Zobrazeno 1 - 10
of 24 110
pro vyhledávání: '"Juan, Jose"'
This study aimed to identify and analyze the characteristics of highly cited publications in the field of artificial intelligence within the Science Citation Index Expanded from 1991 to 2022. The assessment focused on documents that garnered 100 cita
Externí odkaz:
http://arxiv.org/abs/2411.10491
Publikováno v:
Revista Espanola de Documentacion Cientifica - Vol. 47 N. 3 (2024)
The current situation of open research data in Spanish university repositories is analyzed by means of twelve indicators that allow us to compare them with each other. The twelve self-developed indicators deal with research datasets and institutional
Externí odkaz:
http://arxiv.org/abs/2411.10470
Experiments have confirmed the presence of a mass gap between the Standard Model and potential New Physics. Consequently, the exploration of effective field theories to detect signals indicative of Physics Beyond the Standard Model is of great intere
Externí odkaz:
http://arxiv.org/abs/2410.15826
Autor:
Galano-Frutos, Juan José, Bergamasco, Luca, Vigo, Paolo, Morciano, Matteo, Fasano, Matteo, Pirolli, Davide, Chiavazzo, Eliodoro, de Rosa, Maria Cristina
Aquaporins play a key role for the regulation of water transport and solute selectivity in biological cells and tissues. Due to their unique properties, during the last years aquaporins (AQPs) have attracted increasing interest for their use in the d
Externí odkaz:
http://arxiv.org/abs/2410.14355
Autor:
Beck, Jacob, Surana, Shikha, McAuliffe, Manus, Bent, Oliver, Barrett, Thomas D., Luis, Juan Jose Garau, Duckworth, Paul
Predicting the biophysical and functional properties of proteins is essential for in silico protein design. Machine learning has emerged as a promising technique for such prediction tasks. However, the relative scarcity of in vitro annotations means
Externí odkaz:
http://arxiv.org/abs/2410.08355
Autor:
Rodríguez-Aldavero, Juan José, García-Molina, Paula, Tagliacozzo, Luca, García-Ripoll, Juan José
This work explores the representation of univariate and multivariate functions as matrix product states (MPS), also known as quantized tensor-trains (QTT). It proposes an algorithm that employs iterative Chebyshev expansions and Clenshaw evaluations
Externí odkaz:
http://arxiv.org/abs/2407.09609
Autor:
Lima-Pereira, Bárbara K., Nuño-Ballesteros, Juan José, Oréfice-Okamoto, Bruna, Tomazella, João Nivaldo
We relate the Bruce-Roberts numbers of a 1-form with respect to an ICIS to other invariants as the GSV-index, Tjurina and Milnor numbers.
Externí odkaz:
http://arxiv.org/abs/2409.08380
We introduce a variational algorithm based on Matrix Product States that is trained by minimizing a generalized free energy defined using Tsallis entropy instead of the standard Gibbs entropy. As a result, our model can generate the probability distr
Externí odkaz:
http://arxiv.org/abs/2409.08352
This research focuses on solving time-dependent partial differential equations (PDEs), in particular the time-dependent Schr\"odinger equation, using matrix product states (MPS). We propose an extension of Hermite Distributed Approximating Functional
Externí odkaz:
http://arxiv.org/abs/2409.02916
Reinforcement learning is a subfield of machine learning that is having a huge impact in the different conventional disciplines, including physical sciences. Here, we show how reinforcement learning methods can be applied to solve optimization proble
Externí odkaz:
http://arxiv.org/abs/2408.15727