342 research outputs found
Performance of Private Enterprises Under the Background of New Round of Expansion of State-Owned Enterprises
In this paper, we used the super-efficient DEA method to analyze the performance of Chinese private enterprises. Talked 100 best private enterprises from 2008 to 2012 in China as the representative, we analyze the performance of private enterprises. The results showed that the efficiency levels of the private enterprises had continuously improved from 2008 to 2012, but the overall efficiency level of the private enterprise was lower. There existed large different among the private companies, and nearly half of the efficiency values of the enterprises were at 0.7658 or less. Therefore, the state should pay more attention to the living environment of the private enterprises, the private enterprises should play its due the potential to promote the country’s sustainable development
Dream the Impossible: Outlier Imagination with Diffusion Models
Utilizing auxiliary outlier datasets to regularize the machine learning model
has demonstrated promise for out-of-distribution (OOD) detection and safe
prediction. Due to the labor intensity in data collection and cleaning,
automating outlier data generation has been a long-desired alternative. Despite
the appeal, generating photo-realistic outliers in the high dimensional pixel
space has been an open challenge for the field. To tackle the problem, this
paper proposes a new framework DREAM-OOD, which enables imagining
photo-realistic outliers by way of diffusion models, provided with only the
in-distribution (ID) data and classes. Specifically, DREAM-OOD learns a
text-conditioned latent space based on ID data, and then samples outliers in
the low-likelihood region via the latent, which can be decoded into images by
the diffusion model. Different from prior works, DREAM-OOD enables visualizing
and understanding the imagined outliers, directly in the pixel space. We
conduct comprehensive quantitative and qualitative studies to understand the
efficacy of DREAM-OOD, and show that training with the samples generated by
DREAM-OOD can benefit OOD detection performance. Code is publicly available at
https://github.com/deeplearning-wisc/dream-ood.Comment: NeurIPS 202
Driver’s Shy Away Effect in Urban Extra-Long Underwater Tunnel
For urban extra-long underwater tunnels, the obstacle space formed by the tunnel walls on both sides has an impact on the driver\u27s driving. The aim of this study is to investigate the shy away characteristics of drivers in urban extra-long underwater tunnels. Using trajectory offset and speed data obtained from real vehicle tests, the driving behaviour at different lanes of an urban extra-long underwater tunnel was investigated, and a theory of shy away effects and indicators of sidewall shy away deviation for quantitative analysis were proposed. The results show that the left-hand lane has the largest offset and driving speed from the sidewall compared to the other two lanes. In the centre lane there is a large fluctuation in the amount of deflection per 50 seconds of driving, increasing the risk of two-lane collisions. When the lateral clearances are increased from 0.5 m to 2.19 m on the left and 1.29 m on the right, the safety needs of drivers can be better met. The results of this study have implications for improving traffic safety in urban extra-long underwater tunnels and for the improvement of tunnel traffic safety facilities
PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels
Semi-supervised node classification, as a fundamental problem in graph
learning, leverages unlabeled nodes along with a small portion of labeled nodes
for training. Existing methods rely heavily on high-quality labels, which,
however, are expensive to obtain in real-world applications since certain
noises are inevitably involved during the labeling process. It hence poses an
unavoidable challenge for the learning algorithm to generalize well. In this
paper, we propose a novel robust learning objective dubbed pairwise
interactions (PI) for the model, such as Graph Neural Network (GNN) to combat
noisy labels. Unlike classic robust training approaches that operate on the
pointwise interactions between node and class label pairs, PI explicitly forces
the embeddings for node pairs that hold a positive PI label to be close to each
other, which can be applied to both labeled and unlabeled nodes. We design
several instantiations for PI labels based on the graph structure and the node
class labels, and further propose a new uncertainty-aware training technique to
mitigate the negative effect of the sub-optimal PI labels. Extensive
experiments on different datasets and GNN architectures demonstrate the
effectiveness of PI, yielding a promising improvement over the state-of-the-art
methods.Comment: 16 pages, 3 figure
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