22 nd Annual Fall Workshop on Computational Geometry

14th Annual Fall Workshop on Computational Geometry with a Focus on Open Problems

Meeting Report: The 2nd Annual Argonne Soils Workshop, Argonne National Laboratory, Chicago Illinois, USA, October 6-8, 2010

This report summarizes the proceedings of the 2(nd) Annual Argonne Soils Workshop held at Argonne National Laboratory October 6-8, 2010. The workshop assembled a diverse group of soil ecologists, microbiologists, molecular biologists, and computational scientists to discuss the challenges and opportunities related to implementation of metagenomics approaches in soil microbial ecology. The overa...

The Impact of Health Information Technology on Hospital Demand

Acknowledgements: Portions of this paper were developed in Eric Barrette’s dissertation and funded by AHRQ R36 dissertation grant #HS18272-01. We are grateful for insightful and constructive comments from Roger Feldman, Stephen Parente, Alok Gupta, Shane Greenstein, Ira Moscovice and participants at the 22 nd Annual Health Economics Conference and 2 nd Annual Workshop on Health IT and Economics...

Canada-Japan Workshop on Discrete and Computational Geometry

This workshop intends to celebrate and encourage international research collaboration in the area of discrete and computational geometry, particularly between Canada and Japan. The workshop features talks on theoretical and applied aspects of discrete geometry, with an emphasis on computational questions, and computational approaches to mathematics. Topics of particular interest include geometr...

Coling 2008 22 nd International Conference on Computational Linguistics Proceedings of the workshop on Speech Processing for Safety Critical Translation and Pervasive Applications

The concept classifier has been used as a translation unit in speech-to-speech translation systems. However, the sparsity of the training data is the bottle neck of its effectiveness. Here, a new method based on using a statistical machine translation system has been introduced to mitigate the effects of data sparsity for training classifiers. Also, the effects of the background model which is ...