Switchable valley functionalities of an n−n−−nn-n^{-}-n junction in 2D semiconductors

We show that an $n-n^{-}-n$ junction in 2D semiconductors can flexibly realize two basic valleytronic functions, i.e. valley filter and valley source, with gate controlled switchability between the two. Upon carrier flux passing through the junction, the valley filter and valley source functions are enabled respectively by intra- and inter-valley scatterings, and the two functions dominate respectively at small and large band-offset between the $n$ and $n^{-}$ regions. It can be generally shown that, the valley filter effect has an angular dependent polarity and vanishes under angular integration, by the same constraint from time-reversal symmetry that leads to its absence in one-dimension. These findings are demonstrated for monolayer transition metal dichalcogenides and graphene using tight-binding calculations. We further show that junction along chiral directions can concentrate the valley pump in an angular interval largely separated from the bias direction, allowing efficient havest of valley polarization in a cross-bar device

Leading Effect of CP Violation with Four Generations

In the Standard Model with a fourth generation of quarks, we study the relation between the Jarlskog invariants and the triangle areas in the 4-by-4 CKM matrix. To identify the leading effects that may probe the CP violation in processes involving quarks, we invoke small mass and small angle expansions, and show that these leading effects are enhanced considerably compared to the three generation case by the large masses of fourth generation quarks. We discuss the leading effect in several cases, in particular the possibility of large CP violation in $b \to s$ processes, which echoes the heightened recent interest because of experimental hints.Comment: 12 pages, no figur

ES2Net: An Efficient Spectral-Spatial Network for Hyperspectral Image Change Detection

Hyperspectral image change detection (HSI-CD) aims to identify the differences in bitemporal HSIs. To mitigate spectral redundancy and improve the discriminativeness of changing features, some methods introduced band selection technology to select bands conducive for CD. However, these methods are limited by the inability to end-to-end training with the deep learning-based feature extractor and lack considering the complex nonlinear relationship among bands. In this paper, we propose an end-to-end efficient spectral-spatial change detection network (ES2Net) to address these issues. Specifically, we devised a learnable band selection module to automatically select bands conducive to CD. It can be jointly optimized with a feature extraction network and capture the complex nonlinear relationships among bands. Moreover, considering the large spatial feature distribution differences among different bands, we design the cluster-wise spatial attention mechanism that assigns a spatial attention factor to each individual band to individually improve the feature discriminativeness for each band. Experiments on three widely used HSI-CD datasets demonstrate the effectiveness and superiority of this method compared with other state-of-the-art methods