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pro vyhledávání: '"Ayoub AT"'
Large language models (LLMs) have become increasingly pivotal in various domains due the recent advancements in their performance capabilities. However, concerns persist regarding biases in LLMs, including gender, racial, and cultural biases derived
Externí odkaz:
http://arxiv.org/abs/2412.00962
Prior research has demonstrated that language models can, to a limited extent, represent moral norms in a variety of cultural contexts. This research aims to replicate these findings and further explore their validity, concentrating on issues like 'h
Externí odkaz:
http://arxiv.org/abs/2412.00956
Named-entity recognition (NER) is a task that typically requires large annotated datasets, which limits its applicability across domains with varying entity definitions. This paper addresses few-shot NER, aiming to transfer knowledge to new domains w
Externí odkaz:
http://arxiv.org/abs/2412.00426
Given a collection of feature maps indexed by a set $\mathcal{T}$, we study the performance of empirical risk minimization (ERM) on regression problems with square loss over the union of the linear classes induced by these feature maps. This setup ai
Externí odkaz:
http://arxiv.org/abs/2411.12029
Using the Wigner function in phase space, we study quantum steering and entanglement between two coupled harmonic oscillators. We derive expressions for purity and quantum steering in both directions and identify several important selection rules. Ou
Externí odkaz:
http://arxiv.org/abs/2411.07010
Model reduction is a key technology for large-scale physical systems in science and engineering, as it brings behavior expressed in many degrees of freedom to a more manageable size that subsequently allows control, optimization, and analysis with mu
Externí odkaz:
http://arxiv.org/abs/2411.08071
Autor:
Jadouli, Ayoub, Amrani, Chaker El
Wildfires pose significant threats to ecosystems, economies, and communities worldwide, necessitating advanced predictive methods for effective mitigation. This study introduces a novel and comprehensive dataset specifically designed for wildfire pre
Externí odkaz:
http://arxiv.org/abs/2411.06202
Autor:
Jadouli, Ayoub, Amrani, Chaker El
Traditional Feed-Forward Neural Networks (FFNN) and one-dimensional Convolutional Neural Networks (1D CNN) often encounter difficulties when dealing with long, columnar datasets that contain numerous features. The challenge arises from two primary fa
Externí odkaz:
http://arxiv.org/abs/2411.06020
Autor:
Tangui, Sophia, Hurand, Simon, Aljasmi, Rashed, Benmoumen, Ayoub, David, Marie-Laure, Moreau, Philippe, Morisset, Sophie, Célérier, Stéphane, Mauchamp, Vincent
Publikováno v:
Small (2024): 2406334
MXenes stand out from other 2D materials because they combine very good electrical conductivity with hydrophilicity, allowing cost-effective processing as thin films. Therefore, there is a high fundamental interest in unraveling the electronic transp
Externí odkaz:
http://arxiv.org/abs/2411.05461
Autor:
Foussoul, Ayoub, Goyal, Vineet
We consider a fundamental generalization of the classical newsvendor problem where the seller needs to decide on the inventory of a product jointly for multiple locations on a metric as well as a fulfillment policy to satisfy the uncertain demand tha
Externí odkaz:
http://arxiv.org/abs/2410.12134