Zobrazeno 1 - 10
of 94 689
pro vyhledávání: '"P, Daniele"'
We study particular integrated correlation functions of two superconformal primary operators of the stress tensor multiplet in the presence of a half-BPS line defect labelled by electromagnetic charges $(p,q)$ in $\mathcal{N}=4$ supersymmetric Yang-M
Externí odkaz:
http://arxiv.org/abs/2409.12786
Autor:
De Sensi, Daniele, Pichetti, Lorenzo, Vella, Flavio, De Matteis, Tiziano, Ren, Zebin, Fusco, Luigi, Turisini, Matteo, Cesarini, Daniele, Lust, Kurt, Trivedi, Animesh, Roweth, Duncan, Spiga, Filippo, Di Girolamo, Salvatore, Hoefler, Torsten
Publikováno v:
Published in Proceedings of The International Conference for High Performance Computing Networking, Storage, and Analysis (SC '24) (2024)
Multi-GPU nodes are increasingly common in the rapidly evolving landscape of exascale supercomputers. On these systems, GPUs on the same node are connected through dedicated networks, with bandwidths up to a few terabits per second. However, gauging
Externí odkaz:
http://arxiv.org/abs/2408.14090
Autor:
Cambrin, Daniele Rege, Militone, Gabriele Scaffidi, Colomba, Luca, Malnati, Giovanni, Apiletti, Daniele, Garza, Paolo
Designing effective game tutorials is crucial for a smooth learning curve for new players, especially in games with many rules and complex core mechanics. Evaluating the effectiveness of these tutorials usually requires multiple iterations with teste
Externí odkaz:
http://arxiv.org/abs/2408.08396
Publikováno v:
Crop Protection, Volume 184, October 2024, 106848
The use of deep learning methods for precision farming is gaining increasing interest. However, collecting training data in this application field is particularly challenging and costly due to the need of acquiring information during the different gr
Externí odkaz:
http://arxiv.org/abs/2407.14119
Autor:
Kryston, Michele, Marangone, Edoardo, Di Ciccio, Claudio, Friolo, Daniele, Nemmi, Eugenio Nerio, Samory, Mattia, Spina, Michele, Venturi, Daniele, Weber, Ingo
Blockchain technology streamlines multi-party collaborations in decentralized settings, especially where trust is limited. While public blockchains enhance transparency and reliability, they conflict with confidentiality. To address this, we introduc
Externí odkaz:
http://arxiv.org/abs/2407.10684
Large Language Models (LLMs) have demonstrated impressive performance across various tasks. However, current training approaches combine standard cross-entropy loss with extensive data, human feedback, or ad hoc methods to enhance performance. These
Externí odkaz:
http://arxiv.org/abs/2409.13641
Autor:
Cornet, Antoine, Shen, Jie, Ronca, Alberto, Li, Shubin, Neuber, Nico, Frey, Maximilian, Pineda, Eloi, Deschamps, Thierry, Martinet, Christine, Floch, Sylvie Le, Cangialosi, Daniele, Chushkin, Yuriy, Zontone, Federico, Cammarata, Marco, Vaughan, Gavin B. M., di Michiel, Marco, Garbarino, Gaston, Busch, Ralf, Gallino, Isabella, Goujon, Celine, Legendre, Murielle, Manthilake, Geeth, Ruta, Beatrice
It is usually assumed that the memory of any thermo-mechanical protocol applied to a glass can be erased by heating the material in the supercooled liquid. While this is true for thermally treated amorphous solids, we show that hydrostatic compressio
Externí odkaz:
http://arxiv.org/abs/2409.13636
Autor:
Cacciatore, Alessandro, Morelli, Valerio, Paganica, Federica, Frontoni, Emanuele, Migliorelli, Lucia, Berardini, Daniele
Deep learning has long been dominated by multi-layer perceptrons (MLPs), which have demonstrated superiority over other optimizable models in various domains. Recently, a new alternative to MLPs has emerged - Kolmogorov-Arnold Networks (KAN)- which a
Externí odkaz:
http://arxiv.org/abs/2409.13550
We present Flotta, a Federated Learning framework designed to train machine learning models on sensitive data distributed across a multi-party consortium conducting research in contexts requiring high levels of security, such as the biomedical field.
Externí odkaz:
http://arxiv.org/abs/2409.13473
Autor:
Malpetti, Daniele, Azzimonti, Laura
We present a novel strategy for detecting global outliers in a federated learning setting, targeting in particular cross-silo scenarios. Our approach involves the use of two servers and the transmission of masked local data from clients to one of the
Externí odkaz:
http://arxiv.org/abs/2409.13466