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pro vyhledávání: '"A, Hadid"'
Extent of the Magnetotail of Venus From the Solar Orbiter, Parker Solar Probe and BepiColombo Flybys
Autor:
Edberg, Niklas J. T., Andrews, David J., Boldú, J. Jordi, Dimmock, Andrew P., Khotyaintsev, Yuri V., Kim, Konstantin, Persson, Moa, Auster, Uli, Constantinescu, Dragos, Heyner, Daniel, Mieth, Johannes, Richter, Ingo, Curry, Shannon M., Hadid, Lina Z., Pisa, David, Sorriso-Valvo, Luca, Lester, Mark, Sánchez-Cano, Beatriz, Stergiopoulou, Katerina, Romanelli, Norberto, Fischer, David, Schmid, Daniel, Volwerk, Martin
Publikováno v:
Journal of Geophysical Research: Space Physics, 129, e2024JA032603
We analyze data from multiple flybys by the Solar Orbiter, BepiColombo, and Parker Solar Probe (PSP) missions to study the interaction between Venus' plasma environment and the solar wind forming the induced magnetosphere. Through examination of magn
Externí odkaz:
http://arxiv.org/abs/2410.21856
Autor:
Saadi, Ibtissam, Cunningham, Douglas W., Abdelmalik, Taleb-ahmed, Hadid, Abdenour, Hillali, Yassin El
Existing methods for driver facial expression recognition (DFER) are often computationally intensive, rendering them unsuitable for real-time applications. In this work, we introduce a novel transfer learning-based dual architecture, named ShuffViT-D
Externí odkaz:
http://arxiv.org/abs/2409.03438
Graph neural networks (GNN) have shown significant capabilities in handling structured data, yet their application to dynamic, temporal data remains limited. This paper presents a new type of graph attention network, called TempoKGAT, which combines
Externí odkaz:
http://arxiv.org/abs/2408.16391
Accurately forecasting dynamic processes on graphs, such as traffic flow or disease spread, remains a challenge. While Graph Neural Networks (GNNs) excel at modeling and forecasting spatio-temporal data, they often lack the ability to directly incorp
Externí odkaz:
http://arxiv.org/abs/2408.16379
Autor:
Keita, Mamadou, Hamidouche, Wassim, Eutamene, Hessen Bougueffa, Taleb-Ahmed, Abdelmalik, Hadid, Abdenour
We introduce FIDAVL: Fake Image Detection and Attribution using a Vision-Language Model. FIDAVL is a novel and efficient mul-titask approach inspired by the synergies between vision and language processing. Leveraging the benefits of zero-shot learni
Externí odkaz:
http://arxiv.org/abs/2409.03109
Physics-informed neural networks (PINNs) have gained significant prominence as a powerful tool in the field of scientific computing and simulations. Their ability to seamlessly integrate physical principles into deep learning architectures has revolu
Externí odkaz:
http://arxiv.org/abs/2404.03240
Autor:
Keita, Mamadou, Hamidouche, Wassim, Bougueffa, Hassen, Hadid, Abdenour, Taleb-Ahmed, Abdelmalik
In recent years, the emergence of models capable of generating images from text has attracted considerable interest, offering the possibility of creating realistic images from text descriptions. Yet these advances have also raised concerns about the
Externí odkaz:
http://arxiv.org/abs/2404.02726
Autor:
Keita, Mamadou, Hamidouche, Wassim, Eutamene, Hessen Bougueffa, Hadid, Abdenour, Taleb-Ahmed, Abdelmalik
Advancements in deep image synthesis techniques, such as generative adversarial networks (GANs) and diffusion models (DMs), have ushered in an era of generating highly realistic images. While this technological progress has captured significant inter
Externí odkaz:
http://arxiv.org/abs/2404.01959
Facial super-resolution/hallucination is an important area of research that seeks to enhance low-resolution facial images for a variety of applications. While Generative Adversarial Networks (GANs) have shown promise in this area, their ability to ad
Externí odkaz:
http://arxiv.org/abs/2401.15366
Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from bio
Externí odkaz:
http://arxiv.org/abs/2402.03349