文章基于中国家庭金融调查(CHFS) 2019年的数据,深入探讨了金融关注度对家庭消费升级的影响及其机制。研究发现,金融关注度的提高有助于促进家庭消费升级,并且在采用工具变量法和替换被解释变量后,结果依然稳健。机制分析表明,金融关注度可以通过影响信贷约束和收入不确定性来影响家庭消费升级。此外,异质性分析显示,金融关注度能够促进中部、西部和东部地区的消费升级,但在东北地区这一效应并不显著。对于具有城乡背景的家庭群体,金融关注度对消费升级具有促进作用,但这种促进效果存在差异。Based on data from the China Household Finance Survey (CHFS) in 2019, this article conducted an in-depth study on the impact and mechanism of financial attention on household consumption upgrading. The study found that the increase in financial attention can help promote household consumption upgrading, and the results remain robust after using the instrumental variable method and substituting the explained variables. The mechanism analysis shows that financial attention can affect household consumption upgrading by influencing credit constraints and income uncertainty. In addition, heterogeneity analysis shows that financial attention can promote consumption upgrading in the central, western, and eastern regions, but this effect is not obvious in the northeast region. Financial attention has a promotion effect on consumption upgrading among household groups with urban and rural backgrounds, but there are differences in this promotion effect.
近年来深度卷积神经网络在图像去噪中的应用引起了越来越多的研究兴趣。然而,对于复杂的任务,如真实的噪声图像,普通网络无法恢复精细的细节。提出了一种经过注意力机制引导的双重去噪网络来恢复干净的图像。具体来说,该网络由四个模块组成,扩张特征提取块Dilated Feature Extraction Block (DFEB)、动态卷积块Dynamic convolution structure diagram、注意力模块,重建模块。具有稀疏机制的特征提取模块经由两个子网络提取全局和局部特征。增强块收集并融合全局和局部特征,为后者的网络提供补充信息。压缩块细化所提取的信息并压缩网络。最后,利用重建区块重建去噪影像。该网络具有以下优点:1) 双网络结构具有稀疏机制,可以提取不同的特征,增强去噪器的泛化能力。2) 融合全局和局部特征可以提取显著特征,从而恢复复杂噪声图像的细节。大量的实验结果表明,该网络有较好的去噪效果。In recent years, the application of deep convolutional neural networks in image denoising has attracted more and more research interest. However, for complex tasks, such as real noisy images, ordinary networks cannot recover fine details. A dual denoising network guided by attention mechanism is proposed to restore clean images. Specifically, the network consists of four modules: Dilated Feature Extraction Block (DFEB), Dynamic convolution structure diagram, attention module and reconstruction module. Feature extraction blocks with sparse mechanism extract global and local features through two subnetworks. Enhancement blocks collect and fuse global and local features to provide supplementary information to the latter’s network. The compressed block refines the extracted information and compresses the network. Finally, the reconstructed block is used to reconstruct the denoised image. The network has the following advantages: 1) the dual network structure has a sparse mechanism, which can extract different features and enhance the generalization ability of the noise reducer. 2) Fusion of global and local features can extract significant features to recover the details of complex noise images. A large number of experimental results show that the network has a good noise reduction effect.
The S transform, which is a time-frequency representation known for its local spectral phase properties in signal processing, uniquely combines elements of wavelet transforms and the short-time Fourier transform （STF...
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The S transform, which is a time-frequency representation known for its local spectral phase properties in signal processing, uniquely combines elements of wavelet transforms and the short-time Fourier transform （STFT）. The fractional Fourier transform is a tool for non-stationary signal analysis. In this paper, we define the concept of the fractional S transform （FRST） of a signal, based on the idea of the fractional Fourier transform （FRFT） and S transform （ST）, extend the S transform to the time-fractional frequency domain from the time- frequency domain to obtain the inverse transform, and study the FRST mathematical properties. The FRST, which has the advantages of FRFT and ST, can enhance the ST flexibility to process signals. Compared to the S transform, the FRST can effectively improve the signal time- frequency resolution capacity. Simulation results show that the proposed method is effective.
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