Zobrazeno 1 - 10
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pro vyhledávání: '"Chen, Fei"'
We investigate the first and second order cosmological perturbation equations in f(R) modified gravity theory and provide the equation of motion of second order scalar induced gravitational waves. We find that the effects of modified gravity not only
Externí odkaz:
http://arxiv.org/abs/2409.07702
Minimum Variance Distortionless Response (MVDR) is a classical adaptive beamformer that theoretically ensures the distortionless transmission of signals in the target direction, which makes it popular in real applications. Its noise reduction perform
Externí odkaz:
http://arxiv.org/abs/2409.06456
Generic deep learning (DL) networks for image restoration like denoising and interpolation lack mathematical interpretability, require voluminous training data to tune a large parameter set, and are fragile in the face of covariate shift. To address
Externí odkaz:
http://arxiv.org/abs/2407.01469
Autor:
Chen, Fei Yu
Given a ring $R$, we have a classical result stating that the ordinary category of modules is the abelianization of the category of augmented $R$-algebras. Analogously, using the framework of infinity categories and higher algebra, Francis showed tha
Externí odkaz:
http://arxiv.org/abs/2404.08082
Autor:
Tudosiu, Petru-Daniel, Yang, Yongxin, Zhang, Shifeng, Chen, Fei, McDonagh, Steven, Lampouras, Gerasimos, Iacobacci, Ignacio, Parisot, Sarah
Text-to-image generation has achieved astonishing results, yet precise spatial controllability and prompt fidelity remain highly challenging. This limitation is typically addressed through cumbersome prompt engineering, scene layout conditioning, or
Externí odkaz:
http://arxiv.org/abs/2404.02790
Autor:
Hu, Yingbai, Abu-Dakka, Fares J., Chen, Fei, Luo, Xiao, Li, Zheng, Knoll, Alois, Ding, Weiping
Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitating adept navigation of complex scenarios. A persistent challenge in IL
Externí odkaz:
http://arxiv.org/abs/2403.19916
Autor:
Xue, Shuchen, Liu, Zhaoqiang, Chen, Fei, Zhang, Shifeng, Hu, Tianyang, Xie, Enze, Li, Zhenguo
Diffusion probabilistic models (DPMs) have shown remarkable performance in high-resolution image synthesis, but their sampling efficiency is still to be desired due to the typically large number of sampling steps. Recent advancements in high-order nu
Externí odkaz:
http://arxiv.org/abs/2402.17376
Autor:
Ding, Yuting, Chen, Fei
Auditory spatial attention detection (ASAD) is used to determine the direction of a listener's attention to a speaker by analyzing her/his electroencephalographic (EEG) signals. This study aimed to further improve the performance of ASAD with a short
Externí odkaz:
http://arxiv.org/abs/2401.05819
Autor:
Min, Qi, Xu, Ziyang, He, Siqi, Lu, Haidong, Liu, Xingbang, Shen, Ruizi, Wu, Yanhong, Pan, Qikun, Zhao, Chongxiao, Chen, Fei, Su, Maogen, Dong, Chenzhong
We introduce the RHDLPP, a flux-limited multigroup radiation hydrodynamics numerical code designed for simulating laser-produced plasmas in diverse environments. The code bifurcates into two packages: RHDLPP-LTP for low-temperature plasmas generated
Externí odkaz:
http://arxiv.org/abs/2401.01718
Autor:
Chen, Fei, Van Nguyen, Hoa, Leong, Alex S., Panicker, Sabita, Baker, Robin, Ranasinghe, Damith C.
Publikováno v:
Signal Processing (2024)
We consider the problem of tracking multiple, unknown, and time-varying numbers of objects using a distributed network of heterogeneous sensors. In an effort to derive a formulation for practical settings, we consider limited and unknown sensor field
Externí odkaz:
http://arxiv.org/abs/2401.00605