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.     

论我国刑事诉讼中“非法证据”的界定标准

在刑事非法证据排除规则的具体运用中,"非法证据"的界定是当然前提。非法证据并非是任一证据合法性要件欠缺的广义非法证据,而专指取证主体通过严重非法的手段或者侵犯公民基本权利的方式所获取的证据材料。我国刑事非法证据包括非法言词证据和非法实物证据两类,两类非法证据在实际判断中虽存在差异,但都应围绕取证手段的非法性展开,分别考察取证手段是否违反法律规定以及违法的严重程度,以准确界定刑事非法证据的范围。     

数据驱动下收货方质量偏好与电商配送服务质量优化

针对"互联网+大数据"优化电商配送服务质量问题，明确不同收货方的质量需求稳定性，引导电商根据收货方不同质量敏感性提供相对精准服务，提升配送服务质量。模拟投票结果的形成过程聚类得到收货方对服务质量偏好的记忆性特征：（1）"无记忆"型收货方；（2）"记忆"型收货方；（3）"不确定"型收货方；（4）收货方总体。进一步推导不同规划类型求解空间，设计得到"无记忆"型收货方动态规划精确求解方法，及其他三种类型近似求解粒子群算法。研究过程中，配送资源质量感知度被嵌入到模型约束；"无记忆"型收货方的质量需求规划问题转化为零库存策略最优解问题，进而证明存在精确解；"记忆"型收货方呈现出对质量感知的累积；"不确定"型收货方模型通过赋值即可得总体收货方表达式。结果表明：数据驱动研究框架借助大数据资源，使得电商更易通过收货方的质量偏好设计更加匹配的配送方案；不确定服务需求得到有效满足，投入成本的利用率更高；通过特征分类的方式，尽可能地抽取能够精确最优化的部分，缩小NP范围，提高整体求解的精确度。     

高含水期油藏层间压力结构差异量化方法研究

由于长期注水开发和层间非均质性影响，高含水期油藏各小层层间压力结构差异严重，掌握差异状况是合理调整的重要依据。针对现场分层测压资料少，单层压力资料获取困难的情况，应用渗流力学原理建立了高含水期地层层间压力结构差异量化数学模型，模型的关键参数可以通过生产数据计算获得。枣园油田的实例验证表明，高含水油藏层间压力存在明显差异，该方法能够确定各小层的压力差异状况。量化计算结果和实测资料相接近，有利于认清开发矛盾，为合理调整提供技术依据。     

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.     

知识分布、衰减程度与产业集群创新模式的内在机制研究

以知识流动为研究视角，将集群创新过程中的知识溢出、吸收能力内生化，构建包含三种创新模式下的产业集群演化模型，重点分析集群知识分布和知识衰减程度与集群创新模式的内在机制及其对创新产出的影响，并进行仿真实验。研究表明：不同知识分布下，混合创新模式的创新产出表现最好，外部吸收创新模式与内部自主创新模式呈现出不同的创新产出演化路径；在外部吸收创新和混合创新模式下，均匀分布型产业集群的创新产出表现最好，集中分布型表现次之，异质分布型表现最差，而在内部自主创新模式下，集中分布型则表现最好；不同创新模式对知识衰减的敏感程度呈现阶段性差异特征。这些结论对产业集群创新和可持续发展具有很好的理论和实践指导意义。     

The Welfare State as Safety Net in Migration Preferences: Empirical Evidence from an Experiment Among Dutch Master Students

Population Research and Policy Review - The welfare state can be perceived as a safety net which helps individuals adjust to situations of risk or transition. Starting from this idea of the welfare...     

论基层党组织建设与牧区村落社会发展——以青海牧区塔村为个案

农村基层党组织是党在农村全部工作和战斗力的基础,是贯彻落实党的乡村振兴战略、扶贫开发政策实施和乡村社会建设工作的战斗堡垒。在青海牧区乡村,基层组织建设事关党的政策在牧区基层社会贯彻落实,事关牧区乡村社会稳定与发展,事关推进牧区基层社会治理体系和治理能力现代化。文章以青海牧区高原美丽乡村建设中的塔村为个案,观察基层党组织在乡村制度建设、文化建设和经济建设中的核心作用。文章认为,基层党组织在牧区社会中,不仅夯实了基层组织,建成了敢于担当、为民务实的党员干部队伍,还改变了村容村貌,激发了村民的主动性和积极性,有力地促进了牧区乡村社会发展。     

Inequality Assessment by Probabilistic Development Indices

Social Indicators Research - This paper analyses the Human Development Index (HDI) time series from 2010 to 2017. An alternative index is studied, which combines the same components of the HDI by...