440 research outputs found
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
- …