1,726,400 research outputs found
Local effects of cryospheric change on agriculture and pasture in Nepal Trans Himalaya.
No full textYoung researcher Keshav Prasad Paudel, University of Bergen, Norway / Nepal, talks here in the NRF 6th open assembly in Hveragerði, Iceland, in September of 2011.
Please click on the link above to see the video
Supplemental data for "Spectral Normalization and Voigt–Reuss net: A universal approach to microstructure‐property forecasting with physical guarantees"
No full textThis repository contains supplemental data for the article
"Spectral Normalization and Voigt-Reuss net: A universal approach to microstructure‐property forecasting with physical guarantees",
accepted for publication in GAMM-Mitteilungen by Sanath Keshav, Julius Herb, and Felix Fritzen [1]. The data contained in this DaRUS repository acts as an extension to the GitHub repository for the so-called Voigt-Reuss net. The data in this dataset is generated by solving thermal homogenization problems for an abundance of different microstructures. The microstructures are defined by periodic representative volume elements (RVE) and periodic boundary conditions are applied to the temperature fluctuations.
We consider bi-phasic two-dimensional microstructures with a resolution of 400 × 400 pixels, as published in [2], and three-dimensional microstructures with a resolution of 192 × 192 × 192 voxels, as published in [3]. For both microstructure datasets, we provide the effective thermal conductivity tensor that is obtained by solving homogenization problems on the full microstructure for different material parameters in the two phases. For the simulation, we used our implementation of Fourier-Accelerated Nodal Solvers (FANS, [4]) that is based on a Finite Element Method (FEM) discretization. Further details are provided in the README.md file of this dataset, in our manuscript [1], and in the GitHub repository.
[1] Keshav, S., Herb, J., and Fritzen, F. (2025). Spectral Normalization and Voigt–Reuss net: A universal approach to microstructure‐property forecasting with physical guarantees, GAMM‐Mitteilungen. (2025), e70005.
https://doi.org/10.1002/gamm.70005
[2] Lißner, J. (2020). 2d microstructure data (Version V2) [dataset]. DaRUS.
https://doi.org/doi:10.18419/DARUS-1151
[3] Prifling, B., Röding, M., Townsend, P., Neumann, M., and Schmidt, V. (2020). Large-scale statistical learning for mass transport prediction in porous materials using 90,000 artificially generated microstructures [dataset]. Zenodo. https://doi.org/10.5281/zenodo.4047774
[4] Leuschner, M., and Fritzen, F. (2018). Fourier-Accelerated Nodal Solvers (FANS) for homogenization problems. Computational Mechanics, 62(3), 359-392.
https://doi.org/10.1007/s00466-017-1501-5 <br
Supplemental data for "Robust inverse material design with physical guarantees using the Voigt-Reuss Net"
No full textThis repository contains supplemental data for the article
"Robust inverse material design with physical guarantees using the Voigt-Reuss Net",
accepted for publication in the International Journal for Numerical Methods in Engineering by Sanath Keshav and Felix Fritzen.
The data in this DaRUS repository complements the accompanying open-source implementation of the Voigt-Reuss net and enables
reproducibility of the numerical results in the manuscript [1]. The datasets are generated by solving periodic
small-strain linear elasticity homogenization problems for a large number of biphasic periodic RVEs.
Effective stiffness tensors were computed with our open-source implementation of the
Fourier-Accelerated Nodal Solvers (FANS) [4] on voxelized microstructures under periodic boundary conditions.
The repository contains two labeled datasets:
(i) 3D linear elasticity.
We use an open 3D microstructure dataset (90,000 stochastic microstructures, resolution 192x192x192),
together with 236 scalar, image-derived morphological descriptors per microstructure [2].
For each microstructure, we sample three non-dimensional parameters that encode the bulk and shear moduli of the two phases, and compute the corresponding effective stiffness
tensor (symmetric positive definite, 6x6 in Mandel notation).
Overall, the dataset contains ~1.18 million microstructure-material combinations and includes the
train/validation/test splits used in the paper.
(ii) 2D plane-strain elasticity.
We use periodic microstructures obtained from thresholded trigonometric fields parameterized by an amplitude matrix A and a threshold [3].
For each sample, we provide the generator parameters, the rendered microstructure image,
and the homogenized plane-strain stiffness tensor (symmetric positive definite, 3x3 in Voigt notation). The same split definitions and metadata used for training and evaluation are included to reproduce the forward-prediction comparisons and inverse-design experiments.
Further details on file formats, naming conventions, and the exact contents of each file are provided in README.md.
[1] Keshav, S., and Fritzen, F. (2026). Robust inverse material design with physical guarantees using the Voigt-Reuss Net, International Journal for Numerical Methods in Engineering. https://doi.org/10.1002/nme.70296
[2] Prifling, B., Röding, M., Townsend, P., Neumann, M., and Schmidt, V. (2020). Large-scale statistical learning for mass transport prediction in porous materials using 90,000 artificially generated microstructures [dataset]. Zenodo. https://doi.org/10.5281/zenodo.4047774
[3] Boddapati, J., & Daraio, C. (2024). Planar structured materials with extreme elastic anisotropy. Materials & Design, 246, 113348.
https://doi.org/10.1016/j.matdes.2024.113348
[4] Leuschner, M., and Fritzen, F. (2018). Fourier-Accelerated Nodal Solvers (FANS) for homogenization problems. Computational Mechanics, 62(3), 359-392.
https://doi.org/10.1007/s00466-017-1501-5 <br
Keshav Datta's Quick Files
No full textThe Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity
Diagrama do processo de "How to Read a Paper" de S. Keshav (2016)
No full textThis diagram presents the process prescribed by Srinivasan Keshav in the 2016 update of his article originally published in ACM SIGCOMM Computer Communication Review (2007), vol. 36, iss. 3, 83-84. The diagram is provided in PDF and editable drawio format. This 2016 update was retrieved from Keshav's website on https://svr-sk818-web.cl.cam.ac.uk/keshav/wiki/index.php/HTRAPN/
Keshav Datta's Quick Files
No full textThe Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity
Selling Vancouver - Interview with Naren Keshav:
No full textNaren talks about how he tried to sell his wife and mom on Vancouver by showcasing the natural beauty
Egok360 A 360 Egocentric Kinetic Human Activity Video Dataset
No full textRecently, there has been a growing interest in wearable sensors which provides new research perspectives for 360 ° video analysis. However, the lack of 360 ° datasets in literature hinders the research in this field. To bridge this gap, in this paper we propose a novel Egocentric (first-person) 360° Kinetic human activity video dataset (EgoK360). The EgoK360 dataset contains annotations of human activity with different sub-actions, e.g., activity Ping-Pong with four sub-actions which are pickup-ball, hit, bounce-ball and serve. To the best of our knowledge, EgoK360 is the first dataset in the domain of first-person activity recognition with a 360° environmental setup, which will facilitate the egocentric 360 ° video understanding. We provide experimental results and comprehensive analysis of variants of the two-stream network for 360 egocentric activity recognition. The EgoK360 dataset can be downloaded from https://egok360.github.io/
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