Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Cooperative innovation through a talent management pool: A qualitative study on coopetition in healthcare

In recent years, the Dutch healthcare sector has been confronted with increased competition. Not only are financial resources scarce, Dutch hospitals also need to compete with other hospitals in the same geographic area to attract and retain talented employees due to considerable labour shortages. However, four hospitals operating in the same region are cooperating to cope with these shortages by developing a joint Talent Management Pool. ‘Coopetiton’ is a concept used for simultaneous cooperation and competition. In this paper, a case study is performed in order to enhance our understanding of coopetition. Among other things, the findings suggest that perceptions of organizational actors on competition differ and might hinder cooperative innovation with competitors, while perceived shared problems and resource constraints stimulate coopetition. We reflect on the current coopetition literature in light of the research findings, which have implications for future research on this topic.     

习近平总书记关于高校体育工作重要论述的逻辑意蕴与实践路径

习近平总书记关于高校体育工作重要论述是新时代中华民族所认同体育文化的价值取向、精神气质、思维方式与行动纲要的总和，它是大学生思想教育工作发展创新的内在需要。高校通过以中华体育文化精神为基石，融入社会主义主流价值；优化体育文化育人环境，让体育文化叫好又叫座；深入挖掘体育文化活动内涵，打造经典体育文化品牌；丰富体育文化载体形式，使体育文化育人产生倍增效果，多种实践途径践行习近平总书记关于高校体育工作的重要论述，促推中华民族体育强国梦的实现。     

快播“天价罚单”背后的网络盗版侵权思考

以快播公司为典型的网络盗版侵权现象,反映了网络技术的发展所引起的作品传播技术的变革与著作权保护制度之间的激烈碰撞。治理网络盗版侵权,首先应坚持知识传播与权利保护相平衡的原则,长远任务在于充分利用网络在权利所有人和终端使用人之间开通的市场之路,尽快建立著作权人与网站、用户三方互利共赢的成熟的商业机制,当前则可以通过加大监管与惩罚力度乃至追究刑事责任等方式打击盗版侵权。     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

经济新常态下产业园区的发展趋势

经济发展进入到一个新常态,向形态更高级、分工更复杂、结构更合理的阶段转换。在这个过程中,产业园区的发展可能就是形态更加高级,内部分工可能更加复杂,整个结构可能更加趋向合理和科学的发展方式。在经济新常态下,产业园区发展会出现一些新特点和新趋势:第一,从量的快速扩张向质的提升转变;第二,土地资源驱动园区发展模式的转换;第三,园区的产业链式和集群化的发展趋势;第四,产业园区要素与城区功能结合整合的趋势;第五,创新型科技园区加快建设和发展趋势;第六,生态化园区进一步发展趋势;第七,多样化园区融资体系探索和创新模式;第八,自由贸易园区的探索和发展。     

The Abject Embodiment of Cancer Patients: Dignity,Selfhood, and the Grotesque Body

The body is the empirical quintessence of the self. Because selfhood is symbolic, embodiment represents the personification and materialization of otherwise invisible qualities of personhood. The body and experiences of embodiment are central to our sense of being, who we think we are, and what others attribute to us. What happens, then, when one's body is humiliating? How does the self handle the implications of a gruesome body? How do people manage selfhood in light of grotesque physical appearances? This study explores these questions in the experiences of dying cancer patients and seeks to better understand relationships among body, self, and situated social interaction.     

我国西部地区生态环境存在的问题及对策

西部地区虽然经济欠发达,但在我国经济社会发展中占有重要位置。一方面西部地区是我国的重要能源基地,也是长江、澜沧江、黄河、黑河、珠江等大江大河的发源地和主要集水区;另一方面,西部地区野生动植物资源丰富,是我国许多珍稀野生动植物的主要栖息地。因此,西部地区的生态环境建设不仅对西部地区自身发     

浅析建筑市场监理运行现状与对策

文章透析了监理单位的外部管理现状 ,内部运行机制及监理业务的竞争环境 ,并提出了相应的对策与建议