Zobrazeno 1 - 10
of 143
pro vyhledávání: '"Zhang Ruixi"'
We introduce a new hyperbolic approximation to the incompressible Navier-Stokes equations by incorporating a first-order relaxation and using the artificial compressibility method. With two relaxation parameters in the model, we rigorously prove the
Externí odkaz:
http://arxiv.org/abs/2411.15575
This paper presents a dissipativeness analysis of a quadrature method of moments (called HyQMOM) for the one-dimensional BGK equation. The method has exhibited its good performance in numerous applications. However, its mathematical foundation has no
Externí odkaz:
http://arxiv.org/abs/2406.13931
Autor:
Ng, Dianwen, Zhang, Chong, Zhang, Ruixi, Ma, Yukun, Ritter-Gutierrez, Fabian, Nguyen, Trung Hieu, Ni, Chongjia, Zhao, Shengkui, Chng, Eng Siong, Ma, Bin
Large self-supervised pre-trained speech models require computationally expensive fine-tuning for downstream tasks. Soft prompt tuning offers a simple parameter-efficient alternative by utilizing minimal soft prompt guidance, enhancing portability wh
Externí odkaz:
http://arxiv.org/abs/2309.09413
This work is concerned with kinetic equations with velocity of constant magnitude. We propose a quadrature method of moments based on the Poisson kernel, called Poisson-EQMOM. The derived moment closure systems are well defined for all physically rel
Externí odkaz:
http://arxiv.org/abs/2308.10083
This paper performs a stability analysis of a class of moment closure systems derived with an extended quadrature method of moments (EQMOM) for the one-dimensional BGK equation. The class is characterized with a kernel function. A sufficient conditio
Externí odkaz:
http://arxiv.org/abs/2306.07945
Autor:
Ng, Dianwen, Zhang, Ruixi, Yip, Jia Qi, Zhang, Chong, Ma, Yukun, Nguyen, Trung Hieu, Ni, Chongjia, Chng, Eng Siong, Ma, Bin
Most of the existing neural-based models for keyword spotting (KWS) in smart devices require thousands of training samples to learn a decent audio representation. However, with the rising demand for smart devices to become more personalized, KWS mode
Externí odkaz:
http://arxiv.org/abs/2305.01170
Autor:
Ng, Dianwen, Zhang, Ruixi, Yip, Jia Qi, Yang, Zhao, Ni, Jinjie, Zhang, Chong, Ma, Yukun, Ni, Chongjia, Chng, Eng Siong, Ma, Bin
Existing self-supervised pre-trained speech models have offered an effective way to leverage massive unannotated corpora to build good automatic speech recognition (ASR). However, many current models are trained on a clean corpus from a single source
Externí odkaz:
http://arxiv.org/abs/2302.14597
Autor:
Hashizume, Naoya a, ⁎, Takano, Riki a, Umehara, Noritsugu a, Tokoroyama, Takayuki a, Zhang, Ruixi a, Hanyuda, Kiyoshi b, Otsuka, Ayano b, Ueda, Mao b
Publikováno v:
In Tribology International December 2024 200
Autor:
Liu, Miaomiao a, b, 1, Hu, Mengyu a, 1, Liu, Rong a, 1, Wang, Ling a, Wang, Jingtong a, Wang, Yun a, Zhang, Ruixi a, b, Wang, Hui a, Liu, Mengru a, Zhang, Yi a, Wang, Lizhuo a, b, Pei, Wenjun a, b, ⁎, Zhang, Yao a, b, ⁎
Publikováno v:
In Gene 20 October 2024 925
Publikováno v:
In Tribology International October 2024 198