Converting normal insulators into topological insulators via tuning orbital levels

Tuning the spin-orbit coupling strength via foreign element doping and/or modifying bonding strength via strain engineering are the major routes to convert normal insulators to topological insulators. We here propose an alternative strategy to realize topological phase transition by tuning the orbital level. Following this strategy, our first-principles calculations demonstrate that a topological phase transition in some cubic perovskite-type compounds CsGeBr$_3$ and CsSnBr$_3$ could be facilitated by carbon substitutional doping. Such unique topological phase transition predominantly results from the lower orbital energy of the carbon dopant, which can pull down the conduction bands and even induce band inversion. Beyond conventional approaches, our finding of tuning the orbital level may greatly expand the range of topologically nontrivial materials

Quantized spin Hall effect in 3He-A and other p-wave paired Fermi systems

Journal ArticleIn this paper, we propose the quantized spin Hall effect (SHE) in the vortex state of a rotating p-wave paired Fermi system in an inhomogeneous magnetic field and in a weak periodic potential. It is the three-dimensional extension of the spin Hall effect for a 3He-A superfluid film previously studied in the literature. It may also be considered as a generalization of the three-dimensional quantized charge Hall effect of Bloch electrons in the work previously studied in the literature to the spin transport. The A phase of 3He or, more generally, the p-wave paired phase of a cold Fermi atomic gas under suitable conditions should be a good candidate to observe the SHE because the system has a conserved spin current (with no spin-orbit couplings)

Backstepping controller design for a class of stochastic nonlinear systems with Markovian switching

A more general class of stochastic nonlinear systems with irreducible homogenous Markovian switching are considered in this paper. As preliminaries, the stability criteria and the existence theorem of strong solutions are first presented by using the inequality of mathematic expectation of a Lyapunov function. The state-feedback controller is designed by regarding Markovian switching as constant such that the closed-loop system has a unique solution, and the equilibrium is asymptotically stable in probability in the large. The output-feedback controller is designed based on a quadratic-plus-quartic-form Lyapunov function such that the closed-loop system has a unique solution with the equilibrium being asymptotically stable in probability in the large in the unbiased case and has a unique bounded-in-probability solution in the biased case

SCOPE: Scalable Composite Optimization for Learning on Spark

Many machine learning models, such as logistic regression~(LR) and support vector machine~(SVM), can be formulated as composite optimization problems. Recently, many distributed stochastic optimization~(DSO) methods have been proposed to solve the large-scale composite optimization problems, which have shown better performance than traditional batch methods. However, most of these DSO methods are not scalable enough. In this paper, we propose a novel DSO method, called \underline{s}calable \underline{c}omposite \underline{op}timization for l\underline{e}arning~({SCOPE}), and implement it on the fault-tolerant distributed platform \mbox{Spark}. SCOPE is both computation-efficient and communication-efficient. Theoretical analysis shows that SCOPE is convergent with linear convergence rate when the objective function is convex. Furthermore, empirical results on real datasets show that SCOPE can outperform other state-of-the-art distributed learning methods on Spark, including both batch learning methods and DSO methods

Promoting hydrogen production and minimizing catalyst deactivation from the pyrolysis-catalytic steam reforming of biomass on nanosized NiZnAlOx catalysts

Hydrogen production from the thermochemical conversion of biomass was carried out with nano-sized NiZnAlOx catalysts using a two-stage fixed bed reactor system. The gases derived from the pyrolysis of wood sawdust in the first stage were catalytically steam reformed in the second stage. The NiZnAlOx catalysts were synthesized by a co-precipitation method with different Ni molar fractions (5, 10, 15, 25 and 35%) and a constant Zn:Al molar ratio of 1:4. The catalysts were characterized by a wide range of techniques, including N2 adsorption, SEM, XRD, TEM and temperature-programmed oxidation (TPO) and reduction (TPR). Fine metal particles of size around 10–11 nm were obtained and the catalysts had high stability characteristics, which improved the dispersion of active centers during the reaction and promoted the performance of the catalysts. The yield of gas was increased from 49.3 to 74.8 wt.%, and the volumetric concentration of hydrogen was increased from 34.7 to 48.1 vol.%, when the amount of Ni loading was increased from 5 to 35%. Meanwhile, the CH4 fraction decreased from 10.2 to 0.2 vol.% and the C2–C4 fraction was reduced from 2.4 vol.% to 0.0 vol.%. During the reaction, the crystal size of all catalysts was successfully maintained at around 10–11 nm with lowered catalyst coke formation, (particularly for the 35NiZn4Al catalyst where negligible coke was found) and additionally no obvious catalyst sintering was detected. The efficient production of hydrogen from the thermochemical conversion of renewable biomass indicates that it is a promising sustainable route to generate hydrogen from biomass using the NiZnAl metal oxide catalyst prepared in this work via a two-stage reaction system

Reconsideration of the QCD corrections to the ηc\eta_c decays into light hadrons using the principle of maximum conformality

In the paper, we analyze the $\eta_c$ decays into light hadrons at the next-to-leading order QCD corrections by applying the principle of maximum conformality (PMC). The relativistic correction at the ${\cal{O}}(\alpha_s v^2)$-order level has been included in the discussion, which gives about $10\%$ contribution to the ratio $R$. The PMC, which satisfies the renormalization group invariance, is designed to obtain a scale-fixed and scheme-independent prediction at any fixed order. To avoid the confusion of treating $n_f$-terms, we transform the usual $\overline{\rm MS}$ pQCD series into the one under the minimal momentum space subtraction scheme. To compare with the prediction under conventional scale setting, $R_{\rm{Conv,mMOM}-r}= \left(4.12^{+0.30}_{-0.28}\right)\times10^3$, after applying the PMC, we obtain $R_{\rm PMC,mMOM-r}=\left(6.09^{+0.62}_{-0.55}\right) \times10^3$, where the errors are squared averages of the ones caused by $m_c$ and $\Lambda_{\rm mMOM}$. The PMC prediction agrees with the recent PDG value within errors, i.e. $R^{\rm exp}=\left(6.3\pm0.5\right)\times10^3$. Thus we think the mismatching of the prediction under conventional scale-setting with the data is due to improper choice of scale, which however can be solved by using the PMC.Comment: 5 pages, 2 figure