Zobrazeno 1 - 10
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pro vyhledávání: '"Nicoli, P"'
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
Autor:
Boiano, Antonio, Di Gennaro, Marco, Barbieri, Luca, Carminati, Michele, Nicoli, Monica, Redondi, Alessandro, Savazzi, Stefano, Aillet, Albert Sund, Santos, Diogo Reis, Serio, Luigi
Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particularly in sensitive domains such as healthcare. In this context, the TRUSTroke project aims to leverage FL to assist clinicians in ischemic str
Externí odkaz:
http://arxiv.org/abs/2404.11698
Publikováno v:
IEEE Transactions on Signal Processing, 2024
Accurate positioning is known to be a fundamental requirement for the deployment of Connected Automated Vehicles (CAVs). To meet this need, a new emerging trend is represented by cooperative methods where vehicles fuse information coming from navigat
Externí odkaz:
http://arxiv.org/abs/2402.16656
Autor:
Italiano, Lorenzo, Tedeschini, Bernardo Camajori, Brambilla, Mattia, Huang, Huiping, Nicoli, Monica, Wymeersch, Henk
The widespread adoption of the fifth generation (5G) of cellular networks has brought new opportunities for the development of localization-based services. High-accuracy positioning use cases and functionalities defined by the standards are drawing t
Externí odkaz:
http://arxiv.org/abs/2311.10551
Publikováno v:
2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
Federated Learning (FL) methods adopt efficient communication technologies to distribute machine learning tasks across edge devices, reducing the overhead in terms of data storage and computational complexity compared to centralized solutions. Rather
Externí odkaz:
http://arxiv.org/abs/2310.08087