898 research outputs found
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Recently exciting progress has been made on protein contact prediction, but
the predicted contacts for proteins without many sequence homologs is still of
low quality and not very useful for de novo structure prediction. This paper
presents a new deep learning method that predicts contacts by integrating both
evolutionary coupling (EC) and sequence conservation information through an
ultra-deep neural network formed by two deep residual networks. This deep
neural network allows us to model very complex sequence-contact relationship as
well as long-range inter-contact correlation. Our method greatly outperforms
existing contact prediction methods and leads to much more accurate
contact-assisted protein folding. Tested on three datasets of 579 proteins, the
average top L long-range prediction accuracy obtained our method, the
representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21
and 0.30, respectively; the average top L/10 long-range accuracy of our method,
CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding
using our predicted contacts as restraints can yield correct folds (i.e.,
TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and
CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively.
Further, our contact-assisted models have much better quality than
template-based models. Using our predicted contacts as restraints, we can (ab
initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast,
when the training proteins of our method are used as templates, homology
modeling can only do so for 10 of them. One interesting finding is that even if
we do not train our prediction models with any membrane proteins, our method
works very well on membrane protein prediction. Finally, in recent blind CAMEO
benchmark our method successfully folded 5 test proteins with a novel fold
Eternal solutions of the Boltzmann equation near travelling Maxwellians
AbstractIt is shown in this paper that the Cauchy problem of the Boltzmann equation, with a cut-off soft potential and an initial datum close to a travelling Maxwellian, has a unique positive eternal solution. This eternal solution is exponentially decreasing at infinity for all t∈(−∞,∞), consequently the moments of any order are finite. This result gives a negative answer to the conjecture of Villani in the spatially inhomogeneous case
The Cauchy problem for the BGK equation with an external force
AbstractIn this paper, we study the Cauchy problem for the BGK equation with an external force. Firstly, we establish an L∞ existence result for this equation, and obtain some weighted L∞ estimates. Then, by means of the regularizing effects to the initial datum, we construct the approximate solutions and obtain some uniform estimates of the approximate solutions. Finally by using compactness method and passing to the limit, we prove the existence theorems of the L1 and Lp solutions and establish the propagation properties of the Lp moments
Light Ring behind Wormhole Throat: Geodesics, Images and Shadows
The geodesics of the Ellis-Bronnikov wormhole with two parameters are
studied. The asymmetric wormhole has only one light ring and one innermost
stable circular orbit located on one side of the wormhole throat. Consequently,
certain light rays can be reflected back by the wormhole. Additionally, the
same wormhole can have different appearances on both sides of the throat. We
present novel images of the wormhole with a light ring behind the throat in a
scenario with an accretion disk as the light source and in a backlit wormhole
scenario, which are distinct from the images of other compact objects and have
the potential to be observed.Comment: 26 pages, 14 figures, add reference
Practical Stabilization of Uncertain Nonholonomic Mobile Robots Based on Visual Servoing Model with Uncalibrated Camera Parameters
The practical stabilization problem is addressed for a class of uncertain nonholonomic mobile robots with uncalibrated visual parameters. Based on the visual servoing kinematic model, a new switching controller is presented in the presence of parametric uncertainties associated with the camera system. In comparison with existing methods, the new design method is directly used to control the original system without any state or input transformation, which is effective to avoid singularity. Under the proposed control law, it is rigorously proved that all the states of closed-loop system can be stabilized to a prescribed arbitrarily small neighborhood of the zero equilibrium point. Furthermore, this switching control technique can be applied to solve the practical stabilization problem of a kind of mobile robots with uncertain parameters (and angle measurement disturbance) which appeared in some literatures such as Morin et al. (1998), Hespanha et al. (1999), Jiang (2000), and Hong et al. (2005). Finally, the simulation results show the effectiveness of the proposed controller design approach
Real-World Image Variation by Aligning Diffusion Inversion Chain
Recent diffusion model advancements have enabled high-fidelity images to be
generated using text prompts. However, a domain gap exists between generated
images and real-world images, which poses a challenge in generating
high-quality variations of real-world images. Our investigation uncovers that
this domain gap originates from a latents' distribution gap in different
diffusion processes. To address this issue, we propose a novel inference
pipeline called Real-world Image Variation by ALignment (RIVAL) that utilizes
diffusion models to generate image variations from a single image exemplar. Our
pipeline enhances the generation quality of image variations by aligning the
image generation process to the source image's inversion chain. Specifically,
we demonstrate that step-wise latent distribution alignment is essential for
generating high-quality variations. To attain this, we design a cross-image
self-attention injection for feature interaction and a step-wise distribution
normalization to align the latent features. Incorporating these alignment
processes into a diffusion model allows RIVAL to generate high-quality image
variations without further parameter optimization. Our experimental results
demonstrate that our proposed approach outperforms existing methods with
respect to semantic-condition similarity and perceptual quality. Furthermore,
this generalized inference pipeline can be easily applied to other
diffusion-based generation tasks, such as image-conditioned text-to-image
generation and example-based image inpainting.Comment: 19 pages; Project page: https://rival-diff.github.io/ Code(release
later): https://github.com/julianjuaner/RIVAL
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