Adult Education for China‟s “Floating Population”:A Conceptual Framework to Guide Policy, Practice and Research

Since the 1980s, the demand for workers in the China‘s industrial cities, coupled with the extreme poverty of rural villages, has resulted in a massive migrant population comprised of adult workers from the countryside who move to industrial cities seeking work while maintaining strong ties to the villages in which they have permanent residence permits. Approximately 200 million Nong-ming-gong (rural migrant workers) now constitute the largest mass migration in human history. These migrant workers have provided China with an indispensable resource for city construction and economic prosperity. However, the cost to the migrant workers themselves has been extreme, as geographic relocation results in widespread psychological and social dislocation

Research on the Endogenous Problems of Rural Farmers’ Spiritual and Cultural Education

The author conducts research and analysis on the endogenous problems of rural farmers’ spiritual and cultural education and finds that: the current endogenous problems of rural farmers’ spiritual and cultural education in China are mainly manifested in the oldness of spiritual and cultural education concepts, the monotonousness of spiritual and cultural education content, the blurring of spiritual and cultural education forms, the simplicity of spiritual and cultural education methods, and the lag of spiritual and cultural education evaluation and etc. Meanwhile, the author proposes to build a scientific system of spiritual and cultural education policy, create a healthy and harmonious policy implementation atmosphere, and construct a full policy supervision system and some other policy suggestions

SWOT Analysis and Strategies of Constructing Rural Farmers’ Spiritual and Cultural Education System

Opportunities and challenges coexist in constructing rural farmers’ spiritual and cultural education system. Conducting SWOT analysis to analyze various factors from a holistic perspective will facilitate grasping advantages and opportunities, recognizing weaknesses and threats, and seeking a path suitable for local realities. Finally, the author proposes specific policy suggestions regarding awareness improvement, policy improvement, economic development and educational resource integration and so on

Delving StyleGAN Inversion for Image Editing: A Foundation Latent Space Viewpoint

GAN inversion and editing via StyleGAN maps an input image into the embedding spaces ($\mathcal{W}$, $\mathcal{W^+}$, and $\mathcal{F}$) to simultaneously maintain image fidelity and meaningful manipulation. From latent space $\mathcal{W}$ to extended latent space $\mathcal{W^+}$ to feature space $\mathcal{F}$ in StyleGAN, the editability of GAN inversion decreases while its reconstruction quality increases. Recent GAN inversion methods typically explore $\mathcal{W^+}$ and $\mathcal{F}$ rather than $\mathcal{W}$ to improve reconstruction fidelity while maintaining editability. As $\mathcal{W^+}$ and $\mathcal{F}$ are derived from $\mathcal{W}$ that is essentially the foundation latent space of StyleGAN, these GAN inversion methods focusing on $\mathcal{W^+}$ and $\mathcal{F}$ spaces could be improved by stepping back to $\mathcal{W}$. In this work, we propose to first obtain the precise latent code in foundation latent space $\mathcal{W}$. We introduce contrastive learning to align $\mathcal{W}$ and the image space for precise latent code discovery. %The obtaining process is by using contrastive learning to align $\mathcal{W}$ and the image space. Then, we leverage a cross-attention encoder to transform the obtained latent code in $\mathcal{W}$ into $\mathcal{W^+}$ and $\mathcal{F}$, accordingly. Our experiments show that our exploration of the foundation latent space $\mathcal{W}$ improves the representation ability of latent codes in $\mathcal{W^+}$ and features in $\mathcal{F}$, which yields state-of-the-art reconstruction fidelity and editability results on the standard benchmarks. Project page: \url{https://github.com/KumapowerLIU/CLCAE}

Multiple Data-Dependent Kernel Fisher Discriminant Analysis for Face Recognition

Kernel Fisher discriminant analysis (KFDA) method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variations of face images. However, a single kernel is not always suitable for the applications of face recognition which contain data from multiple, heterogeneous sources, such as face images under huge variations of pose, illumination, and facial expression. To improve the performance of KFDA in face recognition, a novel algorithm named multiple data-dependent kernel Fisher discriminant analysis (MDKFDA) is proposed in this paper. The constructed multiple data-dependent kernel (MDK) is a combination of several base kernels with a data-dependent kernel constraint on their weights. By solving the optimization equation based on Fisher criterion and maximizing the margin criterion, the parameter optimization of data-dependent kernel and multiple base kernels is achieved. Experimental results on the three face databases validate the effectiveness of the proposed algorithm

On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling

A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology. However, current node drop pooling methods usually keep the top-k nodes according to their significance scores, which ignore the graph diversity in terms of the node features and the graph structures, thus resulting in suboptimal graph-level representations. To address the aforementioned issue, we propose a novel plug-and-play score scheme and refer to it as MID, which consists of a \textbf{M}ultidimensional score space with two operations, \textit{i.e.}, fl\textbf{I}pscore and \textbf{D}ropscore. Specifically, the multidimensional score space depicts the significance of nodes through multiple criteria; the flipscore encourages the maintenance of dissimilar node features; and the dropscore forces the model to notice diverse graph structures instead of being stuck in significant local structures. To evaluate the effectiveness of our proposed MID, we perform extensive experiments by applying it to a wide variety of recent node drop pooling methods, including TopKPool, SAGPool, GSAPool, and ASAP. Specifically, the proposed MID can efficiently and consistently achieve about 2.8\% average improvements over the above four methods on seventeen real-world graph classification datasets, including four social datasets (IMDB-BINARY, IMDB-MULTI, REDDIT-BINARY, and COLLAB), and thirteen biochemical datasets (D\&D, PROTEINS, NCI1, MUTAG, PTC-MR, NCI109, ENZYMES, MUTAGENICITY, FRANKENSTEIN, HIV, BBBP, TOXCAST, and TOX21). Code is available at~\url{https://github.com/whuchuang/mid}.Comment: 14 pages, 14 figure