Quantum plateau of Andreev reflection induced by spin-orbit coupling

In this work we uncover an interesting quantum plateau behavior for the Andreev reflection between a one-dimensional quantum wire and superconductor. The quantum plateau is achieved by properly tuning the interplay of the spin-orbit coupling within the quantum wire and its tunnel coupling to the superconductor. This plateau behavior is justified to be unique by excluding possible existences in the cases associated with multi-channel quantum wire, the Blonder-Tinkham-Klapwijk continuous model with a barrier, and lattice system with on-site impurity at the interface.Comment: 6 pages, 3 figures

A Bootstrap Lasso + Partial Ridge Method to Construct Confidence Intervals for Parameters in High-dimensional Sparse Linear Models

Constructing confidence intervals for the coefficients of high-dimensional sparse linear models remains a challenge, mainly because of the complicated limiting distributions of the widely used estimators, such as the lasso. Several methods have been developed for constructing such intervals. Bootstrap lasso+ols is notable for its technical simplicity, good interpretability, and performance that is comparable with that of other more complicated methods. However, bootstrap lasso+ols depends on the beta-min assumption, a theoretic criterion that is often violated in practice. Thus, we introduce a new method, called bootstrap lasso+partial ridge, to relax this assumption. Lasso+partial ridge is a two-stage estimator. First, the lasso is used to select features. Then, the partial ridge is used to refit the coefficients. Simulation results show that bootstrap lasso+partial ridge outperforms bootstrap lasso+ols when there exist small, but nonzero coefficients, a common situation that violates the beta-min assumption. For such coefficients, the confidence intervals constructed using bootstrap lasso+partial ridge have, on average, $50\%$ larger coverage probabilities than those of bootstrap lasso+ols. Bootstrap lasso+partial ridge also has, on average, $35\%$ shorter confidence interval lengths than those of the de-sparsified lasso methods, regardless of whether the linear models are misspecified. Additionally, we provide theoretical guarantees for bootstrap lasso+partial ridge under appropriate conditions, and implement it in the R package "HDCI.

Revisit the spin-FET: Multiple reflections, inelastic scattering, and lateral size effects

We revisit the spin-injected field effect transistor (spin-FET) by simulating a lattice model based on recursive lattice Green's function approach. In the one-dimensional case and coherent regime, the simulated results reveal noticeable differences from the celebrated Datta-Das model, which motivate thus an improved treatment and lead to analytic and generalized result. The simulation also allows us to address inelastic scattering (using B\"uttiker's fictitious reservoir approach) and lateral confinement effects on the control of spins which are important issues in the spin-FET device.Comment: 9 pages, 4 figure

Sentiment Community a New Way to Learn Users’ Sentiments in Social Network- A Preliminary Study

Many enterprises begin to use Social Network Sites (SNS) as an important channel and platform to do online marketing and reputation management, because users’ interactions in SNS have more effective impacts on customers’ buying decisions and images of enterprises than traditional websites. To do this, the enterprises need to learn and trace users’ sentiments on their products/services for designing appropriate business strategies. In this study, the sentiment community is proposed as a method for this. The sentiment communities with different polarities in SNS usually represent groups of users with different preferences, and discovered sentiment communities is very useful for enterprises to do customer segmentation and target marketing. Also the evolvement of sentiment communities is explored, so that enterprises can easily trace users’ sentiments and learn their diffusions in SNS. In this paper, a novel method is proposed for discovering users’ sentiment communities, and an initial experimental evaluation is executed