Zobrazeno 1 - 10
of 48 282
pro vyhledávání: '"A A, Hamdi"'
Autor:
Held, Jan, Vandeghen, Renaud, Hamdi, Abdullah, Deliege, Adrien, Cioppa, Anthony, Giancola, Silvio, Vedaldi, Andrea, Ghanem, Bernard, Van Droogenbroeck, Marc
Recent advances in radiance field reconstruction, such as 3D Gaussian Splatting (3DGS), have achieved high-quality novel view synthesis and fast rendering by representing scenes with compositions of Gaussian primitives. However, 3D Gaussians present
Externí odkaz:
http://arxiv.org/abs/2411.14974
Autor:
Manoorkar, Sojwal, Pakkaner, Gülce Kalyoncu, Omar, Hamdi, Barbaix, Soetkin, Ceursters, Dominique, Lathinis, Maxime, Van Offenwert, Stefanie, Bultreys, Tom
Underground hydrogen storage in saline aquifers is a potential solution for seasonal renewable energy storage. Among potential storage sites, facilities used for underground natural gas storage have advantages, including well-characterized cyclical i
Externí odkaz:
http://arxiv.org/abs/2411.14122
Autor:
Chapagain, S., Zhao, Y., Rohleen, T. K., Hamdi, S. M., Boubrahimi, S. F., Flinn, R. E., Lund, E. M., Klooster, D., Scheer, J. R., Cascalheira, C. J.
Individuals who identify as sexual and gender minorities, including lesbian, gay, bisexual, transgender, queer, and others (LGBTQ+) are more likely to experience poorer health than their heterosexual and cisgender counterparts. One primary source tha
Externí odkaz:
http://arxiv.org/abs/2411.13534
In heliophysics research, predicting solar flares is crucial due to their potential to impact both space-based systems and Earth's infrastructure substantially. Magnetic field data from solar active regions, recorded by solar imaging observatories, a
Externí odkaz:
http://arxiv.org/abs/2411.11249
Autor:
Caputo, Jean-Guy, Hamdi, Adel
We study the nonlinear inverse source problem of detecting, localizing and identifying unknown accidental disturbances on forced and damped transmission networks. A first result is that strategic observation sets are enough to guarantee detection of
Externí odkaz:
http://arxiv.org/abs/2411.05462
This paper introduces a novel approach to urban pathfinding by transforming traditional heuristic-based algorithms into deep learning models that leverage real-time contextual data, such as traffic and weather conditions. We propose two methods: an e
Externí odkaz:
http://arxiv.org/abs/2411.05044
M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps
Over the past decade, multivariate time series classification has received great attention. Machine learning (ML) models for multivariate time series classification have made significant strides and achieved impressive success in a wide range of appl
Externí odkaz:
http://arxiv.org/abs/2411.02649
Current Generative Adversarial Network (GAN)-based approaches for time series generation face challenges such as suboptimal convergence, information loss in embedding spaces, and instability. To overcome these challenges, we introduce an advanced fra
Externí odkaz:
http://arxiv.org/abs/2410.21203
Autor:
Li, Peiyu, Bahri, Omar, Hosseinzadeh, Pouya, Boubrahimi, Soukaïna Filali, Hamdi, Shah Muhammad
As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for model decisions. This is necessary for building trust and transparency in
Externí odkaz:
http://arxiv.org/abs/2410.20539
X-ray micro-computed tomography (X-ray micro-CT) is widely employed to investigate flow phenomena in porous media, providing a powerful alternative to core-scale experiments for estimating traditional petrophysical properties such as porosity, single
Externí odkaz:
http://arxiv.org/abs/2410.18937