Some Problems in the Description of English Accentuation

Sponsored in part by the National Science Foundation through Grant Gn-534.1 from the Office of Science Information Service to the Computer and Information Science Research Center, The Ohio State University

On Selection, Projection, Meaning, and Semantic Content

Sponsored in part by the National Science Foundation through Grant GN-534 from the Office of Science Information Service to the Information Sciences Research Center, The Ohio State University

The Accessibility of Deep (Semantic) Structures

Sponsored in part by the National Science Foundation through Grant GN-534 from the Office of Science Information Service to the Information Sciences Research Center, The Ohio State University

Modeling of Fluvial Geomorphic Processes in River Channels Impacted by Agriculture

Mini-Symposium: Modeling Methodology for Agricultural Researc

Self-stabilizing Numerical Iterative Computation

Many challenging tasks in sensor networks, including sensor calibration, ranking of nodes, monitoring, event region detection, collaborative filtering, collaborative signal processing, {\em etc.}, can be formulated as a problem of solving a linear system of equations. Several recent works propose different distributed algorithms for solving these problems, usually by using linear iterative numerical methods. In this work, we extend the settings of the above approaches, by adding another dimension to the problem. Specifically, we are interested in {\em self-stabilizing} algorithms, that continuously run and converge to a solution from any initial state. This aspect of the problem is highly important due to the dynamic nature of the network and the frequent changes in the measured environment. In this paper, we link together algorithms from two different domains. On the one hand, we use the rich linear algebra literature of linear iterative methods for solving systems of linear equations, which are naturally distributed with rapid convergence properties. On the other hand, we are interested in self-stabilizing algorithms, where the input to the computation is constantly changing, and we would like the algorithms to converge from any initial state. We propose a simple novel method called \syncAlg as a self-stabilizing variant of the linear iterative methods. We prove that under mild conditions the self-stabilizing algorithm converges to a desired result. We further extend these results to handle the asynchronous case. As a case study, we discuss the sensor calibration problem and provide simulation results to support the applicability of our approach

A road map for interoperable language resource metadata

LRs remain expensive to create and thus rare relative to demand across languages and technology types. The accidental re-creation of an LR that already exists is a nearly unforgiveable waste of scarce resources that is unfortunately not so easy to avoid. The number of catalogs the HLT researcher must search, with their different formats, make it possible to overlook an existing resource. This paper sketches the sources of this problem and outlines a proposal to rectify along with a new vision of LR cataloging that will to facilitates the documentation and exploitation of a much wider range of LRs than previously considered