Zobrazeno 1 - 10
of 3 233
pro vyhledávání: '"Nicoli P"'
Autor:
Nicoli Paganoti de Mello, Fernando Carlos Ramos Espinoza, Gustavo da Silva Claudiano, Jefferson Yunis-Aguinaga, Janaina Graça de Oliveira Carvalho, Josiane Elizabeth Almeida Silva, Elaine Cristina Pacheco de Oliveira, Julieta Rodini Engrácia de Moraes
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Tilapia is one of the most important farmed fish in the world and the most cultivated in Brazil. The increase of this farming favors the appearance of diseases, including bacterial diseases. Therefore, the aim of this study was to evaluate t
Externí odkaz:
https://doaj.org/article/e142c79040744d289a6c0dcdcd60f8f1
Autor:
Fernando Carlos Ramos‐Espinoza, Victor Alexander Cueva‐Quiroz, Norquis Caled Alvarez‐Rubio, Nicoli Paganoti de Mello, Julieta Rodini Engrácia deMoraes
Publikováno v:
Journal of the World Aquaculture Society, Vol 55, Iss 1, Pp 257-272 (2024)
Abstract Mannan oligosaccharides (MOS) have shown to stimulate immune response in different fish species, but the results may appear contradictory and have not been tested in conjunction with vaccination. We hypothesized that dietary MOS supplementat
Externí odkaz:
https://doaj.org/article/aaa64158e0bd4922b7fe85b538842671
Autor:
Bulgarelli, Andrea, Cellini, Elia, Jansen, Karl, Kühn, Stefan, Nada, Alessandro, Nakajima, Shinichi, Nicoli, Kim A., Panero, Marco
We introduce a novel technique to numerically calculate R\'enyi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches can be combined with the replica trick using a custom neural-network
Externí odkaz:
http://arxiv.org/abs/2410.14466
Autor:
Santos, Diogo Reis, Aillet, Albert Sund, Boiano, Antonio, Milasheuski, Usevalad, Giusti, Lorenzo, Di Gennaro, Marco, Kianoush, Sanaz, Barbieri, Luca, Nicoli, Monica, Carminati, Michele, Redondi, Alessandro E. C., Savazzi, Stefano, Serio, Luigi
The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorit
Externí odkaz:
http://arxiv.org/abs/2410.13869
A novel fully atomistic multiscale classical approach to model the optical response of solvated real-size plasmonic nanoparticles (NPs) is presented. The model is based on the coupling of the Frequency Dependent Fluctuating Charges and Fluctuating Di
Externí odkaz:
http://arxiv.org/abs/2407.02650
Autor:
Pardo, Fernando De Meer, Lehmann, Claude, Gehrig, Dennis, Nagy, Andrea, Nicoli, Stefano, Misheva, Branka Hadji, Braschler, Martin, Stockinger, Kurt
In this paper, we present an end-to-end multi-source Entity Matching problem, which we call entity group matching, where the goal is to assign to the same group, records originating from multiple data sources but representing the same real-world enti
Externí odkaz:
http://arxiv.org/abs/2406.15015
Autor:
Lafiosca, Piero, Nicoli, Luca, Pipolo, Silvio, Corni, Stefano, Giovannini, Tommaso, Cappelli, Chiara
Investigating nanoplasmonics using time-dependent approaches permits shedding light on the dynamic optical properties of plasmonic structures, which are intrinsically connected with their potential applications in photochemistry and photoreactivity.
Externí odkaz:
http://arxiv.org/abs/2406.10926
Autor:
Nicoli, Kim A., Anders, Christopher J., Funcke, Lena, Hartung, Tobias, Jansen, Karl, Kühn, Stefan, Müller, Klaus-Robert, Stornati, Paolo, Kessel, Pan, Nakajima, Shinichi
In this paper, we propose a novel and powerful method to harness Bayesian optimization for Variational Quantum Eigensolvers (VQEs) -- a hybrid quantum-classical protocol used to approximate the ground state of a quantum Hamiltonian. Specifically, we
Externí odkaz:
http://arxiv.org/abs/2406.06150
Bayesian Federated Learning (FL) has been recently introduced to provide well-calibrated Machine Learning (ML) models quantifying the uncertainty of their predictions. Despite their advantages compared to frequentist FL setups, Bayesian FL tools impl
Externí odkaz:
http://arxiv.org/abs/2405.05855
Autor:
Milasheuski, Usevalad, Barbieri, Luca, Tedeschini, Bernardo Camajori, Nicoli, Monica, Savazzi, Stefano
Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information. One of those domains is healthcare, where groups of silos collaborate in order
Externí odkaz:
http://arxiv.org/abs/2404.18519