我国海洋第三产业发展特征分析及对策研究

海洋第三产业是从事海洋非物质生产活动的产业,它主要包括海洋交通运输业、滨海旅游业以及海洋服务业等。国外海洋经济发达国家,海洋第三产业所占的比重都比较大。而我国海洋第三产业相对比较落后,虽发展势头良好,但也存在一些问题。大力扶持我国海洋第三产业的发展,提高它在海洋产业中的比重及国民经济贡献度,是建设＂海上中国＂的一项重要任务。本文就当前我国海洋第三产业的发展特征进行论述,并据此提出下一步的发展对策。     

比较叙事学:"中国叙事学"研究之一途

叙事学研究已得到长足发展,并成为国际学术领域中的显学之一。如何使中国的叙事学研究在已有的基础上形成自己的特色,在国际叙事学领域占有一席之地,比较叙事学研究或许是中国叙事学研究与中国叙事学者可以承担的任务。比较叙事学将在沟通中外叙事理论与实践、挖掘中国丰富的叙事理论资源、扩展与深化叙事学研究中显现出自己的优势,并对叙事学研究产生积极的影响。     

山西科技型人才创业环境研究

科技型人才是企业稀缺的人力资源,是企业核心竞争力的根本来源。科技型人才创业环境从宏观与微观两个层面分析,都有各自的要素和特点。山西科技型人才创业环境宏观方面存在着政府政策实施力度不够,管理使用机制僵化等问题;微观方面存在着科技与经济结合不够,资金支持力度不高、内部运行机制不完善等问题。为此,应从努力构建良好宏观与微观环境入手,实现山西科技型人才创业环境的优化。     

浅谈和谐视野下高校学生思想政治教育模式

高等学校作为培养、造就德智体美全面发展的社会主义事业建设者和接班人的摇篮,是构建社会主义和谐社会的重要阵地。因此,构建高校学生思想政治教育模式应以和谐为理念。和谐的本质和内涵、提出的时代背景与中国传统文化中丰富的和谐思想,决定了将高校学生思想政治教育纳入和谐视野下。和谐视野下的高校学生思想政治教育模式包括从教育目标到教育内容、教育主客体、教育环境、教育方法、教育管理等各方面都协调、匹配,共同作用于高校学生的全面、协调发展。     

城乡一体化:一个社会选择理论的分析视角

社会选择理论承认社会成员在偏好、环境资源、条件和机遇等方面存在差异的前提下,探索采取何种制度安排、政策决策以实现社会选择,是研究个人价值与社会选择之间的冲突与一致性的条件。党的十七届三中全会指出的建立促进城乡经济社会发展一体化制度就是要充分考虑城乡社会不同层面、城乡之间的制度分割等条件,建立适合中国社会的城乡一体化制度。在社会选择理论的视角下,建立城乡一体化制度,就是追求社会最优。因此,必须提高应对社会风险的能力;必须充分调动农民的积极性;必须建立城乡对接的社会保障制度。     

中华民族共同体意识视域下的民族地区"四史"教育——以甘孜藏区基层干部、中小学教师的调查为例

以“四史”教育为切入点,既丰富了铸牢中华民族共同体意识的研究内容,又是贯彻落实习总书记有关“四史”教育重要讲话精神的需要。通过分析中华民族共同体意识深刻内涵的“五个认同”核心要义,探讨中华民族共同体意识与“四史”教育的内在统一性,明确中华民族共同体意识与认同国家历史、认同党的领导、认同共同理想和认同远大理想的内在关联。根据民族地区基层干部、中小学教师的调查,分析在民族地区加强“四史”教育的作用、必要性及其与铸牢中华民族共同体意识的影响相关性,进而提出民族地区“四史”教育要以“共情”教育为抓手、“共鸣”教育为主线和“共行”教育为目标的三条路径。     

Implementation of a Bayesian adaptive design in a proof of concept study

With increased costs of drug development the need for efficient studies has become critical. A key decision point on the development pathway has become the proof of concept study. These studies must provide clear information to the project teams to enable decision making about further developing a drug candidate but also to gain evidence that any effect size is sufficient to warrant this development given the current market environment. Our case study outlines one such proof of concept trial where a new candidate therapy for neuropathic pain was investigated to assess dose-response and to evaluate the magnitude of its effect compared to placebo. A Normal Dynamic Linear Model was used to estimate the dose-response--enforcing some smoothness in the dose-response, but allowing for the fact that the dose-response may be non-monotonic. A pragmatic, parallel group study design was used with interim analyses scheduled to allow the sponsor to drop ineffective doses or to stop the study. Simulations were performed to assess the operating characteristics of the study design. The study results are presented. Significant cost savings were made when it transpired that the new candidate drug did not show superior efficacy when compared placebo and the study was stopped.     

非自治时滞差分方程的渐近稳定性

研究非线性非自治时滞差分万程 △χ_(ｎ)＋α_(ｎ)χ_(ｎ)＋ｆ（ｎ,χ_(ｎ－ｋ),χ_(ｎ－ｋ_(２)),…,χｎ－ｋｍ）＝ ０的一致稳定性和一致渐近稳定性,用不同的方法推广已有的相关定理。     

The LASSO Method for Bilinear Time Series Models

In this article we propose a method called GLLS for the fitting of bilinear time series models. The GLLS procedure is the combination of the LASSO method, the generalized cross-validation method, the least angle regression method, and the stepwise regression method. Compared with the traditional methods such as the repeated residual method and the genetic algorithm, GLLS has the advantage of shrinking the coefficients of the models and saving the computational time. The Monte Carlo simulation studies and a real data example are reported to assess the performance of the proposed GLLS method.     

Variational inference for sparse spectrum Gaussian process regression

We develop a fast variational approximation scheme for Gaussian process (GP) regression, where the spectrum of the covariance function is subjected to a sparse approximation. Our approach enables uncertainty in covariance function hyperparameters to be treated without using Monte Carlo methods and is robust to overfitting. Our article makes three contributions. First, we present a variational Bayes algorithm for fitting sparse spectrum GP regression models that uses nonconjugate variational message passing to derive fast and efficient updates. Second, we propose a novel adaptive neighbourhood technique for obtaining predictive inference that is effective in dealing with nonstationarity. Regression is performed locally at each point to be predicted and the neighbourhood is determined using a measure defined based on lengthscales estimated from an initial fit. Weighting dimensions according to lengthscales, this downweights variables of little relevance, leading to automatic variable selection and improved prediction. Third, we introduce a technique for accelerating convergence in nonconjugate variational message passing by adapting step sizes in the direction of the natural gradient of the lower bound. Our adaptive strategy can be easily implemented and empirical results indicate significant speedups.