing and discriminative training. With the ever-increasing popularity of SLM, we think that it is the right time to assemble a special issue reflecting recent advances in both its theory and applications. It __________________________________________________________________________________________
15307682
Accommodating polymorphic data decompositions in explicitly parallel programs
Explicitly parallel programs have the potential for greater performance than their implicitly parallel counterparts. However, this benefit can be accompanied by additional programming difficulties. We address one particular problem that has implications for both scalability and portability: the need for programs do accommodate diverse data decompositions. We explain why programs with explicit communication have difficulties in handling changes in data decomposition, and we present a solution to this problem which involves the notions of derivative functions and configuration parameters. We illustrate the technique by using three different data decompositions to solve the Modified Gram-Schmidt method on four parallel machines.<<ETX>>