Optimal Randomness for Stochastic Configuration Network (SCN) with Heavy-Tailed Distributions

Stochastic Configuration Network (SCN) has a powerful capability for regression and classification analysis. Traditionally, it is quite challenging to correctly determine an appropriate architecture for a neural network so that the trained model can achieve excellent performance for both learning and generalization. Compared with the known randomized learning algorithms for single hidden layer feed-forward neural networks, such as Randomized Radial Basis Function (RBF) Networks and Random Vector Functional-link (RVFL), the SCN randomly assigns the input weights and biases of the hidden nodes in a supervisory mechanism. Since the parameters in the hidden layers are randomly generated in uniform distribution, hypothetically, there is optimal randomness. Heavy-tailed distribution has shown optimal randomness in an unknown environment for finding some targets. Therefore, in this research, the authors used heavy-tailed distributions to randomly initialize weights and biases to see if the new SCN models can achieve better performance than the original SCN. Heavy-tailed distributions, such as Lévy distribution, Cauchy distribution, and Weibull distribution, have been used. Since some mixed distributions show heavy-tailed properties, the mixed Gaussian and Laplace distributions were also studied in this research work. Experimental results showed improved performance for SCN with heavy-tailed distributions. For the regression model, SCN-Lévy, SCN-Mixture, SCN-Cauchy, and SCN-Weibull used less hidden nodes to achieve similar performance with SCN. For the classification model, SCN-Mixture, SCN-Lévy, and SCN-Cauchy have higher test accuracy of 91.5%, 91.7% and 92.4%, respectively. Both are higher than the test accuracy of the original SCN.     

基于双阈值视觉注意模型的图像关注焦点检测

 图像关注焦点（FOA）检测是基于人眼视觉关注模型的图像感兴趣区提取的关键技术。为了更加精确、合理地搜索图像关注焦点,提出一种基于双阈值视觉关注模型的FOA检测算法。算法首先提取图像的亮度、颜色、方向和离散矩变换（DMT）特征,生成各个特征对应的特征图;然后将各特征图合并为一张包含多种特征的显著图;最后根据显著图的灰度直方图建立静态阈值与动态阈值,确定图像关注焦点的位置与数量。实验结果表明：新算法在图像关注焦点检测的准确性上较Itti模型有更为优秀的表现,更符合人眼视觉的关注习惯。     

Optimal reinsurance and investment strategies for insurer under interest rate and inflation risks

In this paper, we investigate an optimal reinsurance and investment problem for an insurer whose surplus process is approximated by a drifted Brownian motion. Proportional reinsurance is to hedge the risk of insurance. Interest rate risk and inflation risk are considered. We suppose that the instantaneous nominal interest rate follows an Ornstein–Uhlenbeck process, and the inflation index is given by a generalized Fisher equation. To make the market complete, zero-coupon bonds and Treasury Inflation Protected Securities (TIPS) are included in the market. The financial market consists of cash, zero-coupon bond, TIPS and stock. We employ the stochastic dynamic programming to derive the closed-forms of the optimal reinsurance and investment strategies as well as the optimal utility function under the constant relative risk aversion (CRRA) utility maximization. Sensitivity analysis is given to show the economic behavior of the optimal strategies and optimal utility.     

常利率复合二项双险种风险模型的研究

提出了含利率因素的复合二项双险种风险模型,并在有关假设的基础上,给出了此模型下保险公司稳定经营的必要条件;证明了索赔时刻的盈余过程是一马氏过程和调节系数的存在性,并采用递归方法得到了模型的破产概率的上界估计.     

Segment-based region of interest generation for pedestrian detection in far-infrared images

We present a region of interest (ROI) generation method specialized for nighttime pedestrian detection using far-infrared (FIR) images. Because pedestrians typically appear brighter than background in FIR images, previous research efforts primarily attempted to extract ROIs based on the intensity threshold. However this approach has problems resulting from the intensity variances of pedestrians due to their clothing and, especially in urban scenarios, and other heat sources that emit more heat than the pedestrians. In this paper, we propose a novel ROI generation method that is based on combining image segments instead of using the intensity threshold. In order to minimize dependence on brightness, we utilize the low-frequency characteristics of FIR images. As a result, our proposed method generates a small number of ROIs at an acceptable miss rate and the generated ROIs provide advantages for classification because the pedestrians are satisfactorily arranged within a bounding box. Experiments conducted indicate that our proposed method performs reliably in urban scenarios.     

基金存储的一个优化模型

研究各期存款(包括购国库券)利率及银行取款政策后发现:每年奖金额均来自于存款的利息;若每笔本金到取款时能获得最大综合本利率,则奖金额就可获得最大值.据此,建立了基金使用模型,设计了一个有效的算法,并通过编程在计算机上实现了这个算法,成功地解决了三种情况下(①只存款不购国库券;②可存款也可购国库券;③第3年的奖金比其他年度多20%)最佳基金存储设计问题,并对每年奖金额不等、年数n任意等都有效.     

一类随机利率模型下的年金精算现值

年金在日常生活中被广泛应用,但已往大多研究的是固定年金以及随机利率下的确定年金.本文在前人研究成果的基础上考虑了利率随机波动对生命年金的影响,运用随机利率模型,得出年金精算现值较为简单的递推关系式,并举例说明利率的随机波动对年金精算现值的影响程度,结果表明利率的波动对年金的定价影响非常大,绝对不容忽视.     

考虑利率因素的保险破产模型研究

本文首先介绍了在一般化破产模型基础上够造的保险费收取次数为Poisson过程的破产模型,并进一步在此模型上考虑了利率因素,使得相应的破产概率更具有实际意义。     

随机利率下的风险模型

本文考虑一种具有随机利率的风险模型。对随机利率则取一般的独立增量过程 ,得到总索赔额精算现值的各阶矩 ,并在某些条件下给出矩的具体表达式     

长三角地区房地产市场是如何实现"去库存"的?

长三角地区在全国房地产"库存"的背景下,实现了房地产市场的不断向好.对长三角地区有关面板数据进行实证研究,实证结果表明房地产开发投资、医院床位数、工业生产总值与人均房地产成交面积呈正相关.城镇人口数,中、长期贷款利率与人均房地产成交面积呈负相关,高收入城市比低收入城市人均房地产成交面积低14.%,教育支出和城镇居民总的可支配收入对人均房地产成交面积影响不显著.