LQ理论在一类随机参数的跨国资产投资组合优化决策中的应用

考察在连续时间情形下,一类随机系数的跨国(主要研究两国之间)证券投资组合在均值-方差(M-V)优化准则下的最优投资策略(u*(t)),并进一步对该投资组合的有效边界进行研究,得出均值和方差之间的具体表达式.     

Model selection method based on maximal information coefficient of residuals

The traditional model selection criterions try to make a balance between fitted error and model complexity. Assumptions on the distribution of the response or the noise, which may be misspecified, should be made before using the traditional ones. In this article, we give a new model selection criterion, based on the assumption that noise term in the model is independent with explanatory variables, of minimizing the association strength between regression residuals and the response, with fewer assumptions. Maximal Information Coefficient (MIC), a recently proposed dependence measure, captures a wide range of associations, and gives almost the same score to different type of relationships with equal noise, so MIC is used to measure the association strength. Furthermore, partial maximal information coefficient (PMIC) is introduced to capture the association between two variables removing a third controlling random variable. In addition, the definition of general partial relationship is given.     

Novel selection schemes for harmony search

Selection is a vital component used in Evolutionary Algorithms (EA) where the fitness value of the solution has influence on the evolution process. Normally, any efficient selection method makes use of the Darwinian principle of natural selection (i.e., survival of the fittest). Harmony search (HS) is a recent EA inspired by musical improvisation process to seek a pleasing harmony. Originally, two selection methods are used in HS: (i) memory consideration selection method where the values of the decision variables are randomly selected from the population (or solutions stored in harmony memory (HM)) to generate a new harmony, and (ii) selecting a new solution in HM whereby a greedy selection is used to update the HM. The memory consideration selection, the focal point of this paper, is not based on natural selection principle which draws heavily on random selection. In this paper, novel selection schemes which replace the random selection scheme in memory consideration are investigated, comprising global-best, fitness-proportional, tournament, linear rank and exponential rank. The proposed selection schemes are individually altered and incorporated in the process of memory consideration and each adoption is realized as a new HS variation. The performance of the proposed HS variations are evaluated and a comparative study is conducted. The experimental results using benchmark functions show that the selection schemes incorporated in memory consideration directly affect the performance of HS algorithm. Finally, a parameter sensitivity analysis of the proposed HS variations is analyzed.     

Multi-segmental representations and approximation of set-valued functions with 1D images

In this work univariate set-valued functions (SVFs, multifunctions) with 1D compact sets as images are considered. For such a continuous SFV of bounded variation (CBV multifunction), we show that the boundaries of its graph are continuous, and inherit the continuity properties of the SVF. Based on these results we introduce a special class of representations of CBV multifunctions with a finite number of ‘holes’ in their graphs. Each such representation is a finite union of SVFs with compact convex images having boundaries with continuity properties as those of the represented SVF. With the help of these representations, positive linear operators are adapted to SVFs. For specific positive approximation operators error estimates are obtained in terms of the continuity properties of the approximated multifunction.     

奖学金的评定与模糊优选数学模型

本文运用模糊数学思想 ,按照相对隶属度原则 ,通过制定各门课程之间的权重系数 ,建立了一种新的奖学金评定的模糊优选数学模型 .该模型解决了按总成绩排名评定法中所存在的一些问题 .使评定的结果更具科学性与合理性 ,同时模型可推广到其它评比当中     

基于光谱波段优化选择的FTIR测量与分析

在主动式红外遥感测量中,大气中痕量气体的红外吸收与不同红外波段的透过率光谱有关。在很多情况下,透过率光谱在光谱定量分析中起到重要的作用,因此,对测量和仿真的透过率光谱的波段进行优化选择是定量分析的关键。文章对最佳透过率测量范围进行了理论分析, 得到了对应于待测气体浓度反演相对误差最小的理论最佳透过率值;基于谱线的洛伦兹线型通过计算得到了待测气体分子的吸收截面, 同时给出了透过率光谱的校准训练集;确定了单组分CO₂光谱测量分析的波段,优化了多组分CO, CO₂和N₂O光谱同时测量分析的波段并成功地应用于开放光路光谱仪系统。光谱拟合分析结果表明,测量光谱与参考光谱得到了很好的拟合,拟合均方根误差小于1%。     

Display Selection of a Hybrid Foldamer–Peptide Macrocycle

Expanding the chemical diversity of peptide macrocycle libraries for display selection is desirable to improve their potential to bind biomolecular targets. We now have implemented a considerable expansion through a large aromatic helical foldamer inclusion. A foldamer was first identified that undergoes flexizyme-mediated tRNA acylation and that is capable of initiating ribosomal translation with yields sufficiently high to perform an mRNA display selection of macrocyclic foldamer–peptide hybrids. A hybrid macrocyclic nanomolar binder to the C-lobe of the E6AP HECT domain was selected that showed a highly converged peptide sequence. A crystal structure and molecular dynamics simulations revealed that both the peptide and foldamer are helical in an intriguing reciprocal stapling fashion. The strong residue convergence could be rationalized based on their involvement in specific interactions with the target protein. The foldamer stabilizes the peptide helix through stapling and through contacts with key residues. These results altogether represent a significant extension of the chemical space amenable to display selection and highlight possible benefits of inserting an aromatic foldamer into a peptide macrocycle for the purpose of protein recognition.