农地产权结构变迁视域下土地增值收益的公平分享

农地产权结构是搭建土地增值收益分配与宪法秩序有效链接的重要桥梁.从农地产权结构变迁的视角看,我国传统土地增值收益的国有化机制实际在很大程度上符合1982年宪法所确立的社会主义地租国有原则及地利共享秩序.然而,由于宪法土地条款一开始就被注入了地利分配具有倾向性的思想基因,长此以往便忽视了农民作为整体分享土地增值收益的"另一半宪法秩序".新型城镇化背景下,国家进行了"同地同权"、赋权于"民"的农村土地管理制度改革,其实质是宪法秩序的延续而非替代,我国土地增值收益分配的主要机制仍是征地补偿制度.要实现我国土地增值收益的公平分享,必须回到社会主义"国家—集体"一元论的完整地利共享秩序中来,并遵循实质平等的要求,通过以"人的城镇化"为目标对区片综合地价进行限定、采取倾向于农村及农业发展的"土地财政"政策、打破城乡户籍二元制实现城乡一体化发展等方案,推动农民全过程参与、共享土地利益.     

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.     

调整和优化我国社会结构的机制路径选择

改革开放以来，我国社会获得前所未有的飞跃发展，而社会结构却明显滞后于经济结构，社会管理中的各种问题和矛盾日益凸显，社会结构基本呈现亚稳态特征。当今社会建设的核心是创新社会管理机制，优化调整社会结构。     

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.     

英文影片的中国方言版字幕翻译

2012年3D 版《泰坦尼克号》的华丽上映曾掀起影迷们对它的新一轮热捧。文章就同样备受中国观众喜爱的中国方言版《泰坦尼克号》为案例，结合功能目的论来探寻中国方言版字幕翻译的目的与性质，进而分析、总结英文影片字幕的中国方言版译制活动过程。     

社会学民族志的力量:重返早期社会研究的田野工作

自20世纪70年代以来，社会学民族志在田野工作和叙事复兴中与传统人文学汇聚，显著扩展了自己的学科边界。但是，这种扩展并没有解决社会学民族志在深度的经验发生和强烈的理论诉求之间遭遇的紧张。对于这种紧张，本文主张再访早期社会研究的田野，即19世纪末期到一战之前的若干典型研究，从中反思早期社会学田野工作是如何在“直接经验”的倾向中完成类型化，以及如何在这个经验感获得的同时改变对社会世界的道德想象。早期社会学的田野工作的力量之一不在于以规范的诉求压制经验感，而是以独特的情感结构连接经验的观察和道德理想的重生。由此，我们不仅需要反思诸如社会调查到社区研究这样的简单思路，更要重新理解理论史上所谓的“进步主义式”的道德本身。这一工作对重新认识早期社会研究和今天的社会学民族志都具有意义上的准备。     

Fast and approximate exhaustive variable selection for generalised linear models with APES

We present APproximated Exhaustive Search (APES), which enables fast and approximated exhaustive variable selection in Generalised Linear Models (GLMs). While exhaustive variable selection remains as the gold standard in many model selection contexts, traditional exhaustive variable selection suffers from computational feasibility issues. More precisely, there is often a high cost associated with computing maximum likelihood estimates (MLE) for all subsets of GLMs. Efficient algorithms for exhaustive searches exist for linear models, most notably the leaps‐and‐bound algorithm and, more recently, the mixed integer optimisation (MIO) algorithm. The APES method learns from observational weights in a generalised linear regression super‐model and reformulates the GLM problem as a linear regression problem. In this way, APES can approximate a true exhaustive search in the original GLM space. Where exhaustive variable selection is not computationally feasible, we propose a best‐subset search, which also closely approximates a true exhaustive search. APES is made available in both as a standalone R package as well as part of the already existing mplot package.     

基于债务协商视角的债转股定量研究

针对我国政府、企业和银行等金融机构共同关注的债转股问题，基于债务协商谈判思想，建立部分债务股权互换模型，计算公司证券价格，探讨了债转股对公司价值、破产概率、破产损失成本和资本结构的影响，给出了银行等债权人愿意债转股的充分条件。结果表明：在事先破产清算协议贷款下，事后全部债转股总能提高公司股权价值，但并不一定能提高债券价值。只有其协商谈判能力满足一定条件，公司债权人才愿意事后选择债转股，实现帕累托改进、提高社会福利水平。其次，在公司股东协商谈判能力的一定范围内，部分债转股能提高公司价值，其最优转股债息比例随着公司资产风险的增大而增加。再次，债转股能降低公司破产风险和破产损失成本，但同时也提高了债券风险溢价。最后，随着股东谈判能力增强，最优协商转股债务比例、杠杆率都减少，而债券风险溢价增大。本文所得结果对我国政府、企业和银行如何实施债转股提供理论参考和实践指导。     

伸展构造背景下倾角资料的构造解释

系统总结了在伸展构造背景下，导致反映构造特征的地层倾角模式复杂化的多种因素。沉积相的不同、岩性变化、井眼状况和测井作业时间等多种因素都可能使地层倾角资料复杂化。进一步指出在拉张盆地中，进行倾角资料的构造解释，必须了解其构造样式的多样性和复杂性，解释存在的多解性。重点解剖了铲式断层在横剖面情况下，对于不同构造位置下的倾角模式。详细分析了一个倾角模式对应多种构造样式的倾角构造解释实例。倾角资料的构造解释应是综合解释，既要综合井筒的相关地质资料，又要综合测井与地质知识，还必须考虑综合地震资料。