Topological Dirac states beyond π\pi orbitals for silicene on SiC(0001) surface

The discovery of intriguing properties related to the Dirac states in graphene has spurred huge interest in exploring its two-dimensional group-IV counterparts, such as silicene, germanene, and stanene. However, these materials have to be obtained via synthesizing on substrates with strong interfacial interactions, which usually destroy their intrinsic $\pi$($p_z$)-orbital Dirac states. Here we report a theoretical study on the existence of Dirac states arising from the $p_{x,y}$ orbitals instead of $p_z$ orbitals in silicene on 4H-SiC(0001), which survive in spite of the strong interfacial interactions. We also show that the exchange field together with the spin-orbital coupling give rise to a detectable band gap of 1.3 meV. Berry curvature calculations demonstrate the nontrivial topological nature of such Dirac states with a Chern number $C = 2$, presenting the potential of realizing quantum anomalous Hall effect for silicene on SiC(0001). Finally, we construct a minimal effective model to capture the low-energy physics of this system. This finding is expected to be also applicable to germanene and stanene, and imply great application potentials in nanoelectronics.Comment: 6 Figures , Accepted by Nano Letter

The Impact of Social Media Use on Online Collective Action During China’s COVID-19 Pandemic Mitigation: A Social Identity Model of Collective Action (SIMCA) Perspective

The role of social media in fostering collective action in China is under constant debate, and the mechanism underlying the effects of social media use on collective action has not garnered sufficient scholarly attention. This study aims to investigate the (in)direct effects of attention to social media—administered by the governmental (gov) and nongovernmental sectors (nongov), respectively—for information about COVID-19 mitigation in China on intention to participate in online collective action (IPOCA). Findings from a survey suggest that attention to both social media (gov) and social media (nongov) directly predicted IPOCA. The indirect effect of attention to social media (gov) on IPOCA was significantly mediated by social identification. This study evidences the impact of social media on collective action in China and theoretically underpins its mechanisms through the social identity model of collective action

Hamiltonian formalism in Friedmann cosmology and its quantization

We propose a Hamiltonian formalism for a generalized Friedmann-Roberson-Walker cosmology model in the presence of both a variable equation of state (EOS) parameter $w(a)$ and a variable cosmological constant $\Lambda(a)$, where $a$ is the scale factor. This Hamiltonian system containing 1 degree of freedom and without constraint, gives Friedmann equations as the equation of motion, which describes a mechanical system with a variable mass object moving in a potential field. After an appropriate transformation of the scale factor, this system can be further simplified to an object with constant mass moving in an effective potential field. In this framework, the $\Lambda$ cold dark matter model as the current standard model of cosmology corresponds to a harmonic oscillator. We further generalize this formalism to take into account the bulk viscosity and other cases. The Hamiltonian can be quantized straightforwardly, but this is different from the approach of the Wheeler-DeWitt equation in quantum cosmology.Comment: 7 pages, no figure; v2: matches the version accepted by PR

Fast Differentially Private Matrix Factorization

Differentially private collaborative filtering is a challenging task, both in terms of accuracy and speed. We present a simple algorithm that is provably differentially private, while offering good performance, using a novel connection of differential privacy to Bayesian posterior sampling via Stochastic Gradient Langevin Dynamics. Due to its simplicity the algorithm lends itself to efficient implementation. By careful systems design and by exploiting the power law behavior of the data to maximize CPU cache bandwidth we are able to generate 1024 dimensional models at a rate of 8.5 million recommendations per second on a single PC