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pro vyhledávání: '"Luong, A"'
Diffuse domain methods (DDMs) have garnered significant attention for approximating solutions to partial differential equations on complex geometries. These methods implicitly represent the geometry by replacing the sharp boundary interface with a di
Externí odkaz:
http://arxiv.org/abs/2412.07007
Autor:
Motte, F., Pouteau, Y., Nony, T., Dell'Ova, P., Gusdorf, A., Brouillet, N., Stutz, A. M., Bontemps, S., Ginsburg, A., Csengeri, T., Men'shchikov, A., Valeille-Manet, M., Louvet, F., Bonfand, M., Galván-Madrid, R., Álvarez-Gutiérrez, R. H., Armante, M., Bronfman, L., Chen, H. -R. V., Cunningham, N., Díaz-González, D., Didelon, P., Fernández-López, M., Herpin, F., Kessler, N., Koley, A., Lefloch, B., Nestour, N. Le, Liu, H. -L., Moraux, E., Luong, Q. Nguyen, Olguin, F., Salinas, J., Sandoval-Garrido, N. A., Sanhueza, P., Veyry, R., Yoo, T.
ALMA-IMF imaged 15 massive protoclusters down to a resolution of of 2 kau scales, identifying about 1000 star-forming cores. The mass and luminosity of these cores, which are fundamental physical characteristics, are difficult to determine, a problem
Externí odkaz:
http://arxiv.org/abs/2412.02011
Noisy labels pose a substantial challenge in machine learning, often resulting in overfitting and poor generalization. Sharpness-Aware Minimization (SAM), as demonstrated in Foret et al. (2021), improves generalization over traditional Stochastic Gra
Externí odkaz:
http://arxiv.org/abs/2411.17132
This paper concerns the numerical approximation for the invariant distribution of Markovian switching L\'evy-driven stochastic differential equations. By combining the tamed-adaptive Euler-Maruyama scheme with the Multi-level Monte Carlo method, we p
Externí odkaz:
http://arxiv.org/abs/2411.04081
We propose a tamed-adaptive Milstein scheme for stochastic differential equations in which the first-order derivatives of the coefficients are locally H\"older continuous of order $\alpha$. We show that the scheme converges in the $L_2$-norm with a r
Externí odkaz:
http://arxiv.org/abs/2411.01849
Image registration techniques usually assume that the images to be registered are of a certain type (e.g. single- vs. multi-modal, 2D vs. 3D, rigid vs. deformable) and there lacks a general method that can work for data under all conditions. We propo
Externí odkaz:
http://arxiv.org/abs/2411.02672
Mixture of Experts (MoEs) plays an important role in the development of more efficient and effective large language models (LLMs). Due to the enormous resource requirements, studying large scale MoE algorithms remain in-accessible to many researchers
Externí odkaz:
http://arxiv.org/abs/2411.00918
Autor:
Pham, Loc Bao, Luong, Huong Hoang, Tran, Phu Thien, Ngo, Phuc Hoang, Nguyen, Vi Hoang, Nguyen, Thinh
Publikováno v:
An approach to hummed tune and song sequences matching Communications in Computer and Information Science (2022) 690-697
Melody stuck in your head, also known as "earworm", is tough to get rid of, unless you listen to it again or sing it out loud. But what if you can not find the name of that song? It must be an intolerable feeling. Recognizing a song name base on humm
Externí odkaz:
http://arxiv.org/abs/2410.20352
Autor:
Van Chien, Trinh, Duc, Bui Trong, Luong, Ho Viet Duc, Binh, Huynh Thi Thanh, Ngo, Hien Quoc, Chatzinotas, Symeon
This research exploits the applications of reconfigurable intelligent surface (RIS)-assisted multiple input multiple output (MIMO) systems, specifically addressing the enhancement of communication reliability with modulated signals. Specifically, we
Externí odkaz:
http://arxiv.org/abs/2410.05961
Publikováno v:
2024 Challenge. Proc. 4th Symposium on Security and Privacy in Speech Communication, 72-79
In this work, we describe our submissions for the Voice Privacy Challenge 2024. Rather than proposing a novel speech anonymization system, we enhance the provided baselines to meet all required conditions and improve evaluated metrics. Specifically,
Externí odkaz:
http://arxiv.org/abs/2410.02371