SFB/Transregio 161 "Quantitative Methods for Visual Computing"

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181 to 190 of 3,690 Results

YT_interruption_Videos_list.txt Sep 16, 2024 - Dataset for "Short-Form Videos Degrade Our Capacity to Retain Intentions: Effect of Context Switching On Prospective Memory" Plain Text - 1.2 KB - MD5: 34563dbdbcaccb4352772a47b1f6f2e2
Supplementary Material for Uncertainty-aware Spectral Visualization Sep 16, 2024 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing" Evers, Marina; Weiskopf, Daniel, 2024, "Supplementary Material for Uncertainty-aware Spectral Visualization", https://doi.org/10.18419/DARUS-4447, DaRUS, V1 In this supplemental material, we provide supplemental information (PDF document with derivations of the results presented in the paper and two additional use cases) and the supplementary video for uncertainty-aware spectral analysis. We model an uncertain time series as a multivariate Gaussian process. We propagate the uncertainty and explicitly c...
supplementary.pdf Sep 16, 2024 - Supplementary Material for Uncertainty-aware Spectral Visualization Adobe PDF - 1.2 MB - MD5: fe2fd8c5657618b68619ddcf155e10b9 The supplemental material which contains mathematical derivations for the results presented in the main paper, a comparison to Monte Carlo sampling and additional use cases.
video.mp4 Sep 16, 2024 - Supplementary Material for Uncertainty-aware Spectral Visualization MPEG-4 Video - 11.4 MB - MD5: 056e8ced50d7010a4b3a53c8db8dcaed A video that shows how the visual analysis approach can be used for uncertainty-aware spectral analaysis.
Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions Sep 2, 2024 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing" Reichmann, Luca; Hägele, David; Weiskopf, Daniel, 2024, "Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions", https://doi.org/10.18419/DARUS-4441, DaRUS, V1, UNF:6:WoQ4MNffz92VcvZ/qCGL5w== [fileUNF] This dataset contains the supplemental material for "Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions". The contents and usage of this dataset are described in the README.md files.
amiramesh.py Sep 2, 2024 - Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions Python Source Code - 1.6 KB - MD5: f46840a8ba5f5eb6c0cd448328d45eb6 Code
download_embeddings.py Sep 2, 2024 - Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions Python Source Code - 1.7 KB - MD5: 9e512511bd521fe8dc7f5e48adaec823 script to download all of the embeddings. On command line use: python3 download_embeddings.py
download_images.py Sep 2, 2024 - Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions Python Source Code - 1.5 KB - MD5: ed4de7d69bd1fade0bbfe5d9f17fe968 script to download all images. On command line use: python3 download_images.py
download.sh Sep 2, 2024 - Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions Shell Script - 821 B - MD5: a43a6368829cde23690be83cb7b85f8a Code
flows.py Sep 2, 2024 - Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions Python Source Code - 9.4 KB - MD5: 010dc37db9478df3ccfc301d1cda4410 Code

YT_interruption_Videos_list.txt

Sep 16, 2024 - Dataset for "Short-Form Videos Degrade Our Capacity to Retain Intentions: Effect of Context Switching On Prospective Memory"

Plain Text - 1.2 KB -

Supplementary Material for Uncertainty-aware Spectral Visualization

Sep 16, 2024 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"

Evers, Marina; Weiskopf, Daniel, 2024, "Supplementary Material for Uncertainty-aware Spectral Visualization", https://doi.org/10.18419/DARUS-4447, DaRUS, V1

In this supplemental material, we provide supplemental information (PDF document with derivations of the results presented in the paper and two additional use cases) and the supplementary video for uncertainty-aware spectral analysis. We model an uncertain time series as a multivariate Gaussian process. We propagate the uncertainty and explicitly c...

supplementary.pdf

Sep 16, 2024 - Supplementary Material for Uncertainty-aware Spectral Visualization

Adobe PDF - 1.2 MB -

The supplemental material which contains mathematical derivations for the results presented in the main paper, a comparison to Monte Carlo sampling and additional use cases.

video.mp4

Sep 16, 2024 - Supplementary Material for Uncertainty-aware Spectral Visualization

MPEG-4 Video - 11.4 MB -

A video that shows how the visual analysis approach can be used for uncertainty-aware spectral analaysis.

Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions

Sep 2, 2024 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"

Reichmann, Luca; Hägele, David; Weiskopf, Daniel, 2024, "Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions", https://doi.org/10.18419/DARUS-4441, DaRUS, V1, UNF:6:WoQ4MNffz92VcvZ/qCGL5w== [fileUNF]

This dataset contains the supplemental material for "Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions". The contents and usage of this dataset are described in the README.md files.

amiramesh.py

Sep 2, 2024 - Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions

Python Source Code - 1.6 KB -

Code

download_embeddings.py

Sep 2, 2024 - Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions

Python Source Code - 1.7 KB -

script to download all of the embeddings. On command line use: python3 download_embeddings.py

download_images.py

Sep 2, 2024 - Supplemental Material for Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions

Python Source Code - 1.5 KB -

script to download all images. On command line use: python3 download_images.py