Subject
- Mathematics 286
- Biomedical Engineering 2
- Interdisciplinary Research 2
- Life Sciences 2
- Civil Engineering 1
- Communication 1
- Library Science 1
Creator
- Walter Gautschi, 0000-0001-9184-8899 287
- 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
Date
Total is 303 Results
32-digit values of the first 100 recurrence coefficients for the Fermi-Dirac-type weight function with exponent r=4
10.4231/R708639Z
12/09/2016
32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=[1/(exp(x)+1)]^r on [0,Inf], r=4
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 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
Graph of Flickr Photo-Sharing Social Network Crawled in May 2006
10.4231/D39P2W550
02/22/2012
Crawl of the Flickr photo-sharing social network from May 2006 returning a graph with 820,878 nodes and 9,837,214 edges. Dataset is distributed as a SMAT file with README file with code to read file in Python and MATLAB.
Controlling the cost and increasing the utility of network measurement infrastructures
10.4231/D3TH8BM5D
09/13/2012
Dissertation on the cost and utility of network measurement infrastructures, including mechanisms for admission control, identifying measurement patterns that will benefit from inference, and selecting vantage points for wide-scale measurement.
Simplicity of K-means versus deepness of Deep Learning. A Case of Unsupervised Feature Learning with Limited Data
10.4231/R7N58J9Z
Baichuan Zhang , Bartlomiej P. Rajwa , Murat Dundar , Qiang Kou , Yicheng He
10/01/2015
A study contrasting K-means-based unsupervised feature learning and deep learning techniques for small data sets with limited intra- as well as inter-class diversity
bacterial colonies BARDOT Biomedical Engineering Computer Science deep learning Elastic light scattering Interdisciplinary Research K-Means Clustering Life Sciences Machine Learning representation learning
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