SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"

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2,151 to 2,159 of 2,159 Results

additional-figures.tar.gz Oct 13, 2022 - Supplemental Material for Uncertainty-Aware Multidimensional Scaling Gzip Archive - 15.7 MB - MD5: 0a1c73072bb6926d695f3719116a77aa Archive containing the image files of the additional figures.
uamds-supplemental-material.pdf Oct 13, 2022 - Supplemental Material for Uncertainty-Aware Multidimensional Scaling Adobe PDF - 7.1 MB - MD5: c4aa9442824f73455591c1b1e8ba108f Supplemental material containing the mathematical derivation of the stress term and gradient of UAMDS for normal distributions. Also contains additional figures.
Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5 Oct 11, 2022 - SFB-TRR 161 B07 "Computational Uncertainty Quantification" Beschle, Cedric; Barth, Andrea, 2022, "Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5", https://doi.org/10.18419/DARUS-3154, DaRUS, V1 Python code to generate the meshes and FEM solutions to Section 5 of the paper Uncertainty visualization: Fundamentals and recent developments. Comments are in the code to explain it. Paraview is used for the visualization.
main_section_5.py Oct 11, 2022 - Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5 Python Source Code - 14.6 KB - MD5: 4d80e1df0b5b71d0920560ad40eda65e
Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution Sep 21, 2022 - SFB-TRR 161 B01 "Adaptive Self-Consistent Visualization" Rodrigues, Nils; Schulz, Christoph; Döring, Sören; Baumgartner, Daniel; Krake, Tim; Weiskopf, Daniel, 2022, "Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution", https://doi.org/10.18419/DARUS-3055, DaRUS, V1 Supplemental material for the paper "Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution". Contains: math behind Relaxed Dot Plots additional images pseudo-anonymous study data source code for library and test application To view the material, extract supplemental.zip and open index.html in a web browser.
supplemental.zip Sep 21, 2022 - Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution ZIP Archive - 169.9 MB - MD5: 3d83b74d9c8f3607dc0db81aed8eaf82
Source Code for Uncertainty-Aware Multidimensional Scaling Aug 8, 2022 Hägele, David; Krake, Tim, 2022, "Source Code for Uncertainty-Aware Multidimensional Scaling", https://doi.org/10.18419/DARUS-2995, DaRUS, V1 This dataset contains the source code for the uncertainty-aware multidimensional scaling (UAMDS) algorithm implemented in the Java programming language. UAMDS is a nonlinear dimensionality reduction technique for sets of random vectors. The implemented UAMDS model allows to project a set of multivariate normal distributions to low-dimensional space...
uamds-core-0.0.2-jar-with-dependencies.jar Aug 8, 2022 - Source Code for Uncertainty-Aware Multidimensional Scaling application/java-archive - 1.6 MB - MD5: 27646e42ce06d907e9be697f4fe4c3ad All-in-one Java library .jar file. Contains compiled classes, their .java sources, and dependency libraries.
uamds-src.tar.gz Aug 8, 2022 - Source Code for Uncertainty-Aware Multidimensional Scaling Gzip Archive - 27.2 KB - MD5: 90d4d720ac5024af8fab2b95c82a65df Tarball archive containing the Java project with build and readme files.

additional-figures.tar.gz

Oct 13, 2022 - Supplemental Material for Uncertainty-Aware Multidimensional Scaling

Gzip Archive - 15.7 MB -

Archive containing the image files of the additional figures.

uamds-supplemental-material.pdf

Oct 13, 2022 - Supplemental Material for Uncertainty-Aware Multidimensional Scaling

Adobe PDF - 7.1 MB -

Supplemental material containing the mathematical derivation of the stress term and gradient of UAMDS for normal distributions. Also contains additional figures.

Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5

Oct 11, 2022 - SFB-TRR 161 B07 "Computational Uncertainty Quantification"

Beschle, Cedric; Barth, Andrea, 2022, "Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5", https://doi.org/10.18419/DARUS-3154, DaRUS, V1

Python code to generate the meshes and FEM solutions to Section 5 of the paper Uncertainty visualization: Fundamentals and recent developments. Comments are in the code to explain it. Paraview is used for the visualization.

main_section_5.py

Oct 11, 2022 - Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5

Python Source Code - 14.6 KB -

Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution

Sep 21, 2022 - SFB-TRR 161 B01 "Adaptive Self-Consistent Visualization"

Rodrigues, Nils; Schulz, Christoph; Döring, Sören; Baumgartner, Daniel; Krake, Tim; Weiskopf, Daniel, 2022, "Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution", https://doi.org/10.18419/DARUS-3055, DaRUS, V1

Supplemental material for the paper "Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution". Contains: math behind Relaxed Dot Plots additional images pseudo-anonymous study data source code for library and test application To view the material, extract supplemental.zip and open index.html in a web browser.

supplemental.zip

Sep 21, 2022 - Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution

ZIP Archive - 169.9 MB -

Source Code for Uncertainty-Aware Multidimensional Scaling

Aug 8, 2022

Hägele, David; Krake, Tim, 2022, "Source Code for Uncertainty-Aware Multidimensional Scaling", https://doi.org/10.18419/DARUS-2995, DaRUS, V1

This dataset contains the source code for the uncertainty-aware multidimensional scaling (UAMDS) algorithm implemented in the Java programming language. UAMDS is a nonlinear dimensionality reduction technique for sets of random vectors. The implemented UAMDS model allows to project a set of multivariate normal distributions to low-dimensional space...

uamds-core-0.0.2-jar-with-dependencies.jar

Aug 8, 2022 - Source Code for Uncertainty-Aware Multidimensional Scaling

application/java-archive - 1.6 MB -

All-in-one Java library .jar file. Contains compiled classes, their .java sources, and dependency libraries.

uamds-src.tar.gz

Aug 8, 2022 - Source Code for Uncertainty-Aware Multidimensional Scaling

Gzip Archive - 27.2 KB -

Tarball archive containing the Java project with build and readme files.

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