“隐藏的法律”理论——解决司法判决确定性和正确性紧张关系的路径

由于立法者理性的有限性,由于语言表达的有限性,由于法律赖以成长的物质生活方式的变化,立法者所制定的法律不能够涵摄将要发生的所有法律事实。立法者所制定的法律是"显露"的法律,当显露的法律出现不能够适用于法律事实的漏洞时,实证主义求助于"自由裁量权"是不可取的,而应该适用"隐藏"的法律。"隐藏的法律"来源于人类的"类比思维",通过这种思维机器的加工,人类"有限的语言"能够表达"无限的意思",因为通过类比思维"机器"的转换,"有限的语言"可以符合规则地、具有效力地、随机应变地转换成"无限的语言","隐藏的法律"通过类比思维机器的转换显露出来,变成可适用的法律,以填补法律漏洞和实现个案正义。     

Robust estimation for order of hidden Markov models based on density power divergences

In this paper, we study the robust estimation for the order of hidden Markov model (HMM) based on a penalized minimum density power divergence estimator, which is obtained by utilizing the finite mixture marginal distribution of HMM. For this task, we adopt the locally conic parametrization method used in [D. Dacunha-Castelle and E. Gassiate, Testing in locally conic models and application to mixture models. ESAIM Probab. Stat. (1997), pp. 285–317; D. Dacunha-Castelle and E. Gassiate, Testing the order of a model using locally conic parametrization: population mixtures and stationary arma processes, Ann. Statist. 27 (1999), pp. 1178–1209; T. Lee and S. Lee, Robust and consistent estimation of the order of finite mixture models based on the minimizing a density power divergence estimator, Metrika 68 (2008), pp. 365–390] to avoid the difficulties that arise in handling mixture marginal models, such as the non-identifiability of the parameter space and the singularity problem with the asymptotic variance. We verify that the estimated order is consistent and simulation results are provided for illustration.     

从伦理社会学入手构建三级导向的当代灾疫预警体系

在灾疫日益全球化和日常生活化的当代社会,重建与时俱进的社会化灾疫预警方式和灾疫预警体系,成为卓有成效地展开灾疫预防的关键环节。当代灾疫预警必须从人出发,以“生命重于一切”和“生命价值高于一切”为根本价值诉求,遵循生命安全和财产安全的伦理原则,以灾疫情事实本真真实和全民问责为基本内容,创建并不断完善以专业·政府·公民为三级导向的灾疫预警体系和“灾疫情高于一切”的畅通预警机制。     

‘Hidden identities’: perceptions of sexual identity in Beijing

This article draws upon responses given by volunteers who work in the Beijing LGBT Centre regarding perceptions of sexual identity, and how Chinese culture affects hidden or open sexual identities of Chinese lesbian and gay people in this region. The insights gained from those working carefully to create social change offers an important and original contribution to the field of gay and lesbian studies in China. The findings indicate the volunteers at the Beijing LGBT Centre are frustrated by the lack of acceptance of non‐heterosexual relationships among Chinese culture and society, and by the disregard of lesbian gay and bisexual (LGB) people by the Chinese government. The findings also illustrate stigmatization of homosexuality in China is enacted in structural terms (such as in the lack of policy, legislation and positive endorsement by governmental and socio‐political organizations), public expression (such as negative attitudes, beliefs or reactions towards LGB people) and internalized repression (through fear of stigmatization, and subsequent abuse due to negative societal attitudes and discrimination). Influenced by the Chinese tradition of conforming to group values, the findings from this study show that volunteers at the Beijing LGBT Centre believe LGB people in China are generally hesitant to disclose their sexual identities, and reject the idea that there had been a collective shift in Chinese culture regarding increased acceptance of LGB people. It also finds volunteers at the LGBT Centre in Beijing blame Chinese culture for its lack of acceptance of non‐heterosexual relationships, and state stigmatization of homosexuality in China is due to deep‐rooted cultural homophobia.     

Reaching African-American Men on the “Down Low”: Sampling Hidden Populations: Implications for HIV Prevention

ABSTRACT

Studies on the impact of the HIV epidemic among African-American men who have sex with men (MSM) have largely neglected men who self-identify as heterosexual but engage in same-sex behavior. These men, commonly referred to as men on the “down low,” pose formidable challenges to researchers conducting prevention studies. This article addresses the methodological issues that create limitations in sampling this population. It presents a novel approach to locate and access these men, describes the success in using this approach, and suggests implications for future research employing this sampling approach.     

Nonparametric Bayesian analysis for multi-site hidden Markov model

The hidden Markov model (HMM) provides an attractive framework for modeling long-term persistence in a variety of applications including pattern recognition. Unlike typical mixture models, hidden Markov states can represent the heterogeneity in data and it can be extended to a multivariate case using a hierarchical Bayesian approach. This article provides a nonparametric Bayesian modeling approach to the multi-site HMM by considering stick-breaking priors for each row of an infinite state transition matrix. This extension has many advantages over a parametric HMM. For example, it can provide more flexible information for identifying the structure of the HMM than parametric HMM analysis, such as the number of states in HMM. We exploit a simulation example and a real dataset to evaluate the proposed approach.     

Semiparametric hidden Markov model with non-parametric regression

The hidden Markov model regression (HMMR) has been popularly used in many fields such as gene expression and activity recognition. However, the traditional HMMR requires the strong linearity assumption for the emission model. In this article, we propose a hidden Markov model with non-parametric regression (HMM-NR), where the mean and variance of emission model are unknown smooth functions. The new semiparametric model might greatly reduce the modeling bias and thus enhance the applicability of the traditional hidden Markov model regression. We propose an estimation procedure for the transition probability matrix and the non-parametric mean and variance functions by combining the ideas of the EM algorithm and the kernel regression. Simulation studies and a real data set application are used to demonstrate the effectiveness of the new estimation procedure.     

Parallelizing particle filters with butterfly interactions

The bootstrap particle filter (BPF) is the cornerstone of many algorithms used for solving generally intractable inference problems with hidden Markov models. The long-term stability of the BPF arises from particle interactions that typically make parallel implementations of the BPF nontrivial. We propose a method whereby particle interaction is done in several stages. With the proposed method, full interaction can be accomplished even if we allow only pairwise communications between processing elements at each stage. We show that our method preserves the consistency and the long-term stability of the BPF, although our analysis suggests that the constraints on the stagewise interactions introduce errors leading to a lower convergence rate than standard Monte Carlo. The proposed method also suggests a new, more flexible, adaptive resampling scheme, which, according to our numerical experiments, is the method of choice, displaying a notable gain in efficiency in certain parallel computing scenarios.     

广义优先关系下隐性时间、隐性时差和伪时差

在广义优先关系下的工序网络中，发现了奇异现象． 例如，某些工序的开始时间可早于最早开始时间，在不影响总工期的条件下，某些工序的工期延长量可超过总时差，而某些工序的结束时间可迟于最迟结束时间等． 这些现象无法用现有的时间参数值来解释． 通过研究奇异现象，揭示了工序的一些隐性特征，提出了隐性时间、隐性时差和伪时差的新概念，并给出了计算方法． 在很多情况下( 如调整工序的工期时) ，现有的时间参数值与实际不符，而上述隐性时间、隐性时差和伪时差能准确应用于实际． 另外，调整工序的工期是项目调度的重要措施，针对工序的工期在不影响总工期条件下的最大可延长量，发现常识性观点( 认为其等于现有总时差) 是错误的，并根据隐性时间参数值，给出了正确的算法．