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We are given spans of the target text which align to concepts in the AMR graph.These alignment do not cover every token in the target sentnce. Typically function words are not aligned to any graph fragment. Next, we obtain word alignments between the target sentence and source sentence. Since we have word alignments between target and source, and phrase alignments between target and AMR graph, we must convert the word alingments into phrase alignments. The phrases on the source side will then be projected to the AMR concepts via the target sentence

"ModernCV" CV and Cover Letter LaTeX Template Version 1.11 (19/6/14) This template has been downloaded from: http://www.LaTeXTemplates.com Original author: Xavier Danaux (xdanaux@gmail.com) License: CC BY-NC-SA 3.0 (http://creativecommons.org/licenses/by-nc-sa/3.0/) Important note: This template requires the moderncv.cls and .sty files to be in the same directory as this .tex file. These files provide the resume style and themes used for structuring the document.

Lecturas tomadas de la clase de M.Sc. Fidel Ordoñez, Carrera de Matemática UNAH, 2014

Information before unblinding regarding the success of confirmatory clinical trials is highly uncertain. Estimates of expected future power which purport to use this information for purposes of sample size adjustment after given interim points need to reflect this uncertainty. Estimates of future power at later interim points need to track the evolution of the clinical trial. We employ sequential models to describe this evolution. We show that current techniques using point estimates of auxiliary parameters for estimating expected power: (i) fail to describe the range of likely power obtained after the anticipated data are observed, (ii) fail to adjust to different kinds of thresholds, and (iii) fail to adjust to the changing patient population. Our algorithms address each of these shortcomings. We show that the uncertainty arising from clinical trials is characterized by filtering later auxiliary parameters through their earlier counterparts and employing the resulting posterior distribution to estimate power. We devise MCMC-based algorithms to implement sample size adjustments after the first interim point. Bayesian models are designed to implement these adjustments in settings where both hard and soft thresholds for distinguishing the presence of treatment effects are present. Sequential MCMC-based algorithms are devised to implement accurate sample size adjustments for multiple interim points. We apply these suggested algorithms to a depression trial for purposes of illustration.

Lecturas de la clase de Elemento Finito impartida en la Carrera de Matemática de la Universidad Nacional Autónoma de Honduras

This article aims to be a model LaTeX document while teaching you the basics of what it is and how to use it. It contains all of the basic constructs you are likely to encounter as you write your first papers and articles. This article will not go into detail about how to get started with a local installation of LaTeX.

This report gives an overview of various Machine Learning algorithms and compare their efficiencies. Also, it gives an insight into various approaches which can be adopted to handle missing data values and confers the impact of discretisation on various machine learning techiques.

Rapportmall i LaTeX - första utkastet Av Magnus Håkansson, et08mh1@student.lth.se Laborationshandledare, FAFA01, labb: Svängningar 2 april 2011 Överförd till writeLaTeX av Magnus Dagbro, elt12mda@student.lu.se Laborationshandledare, FAFA01, labb: Svängningar 12 maj 2014

A quick look at Merkle's puzzles and it's legacy.