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An application of the Ncut algorithm, with an open-source implementation (in the R environment).
An application of the Ncut algorithm, with an open-source implementation (in the R environment).
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.
José Antonio garcia
FSU-MATH2300-Project3
FSU-MATH2300-Project3
This is a project to develop students' understanding of Newton's Method using the tools available in Geogebra. This project was adapted from a similar project developed by folks at Grand Valley State University. (If any of you see this and would like more specific attributions, please let me know.)
Sarah Wright
The Parallelization and Optimization of the N-Body Problem using OpenMP and Cuda
The Parallelization and Optimization of the N-Body Problem using OpenMP and Cuda
This research paper aims at exploiting efficient ways of implementing the N-Body problem. The N-Body problem, in the field of physics, predicts the movements and planets and their gravitational interactions. In this paper, the efficient execution of heavy computational work through usage of different cores in CPU and GPU is looked into; achieved by integrating the OpenMP parallelization API and the Nvidia CUDA into the code. The paper also aims at performance analysis of various algorithms used to solve the same problem. This research not only aids as an alternative to complex simulations but also for bigger data that requires work distribution and computationally expensive procedures.
Tushaar Gangarapu
Algebra Linear
Algebra Linear
Algebra Linear
Azuaite Schneider
Charge to Mass Ratio of the Electron
Charge to Mass Ratio of the Electron
For an electron moving in a circular path in a magnetic field, if we know the magnetic field strength, accelerating voltage, and radius of the electron's trajectory, then we can make an estimation of the electron's charge to mass ratio. We calculated an average charge to mass ratio of \(2.08 \times 10^{11} \pm 1.81 \times 10^8\) Coulombs per kilogram.
Jake Rugh
A Look at Hilbert Spaces
A Look at Hilbert Spaces
A look at Hilbert Spaces, and the question "Are there natural, separable Hilbert Spaces on the Euclidean Ball for which all composition operators are bounded.
Ryan T Whyte
Solucion parcial 3, álgebra para Física, 2018-1
Solucion parcial 3, álgebra para Física, 2018-1
Solución al tercer parcial del curso de Álgebra para la licenciatura de Física, Facultad de Ciencias, UNAM. Semestre 2018-1
Memo Garro
Solucion parcial 2
Solucion parcial 2
Solución al segundo examen parcial de álgebra
Memo Garro
Mimetic postprocessing for LFRic
Mimetic postprocessing for LFRic
We describe what mimetic interpolation is and why it is critical for some pre- and post-processing tasks. A simple test case shows how using bilinear interpolation for a flux calculation introduces numerical errors that depend on the grid, the number of segments and the number of quadrature points. In contrast, mimetic interpolation will return the exact result regardless of the grid resolution and the number of segments.
Alex Pletzer and Wolfgang Hayek and Jorge Bornemann

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