Zobrazeno 1 - 10
of 10 916
pro vyhledávání: '"A. cu"'
Autor:
Wu, Xianhu, Bi, Jieyu, Cu, Guanglei, Liu, Nian, Xia, Gaojie, Sun, Jilong, Jiang, Jiaxin, Lu, Ning, Li, Ping, Zhao, Chunyi, Zuo, Zewen, Gu, Min
The buried interface between the electron transport layer and the perovskite layer suffers from severe interface defects and imperfect energy level alignment. To address this issue, this study employs a multifunctional organic molecule, curcumin, to
Externí odkaz:
http://arxiv.org/abs/2408.17167
Autor:
Cui, Cu
In this paper, we explore the acceleration of tensor product operations in finite element methods, leveraging the computational power of the NVIDIA A100 GPU Tensor Cores. We provide an accessible overview of the necessary mathematical background and
Externí odkaz:
http://arxiv.org/abs/2407.09621
We present a GPU implementation of vertex-patch smoothers for higher order finite element methods in two and three dimensions. Analysis shows that they are not memory bound with respect to GPU DRAM, but with respect to on-chip scratchpad memory. Mult
Externí odkaz:
http://arxiv.org/abs/2405.19004
Autor:
Cui, Cu, Kanschat, Guido
We present a matrix-free multigrid method for high-order discontinuous Galerkin (DG) finite element methods with GPU acceleration. A performance analysis is conducted, comparing various data and compute layouts. Smoother implementations are optimized
Externí odkaz:
http://arxiv.org/abs/2405.18982
Autor:
Le, Xuan Cu
Publikováno v:
VINE Journal of Information and Knowledge Management Systems, 2022, Vol. 54, Issue 5, pp. 973-989.
Publikováno v:
ICCV2023
Domain generalization (DG) seeks to learn robust models that generalize well under unknown distribution shifts. As a critical aspect of DG, optimizer selection has not been explored in depth. Currently, most DG methods follow the widely used benchmar
Externí odkaz:
http://arxiv.org/abs/2307.11108
Pretraining on a large-scale corpus has become a standard method to build general language models (LMs). Adapting a model to new data distributions targeting different downstream tasks poses significant challenges. Naive fine-tuning may incur catastr
Externí odkaz:
http://arxiv.org/abs/2305.12281
Autor:
Le, Xuan Cu
Publikováno v:
Journal of Science and Technology Policy Management, 2023, Vol. 15, Issue 4, pp. 863-885.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JSTPM-07-2022-0105
Autor:
Le, Xuan Cu
Publikováno v:
Revista de Gestão, 2023, Vol. 31, Issue 1, pp. 34-49.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/REGE-08-2021-0165
Autor:
Le, Xuan Cu
Publikováno v:
International Journal of Emerging Markets, 2021, Vol. 18, Issue 10, pp. 4108-4129.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJOEM-12-2020-1594