55 research outputs found
Renormalized solutions of a nonlinear parabolic equation with double degeneracy
In this paper, we consider the initial-boundary value problem of a nonlinear parabolic equation with double degeneracy, and establish the existence and uniqueness theorems of renormalized solutions which are stronger than solutions
MEASUREMENT OF SPANNING TREE PERFORMANCE BETWEEN DIFFERENT PROTOCOLS : Bachelor's Thesis
Network is becoming more and more important in daily life, the performance of the network is most critical issue. In this article, we will talk about the spanning tree protocol which is used to prevent bridge loops, broadcast radiation and provide the protection of convergence in order to provide the nice performance of the network. You can see from this article about what it is the spanning tree, how it works, and what kind of enhanced protocols are used. I will test and measure the original protocol and enhanced protocols, and show the results of the performance of convergence time. We will see which protocol have nice performance. After that we can decide which protocol is suit for our network and provide the best performance.STP has two main functions: one is in the use of spanning tree algorithm to prevent bridge loops and the broadcast radiation. The second is in the Ethernet network topology change,
through the spanning tree protocol to achieve the purpose of protection of convergence
Semi-Supervised Learning for Mars Imagery Classification and Segmentation
With the progress of Mars exploration, numerous Mars image data are collected
and need to be analyzed. However, due to the imbalance and distortion of
Martian data, the performance of existing computer vision models is
unsatisfactory. In this paper, we introduce a semi-supervised framework for
machine vision on Mars and try to resolve two specific tasks: classification
and segmentation. Contrastive learning is a powerful representation learning
technique. However, there is too much information overlap between Martian data
samples, leading to a contradiction between contrastive learning and Martian
data. Our key idea is to reconcile this contradiction with the help of
annotations and further take advantage of unlabeled data to improve
performance. For classification, we propose to ignore inner-class pairs on
labeled data as well as neglect negative pairs on unlabeled data, forming
supervised inter-class contrastive learning and unsupervised similarity
learning. For segmentation, we extend supervised inter-class contrastive
learning into an element-wise mode and use online pseudo labels for supervision
on unlabeled areas. Experimental results show that our learning strategies can
improve the classification and segmentation models by a large margin and
outperform state-of-the-art approaches.Comment: Accepted by ACM Trans. on Multimedia Computing Communications and
Applications (TOMM
SMars: Semi-Supervised Learning for Mars Semantic Segmentation
Deep learning has become a powerful tool for Mars exploration. Mars terrain
semantic segmentation is an important Martian vision task, which is the base of
rover autonomous planning and safe driving. However, there is a lack of
sufficient detailed and high-confidence data annotations, which are exactly
required by most deep learning methods to obtain a good model. To address this
problem, we propose our solution from the perspective of joint data and method
design. We first present a newdataset S5Mars for Semi-SuperviSed learning on
Mars Semantic Segmentation, which contains 6K high-resolution images and is
sparsely annotated based on confidence, ensuring the high quality of labels.
Then to learn from this sparse data, we propose a semi-supervised learning
(SSL) framework for Mars image semantic segmentation, to learn representations
from limited labeled data. Different from the existing SSL methods which are
mostly targeted at the Earth image data, our method takes into account Mars
data characteristics. Specifically, we first investigate the impact of current
widely used natural image augmentations on Mars images. Based on the analysis,
we then proposed two novel and effective augmentations for SSL of Mars
segmentation, AugIN and SAM-Mix, which serve as strong augmentations to boost
the model performance. Meanwhile, to fully leverage the unlabeled data, we
introduce a soft-to-hard consistency learning strategy, learning from different
targets based on prediction confidence. Experimental results show that our
method can outperform state-of-the-art SSL approaches remarkably. Our proposed
dataset is available at https://jhang2020.github.io/S5Mars.github.io/
Path Generation for Wheeled Robots Autonomous Navigation on Vegetated Terrain
Wheeled robot navigation has been widely used in urban environments, but
little research has been conducted on its navigation in wild vegetation.
