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您的检索式：作者名="Chunye GONG"
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| 1 | SARS-CoV-2 Omicron RBD shows weaker binding affinity than the currently dominant Delta variant to human ACE2显示文摘Dear Editor,SARS-coronavirus-2(SARS-CoV-2)Omicron variant(B.1.1.529)is of great concern to the world due to its multiple mutations that may have an impact on transmissibility and immune evasion.1 Compared to the wild type(WT),Omicron carries as many as 30 single point mutations,3 deletion mutation and one insertion mutation on its spike protein.Strikingly,there are 15 mutations observed in the Omicron receptor-binding domain(RBD),10 of which are in the receptor-binding motif(RBM)that human angiotensin-converting enzyme 2(ACE2)and most monoclonal antibodies(mAbs)interact directly with.As a comparison,the currently dominant variant Delta(B.1.617.2)has only 2 mutations(L452R and T478K)in its RBM and additional K417N and E484K mutations sometimes.Therefore,Omicron variant may significantly impact the binding affinity to ACE2 and effectiveness of currently available mAbs.Consequently,Omicron mutant has aroused wide concern,many countries have taken measures on entry restrictions to prevent its rapid spread.However,the transmissibility and immune evasion risk of Omicron have not been properly evaluated. | Leyun Wu Liping Zhou Mengxia Mo Tingting Liu Chengkun Wu Chunye Gong Kai Lu Likun Gong Weiliang Zhu Zhijian Xu | 2022 | Signal Transduction and Targeted Therapy2022，7，2： | 2 |
| 2 | GPU accelerated simulations of 3D deterministic particle transport using discrete ordinates method显示文摘 | Chunye Gong Jie Liu Lihua Chi Haowei Huang Jingyue Fang Zhenghu Gong | 2011 | Journal of Computational Physics2011，，15： | 1 |
| 3 | Transfer learning for deep neural network-based partial differential equations solving显示文摘Deep neural networks(DNNs)have recently shown great potential in solving partial differential equations(PDEs).The success of neural network-based surrogate models is attributed to their ability to learn a rich set of solution-related features.However,learning DNNs usually involves tedious training iterations to converge and requires a very large number of training data,which hinders the application of these models to complex physical contexts.To address this problem,we propose to apply the transfer learning approach to DNN-based PDE solving tasks.In our work,we create pairs of transfer experiments on Helmholtz and Navier-Stokes equations by constructing subtasks with different source terms and Reynolds numbers.We also conduct a series of experiments to investigate the degree of generality of the features between different equations.Our results demonstrate that despite differences in underlying PDE systems,the transfer methodology can lead to a significant improvement in the accuracy of the predicted solutions and achieve a maximum performance boost of 97.3%on widely used surrogate models. | Xinhai Chen Chunye Gong Qian Wan Liang Deng Yunbo Wan Yang Liu Bo Chen Jie Liu | 2021 | Advances in Aerodynamics2021，3，1： | 1 |
| 4 | Customizing the HPL for China accelerator显示文摘HPL is a Linpack benchmark package widely used in high-performance computing tests. Customizing the HPL is crucial for a heterogeneous system equipped with CPU and the China accelerator because of the complexity of the China accelerator and the specified interface on matrix multiplication built in the China accelerator. Therefore, it is advisable to use delicate partition and encapsulation on matrix(DPEM) to expose a friendly testing configuration. More importantly, we propose the orchestrating algorithm for matrix multiplication(OAMM) to enhance the efficiency of the heterogeneous system composed of CPU and China accelerator. Furthermore, optimization at vectorization(OPTVEC) is applied to shield the architectural details of the vector processing element(VPE) equipped in the China accelerator. The experimental results validate DPEM, OPTVEC and OAMM. OPTVEC optimizations would speed up matrix multiplication more than twofold, moreover OAMM would improve productivity by up to 10% compared to the traditional HPL tested in a heterogeneous system. | Xinbiao GAN Yikun HU Jie LIU Lihua CHI Han XU Chunye GONG Shengguo LI Yihui YAN | 2018 | Science China(Information Sciences)2018，61，4： | 0 |
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