Zobrazeno 1 - 10
of 5 238
pro vyhledávání: '"A, Saux"'
Autor:
Jeggle, Kai, Czerkawski, Mikolaj, Serva, Federico, Saux, Bertrand Le, Neubauer, David, Lohmann, Ulrike
IceCloudNet is a novel method based on machine learning able to predict high-quality vertically resolved cloud ice water contents (IWC) and ice crystal number concentrations (N$_\textrm{ice}$). The predictions come at the spatio-temporal coverage and
Externí odkaz:
http://arxiv.org/abs/2410.04135
In this work, a 3.5 GS/s voltage-controlled oscillator (VCO) analog-to-digital converter (ADC) using multi-stage noise shaping (MASH) is presented. This 28nm CMOS ADC achieves second-order noise shaping in an easily-scalable, open-loop configuration.
Externí odkaz:
http://arxiv.org/abs/2409.15066
Autor:
Dethero, M. -G., Pratt, J., Vlaykov, D. G., Baraffe, I., Guillet, T., Goffrey, T., Saux, A. Le, Morison, A.
Publikováno v:
A&A 692, A46 (2024)
Theoretical descriptions of convective overshooting often rely on a one-dimensional parameterization of the flow called the filling factor for convection. Several definitions of the filling factor have been developed, based on: (1) the percentage of
Externí odkaz:
http://arxiv.org/abs/2409.09815
Autor:
Ceschini, Andrea, Mauro, Francesco, De Falco, Francesca, Sebastianelli, Alessandro, Verdone, Alessio, Rosato, Antonello, Saux, Bertrand Le, Panella, Massimo, Gamba, Paolo, Ullo, Silvia L.
Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs), aimed at overcoming the computational and scalability challenges inherent in classical GNNs that are powerful tools for analyzing da
Externí odkaz:
http://arxiv.org/abs/2408.06524
Autor:
Sebastianelli, Alessandro, Mauro, Francesco, Ciabatti, Giulia, Spiller, Dario, Saux, Bertrand Le, Gamba, Paolo, Ullo, Silvia
A significant amount of remotely sensed data is generated daily by many Earth observation (EO) spaceborne and airborne sensors over different countries of our planet. Different applications use those data, such as natural hazard monitoring, global cl
Externí odkaz:
http://arxiv.org/abs/2407.17108
Autor:
Miroszewski, Artur, Asiani, Marco Fellous, Mielczarek, Jakub, Saux, Bertrand Le, Nalepa, Jakub
Quantum Machine Learning (QML) has gathered significant attention through approaches like Quantum Kernel Machines. While these methods hold considerable promise, their quantum nature presents inherent challenges. One major challenge is the limited re
Externí odkaz:
http://arxiv.org/abs/2407.15776
Autor:
Morison, Adrien, Saux, Arthur Le, Baraffe, Isabelle, Morton, Jack, Guillet, Thomas, Vlaykov, Dimitar, Goffrey, Tom, Pratt, Jane
As a massive star evolves along the main sequence, its core contracts, leaving behind a stable stratification in helium. We simulate 2D convection in the core at three different stages of evolution of a $5M_{\odot}$ star, with three different stratif
Externí odkaz:
http://arxiv.org/abs/2407.06047
Autor:
Dionelis, Nikolaos, Fibaek, Casper, Camilleri, Luke, Luyts, Andreas, Bosmans, Jente, Saux, Bertrand Le
When we are primarily interested in solving several problems jointly with a given prescribed high performance accuracy for each target application, then Foundation Models should for most cases be used rather than problem-specific models. We focus on
Externí odkaz:
http://arxiv.org/abs/2406.18295
Quantum generative modeling is among the promising candidates for achieving a practical advantage in data analysis. Nevertheless, one key challenge is to generate large-size images comparable to those generated by their classical counterparts. In thi
Externí odkaz:
http://arxiv.org/abs/2406.02668
Autor:
Saux, Patrick
This thesis aims to study some of the mathematical challenges that arise in the analysis of statistical sequential decision-making algorithms for postoperative patients follow-up. Stochastic bandits (multiarmed, contextual) model the learning of a se
Externí odkaz:
http://arxiv.org/abs/2405.01994