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Here we provide a selection of academic journal templates for articles and papers which automatically format your manuscripts in the style required for submission to that journal. Thanks to the partnerships we're building within the publishing community, you can also now submit your paper directly to a number of journals and other editorial and review services via the publish menu in the editor.

Style and Template for Preprints (arXiv, bio-arXiv)
Style and Template for Preprints (arXiv, bio-arXiv)
An aesthetic and simple LaTeX style suitable for "preprint" publications such as arXiv and bio-arXiv, etc. This styling maintains the esthetic of NIPS but adding and changing features to make it (IMO) even better and nore suitable for preprints. The result looks fairly different from NIPS style so that readers won't get confused to think that the preprint was published in NIPS. This style is still in development. You can get the latest version from: https://github.com/kourgeorge/arxiv-style
George Kour
NanoJ: a high-performance open-source super-resolution microscopy toolbox
NanoJ: a high-performance open-source super-resolution microscopy toolbox
Super-resolution microscopy has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for super-resolution microscopy designed to combine high performance and ease of use. We named it NanoJ - a reference to the popular ImageJ software it was developed for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image reconstruction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). We expect to expand NanoJ in the future through the development of new tools designed to improve quantitative data analysis and measure the reliability of fluorescent microscopy studies.
Romain F. Laine, Kalina L. Tosheva, Nils Gustafsson, Robert D. M. Gray, Pedro Almada, David Albrecht, Gabriel T. Risa, Fredrik Hurtig, Ann-Christin Lindås, Buzz Baum, Jason Mercer, Christophe Leterrier, Pedro M. Pereira, Siân Culley, Ricardo Henriques
Astro2020 Decadal Science White Paper
Astro2020 Decadal Science White Paper
This is an exact copy of the official NRC Astro2020 white paper LaTeX template. Re-published for community use on Overleaf by Jason Tumlinson (STScI / JHU).
Astro2020 Committee (via JT)
Elsevier’s CAS LaTeX Single-Column Template
Elsevier’s CAS LaTeX Single-Column Template
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. 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
Elsevier
Elsevier’s CAS LaTeX Double-Column Template
Elsevier’s CAS LaTeX Double-Column Template
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. 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
Elsevier
Manuscript Template for RAIRO Operations Research
Manuscript Template for RAIRO Operations Research
Manuscript template for RAIRO Operations Research as provided on the journal webpage; downloaded from the journal's FTP site on 16 March, 2019.
EDP Sciences
An Approach Based on Bayesian Networks for Query Selectivity Estimation
An Approach Based on Bayesian Networks for Query Selectivity Estimation
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.
Max Halford, Philippe Saint-Pierre, Franck Morvan
Technische Mechanik (ISSN  2199-9244) Template
Technische Mechanik (ISSN 2199-9244) Template
Technische Mechanik is a scientific journal for fundamentals and applications of engineering mechanics. Established in 1980 the Technische Mechanik is a periodical which publishes scientific papers from the entire field of mechanics ; i.e. all branches of theoretical and applied mechanics including related fields of study. The journal Technische Mechanik is an Open Access journal. All papers are available free of charge for download. Publishing of papers is free of any costs for the authors. Each submitted paper runs through a standard peer review process. Website: http://www.ovgu.de/techmech
Magdeburger Verein für Technische Mechanik e.V.
Template for IEICE journals, technical reports
Template for IEICE journals, technical reports
Japanese template for IEICE journals, technical reports (ieicej v3.2)
IEICE

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