Portfolio Selection with Different Borrowing-Lending Rates: Utility Maximization Model based on Mean and VaR

This article investigates a portfolio selection problem with different borrowing–lending rates and with Value-at-Risk (VaR) as the measure of risk. The problem is formulated as a utility maximization model with a general utility function that is a function of only the mean and the VaR of portfolio return. Several properties of the efficient frontier of the mean-VaR model are first obtained and then used to give some existence conditions and characterizations of the optimal solution to the utility maximization model. Further, a solution method and a numerical algorithm for solving the optimal solution are proposed. Finally, a numerical example using the real data of Chinese stock market is given to show the validity and the practicability of these results.     

Multi-label dimensionality reduction and classification with extreme learning machines

In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and wil hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis （MLKDA）, is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis （LDA）. In the classification process of multi-label data, the extreme learning machine （ELM） is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization （MDDM） and multi-label linear discriminant analysis （MLDA） in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.     

The distributed representation for societal risk classification toward BBS posts

The risk classification of BBS posts is important to the evaluation of societal risk level within a period. Using the posts collected from Tianya forum as the data source, the authors adopted the societal risk indicators from socio psychology, and conduct document-level multiple societal risk classification of BBS posts. To effectively capture the semantics and word order of documents, a shallow neural network as Paragraph Vector is applied to realize the distributed vector representations of the posts in the vector space. Based on the document vectors, the authors apply one classification method KNN to identify the societal risk category of the posts. The experimental results reveal that paragraph vector in document-level societal risk classification achieves much faster training speed and at least 10% improvements of F-measures than Bag-of-Words. Furthermore, the performance of paragraph vector is also superior to edit distance and Lucene-based search method. The present work is the first attempt of combining document embedding method with socio psychology research results to public opinions area.     

F-generation law and recognition of system law

If a system is not disturbed (or invaded) by some law, there is no doubt that each system will move according to the expected law and keep stable. Although such a fact often appears, some unknown law breaks into the system and leads it into turbulence. Using function one direction S-rough sets, this article gives the concept of the F -generation law in the system, the generation model of the F -generation law and the recognition method of the system law. Function one direction singular rough sets is a new theory and method in recognizing the disturbance law existing in the system and recognizing the system law.     

System deployment optimization in architecture design

Optimization of architecture design has recently drawn research interest. System deployment optimization （SDO） refers to the process of optimizing systems that are being deployed to activi- ties. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal so- lutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are mini- mized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm （HGA）, as well as the combination of heuristic method and particle swarm optimization （HPSO）. Experi- ment results show that the HPSO method generates solutions faster than particle swarm optimization （PSO） and genetic algo- rithm （GA） in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution.     

基于支持度的改进证据合成方法

针对Dempster方法在合成高度冲突的证据时结果与直观结论相悖的问题,提出了一种基于支持度的改进证据合成方法。首先根据证据之间的相似性程度判断是否存在证据冲突,对于总数在3条以上且存在冲突的证据组合,应用各证据的支持矩阵,通过其最大特征值对应的特征向量导出各证据的支持度系数,由此对基本概率分配（basic probability assignation, BPA）函数进行转化,再采用Dempster方法进行合成。仿真计算结果表明,该方法能有效处理冲突证据组合,克服了一票否决问题,相对于其它几种典型改进算法具有更快的识别速度,能进一步减小识别结果的不确定性,并保留了Dempster方法满足交换律、结合律的良好特性。     

美国中产阶级的“美国梦”研究——以《兔子,快跑》为例

"美国梦"的概念的出现,反映了美国人积极向上的精神,即人只要努力奋斗就能过上体面人的生活。围绕这种共识,约翰·厄普代克出版的《兔子,快跑》一书,生动地刻画了在美国梦理念下哈利·阿斯特罗姆的人生历程,折射了美国中产阶级面临的困惑与迷茫,并对美国梦进行了深刻的反思。     

瞬变电磁法在一四井煤矿采空区探测中的应用

采用瞬变电磁法(TEM)在河南省新密市境内探查一四井煤矿采空区积水分布情况、断层等构造的含导水性情况及二1煤底板的富水性情况,为矿井防治水工作的合理实施提供依据.     

论减灾系统的远程公平化

远程公平化是偏向于远程的公平，旨在寻求系统整体的优化。在一定的约束条件下，通过域的分隔、变形，中心的定位与相应的远程运作机制，可消减不利因素对系统的干扰，扩大结构瓶颈，调优技术性弱点、难点、远点、易忽视点的指标期望与实现值，抑小扬大，隐近就远，弥补系统缺陷，或于不完备的条件下构建 完备系统，保障全局利益与远效，推进减灾管理的科学化，改善各个方面的减灾工作。     

液压油缸加工方式之探

阐述了液压传动系统中的执行元件-液压油缸缸筒的加工方式，着重介绍了缸筒的加工工序、内孔加工的重要刀具以及注意事项。