Zobrazeno 1 - 10
of 6 859
pro vyhledávání: '"Costa, A. F."'
Surface roughness plays a crucial role in the accuracy of indentation experiments used to measure the elastic properties of materials. In this study, we present a computational analysis of how surface roughness, represented explicitly by fractal geom
Externí odkaz:
http://arxiv.org/abs/2410.17486
Publikováno v:
Phys. Rev. B 110, 115101 (2024)
We study the physics of the strong-coupling Hubbard model in a kagome lattice ribbon under mechanical tension and half-filling. It is known that in the absence of strain, the lattice symmetry of the system and strong electronic interactions induce ma
Externí odkaz:
http://arxiv.org/abs/2406.04514
Autor:
Costa, Antonio F.
Let $S$ be a (compact)\ Riemann surface of genus greater than one. Two automorphism of $S$ are topologically equivalent if they are conjugated by a homeomorphism. The topological classification of automorphisms is a classical problem and its study wa
Externí odkaz:
http://arxiv.org/abs/2406.02805
Autor:
Mendoza, Edgar, Costa, Samuel F. M., Carvajal, Miguel, Pilling, Sérgio, Alves, Márcio O., Galvão, Breno R. L.
Publikováno v:
A&A 687, A149 (2024)
Among the silicon bearing species discovered in the interstellar medium, SiS and SiO stand out as key tracers due to their distinct chemistry and abundances in interstellar and circumstellar environments. Our objective is to enhance the network of Si
Externí odkaz:
http://arxiv.org/abs/2404.07406
The principles of ergodicity and thermalization constitute the foundation of statistical mechanics, positing that a many-body system progressively loses its local information as it evolves. Nevertheless, these principles can be disrupted when thermal
Externí odkaz:
http://arxiv.org/abs/2401.03111
Autor:
Pinaya, Walter H. L., Graham, Mark S., Kerfoot, Eric, Tudosiu, Petru-Daniel, Dafflon, Jessica, Fernandez, Virginia, Sanchez, Pedro, Wolleb, Julia, da Costa, Pedro F., Patel, Ashay, Chung, Hyungjin, Zhao, Can, Peng, Wei, Liu, Zelong, Mei, Xueyan, Lucena, Oeslle, Ye, Jong Chul, Tsaftaris, Sotirios A., Dogra, Prerna, Feng, Andrew, Modat, Marc, Nachev, Parashkev, Ourselin, Sebastien, Cardoso, M. Jorge
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perfor
Externí odkaz:
http://arxiv.org/abs/2307.15208
Autor:
Da Costa, Pedro F, Dafflon, Jessica, Mendes, Sergio Leonardo, Sato, João Ricardo, Cardoso, M. Jorge, Leech, Robert, Jones, Emily JH, Pinaya, Walter H. L.
Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions
Externí odkaz:
http://arxiv.org/abs/2212.04984
Autor:
Costa, Eudriano F. S.1,2 (AUTHOR) eudrianocosta@gmail.com, Menezes, Gui M.1,2 (AUTHOR), Colaço, Ana1,2 (AUTHOR)
Publikováno v:
PLoS ONE. 10/29/2024, Vol. 19 Issue 10, p1-21. 21p.
Autor:
Sousa, M. G.1 (AUTHOR), Costa, R. F. P.1 (AUTHOR), Neto, G. D. de Moraes2,3 (AUTHOR) gdmneto@gmail.com, Vernek, E.1,2 (AUTHOR)
Publikováno v:
Scientific Reports. 10/25/2024, Vol. 14 Issue 1, p1-13. 13p.
Autor:
Pinaya, Walter H. L., Tudosiu, Petru-Daniel, Dafflon, Jessica, da Costa, Pedro F, Fernandez, Virginia, Nachev, Parashkev, Ourselin, Sebastien, Cardoso, M. Jorge
Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects might be hindering their full potential. Generating synthetic data pr
Externí odkaz:
http://arxiv.org/abs/2209.07162