21,372 research outputs found

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

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    We show that an nβˆ’nβˆ’βˆ’nn-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 nn and nβˆ’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

    Kernel Truncated Regression Representation for Robust Subspace Clustering

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    Subspace clustering aims to group data points into multiple clusters of which each corresponds to one subspace. Most existing subspace clustering approaches assume that input data lie on linear subspaces. In practice, however, this assumption usually does not hold. To achieve nonlinear subspace clustering, we propose a novel method, called kernel truncated regression representation. Our method consists of the following four steps: 1) projecting the input data into a hidden space, where each data point can be linearly represented by other data points; 2) calculating the linear representation coefficients of the data representations in the hidden space; 3) truncating the trivial coefficients to achieve robustness and block-diagonality; and 4) executing the graph cutting operation on the coefficient matrix by solving a graph Laplacian problem. Our method has the advantages of a closed-form solution and the capacity of clustering data points that lie on nonlinear subspaces. The first advantage makes our method efficient in handling large-scale datasets, and the second one enables the proposed method to conquer the nonlinear subspace clustering challenge. Extensive experiments on six benchmarks demonstrate the effectiveness and the efficiency of the proposed method in comparison with current state-of-the-art approaches.Comment: 14 page

    High-frequency Light Reflector via Low-frequency Light Control

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    We show that the momentum of light can be reversed via the atomic coherence created by another light with one or two orders of magnitude lower frequency. Both the backward retrieval of single photons from a timed Dicke state and the reflection of continuous waves by high-order photonic band gaps are analysed. The required control field strength scales linearly with the nonlinearity order, which is explained by the dynamics of superradiance lattices. Experiments are proposed with 85^{85}Rb atoms and Be2+^{2+} ions. This holds promise for light-controllable X-ray reflectors.Comment: 5 pages, 5 figure

    ShuttleSet22: Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset

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    In recent years, badminton analytics has drawn attention due to the advancement of artificial intelligence and the efficiency of data collection. While there is a line of effective applications to improve and investigate player performance, there are only a few public badminton datasets that can be used for researchers outside the badminton domain. Existing badminton singles datasets focus on specific matchups; however, they cannot provide comprehensive studies on different players and various matchups. In this paper, we provide a badminton singles dataset, ShuttleSet22, which is collected from high-ranking matches in 2022. ShuttleSet22 consists of 30,172 strokes in 2,888 rallies in the training set, 1,400 strokes in 450 rallies in the validation set, and 2,040 strokes in 654 rallies in the testing set with detailed stroke-level metadata within a rally. To benchmark existing work with ShuttleSet22, we test the state-of-the-art stroke forecasting approach, ShuttleNet, with the corresponding stroke forecasting task, i.e., predict the future strokes based on the given strokes of each rally. We also hold a challenge, Track 2: Forecasting Future Turn-Based Strokes in Badminton Rallies, at CoachAI Badminton Challenge 2023 to boost researchers to tackle this problem. The baseline codes and the dataset will be made available on https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI-Challenge-IJCAI2023.Comment: IT4PSS @ IJCAI-23 and CoachAI Badminton Challenge Track 2 @ IJCAI-23. Challenge website: https://sites.google.com/view/coachai-challenge-2023
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