对积极的一般预防论中量刑基准的反思及其启示

刑罚目的与量刑基准关系密切,量刑时必须考虑预防犯罪的目的。积极的一般预防论在目的刑论中占据着重要地位,它试图从提升公民的法律信仰、发扬公众的守法精神、强化一般人的法律忠诚信念的角度正面论证一般预防的积极效果。但是,它所提倡的量刑基准可能不利于实现预防效果,无助于提供清晰的刑罚限度,导致处罚的扩大化和严厉化。通过反思积极的一般预防论中量刑基准的缺陷,可以给我国刑法理论带来深刻的启示。     

Tuning Parameter Selection in Cox Proportional Hazards Model with a Diverging Number of Parameters

Regularized variable selection is a powerful tool for identifying the true regression model from a large number of candidates by applying penalties to the objective functions. The penalty functions typically involve a tuning parameter that controls the complexity of the selected model. The ability of the regularized variable selection methods to identify the true model critically depends on the correct choice of the tuning parameter. In this study, we develop a consistent tuning parameter selection method for regularized Cox's proportional hazards model with a diverging number of parameters. The tuning parameter is selected by minimizing the generalized information criterion. We prove that, for any penalty that possesses the oracle property, the proposed tuning parameter selection method identifies the true model with probability approaching one as sample size increases. Its finite sample performance is evaluated by simulations. Its practical use is demonstrated in The Cancer Genome Atlas breast cancer data.     

Conditional Akaike information under covariate shift with application to small area estimation

In this study, we consider the problem of selecting explanatory variables of fixed effects in linear mixed models under covariate shift, which is when the values of covariates in the model for prediction differ from those in the model for observed data. We construct a variable selection criterion based on the conditional Akaike information introduced by Vaida & Blanchard (2005). We focus especially on covariate shift in small area estimation and demonstrate the usefulness of the proposed criterion. In addition, numerical performance is investigated through simulations, one of which is a design‐based simulation using a real dataset of land prices. The Canadian Journal of Statistics 46: 316–335; 2018 © 2018 Statistical Society of Canada     

一个痴情者的学术回眸

治学必须充分占有原始资料;评论作品也应熟悉相关的生活内容,采取历史主义态度;对待文学作品,首先应着眼于艺术,思想虽重要,但必须附丽于艺术。还介绍了自己“从疑点入手”,追根究底的可行的方法,以及通过清理三次文学冤案,从理论上挖出“左倾”病根的业绩。     

诱惑侦查合法性判断标准的细化

诱惑侦查合法性判断标准,既是诱惑侦查理论研究的核心指向,也是侦查理论发展的必然要求。在理论支撑上,随着诱惑侦查基础研究的具体化,特别是其理论阐释与马克思主义哲学本体论、认识论、价值论和方法论的契合性,决定了诱惑侦查合法性判断标准细化已经具备哲学和刑事法学基础。在侦查实践中,确立与侦查规律相适应、能够较为准确地呈现"侦查中"和"侦查后"的实践程式,能够指导侦查人员较为准确地判断处于不同阶段的诱惑侦查的合法性。在效用考量上,依托总效应与边际效应、效率与公正、善良与正义等要素构建的经济效用、社会效用和道德伦理效用评判标准,能进一步对诱惑侦查合法性判断标准的细化做出合理评价。     

基于最小最大后悔值的应急救灾网络构建鲁棒优化模型与算法

应急救灾过程分为两个阶段：第一阶段启动应急救灾网络构建,在灾区附近设立临时应急配送中心,并由应急资源供应方向其紧急调配应急资源;第二阶段将应急资源从临时应急配送中心向灾区受灾点进行调度,以保证救灾过程顺利进行。本文研究第一阶段应急救灾网络的构建问题,考虑到突发灾害初期灾情相关参数概率分布情况难以获取,建立了基于情景的最小最大后悔值准则的应急救灾网络构建鲁棒优化模型。求解模型时,利用有限情景集表示第二阶段的不确定性数据,并将模型化为与其等价的混合整数规划模型,利用情景松弛的迭代算法进行求解。数值试验中给出相应的绝对鲁棒模型与本文偏差鲁棒模型作了比较,结果表明基于最小最大后悔值准则的应急救灾网络优化模型具有良好的鲁棒性,而且算法也是有效的。     

优秀实验课程评审指标体系与评价标准

实验课程建设水平的高低直接影响着高校教学质量的高低和所培养人才的状况,评选优秀实验课程能对其他实验课程的建设与发展起到示范和带动作用。而优秀实验课程评审指标设立的合理性,则成为正确评价优秀实验课程的关键。文章以广州大学为例,依据优秀实验课程评审指标体系构建的基本原则,讨论了所构建的评审指标体系和评价等级标准,旨在为高校优秀实验课程评价提供借鉴。     

人工智能生成物独创性标准再探讨——兼论人类干预之司法判断模型的构建

人工智能生成物的独创性标准直接影响到其作品资格的认定,具有重要意义.应该以"独立创作、源于本人"和"最低限度创造性"作为独创性构成要素,采用独创性主观标准,以人类干预引领和保障科技向善.司法实践中,构建人类干预评价模型,将案例抽象为人类干预情形组合,可解决独创性主观标准之客观化问题;构建人类干预条件模型,归纳人类干预情形组合与著作权保护之间的关系,可解决独创性主观标准之门槛问题.     

Selection of linear mixed-effects models for clustered data

We consider model selection for linear mixed-effects models with clustered structure, where conditional Kullback–Leibler (CKL) loss is applied to measure the efficiency of the selection. We estimate the CKL loss by substituting the empirical best linear unbiased predictors (EBLUPs) into random effects with model parameters estimated by maximum likelihood. Although the BLUP approach is commonly used in predicting random effects and future observations, selecting random effects to achieve asymptotic loss efficiency concerning CKL loss is challenging and has not been well studied. In this paper, we propose addressing this difficulty using a conditional generalized information criterion (CGIC) with two tuning parameters. We further consider a challenging but practically relevant situation where the number, $m$, of clusters does not go to infinity with the sample size. Hence the random-effects variances are not consistently estimable. We show that via a novel decomposition of the CKL risk, the CGIC achieves consistency and asymptotic loss efficiency, whether $m$ is fixed or increases to infinity with the sample size. We also conduct numerical experiments to illustrate the theoretical findings.     

交错级数敛散性的一个判别定理

提出了交错级数敛散性的一个新的判别定理.该定理的判别式为极限形式,运用其判别交错级数的敛散性非常简便.