External sensors (LiDAR, camera etc.) are often used to construct point cloud
map of the surrounding environment, however, the supporting rigid ground used
for travelling cannot be detected due to the occlusion of vegetation. This
often causes unsafe or not smooth path during planning process. To address the
drawback, we propose the PE-RRT* algorithm, which effectively combines a novel
support plane estimation method and sampling algorithm to generate real-time
feasible and safe path in vegetation environments. In order to accurately
estimate the support plane, we combine external perception and proprioception,
and use Multivariate Gaussian Processe Regression (MV-GPR) to estimate the
terrain at the sampling nodes. We build a physical experimental platform and
conduct experiments in different outdoor environments. Experimental results
show that our method has high safety, robustness and generalization
To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning
Existing visual instruction tuning methods typically prompt large language
models with textual descriptions to generate instruction-following data.
Despite the promising performance achieved, these descriptions are derived from
image annotations, which are oftentimes coarse-grained. Furthermore, the
instructions might even contradict the visual content without observing the
entire visual context. To address this challenge, we introduce a fine-grained
visual instruction dataset, LVIS-Instruct4V, which contains 220K visually
aligned and context-aware instructions produced by prompting the powerful
GPT-4V with images from LVIS. Through experimental validation and case studies,
we demonstrate that high-quality visual instructional data could improve the
performance of LLaVA-1.5, a state-of-the-art large multimodal model, across a
wide spectrum of benchmarks by clear margins. Notably, by simply replacing the
LLaVA-Instruct with our LVIS-Instruct4V, we achieve better results than LLaVA
on most challenging LMM benchmarks, e.g., LLaVA (76.7 vs. 70.7) and MM-Vet
(40.2 vs. 35.4). We release our data and model at
https://github.com/X2FD/LVIS-INSTRUCT4V.Comment: techical report; work in progres
Linear stability for a free boundary problem modeling the growth of tumor cord with time delay
This paper was concerned with a free boundary problem modeling the growth of tumor cord with a time delay in cell proliferation, in which the cell location was incorporated, the domain was bounded in , and its boundary included two disjoint closed curves, one fixed and the other moving and a priori unknown. A parameter represents the aggressiveness of the tumor. We proved that there exists a unique radially symmetric stationary solution for sufficiently small time delay, and this stationary solution is linearly stable under the nonradially symmetric perturbations for any . Moreover, adding the time delay in the model leads to a larger stationary tumor. If the tumor aggressiveness parameter is bigger, the time delay has a greater effect on the size of the stationary tumor, but it has no effect on the stability of the stationary solution
Conditional DETR for Fast Training Convergence
The recently-developed DETR approach applies the transformer encoder and
decoder architecture to object detection and achieves promising performance. In
this paper, we handle the critical issue, slow training convergence, and
present a conditional cross-attention mechanism for fast DETR training. Our
approach is motivated by that the cross-attention in DETR relies highly on the
content embeddings for localizing the four extremities and predicting the box,
which increases the need for high-quality content embeddings and thus the
training difficulty. Our approach, named conditional DETR, learns a conditional
spatial query from the decoder embedding for decoder multi-head
cross-attention. The benefit is that through the conditional spatial query,
each cross-attention head is able to attend to a band containing a distinct
region, e.g., one object extremity or a region inside the object box. This
narrows down the spatial range for localizing the distinct regions for object
classification and box regression, thus relaxing the dependence on the content
embeddings and easing the training. Empirical results show that conditional
DETR converges 6.7x faster for the backbones R50 and R101 and 10x faster for
stronger backbones DC5-R50 and DC5-R101. Code is available at
https://github.com/Atten4Vis/ConditionalDETR.Comment: Accepted by ICCV 2021. The first two authors share first authorship,
and the order was determined by rolling dic
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