Is a co-jump in prices a sparse jump?

Systematic co-jumps in asset prices are generally thought to account for only a small proportion of overall jumps. In actual observations, however, jumps in asset prices are often persistent, and the time of persistence varies. In this context, we develop a new rule to identify co-jumps and improve traditional tests by considering different sampling frequencies and different sampling starting points to re-evaluate the occurrence rate of systematic co-jumps in financial assets. We conduct a simulation experiment to show that the current test procedures generally underestimate the number of co-jumps when considering persistence, but that the proposed procedure can identify co-jumps more accurately. We also perform an empirical analysis using price data from the Shanghai 50 Index and its 25 constituent stocks in China’s stock market. The average proportion of systematic co-jumps detected by the improved s-BNS is approximately 30%, which shows that the co-jump and even the systematic co-jump are not sparse jumps. The results also reveal the shortcomings of traditional jump tests in estimating persistent jumps and demonstrate that the proposed method can better detect the possible nondiversifiable risks between market indices and their constituent stocks, thereby contributing to financial risk management.     

机场排水系统检查与维护分析

机场排水系统检查和维护工作做到位,可以确保机场顺利运行,保证飞机飞行安全性。本文将通过机场排水系统设施、作用、主要技术手段;系统病害问题等方面,对机场排水系统检查和维护工作进行了探讨。     

Insurance fraud detection: A statistically validated network approach

Fraud is a social phenomenon, and fraudsters often collaborate with other fraudsters, taking on different roles. The challenge for insurance companies is to implement claim assessment and improve fraud detection accuracy. We developed an investigative system based on bipartite networks, highlighting the relationships between subjects and accidents or vehicles and accidents. We formalize filtering rules through probability models and test specific methods to assess the existence of communities in extensive networks and propose new alert metrics for suspicious structures. We apply the methodology to a real database—the Italian Antifraud Integrated Archive—and compare the results to out-of-sample fraud scams under investigation by the judicial authorities.     

LAT模型中基于次用户权值优化的频谱感知

全双工认知无线电LAT（Listen-and-Talk）模型因自干扰消除不完全而使得频谱感知能力下降。为提升频谱感知性能，提出了LAT模型下基于次用户加权的协作式频谱感知方法，分别研究了决策融合和数据融合下次用户最优权值的算法。求解最优权值时利用了凸优化方法，在处理非凸问题时利用了序列规划的方法，得到了较好的优化结果。仿真实验表明，在加权融合机制下的LAT模型得到了趋近于传统LBT（Listen-before-Talk）模型的频谱感知能力。     

微弱信号多站时差协同检测技术

针对现有时差相关检测方法难以有效检测微弱信号的现状,为提升现有检测系统对微弱信号的检测性能，提出了一种针对微弱信号的多站时差协同检测技术。通过在空间域内将多组时差站的相关结果积累，然后搜索二维网格中的最大值得到检验统计量。分析了检验统计量的概率分布，明确检验统计量在纯噪声条件下服从极值I型分布，依据奈威-皮尔逊（Neyman-Pearson）准则得到了检测门限。蒙特卡洛仿真结果表明，相同条件下，该方法比传统的相关检测的处理增益高约10lgK dB（K为时差线数目）     

Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China

The current research aims to launch effective accounting fraud detection models using imbalanced ensemble learning algorithms for China A-Share listed firms. Based on a sample of 33,544 Chinese firm-year instances from 1998 to 2017, this research respectively established one logistic regression and four ensemble learning classifiers (AdaBoost, XGBoost, CUSBoost, and RUSBoost) by 12 financial ratios and 28 raw financial data. Additionally, we divided the sample into the train and test observations to evaluate the classifiers' out-of-sample performance. In detail, we applied two metrics, namely, Area under the ROC (receiver operating characteristic) curve (AUC) and Area under the Precision-Recall curve (AUPR), to evaluate classifiers' discriminability. In the supplement test, this study put forward an algebraic fused model on the basis of the four ensemble learning classifiers and introduced the sliding window technique. The empirical results showed that the ensemble learning classifiers can detect accounting fraud for the imbalanced China A-listed firms far more effectively than the logistic regression model. Moreover, imbalanced ensemble learning classifiers (CUSBoost and RUSBoost) effectively performed better than the common ensemble learning models (AdaBoost and XGBoost) in average. The algebraic fused model in the supplement test also obtained the highest average AUC and AUPR among all the employed algorithms. Our results offer firm support for the potential role of Machine Learning (ML)-based Artificial Intelligence (AI) approaches in reliably predicting accounting fraud with high accuracy. Similarly, for the Chinese settings, our ML-based AI offers utmost advantage in forecasting accounting fraud. Finally, this paper fills the research gap on the applications of imbalanced ensemble learning in accounting fraud detection for Chinese listed firms.     

数字孪生技术在智慧机场建设中的应用综述

数字孪生技术融合多种信息化技术，为虚拟现实和计算机仿真等方面提供了技术支撑，也是未来人类建立虚拟世界的必要条件。作为航班起降港口的机场一直被看作是一个城市甚至一个国家的门户，为此通过建立基于数字孪生技术的虚拟机场，不仅可以为机场管理人员和航班保障人员提供更直观更高效的工作模式，也可为旅客提供更多的服务体验。通过描述数字孪生机场的技术概念和建立方式，并对数字孪生技术在航班保障运行、安全应急处理、旅客出行服务等业务应用场景进行阐述和分析，为数字孪生机场的相关研究和应用提供理论依据。     

机场服务质量评价研究

随着中国经济的快速发展和人民生活质量的提升，我国机场在许多方面仍然面临着问题，特别是机场服务质量问题。通过构建基于SERVQUAL量表的机场服务质量评价指标体系，进行问卷设计和数据采集，对数据样本进行可信度分析以及SERVQUAL量表的统计分析，提出针对机场服务质量的改善措施。     

基于FFT的突发扩频信号兼容快捕算法

针对传统突发扩频信号捕获算法的缺陷,即无法自适应兼容处理不同同步头时长信号,提出了一种改进的基于FFT的突发扩频信号兼容快速捕获算法。根据不同时长信号的实际应用特性,利用同步头信号尾部对齐的格式特点,采用子矩阵形式存储不同时长信号的码相关结果,通过多支路并行捕获处理,实现突发信号的自适应兼容捕获。同时,通过矩阵的FFT运算同步实现全相干累积来提高信号载噪比,通过时频并行处理提高处理效率,最大限度地优化了弱信号条件的捕获性能。仿真验证表明,算法可以实现不同扩频信号的兼容快速捕获,研究为同步头时长不等的低载噪比信号提供了一种有效的兼容快速捕获方法。     

宽带信号数字信道化及跨信道重构侦测技术

针对在无源探测大带宽信号的过程中会产生跨信道失真问题，提出了一种跨信道可重构的信道化方法。首先经过均匀滤波器组粗滤波，再引入检测时间自适应的双门限能量检测机制，对粗滤波信号进行跨信道判决，之后对各个跨信道信号分别进行可重构信道化设计，从而恢复出原始信号。与传统的数字信道化方法相比，该算法能够更新能量检测门限对跨信道信号进行实时判决，对跨信道信号的重构准确率高于98.3%，实现了对跨子信道带宽信号的近似无失真重构。Matlab仿真结果验证了算法的有效性。