Subject
- Mathematics 285
- Biomedical Engineering 2
- Interdisciplinary Research 2
- Life Sciences 2
- Civil Engineering 1
- Communication 1
- Library Science 1
Creator
- Walter Gautschi, 0000-0001-9184-8899 286
- Mireille Boutin, 0000-0002-0837-6577 3
- Bartlomiej P. Rajwa 2
- Courtney Falk 2
- Jonas Hepp 2
- Murat Dundar 2
- Yellamraju Tarun 2
- Adnan Firoze, 0000-0002-2751-009X 1
- Akshaj Uppala 1
- Austin J. Klasa 1
Type
- Dataset 302
Date
subject: Computer Science type: dataset
Total is 302 Results
32-digit values of the first 100 recurrence coefficients for the half-range Freud weight function with exponents mu=0, nu=4
10.4231/R7416V2R
12/09/2016
32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^mu*exp(-x^nu) on [0,Inf], mu=0, nu=4
Computer Science Freud weight function Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions
32-digit values of the first 100 recurrence coefficients for the Schroedinger weight function with exponent mu=5
10.4231/R7QR4V3Z
12/09/2016
32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^mu*exp(-x^4/16) on [0,Inf], mu=5
Computer Science Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Schroedinger weight function 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/R7BK19B6
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=.02
10.4231/R7319SWH
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
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
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
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
Generalized Hermite polynomials
10.4231/R79K4878
12/15/2016
Matlab routine for the first N recurrence coefficients of generalized Hermite polynomials
Computer Science Hermite polynomials Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions
Jacobi polynomials
10.4231/R7PR7SZ5
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
,
Akshaj Uppala
,
Bedrich Benes
,
Brady Hardiman
,
Daniel Aliaga
,
Lindsay Darling
,
Raymond Yeh
,
Songlin Fei
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
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Results 1 - 10 of 302