Contrasting high-resolution characteristic shock-capturing methods in aeroacoustic test problems

Two shock-capturing methods are considered. One is based on a standard conservative Roe scheme with van Leer's MUSCL variable extrapolation method applied to characteristic variables and a Runge-Kutta time stepping scheme. The other is based on the novel CABARET space-time scheme, which uses two sets of staggered variables, one for the conservation step and the other for characteristic splitting into local Riemann invariants. The methods are compared in a range of 2-D inviscid compressible flow test cases. Copyright © 2008 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

A Bayesian framework to predict deformations during supported excavations based on a semi-empirical method

The ground movements induced by the construction of supported excavation systems are generally predicted by empirical/semi-empirical methods in the design stage. However, these methods cannot account for the site-specific conditions and for information that becomes available as an excavation proceeds. A Bayesian updating methodology is proposed to update the predictions of ground movements in the later stages of excavation based on recorded deformation measurements. As an application, the proposed framework is used to predict the three-dimensional deformation shapes at four incremental excavation stages of an actual supported excavation project. © 2011 Taylor & Francis Group, London.

The infinite HMM for unsupervised PoS tagging

We extend previous work on fully unsupervised part-of-speech tagging. Using a non-parametric version of the HMM, called the infinite HMM (iHMM), we address the problem of choosing the number of hidden states in unsupervised Markov models for PoS tagging. We experiment with two non-parametric priors, the Dirichlet and Pitman-Yor processes, on the Wall Street Journal dataset using a parallelized implementation of an iHMM inference algorithm. We evaluate the results with a variety of clustering evaluation metrics and achieve equivalent or better performances than previously reported. Building on this promising result we evaluate the output of the unsupervised PoS tagger as a direct replacement for the output of a fully supervised PoS tagger for the task of shallow parsing and compare the two evaluations. © 2009 ACL and AFNLP.