Recent
![I love math](https://writelatex.s3.amazonaws.com/published_ver/6253.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240630T193022Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240630/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=0de8fc815af0e9b368928f883b78662e0000eb2a5bf44324287de860b59f45a6)
I love math
j'aimes les math par une courbe paramétrique de cœur !
Noureddine
![An application of the Ncut algorithm, with an open-source implementation (in the R environment).](https://writelatex.s3.amazonaws.com/published_ver/8146.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240630T193022Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240630/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=c7b79a76951903454845d431f2d03a6b52d2e317a2563b64a212ac55acfd9bee)
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
![Assignment 1](https://writelatex.s3.amazonaws.com/published_ver/2189.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240630T193022Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240630/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=24aa7e4e0f5e8275b393d92fbdb5a92f67f0657153ea8b4b6ce4dc34e6ccef3e)
Assignment 1
Math 471 Homework 1
Lorenzo Rodriguez
![Si \(AB=I\) entonces \(A\) es invertible y \(A^{-1}=B\)](https://writelatex.s3.amazonaws.com/published_ver/6975.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240630T193022Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240630/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=ceb3b4169a2efb983357ba2813e8ae3e7777eef64912d3a8ffcba4a19c31f093)
Si \(AB=I\) entonces \(A\) es invertible y \(A^{-1}=B\)
Vamos a demostrar el notable teorema que dice que, dadas dos matrices cuadradras \(A\) y \(B\) del mismo tamaño, si \(AB=I\), donde \(I\) es la matriz identidad del mismo tamaño que la matrices \(A\) y \(B\), entonces \(A\) es invertible y \(B^{-1}=A\). La prueba será directa y sólo usaremos el hecho de que si \(|A|\ne0\) entonces \(A\) es invertible. La pregunta es si puedes tú, estimado estudiante, ofrecer otra prueba de la que aquí se sugiere. Sirva además este texto como un ejemplo de escritura con LaTeX.
Memo Garro
![Ejercicios en LaTeX](https://writelatex.s3.amazonaws.com/published_ver/2227.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240630T193022Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240630/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=144dbb02951a7e5ffdf9504febcb478d1bb05d2f577eac2894166c26d9d0f804)
Ejercicios en LaTeX
Mathematic homework exercise in LaTeX
Alvaro Polo Ulloque
![Robust estimators in partly linear regression models on Riemannian manifolds](https://writelatex.s3.amazonaws.com/published_ver/1093.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240630T193022Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240630/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=429b2bb3f85b4706262a7a0ff6c29ea8cfbe4e8860a582941a7001505ad53143)
Robust estimators in partly linear regression models on Riemannian manifolds
Under a partly linear model we study a family of robust estimates for the regression parameter and the regression function when some of the predictors take values on a Riemannian manifold. We obtain the consistency and the asymptotic normality of the proposed estimators. Simulations and an application to a real dataset show the good performance of our proposal under small samples and contamination.
Rajesh Kumar
![FSU-MATH2400-Project6](https://writelatex.s3.amazonaws.com/published_ver/7655.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240630T193022Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240630/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=738e19aca2fde6327c0dc1c6d43c7beb5cf238261468dd4020272c5b46073989)
FSU-MATH2400-Project6
In this calculus project, students use infinite series to investigate Euler's Equation: $e^{i\pi} + 1 = 0$.
Sarah Wright
![MAT 3770 Homework 2/19/2018](https://writelatex.s3.amazonaws.com/published_ver/7535.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240630T193022Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240630/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=1e3b624953596848a51e9ec38323b1a23cd8e74761e29cc836a39b433d8a0c81)
MAT 3770 Homework 2/19/2018
hw
HolyAvocados TV
![Mock American Mathematics Competitions (AMCs)](https://writelatex.s3.amazonaws.com/published_ver/4092.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240630T193022Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240630/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=b70d7672cb8d659c78b6d8e1b0dbb995c6a3bad4ff2c6b06a7225f4cdef1d28b)
Mock American Mathematics Competitions (AMCs)
Mock AMC questions
David Hu