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pro vyhledávání: '"Saadi, A"'
Multi-modal Large Language models (MLLMs) are always trained on data from diverse and unreliable sources, which may contain misaligned or mislabeled text-image pairs. This frequently causes robustness issues and hallucinations, leading to performance
Externí odkaz:
http://arxiv.org/abs/2411.11667
Recent measurements of the lepton flavor universality ratios $R^{\mu e}_{K}$ and $R^{\mu e}_{K^*}$ in $B\to \left(K, K^*\right)\mu^{+}\mu^{-}\left(e^+e^-\right)$ at LHCb align with the Standard Model predictions, necessitating search for the compleme
Externí odkaz:
http://arxiv.org/abs/2410.20633
Autor:
Kanithi, Praveen K, Christophe, Clément, Pimentel, Marco AF, Raha, Tathagata, Saadi, Nada, Javed, Hamza, Maslenkova, Svetlana, Hayat, Nasir, Rajan, Ronnie, Khan, Shadab
The rapid development of Large Language Models (LLMs) for healthcare applications has spurred calls for holistic evaluation beyond frequently-cited benchmarks like USMLE, to better reflect real-world performance. While real-world assessments are valu
Externí odkaz:
http://arxiv.org/abs/2409.07314
Autor:
Natal, Joseph, Al-saadi, Oleksiy
It is shown that computing the configuration of any one-dimensional cellular automaton at generation $n$ can be accelerated by constructing and running a composite one with a radius proportional to $\log n$. The new automaton is the original automato
Externí odkaz:
http://arxiv.org/abs/2409.07065
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
The Standard Model (SM) is lepton flavor universal, and the recent measurements of lepton flavor universality in $B \to (K,K^*)\ell^{+}\ell^{-}$, for $\ell = \mu, \; e$, decays now lie close to the SM predictions. However, this is not the case for th
Externí odkaz:
http://arxiv.org/abs/2409.03388
We quantify the uncertainty of the L\"ammer model of damage evolution when fitted to (noisy) observations of damage evolution in cyclic fatigue experiments with and without dwell time. We therefore develop a bootstrap method by sampling over blocks o
Externí odkaz:
http://arxiv.org/abs/2405.17858
Imaging modalities such as Computed Tomography (CT) and Positron Emission Tomography (PET) are key in cancer detection, inspiring Deep Neural Networks (DNN) models that merge these scans for tumor segmentation. When both CT and PET scans are availabl
Externí odkaz:
http://arxiv.org/abs/2404.13704
Motivated by the study of heavy-light meson production within the framework of heavy quark effective theory (HQET) factorization, we extend the factorization formalism for a rather complicated process $W^+\to B^+\ell^+\ell^-$ in the limit of a non-ze
Externí odkaz:
http://arxiv.org/abs/2404.01696
Autor:
Saadi, Fayssal
We describe the dynamics of a group $\Gamma$ generated by Dehn twists along two filling multi-curves or a family of filling curves on the SU(2)-representation variety of closed surfaces. Consequently, we provide explicit $\Gamma$-invariant rational f
Externí odkaz:
http://arxiv.org/abs/2404.00372