浅析邓小平的思想政治教育理论

邓小平的思想政治教育理论有丰富的内涵,是邓小平理论的重要组成部分。加强思想政治教育对社会主义现代化建设的各个方面都有非常重要的作用。邓小平的思想政治教育不仅有具体的指导原则,丰富的内容,而且还指出了实行思想政治教育的途径。把握邓小平的思想政治教育理论对于我们当前的思想政治工作有着重要的理论和实践意义。     

地方高校毕业生就业工作路径探析

当前,我国大学生就业形势异常严峻,对高等学校尤其是地方高校的毕业生就业工作提出了严峻的挑战。本文分析了地方高校就业工作中的障碍,并在此基础上从地方高校自身的努力出发,提出了相应的工作思路。     

Consumer Culture Theory,Nonverbal Communication,and Contemporary Politics: Considering Context and Embracing Complexity

This commentary addresses Olivola and Todorov’s “Elected in 100 ms: Appearance-Based Trait Inferences and Voting” and considers its valuable contribution to the field of nonverbal communication. This work suggests that contemporary politics and voting behavior are so complex that they can be better understood outside of laboratory settings where the vital elements on culture and context come into play. By using consumer culture theory and other cultural theories of branding, this commentary evaluates elements of Barack Obama’s presidential run, and considers why his campaign resonated so well with voters in contemporary United States culture.     

论庾信入北后创作中的意象

庾信入北后的持节愿望、乡关之思、隐遁之念等心态有时通过种种繁富的动植物意象的运用含蓄表达出来,这是其后期之作呈现出幽冷蕴藉的美感的原因之一.     

中国传统诚信思想的源流及其特点

本文主要对先秦儒、道、墨、法四家的思想家关于“诚”、“信”及“诚信”的论述进行统计、分析,进一步概括出他们论述的主要特点,以加深对传统诚信思想的认识。     

利息的来源和性质新解

商业银行为存款方和贷款方提供双向服务。城乡居民以获取利息为目的而自觉储蓄属于间接投资行为,存款利息是间接投资收益。贷款利息减去存款利息的差额是银行服务价值,来源于银行员工创造的价值。存贷双方分享银行服务,按一定比例支付这种服务价值。把存款利息视为货币使用权的价格,可以从劳动价值论得到合理解释。这种解释有利于充分发挥利息在配置资源中的作用。     

Sparse Markov Chains for Sequence Data

Finite memory sources and variable‐length Markov chains have recently gained popularity in data compression and mining, in particular, for applications in bioinformatics and language modelling. Here, we consider denser data compression and prediction with a family of sparse Bayesian predictive models for Markov chains in finite state spaces. Our approach lumps transition probabilities into classes composed of invariant probabilities, such that the resulting models need not have a hierarchical structure as in context tree‐based approaches. This can lead to a substantially higher rate of data compression, and such non‐hierarchical sparse models can be motivated for instance by data dependence structures existing in the bioinformatics context. We describe a Bayesian inference algorithm for learning sparse Markov models through clustering of transition probabilities. Experiments with DNA sequence and protein data show that our approach is competitive in both prediction and classification when compared with several alternative methods on the basis of variable memory length.