Can energy prices predict stock returns? An extreme bounds analysis

We assess the predictive abilities of energy prices for future US stock market returns using Sala-i-Martin's (1997) extreme bounds analysis (EBA). The EBA results reveal that the predictive power of energy prices varies substantially across the regression models with different combinations of conditioning variables. Energy prices are not robust predictors for the stock returns in the whole sample period from June 1987 to April 2015. However, before the 2008 global financial crisis, energy prices exerted a moderate negative effect on future stock returns and their effects have become strongly positive afterwards. In general, the predictive power declines with the increase in forecast horizon and it varies considerably over time.     

Backtesting portfolio value-at-risk with estimated portfolio weights

This article theoretically and empirically analyzes backtesting portfolio value-at-risk (VaR) with estimation risk in an intrinsically multi-variate framework. It particularly takes into account the estimation of portfolio weights in forecasting portfolio VaR and its impact on backtesting. It shows that the estimation risk from estimating portfolio weights and that from estimating the multi-variate dynamic model make the existing methods in a univariate framework inapplicable. It proposes a general theory to quantify estimation risk applicable to the present problem and suggests practitioners a simple but effective way to implement valid inference to overcome the effect of estimation risk in backtesting portfolio VaR. In particular, we apply our theory to the efficient mean-variance-skewness portfolio for a multi-variate generalized autoregressive conditional heteroscedasticity model with multi-variate general hyperbolic distributed innovations. Some Monte Carlo simulations and an empirical application demonstrate the merits of our method.     

An Application of Interval Methods to Stock Market Forecasting

Stock market forecasting has been a challenging financial research topic for decades. In the literature, there are numerous results based on point methods. However, poor forecasting quality has been a continuous problem. Motivated by the fact that financial data varies within intervals, we apply interval methods on a well known stock pricing model [3] to predict stock market variability as intervals. Empirical results obtained with a few different approaches in this paper consistently suggest that interval forecasts have better overall quality than traditional point forecasts.     

Position Unmanned Aerial Vehicles in the Mobile Ad Hoc Network

This paper considers the problem of employing multiple unmanned aerial vehicles (UAVs) to the mobile ad hoc network (MANET) as relay backbone nodes to construct the backbone network, to improve the network connectivity, and to address many issues in the MANET such as linkage, capacity, load balance, and reliability. With considering the dynamic nature of the problem, this study provides several linear location problem models and their extensions to accommodate these issues. Due to the size of linear location models associated with a large number of constraints, the problem becomes computational challenging even with modest size of nodes. To overcome the computational barrier, we recast these location problem models using a quadratic unconstrained binary optimization (QUBO) framework and solve these QUBO models with a Tabu search heuristic with preprocessing. The analysis of the solutions that are produced by QUBO together with the comparisons made with the linear model highlight both the attractiveness and robustness of the proposed approach. The results of this study provide support to future advanced routing protocol development.     

Autoencoder-based anomaly detection for surface defect inspection

In this paper, the unsupervised autoencoder learning for automated defect detection in manufacturing is evaluated, where only the defect-free samples are required for the model training. The loss function of a Convolutional Autoencoder (CAE) model only aims at minimizing the reconstruction errors, and makes the representative features widely spread. The proposed CAE in this study incorporates a regularization that improves the feature distribution of defect-free samples within a tight range. It makes the representative feature vectors of all training samples as close as possible to the mean feature vector so that a defect sample in the evaluation stage can generate a distinct distance from the trained center of defect-free samples. The proposed CAE model with regularizations has been tested on a variety of material surfaces, including textural and patterned surfaces in images. The experimental results reveal that the proposed CAE with regularizations significantly outperforms the conventional CAE for defect detection applications in the industry.     

A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization

Constrained optimization problems are very important as they are encountered in many science and engineering applications. As a novel evolutionary computation technique, cuckoo search （CS） algorithm has attracted much attention and wide applications, owing to its easy implementation and quick convergence. A hybrid cuckoo pattern search algorithm （HCPS） with feasibility-based rule is proposed for solving constrained numerical and engineering design optimization problems. This algorithm can combine the stochastic exploration of the cuckoo search algorithm and the exploitation capability of the pattern search method. Simulation and comparisons based on several well-known benchmark test functions and structural design optimization problems demonstrate the effectiveness, efficiency and robustness of the proposed HCPS algorithm.     

基于神经网络的快速核子空间人脸识别算法研究

针对现有核子空间人脸识别算法计算量大且速度缓慢的现状,提出了一种基于神经网络的快速核子空间人脸识别算法模型,利用神经网络的隐含层神经元将核特征子空间的基表示进行约减,从而大幅提高了识别速度。进而基于KPCA和KFDA两种核子空间人脸识别算法,建立了神经网络逼近模型,并基于ORL、UMIST和YALE 3种人脸数据库进行了实证分析。实验结果表明,当隐含层神经元个数设置为训练样本总数一半或更少时,基于神经网络的快速核子空间算法能够取得相近甚至相当于核子空间算法的识别率。从而在满足一定识别正确率的条件下,能将识别时间缩短到50%甚至更低。     

VMPlanner: Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10–20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.     

分布式系统中的资源发现机制综述

资源发现问题亦即由给定的资源描述找到满足该描述的资源地址。如何快速并且准确地从分布存储的资源中找到所需的资源,是一个挑战性的问题。传统的网格资源发现系统采用注册中心和索引的方式,但这些方式不能满足网格系统规模不断扩大的需求。而对等网络是一种扩展性好的分布式系统,采用已有的P2P技术解决网格环境下的资源发现问题是一种有效的途径。介绍了网格系统中的资源发现、P2P系统的资源发现以及基于P2P的网格资源发现系统,并对它们进行了比较。