{Overleaf 模板库}LaTeX 模板和示例 — Recent

Plantilla para la elaboración de tesis en la Universidad del Valle-Colombia. Para iniciar, el autor debe editar MisDatos.tex.

Sample document using the v2019 style version of the series OASIcs - OpenAccess Series in Informatics. OASIcs offers a venue for the Open Access and online publication of peer-reviewed proceedings based on international scientific events (workshops, symposia, conferences, ...) that took place outside of Schloss Dagstuhl. See https://www.dagstuhl.de/oasics https://github.com/dagstuhl-publishing/styles/

2018年研究生数学建模LaTeX模板：https://github.com/latexstudio/GMCMthesis 。

About Adversarial Machine Learning

Internship report template for the students of the Université de Technologie de Troyes.

Este template contempla as exigências necessárias de formatação para o curso Superior de Análise e Desenvolvimento de Sistemas (TADS) do Instituto Federal de Educação, Ciência e Tecnologia de Goiás (IFG) - Campus Formosa.

Important Note: This template is no longer supported, but is provided for historical reference. This template for Wiley article submissions was developed by Overleaf for the Overleaf-Wiley pilot which ran during 2017 and 2018. It was last updated in January 2019. If you're planning to submit to a Wiley journal, please check the journal's author guidelines page for the latest information on how to prepare your submission.

The efficiency of a query execution plan depends on the accuracy of the selectivity estimates given to the query optimiser by the cost model. The cost model makes simplifying assumptions in order to produce said estimates in a timely manner. These assumptions lead to selectivity estimation errors that have dramatic effects on the quality of the resulting query execution plans. A convenient assumption that is ubiquitous among current cost models is to assume that attributes are independent with each other. However, it ignores potential correlations which can have a huge negative impact on the accuracy of the cost model. In this paper we attempt to relax the attribute value independence assumption without unreasonably deteriorating the accuracy of the cost model. We propose a novel approach based on a particular type of Bayesian networks called Chow-Liu trees to approximate the distribution of attribute values inside each relation of a database. Our results on the TPC-DS benchmark show that our method is an order of magnitude. more precise than other approaches whilst remaining reasonably efficient in terms of time and space.

Baseado no exemplo do Metropolis