Zobrazeno 1 - 10
of 9 978
pro vyhledávání: '"Martínez Fernández"'
Autor:
Martinez-Sanchez Rafael M., Bretones-García María Dolores, Valdiosera Cristina, Vera-Rodríguez Juan Carlos, López Flores Inmaculada, Simón-Vallejo María D., Ruiz Borrega Pilar, Martínez Fernández María J., Romo Villalba Jorge L., Bermúdez Jiménez Francisco, Martín de los Santos Rafael, Pardo-Gordó Salvador, Cortés Sánchez Miguel
Publikováno v:
Open Archaeology, Vol 8, Iss 1, Pp 892-904 (2022)
The presence of scattered prehistoric human bones in caves and sinkholes is common in many regions of Iberia. These are usually interpreted as erratic elements coming from burial contexts, usually collective associations. These burial contexts are ve
Externí odkaz:
https://doaj.org/article/b5d2c3a45160432596502ead08359322
Autor:
de Martino, Vincenzo, Castaño, Joel, Palomba, Fabio, Franch, Xavier, Martínez-Fernández, Silverio
Context: The emergence of Large Language Models (LLMs) has significantly transformed Software Engineering (SE) by providing innovative methods for analyzing software repositories. Objectives: Our objective is to establish a practical framework for fu
Externí odkaz:
http://arxiv.org/abs/2411.09974
Background: Open-Source Pre-Trained Models (PTMs) and datasets provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailored to Software Engineering (SE) needs. Aims: We apply an SE-oriented c
Externí odkaz:
http://arxiv.org/abs/2411.09683
The proliferation of Machine Learning (ML) models and their open-source implementations has transformed Artificial Intelligence research and applications. Platforms like Hugging Face (HF) enable the development, sharing, and deployment of these model
Externí odkaz:
http://arxiv.org/abs/2411.09645
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