Jump-detection and curve estimation methods for discontinuous regression functions based on the piecewise B-spline function

Jump-detection and curve estimation methods for the discontinuous regression function are proposed in this article. First, two estimators of the regression function based on B-splines are considered. The first estimator is obtained when the knot sequence is quasi-uniform; by adding a knot with multiplicity p + 1 at a fixed point x₀ on support [a, b], we can obtain the second estimator. Then, the jump locations are detected by the performance of the difference of the residual sum of squares DRSS(x₀) (x₀ ∈ (a, b)); subsequently the regression function with jumps can be fitted based on piecewise B-spline function. Asymptotic properties are established under some mild conditions. Several numerical examples using both simulated and real data are presented to evaluate the performance of the proposed method.     

从《牙医谋杀案》看阿加莎·克里斯蒂侦探小说的特殊魅力

本文以《牙医谋杀案》为例,从破案模式、人物形象塑造和案件设计三个角度出发,探讨了阿加莎·克里斯蒂侦探小说的特殊魅力所在。     

铰接平面多杆机构杆件布层的一种自动化算法

本文针对铰接平面多杆机同构同一运动平面层上杆件干涉的原因，提出一种建立在杆件平面几何形状基础之上的自动化在布层算法。该算法通过检查显示构件外轮廓线上象素的方法，来发现是否干涉，从而确定构件所在的运动平面层。     

Improvements over the duplicate performance method for outlier detection in categorical multivariate surveys

Summary The detection of errors and outliers is an important step in data processing, especially those errors arising from data entry operations because they are of the entire responsability of the data processing staff. The duplicate performance method, is commonly used as an attempt to detect such type of errors. It implies typically typing twice the same data without any special precedence. If the errors are uniformly distributed among individuals, retyping a fraction of the total will also remove typically the same fraction of the errors. A new method is presented, which is able to improve that procedure by sorting the records putting first the most unlikely ones. The ability of the present methodology has been tested by a Monte Carlo simulation, using an existing database of categorical answers of housing characteristics in Uruguay. At first, it has been randomly contaiminated, and after that, the proposed procedure applied. The results show that if a partial retyping is done following the proposed order about 50 % of the errors can be removed while keeping the retyping effort between 4 and 14% of the dataset, while to attain a similar result with the standard methodology 50% (on, average) of the database should be processed. The new ordering is based upon the unrotated Principal Component Analysis (PCA) transformation of the previously coded data. No special shape of the multivariate distribution function is assumed or required.     

激光扫描声学显微镜图像信号的分析与处理

提出了激光扫描声学显微镜（ＳＬＡＭ）图像信号的近似模型，针对ＳＬＡＭ图像的统计特性和各个环节噪声特性，提出了密度比较算法，采用数学形态滤波，对图像进行了处理，并给出实验结果。     

力触觉建模及再现技术研究

力触觉再现关键技术之一是物理模型和碰撞检测算法的构建。以7自由度力触觉设备为基本构架,介绍了力触 觉设备的反馈机理和电气组成;分析比较了力触觉建模的特点,研究弹簧一质点模型,给出了弹簧一质点模型的面模型和 体模型的具体表现形式;分析了力触觉再现碰撞检测算法的优劣,实现了AABB包围盒的遍历算法。最后在Chai 3D环 境构建一个操作手与虚拟柱体的力触觉交互反馈模型,分析了在3种交互情况下的反馈力情况。本文可为力触觉再现 技术的深入研究提供理论参考。     

Approximate Bayesian Inference in Spatial Generalized Linear Mixed Models

Abstract.  In this paper we propose fast approximate methods for computing posterior marginals in spatial generalized linear mixed models. We consider the common geostatistical case with a high dimensional latent spatial variable and observations at known registration sites. The methods of inference are deterministic, using no simulation-based inference. The first proposed approximation is fast to compute and is 'practically sufficient', meaning that results do not show any bias or dispersion effects that might affect decision making. Our second approximation, an improvement of the first version, is 'practically exact', meaning that one would have to run MCMC simulations for very much longer than is typically done to detect any indication of error in the approximate results. For small-count data the approximations are slightly worse, but still very accurate. Our methods are limited to likelihood functions that give unimodal full conditionals for the latent variable. The methods help to expand the future scope of non-Gaussian geostatistical models as illustrated by applications of model choice, outlier detection and sampling design. The approximations take seconds or minutes of CPU time, in sharp contrast to overnight MCMC runs for solving such problems.     

可记忆电子分控器设计

设计了一种具有记忆功能的电子分控器,分控器在分步控制过程中彻底断电时,能可靠实时保存当前数据,而且不受断电时间限制。采用法拉电容作为MCU数据保存备用电源,解决了核心器件对锂电池的依赖,从根本解决了由于锂电池使用寿命短带来的弊端。通过实测检验、应用,分控器数据保存可靠实现了记忆功能。     

A Study of Risk Relevance Reasoning Based on a Context Ontology of Railway Accidents

With the application of risk management and accident response in the railway domain, risk detection and prevention have become key research topics. Many dangers and associated risk sources must be considered in collaborative scenarios of heavy-haul railways. In these scenarios, (1) various risk sources are involved in different data sources, and context affects their occurrence, (2) the relationships between contexts and risk sources in the accident cause mechanism need to be explicitly defined, and (3) risk knowledge reasoning needs to integrate knowledge from multiple data sources to achieve comprehensive results. To express the association rules among core concepts, this article constructs two ontologies: The accident-risk ontology and the context ontology. Concept analysis is based on railway domain knowledge and accident analysis reports. To sustainably integrate knowledge, an integrated evolutionary model called scenario-risk-accident chain ontology (SRAC) is constructed by introducing new data sources. The SRAC is integrated through expert rules between the two ontologies, and its evolution process involves new knowledge through a new risk source database. After three versions of the upgrade process, potential risk sources can be mined and evaluated in specific contexts. To evaluate the risk source level, a long short-term memory (LSTM) neural network model is used to capture context and risk text features. A model comparison for different neural network structures is performed to find the optimal evaluation results. Finally, new concepts, such as risk source level, and new instances are updated in the context-aware risk knowledge reasoning framework.     

Facial emotion identification and sexual assault risk detection among college student sexual assault victims and nonvictims

Objective: High rates of sexual victimization among college students necessitate further study of factors associated with sexual assault risk detection. The present study examined how social information processing relates to sexual assault risk detection as a function of sexual assault victimization history. Participants: 225 undergraduates (M_age = 19.12, SD = 1.44; 66% women). Methods: Participants completed an online questionnaire assessing victimization history, an emotion identification task, and a sexual assault risk detection task between June 2013 and May 2014. Results: Emotion identification moderated the association between victimization history and risk detection such that sexual assault survivors with lower emotion identification accuracy also reported the least risk in a sexual assault vignette. Conclusions: Findings suggest that differences in social information processing, specifically recognition of others' emotions, are associated with sexual assault risk detection. College prevention programs could incorporate emotional awareness strategies, particularly for men and women who are sexual assault survivors.