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pro vyhledávání: '"Guo, JingWei"'
The purpose of this paper is twofold. One is to investigate the properties of the zeros of cross-products of Bessel functions or derivatives of ultraspherical Bessel functions, as well as the properties of the zeros of the derivative of the first-kin
Externí odkaz:
http://arxiv.org/abs/2412.14059
While Test-Time Adaptation (TTA) has shown promise in addressing distribution shifts between training and testing data, its effectiveness diminishes with heterogeneous data streams due to uniform target estimation. As previous attempts merely stabili
Externí odkaz:
http://arxiv.org/abs/2411.15173
Medical Image Analysis (MedIA) has become indispensable in modern healthcare, enhancing clinical diagnostics and personalized treatment. Despite the remarkable advancements supported by deep learning (DL) technologies, their practical deployment face
Externí odkaz:
http://arxiv.org/abs/2411.05824
Autor:
Guo, Jingwei, Wang, Meihui, Ilyankou, Ilya, Jongwiriyanurak, Natchapon, Gao, Xiaowei, Christie, Nicola, Haworth, James
Panoramic cycling videos can record 360{\deg} views around the cyclists. Thus, it is essential to conduct automatic road user analysis on them using computer vision models to provide data for studies on cycling safety. However, the features of panora
Externí odkaz:
http://arxiv.org/abs/2407.15199
Whilst spectral Graph Neural Networks (GNNs) are theoretically well-founded in the spectral domain, their practical reliance on polynomial approximation implies a profound linkage to the spatial domain. As previous studies rarely examine spectral GNN
Externí odkaz:
http://arxiv.org/abs/2401.09071
While recent test-time adaptations exhibit efficacy by adjusting batch normalization to narrow domain disparities, their effectiveness diminishes with realistic mini-batches due to inaccurate target estimation. As previous attempts merely introduce s
Externí odkaz:
http://arxiv.org/abs/2312.09486
Publikováno v:
Proceedings of the ACM Web Conference 2023
Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph machine learning, with polynomial filters applied for graph convolutions, where all nodes share the identical filter weights to mine their local contexts. Despite the suc
Externí odkaz:
http://arxiv.org/abs/2312.09041
Autor:
Guo, Jingwei, Zhu, Xiangrong
For symbol $a\in S^{n(\rho-1)/2}_{\rho,1}$ the pseudo-differential operator $T_a$ may not be $L^2$ bounded. However, under some mild extra assumptions on $a$, we show that $T_a$ is bounded from $L^{\infty}$ to $BMO$ and on $L^p$ for $2\leq p<\infty$.
Externí odkaz:
http://arxiv.org/abs/2309.10380
Autor:
Chen, Xuezhi, Guo, Jingwei
We prove a quantitative Roth-type theorem for polynomial corners in $\mathbb{R}^2$. Let $P_1$ and $P_2$ be two linearly independent polynomials with zero constant term. We show that any measurable subset of $[0,1]^2$ with positive measure contains th
Externí odkaz:
http://arxiv.org/abs/2307.00271
Autor:
Qian, Feiyu, Cai, Xianglong, He, Shutong, Sun, Jinglu, Xu, Ming, Jia, Yuxi, Liu, Zhensong, Tan, Yannan, Liu, Wanfa, Guo, Jingwei
Publikováno v:
In Optics Communications 1 January 2025 574
