The Positive Impact of Having Served as a Danwei Leader on Post-retirement Life Satisfaction: Experiences in China

Relevance deprivation syndrome refers to feelings of incompetence among retired people caused by them leaving their high status or influential jobs. The question then arises: do people in positions of power, like Danwei leaders in China, have a lower life satisfaction post-retirement compared to other groups? This study investigated the influence of serving as a Danwei leader before retirement on retirees’ life satisfaction, as well as differences in this influence and the channels through which they are affected. Based on the data of 5,873 respondents of the 2018 China Longitudinal Aging Social Survey, ordinary least-squares, ordered logistic regression, and propensity score matching models were used to investigate the influence, differences, and influential mechanisms of serving as a Danwei leader before retirement on retirees’ life satisfaction. We found that Danwei leaders experience a significantly positive impact on their life satisfaction post-retirement. Second, the positive impact of having served in this role on peoples’ post-retirement life satisfaction is related to the resulting higher income, social status, and better living habits. In contrast to the perspective of relevance deprivation syndrome, in China, having been a Danwei leader before retirement has a significantly positive impact on peoples’ life satisfaction post-retirement, with there being a significant difference observed among different types of retired Danwei leaders.

Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues

Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is challenging in three aspects: (1) the meaning of a context–response pair is built upon language units from multiple granularities (e.g., words, phrases, and sub-sentences, etc.); (2) local (e.g., a small window around a word) and long-range (e.g., words across the context and the response) dependencies may exist in dialogue data; and (3) the relationship between the context and the response candidate lies in multiple relevant semantic clues or relatively implicit semantic clues in some real cases. However, existing approaches usually encode the dialogue with mono-type representation and the interaction processes between the context and the response candidate are executed in a rather shallow manner, which may lead to an inadequate understanding of dialogue content and hinder the recognition of the semantic relevance between the context and response. To tackle these challenges, we propose a representation [ K ] -interaction [ L ] -matching framework that explores multiple types of deep interactive representations to build context-response matching models for response selection. Particularly, we construct different types of representations for utterance–response pairs and deepen them via alternate encoding and interaction. By this means, the model can handle the relation of neighboring elements, phrasal pattern, and long-range dependencies during the representation and make a more accurate prediction through multiple layers of interactions between the context–response pair. Experiment results on three public benchmarks indicate that the proposed model significantly outperforms previous conventional context-response matching models and achieve slightly better results than the BERT model for multi-turn response selection in retrieval-based dialogue systems.

A Densely Connected GRU Neural Network Based on Coattention Mechanism for Chinese Rice-Related Question Similarity Matching

In the question-and-answer (Q&A) communities of the “China Agricultural Technology Extension Information Platform”, thousands of rice-related Chinese questions are newly added every day. The rapid detection of the same semantic question is the key to the success of a rice-related intelligent Q&A system. To allow the fast and automatic detection of the same semantic rice-related questions, we propose a new method based on the Coattention-DenseGRU (Gated Recurrent Unit). According to the rice-related question characteristics, we applied word2vec with the TF-IDF (Term Frequency–Inverse Document Frequency) method to process and analyze the text data and compare it with the Word2vec, GloVe, and TF-IDF methods. Combined with the agricultural word segmentation dictionary, we applied Word2vec with the TF-IDF method, effectively solving the problem of high dimension and sparse data in the rice-related text. Each network layer employed the connection information of features and all previous recursive layers’ hidden features. To alleviate the problem of feature vector size increasing due to dense splicing, an autoencoder was used after dense concatenation. The experimental results show that rice-related question similarity matching based on Coattention-DenseGRU can improve the utilization of text features, reduce the loss of features, and achieve fast and accurate similarity matching of the rice-related question dataset. The precision and F1 values of the proposed model were 96.3% and 96.9%, respectively. Compared with seven other kinds of question similarity matching models, we present a new state-of-the-art method with our rice-related question dataset.

Multi-Scale Fused SAR Image Registration Based on Deep Forest

SAR image registration is a crucial problem in SAR image processing since the registration results with high precision are conducive to improving the quality of other problems, such as change detection of SAR images. Recently, for most DL-based SAR image registration methods, the problem of SAR image registration has been regarded as a binary classification problem with matching and non-matching categories to construct the training model, where a fixed scale is generally set to capture pair image blocks corresponding to key points to generate the training set, whereas it is known that image blocks with different scales contain different information, which affects the performance of registration. Moreover, the number of key points is not enough to generate a mass of class-balance training samples. Hence, we proposed a new method of SAR image registration that meanwhile utilizes the information of multiple scales to construct the matching models. Specifically, considering that the number of training samples is small, deep forest was employed to train multiple matching models. Moreover, a multi-scale fusion strategy is proposed to integrate the multiple predictions and obtain the best pair matching points between the reference image and the sensed image. Finally, experimental results on four datasets illustrate that the proposed method is better than the compared state-of-the-art methods, and the analyses for different scales also indicate that the fusion of multiple scales is more effective and more robust for SAR image registration than one single fixed scale.

Les effets des programmes d’été de littératie: Les théories sur les opportunités d’apprentissage et les élèves « non traditionnels » dans les écoles de langue française en Ontario

According to studies from the United States and English Canada, student achievement gaps grow over the summer months when children are not attending school, but summer literacy interventions can reduce those gaps. This paper presents data from a quasi-experiment conducted in eight Ontario French language school boards in 2010, 2011 and 2012 for 682 children in grades 1-3. Growth in literacy test scores between June and September are compared for 361 attendees of summer literacy programs and 321 control students. Summer program recruits initially had lower prior literacy scores and grades, and tended to hail from relatively disadvantaged social backgrounds. Yet, summer programs narrowed those pre-existing gaps. Effect sizes from a variety of regression and propensity score matching models ranged from .32 to .58, which is quite sizeable by the standards of elementary school interventions and summer programs. Effects were stronger among students whose parents reported not speaking French exclusively at home. Our paper considers learning opportunity theory in light of the “non-traditional” student in Ontario French language schools.