LaTeX templates and examples — Journal articles

Overleaf template for submitting manuscripts to the Journal of Dark Sky Studies

This template has been created for authors submitting articles to Physiome. It is designed for easy editing online with Overleaf. Simply click above to start writing online in your browser. For instructions about the different article types accepted, please see Physiome's article guidelines, and if you're new to Overleaf check out our tutorial for some help getting started.

Adaptive learning rate clipping (ALRC) stabilizes learning by limiting backpropagated losses.

cas-sc.cls and cas-dc.cls are the two new class files created for typesetting Elsevier's journal articles which go through Complex Article Service (CAS) workflow. These are now accepted for submitting articles, both in Elsevier's electronic submission system and elsewhere. These classes are document https://support.stmdocs.in/wiki/index.php?title=Elsarticle_-_CAS. Bugs and problems with elsarticle.cls may be reported to the developers of the class via elsarticle@stmdocs.in. License: The LaTeX Project Public Li­cense 1.2

This template is aimed to be used for articles written in French and submitted to the Journal of Interdisciplinary Methodologies and Issues in Science (JIMIS).

This template is aimed to be used for articles written in French and submitted to the Journal of Interdisciplinary Methodologies and Issues in Science (JIMIS).

Important Note: This template is no longer supported, but is provided for historical reference.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.

This is the template of a regular article to Mathematica Applicanda — the Annales Societatis Mathematicae Polonae Series III published by Polish Mathematical Society.