媒介融合时代地方高校新闻教育的路向

新闻教育要服务于新闻实践,就必须不断调整教学目标和培养方向。当下,媒介形态、生态环境都促使各种媒介趋向融合,也对媒介从业者提出了新要求。新闻教育尤其是地方高校单一学科、单一技能的新闻人才培养模式已不能适应当前媒介融合的需要。地方高校新闻教育要紧跟媒介融合的趋势及新闻实践的变化,就必须打造以新媒体技术为基础的新闻实践教学平台,打破人才评价机制和引进机制,构建适合自身的学科架构,推行特色教育,积极探索融入地方经济社会发展的路径,培养具有融合理念、多种学科知识、多种媒介传播能力的高素质新闻人才。     

人工智能与制造业融合:基本内涵、发展态势与内在逻辑——兼论成都人工智能与制造业融合的探索与实践

人工智能是新一轮科技革命和产业变革的重要驱动力量,人工智能与制造业融合将推动制造业加快转型升级。基于对人工智能与制造业融合的内涵、态势的分析,阐明了人工智能与制造业融合功能区的内在逻辑和分类方式,结合成都的基础条件,对人工智能与制造业融合功能区进行了分类,并借鉴国内先进经验,从加强人工智能技术和产品供给、分类推进制造业智能化、增强基础设施支撑、打造产业融合生态体系四个方面提出建议。     

山西省区域经济差距变动趋势与原因探析——基于1978年-2009年山西省数据的实证分析

长期以来,山西省的区域经济差距显著。δ收敛检验表明,整体上看,1978年到2009年间山西省的区域经济差距呈现“趋异一趋同”的变化过程。然而,β收敛检验表明,在此期间山西省区域经济并没有发生绝对或相对β收敛。基于资本产出比分解法的增长核算分析表明,资本产出弹性是影响山西区域经济差距的重要因素。要素投入对地区差距的影响逐渐下降,而全要素生产率的影响逐渐上升。2003年到2009年,要素投入实际上是缩小区域差距的力量,山西省的区域差距完全来自于全要素生产率。山西省要继续缩小区域差距,必须从提高低收入地区的全要素生产率着手。     

A permutation-based Bayesian approach for inverse covariance estimation

Abstract

Covariance estimation and selection for multivariate datasets in a high-dimensional regime is a fundamental problem in modern statistics. Gaussian graphical models are a popular class of models used for this purpose. Current Bayesian methods for inverse covariance matrix estimation under Gaussian graphical models require the underlying graph and hence the ordering of variables to be known. However, in practice, such information on the true underlying model is often unavailable. We therefore propose a novel permutation-based Bayesian approach to tackle the unknown variable ordering issue. In particular, we utilize multiple maximum a posteriori estimates under the DAG-Wishart prior for each permutation, and subsequently construct the final estimate of the inverse covariance matrix. The proposed estimator has smaller variability and yields order-invariant property. We establish posterior convergence rates under mild assumptions and illustrate that our method outperforms existing approaches in estimating the inverse covariance matrices via simulation studies.     

Approximation to two independent Gaussian processes from a unique Lévy process and applications

Abstract

In this article, we construct two families of processes, from a unique Lévy process, the finite dimensional distributions of which converge in law towards the finite dimensional distributions of the two independent Gaussian processes. As applications of this result, we obtain families of processes that converge in law towards fractional Brownian motion, sub-fractional Brownian motion and bifractional Brownian motion, respectively.     

A Short Note on Almost Sure Convergence of Bayes Factors in the General Set-Up

ABSTRACT

Although there is a significant literature on the asymptotic theory of Bayes factor, the set-ups considered are usually specialized and often involves independent and identically distributed data. Even in such specialized cases, mostly weak consistency results are available. In this article, for the first time ever, we derive the almost sure convergence theory of Bayes factor in the general set-up that includes even dependent data and misspecified models. Somewhat surprisingly, the key to the proof of such a general theory is a simple application of a result of Shalizi to a well-known identity satisfied by the Bayes factor. Supplementary materials for this article are available online.     

双目标用户均衡下的交通流逐日动态演化模型

时间和费用是出行者在路径选择时关注的两个重要目标，许多研究将两者加权组合为单目标来考虑，存在一定缺陷.在静态交通分配模型的研究中，已有学者提出将两者分开考虑的双目标用户均衡.然而，在动态交通分配模型中，现有研究仍多以传统的单目标用户均衡作为交通流演化的最终状态.因此，本文针对双目标用户均衡，提出了一种新的基于路径流量更新的交通流逐日动态演化模型，假设出行者在每天的路径选择中会将出行时间和收费分开比较以决定是否变换路径.论文从理论上证明了该模型的收敛性，以及交通流演化的最终状态会达到双目标用户均衡，同时数值算例也验证了模型的有效性.