The Evolution and Approaches of Information Analysis Service of University Libraries in China

Abstract

In recent years, we have witnessed the great advancement of Deep neural networks (DNNs) in image restoration. However, a critical limitation is that they cannot generalize well to real-world degradations with different degrees or types. In this paper, we are the first to propose a novel training strategy for image restoration from the causality perspective, to improve the generalization ability of DNNs for unknown degradations. Our method, termed Distortion Invariant representation Learning (DIL), treats each distortion type and degree as one specific confounder, and learns the distortion-invariant representation by eliminating the harmful confounding effect of each degradation. We derive our DIL with the back-door criterion in causality by modeling the interventions of different distortions from the optimization perspective. Particularly, we introduce the counterfactual distortion augmentation to simulate the virtual distortion types and degrees as the confounders. Then, we instantiate the intervention of each distortion with a virtually model updating based on corresponding distorted images, and eliminate them from the meta-learning perspective. Extensive experiments demonstrate the effectiveness of our DIL on the generalization capability on unseen distortion types and degrees. Our code will be available at https://github.com/lixinustc/causal-IR-DIL.

The Effect of Urban Spatial Form on Energy Efficiency: A Cross-Sectional Study in China

“…After entering the 21st century, with the reorganization of the world economic pattern and global economic integration, the urban spatial form has further developed in the direction of regionalization and information networking, and its internal mechanism has become more complex (Tanushri and Sarika, 2021). The evolution of urban spatial form is showing unprecedented new mechanisms and characteristics, and has become an important field of urban geography and economic research (Li et al, 2021;(Xiong and Duan, 2020). This is because the urban spatial form can not only comprehensively reflect the social and economic activities of the city in different periods, but also change with the changes of urban economic activities and social culture, showing a dynamic interaction process.…”

“…There are many indicators and methods for measuring energy efficiency in academia, such as parametric and nonparametric estimation methods. Considering that the super-SBM model not only overcomes the limitations of the traditional SBM model, but also has relatively high accuracy of measurement results (Li et al, 2021), this paper establishes a super-SBM model of non angular, non radial and considering undesirable output to measure energy efficiency with reference to the practice of Wu et al, 2021b. The model is constructed as follows:…”