Shengfeng Deng
Central China Normal University
24 Papers
27 Citations
Shengfeng Deng is an academic researcher from Central China Normal University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 4, co-authored 14 publications. Previous affiliations of Shengfeng Deng include Virginia Tech.
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Papers
Supervised and unsupervised learning of directed percolation.
TL;DR: In this paper, the authors show that using some simple techniques of ML, non-steady-state configurations of directed percolation (DP) suffice to capture its essential critical behaviors in both ($1+1$) and ($2+ 1$) dimensions.
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Social distancing and epidemic resurgence in agent-based Susceptible-Infectious-Recovered models
Ruslan I. Mukhamadiarov,Shengfeng Deng,Shengfeng Deng,Shannon R. Serrao,Priyanka,Riya Nandi,Louie Hong Yao,Uwe C. Täuber +7 more
TL;DR: It is robustly found that the intensity and spatial spread of the epidemic recurrence wave can be limited to a manageable extent provided release of social distancing restrictions is delayed sufficiently and long-distance connections are maintained on a low level.
Spreading dynamics of forget-remember mechanism.
Shengfeng Deng,Wei Li,Wei Li +2 more
TL;DR: It is proven that the mean field critical transmissibility for the SIS model and the critical transmission bounds for theSIR model are the lower and the upper bounds of thecritical transmissability for the FRM model, respectively.
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Synchronization Transition of the Second-Order Kuramoto Model on Lattices
Géza Ódor,Shengfeng Deng +1 more
TL;DR: The second-order Kuramoto equation describes the synchronization of coupled oscillators with inertia, which occur, for example, in power grids as mentioned in this paper , where the oscillator phases exhibit a crossover and the frequency is spread over a real phase transition in 3D.
Synchronization transitions on connectome graphs with external force
TL;DR: In this paper , the authors investigate the synchronization transition of the Shinomoto-Kuramoto model on networks of the fruit fly and two large human connectomes and show that these exponents, characterizing the auto-correlations are smaller in the excited system than in the resting state and exhibit module dependence.
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