Gallery — Academic Journal

This template can be used to submit review responses during journal submission. Normally a journal publishing process contains multiple rounds of reviews. The responses to the reviewer comments can be written using this template in a formal and professional manner. Each and every reviewer comment can be addressed separately and clearly with this template.

Template for American Physical Society (APS) and American Institute of Physics (AIP) journals, including Physical Review Letters, Physical Review A-E, Physical Review X, Reviews of Modern Physics, Applied Phyiscs Letters, using the ReVTeX 4.2 document class.

LaTeX Template for I.J. Intelligent Systems and Applications (MECS)

This template is for authors who are preparing a manuscript for a Taylor & Francis journal using the LaTeX document preparation system and the interact class file and the "P" reference style, which is available via selected journals' home pages on the Taylor & Francis website.

Plantilla publicada en la página de la revista de la Facultad de Ciencias Exactas, Físicas y Naturales

This is an unofficial general-purpose template for articles in Nature Reviews Physics based on the information about the journal and the guide to authors available at: https://www.nature.com/natrevphys/

This template can be used by authors to submit their papers to Networks: An International Journal

Japanese template for IEICE journals, technical reports (ieicej v3.2)

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.