Policy Determinants of School Outcomes under Model Uncertainty: Evidence from South Africa

In this paper, we assess the determinants of secondary school outcomes in South Africa. We use Bayesian model averaging techniques to account for uncertainty in the set of underlying factors that are chosen among a very large pool of explanatory variables in order to identify the subset of explanatory variables most strongly associated with the dependent variable. Our analysis indicates that the socio‐economic background of pupils, demographic characteristics such as population groups (Black and White), as well as geographical locations account for a significant variation in pupils’ achievement levels. We also find that the most robust policy determinants of pupils’ test scores are the availability of a library at school, the use of IT in the classroom as well as school climate.     

基于K-S检验与距离相关分析的网络借贷信用评价指标体系构建

网络借贷作为一种新型互联网金融模式,提升了金融资源使用效率,缓解了小企业融资难的困局。构建合理的网络借贷信用评价指标体系,从而对网络借贷的潜在风险及时甄别与预防,对互联网金融健康持续发展意义重大。本文根据K-S检验与距离相关分析相结合,筛选对借款客户违约状态甄别能力强的指标,建立了网络借贷信用评价指标体系,通过P2P网络借贷(peer to peer lending,个人对个人借贷)平台LendingClub交易数据进行实证研究,结果表明:不仅借款金额、借款利率等借款标的特征对借贷者违约具有显著相关性,借款者年龄等个人特征、借款者年收入等财务特征以及借款者违约次数等信用特征均对借贷者违约风险产生显著影响。投资者在出借资金时,往往青睐于已婚、年龄适中、具有一定工作经历、历史违约次数较少的借款人。因此,风险监管部门应构建网络借贷违约风险评估模型,对P2P平台进行风险监测,同时建立关键信息共享机制,融合多源数据,明确审查范围,实现P2P网络借贷行业健康有序发展。     

Systemic financial risk early warning of financial market in China using Attention-LSTM model

We propose an Attention-LSTM neural network model to study the systemic risk early warning of China. Based on text mining, the network public opinion index is constructed and used as a training set to be incorporated into the early warning model to test the early warning effect. The results show that: (i) the network public opinion is the non-linear Granger causality of systemic risk. (ii) The Attention-LSTM neural network has strong generalization ability. Early warning effects have been significantly improved. (iii) Compared with the BP neural network model, the SVR model and the ARIMA model, the LSTM neural network early warning model has a higher accuracy rate, and its average prediction accuracy for systemic risk indicators has been improved over short, medium and long terms. When the attention mechanism is included in the LSTM, the Attention-LSTM neural network model is even more accurate in all the cases.     

中国金融系统风险溢出效应研究——基于溢出指数和波动溢出网络

文章基于溢出指数和波动溢出网络方法，从静态和动态分别度量我国金融系统不同子市场间的风险联动水平及变动趋势。研究结果表明，我国金融系统风险溢出效应整体水平较高，各市场间联动性较强；市场内部滞后效应大于市场之间溢出效应，两两有向溢出效应具有强不对称性；作为我国金融系统的风险中心，货币市场始终处于风险溢出方，但在金融危机时期，其对外风险溢出效应相对减弱，而大宗商品市场、资本市场、房地产市场等其余市场的风险溢出效应相对增强。值得注意的是，当前我国金融系统整体风险溢出水平较高，市场间风险相关性较强，需要进一步加强系统性风险的监测与防范。     

International stock return predictability

We propose a new methodology for predicting international stock returns. Our Bayesian framework performs probabilistic selection of predictors that can shift at multiple unknown structural break dates. The approach generates significantly more accurate forecasts of international stock returns than a range of popular models that are economically meaningful for a risk-averse mean–variance investor. Allowing for regime-specific variable selection reduces considerably the international diversification of an unhedged U.S. investor’s portfolio.     

Balancing risk with virtual private networking during a pandemic

When the pandemic struck and teaching went online worldwide, universities had to make pressing decisions that balanced cybersecurity against other factors, including health and safety, usability, and cost. One such challenge Indiana University (IU) faced was how to accommodate the secure telecommunications needs of 130,000 faculty, staff, and students who would now be teaching, learning, doing research, and working from home. Some universities reflexively promoted virtual private network (VPN) use for all activities. Such an approach would have been unsustainable at IU, however, owing both to the licenses and resources needed for the sheer number of users and to the high-throughput applications on which they rely. Perhaps even worse, it would have increased the chances that the VPN would be unavailable during a critical incident or other situation in which secure communications must be guaranteed. Instead, IU launched an awareness campaign demonstrating exactly when VPN use is and isn’t needed. In addition, network staff employed a VPN feature called split tunneling to reduce the load. This article discusses the advantages and disadvantages of this approach and how IU made the decision to balance both sides of the risk equation to ensure the continued advancement of its mission throughout the pandemic.     

基于卷积神经网络的列车检测算法研究

列车检测作为列车自动驾驶的核心技术,可以有效地降低列车追尾等事故造成的人身危险和财产损失。为实现精准的列车检测,选用改进的卷积神经网络(PVANET)对输入图像进行特征提取,在此基础上,采用候选区域网络,从生成的特征图里滑动搜索,判断出图像中可能为列车的区域位置,并进一步采用快速区域卷积神经网络对每个候选区域进行分类,计算出其所属类别的置信度,同时精确定位列车。经验证,该方法适应范围广、鲁棒性高,可以有效地检测不同环境光强及不同朝向的列车,保障列车安全,为列车自动驾驶及辅助驾驶提供安全保障。     

Geography of Italian student mobility: A network analysis approach

Understanding the phenomenon of intra- and international student mobility has become increasingly relevant to the organization of tertiary education systems. Using microdata information provided by the Italian National Student Archive on the cohorts of students enrolled at university in the academic years 2011–12 and 2014–15, we consider a network analysis approach to investigate the incoming and outgoing student flows between territories and universities. More specifically, the paper aims to shed light on the dynamics of Italian student mobility networks at both the bachelor's and master's degree levels by considering attractiveness indicators combined with network centrality measures, clustering techniques for network data and explanatory models. We analyze the partition of the global network structure by means of blockmodeling analysis and we explain the determinants of the differences among universities in attracting students adopting a quantile regression analysis.     

网络嵌入特征与研发经历对通讯企业预研阶段研发者选择的影响

预研关系到企业在未来行业发展中的竞争优势，但以往相关研究多集中于国防军工企业，对一般民用企业研究较少。从研发者嵌入的网络结构与其自身属性特征两个角度出发，验证通讯企业在3G到4G技术跨越过程中选择预研研发者的影响因素，给出中介中心度、自我中心网结构洞、研发经历3个方面的假设。通过稀有事件logistic回归分析发现，自我中心网中的结构洞个数越多、以往研发技术经历越丰富，研发者越可能被选拔进入企业预研活动。研究结论具有较高的应用价值，可为企业预研中人力资源管理提供决策依据。     

Study of emerging air taxi network operation using discrete-event systems simulation approach

This research focuses on understanding the air taxi operations to determine the number of air taxis required to fulfill the demand for urban air mobility in New York City (NYC). We leverage the Define, Measure, Analyze, Design, and Verify (DMADV) framework and integrate it with the systems simulation approach. Upon investigation, we find that all the parameters linearly impact the vehicle utilization, while other measures are robust, specifically with respect to the seating capacity. It is also recommended to operate initially with 70 air taxis in NYC to achieve a trade-off between customer wait time and vehicle utilization. The proposed approach can act as a recommender system for air taxi companies.