Zobrazeno 1 - 10
of 3 484
pro vyhledávání: '"P A, Cossu"'
Co-based honeycomb magnets have been actively studied recently for the potential realization of emergent quantum magnetism therein such as the Kitaev spin liquid. Here we employ density functional and dynamical mean-field theory methods to examine a
Externí odkaz:
http://arxiv.org/abs/2406.18003
We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics. This new methodology produces actual numerical solutions instead of bounds on them. We extend the original BootSTOP algorithm by adding multiple con
Externí odkaz:
http://arxiv.org/abs/2404.14909
Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data. Unfortunately, CL models tend to forget previous knowledge, thus often underperforming when compared with an offline model tra
Externí odkaz:
http://arxiv.org/abs/2404.07817
Autor:
Nascimento, Julio A. do, Hasnip, Phil J., Cavill, S. A., Cossu, Fabrizio, Kepaptsoglou, Demie, Ramasse, Quentin M., Kerrigan, Adam, Lazarov, Vlado K.
We explore the inelastic spectra of electrons impinging in a magnetic system. The methodology here presented is intended to highlight the charge-dependent interaction of the electron beam in a STEM-EELS experiment, and the local vector potential gene
Externí odkaz:
http://arxiv.org/abs/2401.12302
Autor:
Verwimp, Eli, Aljundi, Rahaf, Ben-David, Shai, Bethge, Matthias, Cossu, Andrea, Gepperth, Alexander, Hayes, Tyler L., Hüllermeier, Eyke, Kanan, Christopher, Kudithipudi, Dhireesha, Lampert, Christoph H., Mundt, Martin, Pascanu, Razvan, Popescu, Adrian, Tolias, Andreas S., van de Weijer, Joost, Liu, Bing, Lomonaco, Vincenzo, Tuytelaars, Tinne, van de Ven, Gido M.
Publikováno v:
Transactions on Machine Learning Research (TMLR), 2024
Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting what was learned in the past. In this work, we take a step back, and ask
Externí odkaz:
http://arxiv.org/abs/2311.11908
Autor:
Piera Grisolia, Rossella Tufano, Clara Iannarone, Antonio De Falco, Francesca Carlino, Cinzia Graziano, Raffaele Addeo, Marianna Scrima, Francesco Caraglia, Anna Ceccarelli, Pier Vitale Nuzzo, Alessia Maria Cossu, Stefano Forte, Raffaella Giuffrida, Michele Orditura, Michele Caraglia, Michele Ceccarelli
Publikováno v:
Journal of Translational Medicine, Vol 22, Iss 1, Pp 1-12 (2024)
Abstract Background Recent studies have highlighted the importance of the cell-free DNA (cfDNA) methylation profile in detecting breast cancer (BC) and its different subtypes. We investigated whether plasma cfDNA methylation, using cell-free Methylat
Externí odkaz:
https://doaj.org/article/b4709ad2f11d40968a2b2c09a3828f5f
Autor:
Antonio Cigliano, Isabella Gigante, Marina Serra, Gianpaolo Vidili, Maria M. Simile, Sara Steinmann, Francesco Urigo, Eleonora Cossu, Giovanni M. Pes, Maria P. Dore, Silvia Ribback, Egle P. Milia, Elena Pizzuto, Serena Mancarella, Li Che, Rosa M. Pascale, Gianluigi Giannelli, Matthias Evert, Xin Chen, Diego F. Calvisi
Publikováno v:
Journal of Experimental & Clinical Cancer Research, Vol 43, Iss 1, Pp 1-17 (2024)
Abstract Background Intrahepatic cholangiocarcinoma (iCCA) is a lethal primary liver tumor characterized by clinical aggressiveness, poor prognosis, and scarce therapeutic possibilities. Therefore, new treatments are urgently needed to render this di
Externí odkaz:
https://doaj.org/article/4358a36380b8455e977ec4053d406fe2
Autor:
Soutif--Cormerais, Albin, Carta, Antonio, Cossu, Andrea, Hurtado, Julio, Hemati, Hamed, Lomonaco, Vincenzo, Van de Weijer, Joost
Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream. In this empirical evaluation, we evalu
Externí odkaz:
http://arxiv.org/abs/2308.10328
Autor:
Ghassemlooy, Z., Khalighi, M. A., Zvanovec, S., Shrestha, A., Ortega, B., Petkovic, M., Pang, X., Sirtori, C., Orsucci, D., Moll, F., Cossu, G., Spirito, V., Ninos, M. P., Ciaramella, E., Bas, J., Amay, M., Huang, S., Safari, M., Gutema, T., Popoola, W., Matus, Vicente, Rabadan, Jose, Perez-Jimenez, Rafael, Panayirci, E., Diamantoulakis, P. D., Haas, H., Ijeh, I. C.
NEWFOCUS is an EU COST Action targeted at exploring radical solutions that could influence the design of future wireless networks. The project aims to address some of the challenges associated with optical wireless communication (OWC) and to establis
Externí odkaz:
http://arxiv.org/abs/2311.02511
Distributed learning on the edge often comprises self-centered devices (SCD) which learn local tasks independently and are unwilling to contribute to the performance of other SDCs. How do we achieve forward transfer at zero cost for the single SCDs?
Externí odkaz:
http://arxiv.org/abs/2303.15888