429 research outputs found

    Emerging Artificial Two-Dimensional van der Waals Heterostructures for Optoelectronics

    Get PDF
    Two-dimensional (2D) materials are attracting explosive attention for their intriguing potential in versatile applications, covering optoelectronics, electronics, sensors, etc. An attractive merit of 2D materials is their viable van der Waals (VdW) stacking in artificial sequence, thus forming different atomic arrangements in vertical direction and enabling unprecedented tailoring of material properties and device application. In this chapter, we summarize the latest progress in assembling VdW heterostructures for optoelectronic applications by beginning with the basic pick-transfer method for assembling 2D materials and then discussing the different combination of 2D materials of semiconductor, conductor, and insulator properties for various optoelectronic devices, e.g., photodiode, phototransistors, optical memories, etc

    MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition

    Full text link
    Spatial-temporal feature learning is of vital importance for video emotion recognition. Previous deep network structures often focused on macro-motion which extends over long time scales, e.g., on the order of seconds. We believe integrating structures capturing information about both micro- and macro-motion will benefit emotion prediction, because human perceive both micro- and macro-expressions. In this paper, we propose to combine micro- and macro-motion features to improve video emotion recognition with a two-stream recurrent network, named MIMAMO (Micro-Macro-Motion) Net. Specifically, smaller and shorter micro-motions are analyzed by a two-stream network, while larger and more sustained macro-motions can be well captured by a subsequent recurrent network. Assigning specific interpretations to the roles of different parts of the network enables us to make choice of parameters based on prior knowledge: choices that turn out to be optimal. One of the important innovations in our model is the use of interframe phase differences rather than optical flow as input to the temporal stream. Compared with the optical flow, phase differences require less computation and are more robust to illumination changes. Our proposed network achieves state of the art performance on two video emotion datasets, the OMG emotion dataset and the Aff-Wild dataset. The most significant gains are for arousal prediction, for which motion information is intuitively more informative. Source code is available at https://github.com/wtomin/MIMAMO-Net.Comment: Accepted by AAAI 202

    Valley-polarized quantum anomalous Hall effect in van der Waals heterostructures based on monolayer jacutingaite family materials

    Full text link
    We numerically study the general valley polarization and anomalous Hall effect in van der Waals (vdW) heterostructures based on monolayer jacutingaite family materials Pt2_{2}AX3_{3} (A = Hg, Cd, Zn; X = S, Se, Te). We perform a systematic study on the atomic, electronic, and topological properties of vdW heterostructures composed of monolayer Pt2_{2}AX3_{3} and two-dimensional ferromagnetic insulators. We show that four kinds of vdW heterostructures exhibit valley-polarized quantum anomalous Hall phase, i.e., Pt2_{2}HgS3_{3}/NiBr2_{2}, Pt2_{2}HgSe3_{3}/CoBr2_{2}, Pt2_{2}HgSe3_{3}/NiBr2_{2}, and Pt2_{2}ZnS3_{3}/CoBr2_{2}, with a maximum valley splitting of 134.2 meV in Pt2_{2}HgSe3_{3}/NiBr2_{2} and sizable global band gap of 58.8 meV in Pt2_{2}HgS3_{3}/NiBr2_{2}. Our findings demonstrate an ideal platform to implement applications on topological valleytronics
    • …
    corecore