Výsledky vyhledávání - "A. NASCIMENTO"

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Complexity and Structural Results for the Hull and Convexity Numbers in Cycle Convexity for Graph Products

Autor: Anand, Bijo S., V., Ullas Chandran S., Nascimento, Julliano R., Nair, Revathy S.

Let $G$ be a graph and $S \subseteq V(G)$. In the cycle convexity, we say that $S$ is \textit{cycle convex} if for any $u\in V(G)\setminus S$, the induced subgraph of $S\cup\{u\}$ contains no cycle that includes $u$. The \textit{cycle convex hull} of

Externí odkaz: http://arxiv.org/abs/2412.19258

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A BCool survey of stellar magnetic cycles

Autor: Bellotti, S., Petit, P., Jeffers, S. V., Marsden, S. C., Morin, J., Vidotto, A. A., Folsom, C. P., See, V., Nascimento Jr, J. -D. do

The magnetic cycle on the Sun consists of two consecutive 11-yr sunspot cycles and exhibits a polarity reversal around sunspot maximum. Although solar dynamo theories have progressively become more sophisticated, the details as to how the dynamo sust

Externí odkaz: http://arxiv.org/abs/2412.09365

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Dual equivalence between the Maxwell-Chern-Simons theory and two self-dual massive models interacting with matter in $\mathcal{N}=2$, $d=3$ superspace

Autor: Gama, F. S., Nascimento, J. R., Petrov, A. Yu., Porfirio, P. J.

We investigate the $\mathcal{N} = 2$ supersymmetric generalization of the dual equivalence between the Maxwell-Chern-Simons model and two massive self-dual models. One of the self-dual models is described by a real scalar superfield, while the other

Externí odkaz: http://arxiv.org/abs/2412.07622

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Enhancing operational wind downscaling capabilities over Canada: Application of a Conditional Wasserstein GAN methodology

Autor: Guevara, Jorge, Nascimento, Victor, Schmude, Johannes, Salles, Daniel, Corbeil-Létourneau, Simon, Surcel, Madalina, Brunet, Dominique

Wind downscaling is essential for improving the spatial resolution of weather forecasts, particularly in operational Numerical Weather Prediction (NWP). This study advances wind downscaling by extending the DownGAN framework introduced by Annau et al

Externí odkaz: http://arxiv.org/abs/2412.06958

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Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data

Autor: Kerim, Abdulrahman, Marcolino, Leandro Soriano, Nascimento, Erickson R., Jiang, Richard

Supervised machine learning methods require large-scale training datasets to perform well in practice. Synthetic data has been showing great progress recently and has been used as a complement to real data. However, there is yet a great urge to asses

Externí odkaz: http://arxiv.org/abs/2412.05466

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Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains

Autor: Nobrega, Lucas Nogueira, de Oliveira, Ewerton, Saska, Martin, Nascimento, Tiago

The human-robot interaction (HRI) is a growing area of research. In HRI, complex command (action) classification is still an open problem that usually prevents the real applicability of such a technique. The literature presents some works that use ne

Externí odkaz: http://arxiv.org/abs/2412.02863

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