Zobrazeno 1 - 10
of 2 046
pro vyhledávání: '"P. Martínez-Fernández"'
As machine learning (ML) and artificial intelligence (AI) technologies become increasingly prevalent in society, concerns about their environmental sustainability have grown. Developing and deploying ML-enabled systems, especially during training and
Externí odkaz:
http://arxiv.org/abs/2410.06708
Background: Given the fast-paced nature of today's technology, which has surpassed human performance in tasks like image classification, visual reasoning, and English understanding, assessing the impact of Machine Learning (ML) on energy consumption
Externí odkaz:
http://arxiv.org/abs/2409.12878
A synthetic Mirnov coils diagnostic is presented and used to study the capabilities of the poloidal array of single-axis coils and the two helical arrays of tri-axial coils installed in the TJ-II stellarator. This tool integrates the plasma currents
Externí odkaz:
http://arxiv.org/abs/2409.04221
Autor:
Omar, Rafiullah, Bogner, Justus, Muccini, Henry, Lago, Patricia, Martínez-Fernández, Silverio, Franch, Xavier
Background: Machine learning (ML) model composition is a popular technique to mitigate shortcomings of a single ML model and to design more effective ML-enabled systems. While ensemble learning, i.e., forwarding the same request to several models and
Externí odkaz:
http://arxiv.org/abs/2407.02914
The latest advancements in machine learning, specifically in foundation models, are revolutionizing the frontiers of existing software engineering (SE) processes. This is a bi-directional phenomona, where 1) software systems are now challenged to pro
Externí odkaz:
http://arxiv.org/abs/2406.18142
Generalized parton distributions (GPDs) are off-forward matrix elements of quark and gluon operators that work as a window to the total angular momentum of partons and their transverse imaging (nucleon tomography). To access GPDs one needs to look in
Externí odkaz:
http://arxiv.org/abs/2406.14640
Autor:
Martinez-Fernandez, Daniel, Pedrosa, Clara, Herranz, Miguel, Foteinopoulou, Katerina, Karayiannis, Nikos Ch., Laso, Manuel
Publikováno v:
J. Chem. Phys. 161, 034902 (2024)
Through extensive Monte Carlo simulations, we systematically study the effect of chain stiffness on the packing ability of linear polymers composed of hard spheres in extremely confined monolayers, corresponding effectively to 2D films. First, we exp
Externí odkaz:
http://arxiv.org/abs/2406.10376
Publikováno v:
Urban Rail Transit, Vol 6, Iss 3, Pp 145-156 (2020)
Abstract Minimizing energy consumption is a key issue from both an environmental and economic perspectives for railways systems; however, it is also important to reduce infrastructure construction costs. In the present work, an artificial neural netw
Externí odkaz:
https://doaj.org/article/7e131b7ea6094d2ea12c6daba1dee953
The growing use of large machine learning models highlights concerns about their increasing computational demands. While the energy consumption of their training phase has received attention, fewer works have considered the inference phase. For ML in
Externí odkaz:
http://arxiv.org/abs/2402.07585
The rapidly evolving fields of Machine Learning (ML) and Artificial Intelligence have witnessed the emergence of platforms like Hugging Face (HF) as central hubs for model development and sharing. This experience report synthesizes insights from two
Externí odkaz:
http://arxiv.org/abs/2402.07323