{overleaf template gallery}LaTeX templates and examples — Recent

Dieses Dokument enthält Notizen aus dem Kurs Mathematik 1 des Studienbefähigungslehrgangs 2014/15 der FH Joanneum Kapfenberg.

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

Dr Driver's standard reading quiz template, downloaded from the github gist on 20th August 2014. The version on writeLaTeX has the commands for choosing the Meta and Meta Serif fonts commented out as these are not currently installed on the system. The template compiles with XeLaTeX.

The natbib package provides automatic numbering, sorting and formatting of in text citations and bibliographic references in LaTeX. It supports both numeric and author-year citation styles. The natbib package is the most commonly used package for handling references in LaTeX, and it is very functional, but the more modern biblatex package is also worth a look.

The XITS fonts provide a Times-like serif typeface for mathematical and scientific publishing. They provide a version of the STIX fonts enriched with the OpenType MATH extension, making them suitable for high quality mathematical typesetting with XeTeX and LuaTeX. XITS fonts are free and open source.

"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.

Template for written Assignments. * bib included with examples for Harvard-Style referencing (natbib) * Arial (?) fonts * 1.5 spacing

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.