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Plantilla para examen/evaluación. Usa las clases exam y mathexam.

An approach to calculating teaching workload (effort) in hours/week/course.

Review Article: A Workload Characterization Study of the 1998 World Cup Web Site

"ModernCV" CV and Cover Letter LaTeX Template Version 1.1 (9/12/12) 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/)

武汉大学本科生毕业论文 LaTeX 模板 https://github.com/mtobeiyf/whu-thesis

Plantilla de tesis que incluye la identidad coportaiva ULL (logos y fuentes). Para modificaciones y sugerencias, enviar pull request a https://github.com/tic-ull/ULL-LaTeX-Plantilla-Tesis

Senior Thesis Template for the Princeton University Department of Operations Research and Financial Engineering

Although the analysis of data is a task that has gained the interest of the statistical community in recent years and whose familiarity with the statistical computing environment, they encourage the current statistical community (to students and teachers of the area) to complete statistical analysis reproducible by means of the tool R. However for years there has been a gap between the calculation of matrices on a large scale and the term "big data", in this work the Normalized Cut algorithm for images is applied. Despite the expected, the R environment to do image analysis is poorly, in comparison with other computing platforms such as the Python language or with specialized software such as OpenCV. Being well known the absence of such function, in this work we share an implementation of the Normalized Cut algorithm in the R environment with extensions to programs and processes performed in C ++, to provide the user with a friendly interface in R to segment images. The article concludes by evaluating the current implementation and looking for ways to generalize the implementation for a large scale context and reuse the developed code. Key words: Normaliced Cut, image segmentation, Lanczos algorithm, eigenvalues and eigenvectors, graphs, similarity matrix, R (the statistical computing environment), open source, large scale and big data.

FRUCT is a large Pan-European cooperation framework that promotes open innovations of academia and industry. FRUCT conference is a high-quality scientific event for meeting academia and business people and setting projects. The average conference is attended by over 150 participants. For more information about the conference deadlines and other practicalities please visit CFP link: www.fruct.org/cfp. Don't forget to Submit your paper at www.fruct.org/submit.