Zobrazeno 1 - 10
of 661
pro vyhledávání: '"Xiong Zhuang"'
Autor:
Li, Min, Chen, Chen, Xiong, Zhuang, Liu, Ying, Rong, Pengfei, Shan, Shanshan, Liu, Feng, Sun, Hongfu, Gao, Yang
Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing technique to extract the distribution of tissue susceptibilities, demonstrating significant potential in studying neurological diseases. However, the ill-conditioned natu
Externí odkaz:
http://arxiv.org/abs/2406.12300
Publikováno v:
Phys. Rev. D 110, 053007 (2024)
Our main objective is to derive the decay rate for the semileptonic decays $D\to V\ell^+\nu_{\ell}\,(\ell=e,\mu)$, where $V$ represents a vector particle. In these decays, the vector particle $V$ decays into three pseudo-scalar particles. To accompli
Externí odkaz:
http://arxiv.org/abs/2404.04816
Quantitative Susceptibility Mapping (QSM) dipole inversion is an ill-posed inverse problem for quantifying magnetic susceptibility distributions from MRI tissue phases. While supervised deep learning methods have shown success in specific QSM tasks,
Externí odkaz:
http://arxiv.org/abs/2403.14070
Supervised deep learning methods have shown promise in undersampled Magnetic Resonance Imaging (MRI) reconstruction, but their requirement for paired data limits their generalizability to the diverse MRI acquisition parameters. Recently, unsupervised
Externí odkaz:
http://arxiv.org/abs/2311.12078
Autor:
Gao, Yang, Xiong, Zhuang, Shan, Shanshan, Liu, Yin, Rong, Pengfei, Li, Min, Wilman, Alan H, Pike, G. Bruce, Liu, Feng, Sun, Hongfu
Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruc
Externí odkaz:
http://arxiv.org/abs/2311.07823
Autor:
Xiong, Zhuang, Hou, Yaoping
In this paper we consider the eigenvalues and the Seidel eigenvalues of a chain graph. An$\dbar$eli\'{c}, da Fonseca, Simi\'{c}, and Du \cite{andelic2020tridiagonal} conjectured that there do not exist non-isomorphic cospectral chain graphs with resp
Externí odkaz:
http://arxiv.org/abs/2310.20230
Autor:
Xiong, Zhuang
Let $\Gamma = (G, \sigma)$ be a signed graph, where $G = (V(G),E(G))$ is an (unsigned) graph, called the underlying graph. The net Laplacian matrix of $\Gamma$ is defined as $L^{\pm}(\Gamma) = D^{\pm}(\Gamma) - A(\Gamma)$, where $D^{\pm}(\Gamma)$ and
Externí odkaz:
http://arxiv.org/abs/2310.12784
The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects. To address this generalization issue in
Externí odkaz:
http://arxiv.org/abs/2308.09467
Publikováno v:
Phys. Lett. B 848 (2024) 138363
We study the $Z_{cs}(3985)$ and $Z_{cs}(4000)$ exotic states in the decays of $\Lambda_b$ baryons through a molecular scenario. In the final state interaction, the $\Lambda_b\to \Lambda_c^+ D_s^{(*)-}$ decays are followed by the $\Lambda_c^+ D_s^{(*)
Externí odkaz:
http://arxiv.org/abs/2305.01352
Affine Transformation Edited and Refined Deep Neural Network for Quantitative Susceptibility Mapping
Deep neural networks have demonstrated great potential in solving dipole inversion for Quantitative Susceptibility Mapping (QSM). However, the performances of most existing deep learning methods drastically degrade with mismatched sequence parameters
Externí odkaz:
http://arxiv.org/abs/2211.13942