Distinct moir\'e textures of in-plane electric polarizations for distinguishing moir\'e origins in homobilayers

In binary compound 2D insulators/semiconductors such as hexagonal boron nitride (hBN), the different electron affinities of atoms can give rise to out-of-plane electric polarizations across inversion asymmetric van der Waals interface of near 0-degree twist angles. Here we show that at a general stacking order where sliding breaks the in-plane C3 rotational symmetry, the interfacial charge redistribution also leads to an in-plane electric polarization, with a comparable magnitude to that of the out-of-plane ones. The effect is demonstrated in hBN bilayers, as well as in biased graphene bilayers with the gate-controlled interlayer charge redistribution. In long wavelength moir\'e patterns, the in-plane electric polarizations determined by the local interlayer stacking registries constitute topologically nontrivial spatial textures. We show that these textures can distinguish moir\'e patterns of different origins from twisting, biaxial- and uniaxial-heterostrain, where vector fields of the electric polarizations feature Bloch type merons, Neel type merons, and anti-merons, respectively. Combinations of twisting and heterostrain can further be exploited for engineering various electric polarization textures including 1D quasiperiodic lattices

YOLOX-PAI: An Improved YOLOX, Stronger and Faster than YOLOv6

We develop an all-in-one computer vision toolbox named EasyCV to facilitate the use of various SOTA computer vision methods. Recently, we add YOLOX-PAI, an improved version of YOLOX, into EasyCV. We conduct ablation studies to investigate the influence of some detection methods on YOLOX. We also provide an easy use for PAI-Blade which is used to accelerate the inference process based on BladeDISC and TensorRT. Finally, we receive 42.8 mAP on COCO dateset within 1.0 ms on a single NVIDIA V100 GPU, which is a bit faster than YOLOv6. A simple but efficient predictor api is also designed in EasyCV to conduct end2end object detection. Codes and models are now available at: https://github.com/alibaba/EasyCV.Comment: 5 pages, 5 figure

Investigating the integrate and fire model as the limit of a random discharge model: a stochastic analysis perspective

In the mean field integrate-and-fire model, the dynamics of a typical neuron within a large network is modeled as a diffusion-jump stochastic process whose jump takes place once the voltage reaches a threshold. In this work, the main goal is to establish the convergence relationship between the regularized process and the original one where in the regularized process, the jump mechanism is replaced by a Poisson dynamic, and jump intensity within the classically forbidden domain goes to infinity as the regularization parameter vanishes. On the macroscopic level, the Fokker-Planck equation for the process with random discharges (i.e. Poisson jumps) are defined on the whole space, while the equation for the limit process is on the half space. However, with the iteration scheme, the difficulty due to the domain differences has been greatly mitigated and the convergence for the stochastic process and the firing rates can be established. Moreover, we find a polynomial-order convergence for the distribution by a re-normalization argument in probability theory. Finally, by numerical experiments, we quantitatively explore the rate and the asymptotic behavior of the convergence for both linear and nonlinear models