3,941 research outputs found
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
We present SegNeXt, a simple convolutional network architecture for semantic
segmentation. Recent transformer-based models have dominated the field of
semantic segmentation due to the efficiency of self-attention in encoding
spatial information. In this paper, we show that convolutional attention is a
more efficient and effective way to encode contextual information than the
self-attention mechanism in transformers. By re-examining the characteristics
owned by successful segmentation models, we discover several key components
leading to the performance improvement of segmentation models. This motivates
us to design a novel convolutional attention network that uses cheap
convolutional operations. Without bells and whistles, our SegNeXt significantly
improves the performance of previous state-of-the-art methods on popular
benchmarks, including ADE20K, Cityscapes, COCO-Stuff, Pascal VOC, Pascal
Context, and iSAID. Notably, SegNeXt outperforms EfficientNet-L2 w/ NAS-FPN and
achieves 90.6% mIoU on the Pascal VOC 2012 test leaderboard using only 1/10
parameters of it. On average, SegNeXt achieves about 2.0% mIoU improvements
compared to the state-of-the-art methods on the ADE20K datasets with the same
or fewer computations. Code is available at https://github.com/uyzhang/JSeg
(Jittor) and https://github.com/Visual-Attention-Network/SegNeXt (Pytorch).Comment: SegNeXt, a simple CNN for semantic segmentation. Code is availabl
Friedmann cosmology with a generalized equation of state and bulk viscosity
The universe media is considered as a non-perfect fluid with bulk viscosity
and described by a more general equation of state. We assume the bulk viscosity
is a linear combination of the two terms: one is constant, and the other is
proportional to the scalar expansion . The equation of state
is described as , where is a parameter. This model
can be used to explain the dark energy dominated universe. Different choices of
the parameters may lead to three kinds of fates of the cosmological evolution:
no future singularity, big rip, or Type III singularity of Ref. [S. Nojiri,
S.D. Odintsov, and S. Tsujikawa, Phys. Rev. D \textbf{71}, 063004 (2005)].Comment: 5 pages and 4 fig
Numerical simulation analysis of four-step variable-diameter pipe by solid-liquid two-phase grinding
In order to investigate the effect of abrasive flow on the polishing effect of variable diameter pipe parts, taking the fourth-order variable-diameter pipe part as the research object, the solid-liquid two-phase abrasive grains are used as the processing method of the fourth-order variable-diameter pipe, the numerical simulation of the machining process of the four-order variable-diameter pipe parts were carried out. Analysis of different inlet speed conditions, the dynamic pressure and the distribution of turbulence intensity of the flow field of the fourth order variable diameter pipe. Through comparative analysis, the effects of the four-stage variable-diameter pipe flow field are studied, which can provide the theoretical basis for the continuous improvement of the abrasive flow precision and ultra precision machining technology, which can improve the efficiency of abrasive flow processing, so that the workpiece fatigue strength is improved, enhance the reliability of the workpiece, extend the service life of the workpiece
An online conserved SSR discovery through cross-species comparison
Simple sequence repeats (SSRs) play important roles in gene regulation and genome evolution. Although there exist several online resources for SSR mining, most of them only extract general SSR patterns without providing functional information. Here, an online search tool, CG-SSR (Comparative Genomics SSR discovery), has been developed for discovering potential functional SSRs from vertebrate genomes through cross-species comparison. In addition to revealing SSR candidates in conserved regions among various species, it also combines accurate coordinate and functional genomics information. CG-SSR is the first comprehensive and efficient online tool for conserved SSR discovery
Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations
Stochastic partial differential equations (SPDEs) are crucial
for modelling dynamics with randomness in many areas including economics, physics, and atmospheric sciences. Recently, using deep learning approaches to learn the PDE solution for accelerating PDE simulation becomes increasingly
popular. However, SPDEs have two unique properties that
require new design on the models. First, the model to approximate the solution of SPDE should be generalizable over
both initial conditions and the random sampled forcing term.
Second, the random forcing terms usually have poor regularity whose statistics may diverge (e.g., the space-time white
noise). To deal with the problems, in this work, we design
a deep neural network called Deep Latent Regularity Net
(DLR-Net). DLR-Net includes a regularity feature block as
the main component, which maps the initial condition and the
random forcing term to a set of regularity features. The processing of regularity features is inspired by regularity structure theory and the features provably compose a set of basis to
represent the SPDE solution. The regularity features are then
fed into a small backbone neural operator to get the output.
We conduct experiments on various SPDEs including the dynamic Φ^{4}_{1} model and the stochastic 2D Navier-Stokes equation to predict their solutions, and the results demonstrate that
the proposed DLR-Net can achieve SOTA accuracy compared
with the baselines. Moreover, the inference time is over 20
times faster than the traditional numerical solver and is comparable with the baseline deep learning models
Deep Random Vortex Method for Simulation and Inference of Navier-Stokes Equations
Navier-Stokes equations are significant partial differential equations that
describe the motion of fluids such as liquids and air. Due to the importance of
Navier-Stokes equations, the development on efficient numerical schemes is
important for both science and engineer. Recently, with the development of AI
techniques, several approaches have been designed to integrate deep neural
networks in simulating and inferring the fluid dynamics governed by
incompressible Navier-Stokes equations, which can accelerate the simulation or
inferring process in a mesh-free and differentiable way. In this paper, we
point out that the capability of existing deep Navier-Stokes informed methods
is limited to handle non-smooth or fractional equations, which are two critical
situations in reality. To this end, we propose the \emph{Deep Random Vortex
Method} (DRVM), which combines the neural network with a random vortex dynamics
system equivalent to the Navier-Stokes equation. Specifically, the random
vortex dynamics motivates a Monte Carlo based loss function for training the
neural network, which avoids the calculation of derivatives through
auto-differentiation. Therefore, DRVM not only can efficiently solve
Navier-Stokes equations involving rough path, non-differentiable initial
conditions and fractional operators, but also inherits the mesh-free and
differentiable benefits of the deep-learning-based solver. We conduct
experiments on the Cauchy problem, parametric solver learning, and the inverse
problem of both 2-d and 3-d incompressible Navier-Stokes equations. The
proposed method achieves accurate results for simulation and inference of
Navier-Stokes equations. Especially for the cases that include singular initial
conditions, DRVM significantly outperforms existing PINN method
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