Zobrazeno 1 - 10
of 2 541
pro vyhledávání: '"A. Miguelez"'
The structure of the normal modes of vibration of rotationally invariant $n$-vortices in the Ginzburg-Landau/Abelian Higgs model is completely unveiled for any value of the coupling constant.
Comment: 5 pages, 4 figures
Comment: 5 pages, 4 figures
Externí odkaz:
http://arxiv.org/abs/2410.08705
The resonant energy transfer mechanism, responsible for the presence of fractal patterns in the velocity diagrams of kink-antikink scattering, is analyzed for a family of two-component scalar field theory models, in which the kink solutions have two
Externí odkaz:
http://arxiv.org/abs/2407.09131
Autor:
Jain, Nitisha, Akhtar, Mubashara, Giner-Miguelez, Joan, Shinde, Rajat, Vanschoren, Joaquin, Vogler, Steffen, Goswami, Sujata, Rao, Yuhan, Santos, Tim, Oala, Luis, Karamousadakis, Michalis, Maskey, Manil, Marcenac, Pierre, Conforti, Costanza, Kuchnik, Michael, Aroyo, Lora, Benjelloun, Omar, Simperl, Elena
Data is critical to advancing AI technologies, yet its quality and documentation remain significant challenges, leading to adverse downstream effects (e.g., potential biases) in AI applications. This paper addresses these issues by introducing Croiss
Externí odkaz:
http://arxiv.org/abs/2407.16883
Autor:
Alonso-Izquierdo, A., Blanco-Pillado, J. J., Miguélez-Caballero, D., Navarro-Obregón, S., Queiruga, J.
The evolution of 1-vortices when their massive bound mode is excited is investigated in detail (both analytically and numerically) in the Abelian-Higgs model for different ranges of the self-coupling constant. The dependence of the spectrum of the 1-
Externí odkaz:
http://arxiv.org/abs/2405.06030
Recent regulatory initiatives like the European AI Act and relevant voices in the Machine Learning (ML) community stress the need to describe datasets along several key dimensions for trustworthy AI, such as the provenance processes and social concer
Externí odkaz:
http://arxiv.org/abs/2404.15320
Autor:
Akhtar, Mubashara, Benjelloun, Omar, Conforti, Costanza, Gijsbers, Pieter, Giner-Miguelez, Joan, Jain, Nitisha, Kuchnik, Michael, Lhoest, Quentin, Marcenac, Pierre, Maskey, Manil, Mattson, Peter, Oala, Luis, Ruyssen, Pierre, Shinde, Rajat, Simperl, Elena, Thomas, Goeffry, Tykhonov, Slava, Vanschoren, Joaquin, van der Velde, Jos, Vogler, Steffen, Wu, Carole-Jean
Data is a critical resource for Machine Learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that simplifies how data is used by ML tools and frameworks. Croissant makes da
Externí odkaz:
http://arxiv.org/abs/2403.19546
Industrial processes generate a massive amount of monitoring data that can be exploited to uncover hidden time losses in the system, leading to enhanced accuracy of maintenance policies and, consequently, increasing the effectiveness of the equipment
Externí odkaz:
http://arxiv.org/abs/2403.07554
To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models. Besides, data-sharing practi
Externí odkaz:
http://arxiv.org/abs/2401.10304
The dynamics of a wobbling kink in a two-component coupled $\phi^4$ scalar field theory (with an excited orthogonal shape mode) is addressed. For this purpose, the vibration spectrum of the second order small kink fluctuation is studied in order to f
Externí odkaz:
http://arxiv.org/abs/2310.15738
We study the problem of valuing and hedging a vulnerable derivative claim with bilateral cash flows between two counterparties in the presence of asymmetric funding costs, defaults and wrong way risk (WWR). We characterize the pre-default claim value
Externí odkaz:
http://arxiv.org/abs/2308.10568