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pro vyhledávání: '"Youssef, A A"'
Machine unlearning, the process of selectively removing data from trained models, is increasingly crucial for addressing privacy concerns and knowledge gaps post-deployment. Despite this importance, existing approaches are often heuristic and lack fo
Externí odkaz:
http://arxiv.org/abs/2412.09119
While the market impact of aggressive orders has been extensively studied, the impact of passive orders, those executed through limit orders, remains less understood. The goal of this paper is to investigate passive market impact by developing a micr
Externí odkaz:
http://arxiv.org/abs/2412.07461
Detecting road obstacles is essential for autonomous vehicles to navigate dynamic and complex traffic environments safely. Current road obstacle detection methods typically assign a score to each pixel and apply a threshold to generate final predicti
Externí odkaz:
http://arxiv.org/abs/2412.05707
Effect of top metallic contacts on energy conversion performances for near-field thermophotovoltaics
Autor:
Jeyar, Youssef, Austry, Kevin, Luo, Minggang, Guizal, Brahim, Zheng, Yi, Messina, Riccardo, Vaillon, Rodolphe, Antezza, Mauro
The design of metallic contact grids on the front side of thermophotovoltaic cells is critical since it can cause significant optical and electrical resistive losses, particularly in the near field. However, from the theoretical point of view, this e
Externí odkaz:
http://arxiv.org/abs/2412.04258
Autor:
Mansour, Youssef, Heckel, Reinhard
We investigate biases in pretraining datasets for large language models (LLMs) through dataset classification experiments. Building on prior work demonstrating the existence of biases in popular computer vision datasets, we analyze popular open-sourc
Externí odkaz:
http://arxiv.org/abs/2412.02857
A slot-based energy storage decision-making approach for optimal Off-Grid telecommunication operator
This paper proposes a slot-based energy storage approach for decision-making in the context of an Off-Grid telecommunication operator. We consider network systems powered by solar panels, where harvest energy is stored in a battery that can also be s
Externí odkaz:
http://arxiv.org/abs/2412.01731
Autor:
Ayad, Youssef, Fahlaoui, Said
We describe the full group of isometries of each left invariant Riemannian metric on the simply connected unimodular nilpotent or solvable $(R)$-type Lie groups of dimension four.
Externí odkaz:
http://arxiv.org/abs/2412.01588
This paper evaluates the use of metamorphic relations to enhance the robustness and real-world performance of machine learning models. We propose a Metamorphic Retraining Framework, which applies metamorphic relations to data and utilizes semi-superv
Externí odkaz:
http://arxiv.org/abs/2412.01958
Autor:
Paschali, Magdalini, Chen, Zhihong, Blankemeier, Louis, Varma, Maya, Youssef, Alaa, Bluethgen, Christian, Langlotz, Curtis, Gatidis, Sergios, Chaudhari, Akshay
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models, are trained on
Externí odkaz:
http://arxiv.org/abs/2411.18730
As the field of quantum computing grows, novel algorithms which take advantage of quantum phenomena need to be developed. As we are currently in the NISQ (noisy intermediate scale quantum) era, quantum algorithm researchers cannot reliably test their
Externí odkaz:
http://arxiv.org/abs/2411.18354