Datasets

subject: Computer Science type: dataset

Total is 302 Results
32-digit values of the first 100 recurrence coefficients for the bimodal weight function with parameter ε=.02

10.4231/R7319SWH

Walter Gautschi ORCID logo

01/03/2017

32-digit values of the first 100 recurrence coefficients for the weight function w(x)=exp[-(x^2-1)^2/(4*ε)] on [-Inf,Inf], ε=.02

Computer Science Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions

32-digit values of the first 100 recurrence coefficients for the bimodal weight function with parameter ε=.1

10.4231/R76T0JNX

Walter Gautschi ORCID logo

01/03/2017

32-digit values of the first 100 recurrence coefficients for the weight function w(x)=exp[-(x^2-1)^2/(4*ε)] on [-Inf,Inf], ε=.1

Computer Science Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions

32-digit values of the first 100 recurrence coefficients for the bimodal weight function with parameter ε=.005

10.4231/R7Z899DM

Walter Gautschi ORCID logo

01/03/2017

32-digit values of the first 100 recurrence coefficients for the weight function w(x)=exp[-(x^2-1)^2/(4*ε)] on [-Inf,Inf], ε=.005

Computer Science Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions

32-digit values of the first 100 recurrence coefficients for the bimodal weight function with parameter ε=.001

10.4231/R7TH8JPW

Walter Gautschi ORCID logo

01/03/2017

32-digit values of the first 100 recurrence coefficients for the weight function w(x)=exp[-(x^2-1)^2/(4*ε)] on [-Inf,Inf], ε=.001

Computer Science Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions

Jacobi polynomials

10.4231/R7PR7SZ5

Walter Gautschi ORCID logo

12/15/2016

Matlab routines for the first N recurrence coefficients of Jacobi polynomials

Computer Science Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions

Localization of Urban Trees across the USA with Generative AI

10.4231/7AK0-1P48

Adnan Firoze ORCID logo , Akshaj Uppala , Bedrich Benes ORCID logo , Brady Hardiman , Daniel Aliaga , Lindsay Darling , Raymond Yeh , Songlin Fei ORCID logo

12/12/2023

We use deep learning to provide a novel solution to map all trees on both public and private lands across 330 United States (U.S.) cities. This repository contains the dataset and code base.

Computer Science Computer Vision Ecology Pattern Recognition urban forestry urban tree

Display #

Results 1 - 10 of 302