Big data driven cycle time parallel prediction for production planning in wafer manufacturing

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.     

The Neutrality Illusion: Biased Economics,Biased Training,and Biased Monetary Policy. Testing the Role of Ideology on FOMC Voting Behaviour

This research is trying to shed light on two myths that are usually widespread: the first one being the idea of the academic economist as a neutral scientist finding uncontestable consensual truths, thanks to uncontestable empirical methods, the second, the idea of the central banker as a Weberian neutral bureaucrat setting aside personal beliefs to act mechanically for the common good. Deconstructing this ‘neutrality illusion’, this work argues that economics is actually a divided and ideologically marked discipline despite its aim at natural-science-type-legitimacy. It argues in a related discussion that such ideological bias also impedes a purely neutral conduct of monetary policy, undermining the very idea of central bank independence. Linking these two arguments, it argues that graduate training in economics is the first place for the formation of biased preferences, because of the substantial ideological sorting that exists across universities. Using a unique database on FOMC members’ votes and ideology, the paper tests this idea empirically and despite unavoidable caveats, finds robust evidence of a systematic impact of the ideological features of their alma mater on FOMC members’ voting behaviour – impact that we found more important than the other traditional determinants of central bankers’ actions.     

关键驱动力作用下的新疆生态系统服务时空格局分析

[目的]恶劣的生态环境和有限的资源驱动着新疆特殊生态系统服务的形成,人类又通过对生态系统服务的消费来满足和提高自身福祉,需要研究其时空变化并辨识主要驱动因素,支持区域可持续发展。[方法]文章构建了融合不同土地利用类型生态系统服务的价值量排序及服务功能强度矩阵的生态系统服务评估体系,并耦合3种关键驱动因子以评估各个因子的驱动强度,实现对生态系统服务热点与冷点变化的驱动及迁移分析。[结果]2000—2010年全疆生态系统服务总体增加,这一时期生态系统服务的热点区大部分转变为2010—2015年的冷点区,且15年来生态系统服务总体呈现小幅下降趋势。3个时期北疆和南疆人为干扰强度均呈增加趋势,由其驱动的生态系统服务则相应减小;南疆地区植被覆盖范围极其有限,故对生态系统服务驱动强度低;降水在北疆和南疆都能够产生高生态系统服务,但降水量的不稳定性能够使生态系统服务产生较大波动。相比于南疆地区,北疆生态系统在外界干扰下能够体现出更强的抗性和整体性,从而实现相对高效的生态系统服务输出。[结论]对关键驱动因子作用下的生态系统服务进行时空变化分析,有助于理解当地生态系统产生变化的原因,从而为当地生态环境保护提供参考。     

Arena-based events and crime: an analysis of hourly robbery data

This article makes use of hourly crime counts to model the relationship between events that take place at the Prudential Center in Newark, NJ and robberies, an arena that has caused local controversy regarding the costs and benefits of hosting such an entertainment venue. Results from the econometric model suggest that the NHL’s New Jersey Devils ice hockey games, concerts, and Disney-themed events are all associated with increases in robbery, while various other event categories such as the NBA basketball games played by the Nets and boxing, and mixed martial arts (MMA) matches are not associated with an increase. These findings support two complementary ecological theories of crime that focus on how events provide additional opportunities for crime by increasing the associated benefits while simultaneously decreasing the cost for economically motivated offenders to take advantage of.     

Schmoozers,not snoozers: Busting the Kevin and Angela stereotypes in public accounting

In contrast to the well-known stereotype of the boring, unsociable accountant, today’s public accounting professionals argue that they are boundary spanners. Working at the intersection of the firm/client interface, these professionals contend that sociability is a requisite skill for routinely navigating the many interactions they have with multiple personnel at client organizations. This research aims to address this disconnect and to confront whether the public perception is warranted. Using the Trait Emotional Intelligence Questionnaire (TEIQue), we assess the sociability of 176 public accounting professionals and discover that, contrary to the stereotype, male public accountants are more sociable than males in the general population. Our analysis also demonstrates that female public accountants are no more or less sociable than females in the general population. Additional analysis shows that while male public accountants are more sociable than female public accountants, this effect is attributable to differences at lower levels of the profession and that male-female differences disappear when comparing male managers and partners to female managers and partners. Given the importance of sociability in public accounting, this research offers recruiting strategies that will help to dispel the myth of the unsociable accountant and provides training tips for enhancing sociability within the ranks.     

Legally astute managers as a source of value in organizations

The legal and compliance departments in organizations have more influence than ever before. Why then are companies so vulnerable to legal liability? The problem may not be with talented legal professionals, but with the lack of legal knowledge held by MBAs and other business school graduates that make day-to-day decisions in modern organizations. Firms can close this knowledge gap and minimize legal liability by training their managers in legal astuteness—the ability of a manager to address legal issues successfully—by instilling four essential traits: respecting the rule of law, recognizing legal issues, resolving problems proactively, and reporting complex legal issues to experts. Job candidates should also be screened for traits of legal astuteness. A legally astute candidate, especially a graduate of a business school that requires legal education for all its students, can be a cultural ambassador for legal astuteness and a valuable liaison between legal and compliance departments and the candidate’s functional area. A legally ignorant candidate will require significant training and a frank assessment of the legal risk they bring to the organization. Modern firms in today’s legal environment face two choices: hire a legally astute manager now or deal with a compliance headache later.     

Training and organisational performance: A meta‐analysis of temporal,institutional and organisational context moderators

Drawing on systems theory, we conducted a moderated meta‐analysis of the training and organisational performance relationship using 119 primary studies. We examined the moderating effects of quality versus quantity of training, time, institutional and organisational context factors in the relationship between training and organisational performance. Our findings reveal that training is positively and directly related to organisational performance with no statistically significant difference between measures of training quality and quantity. We found that the relationship was stronger over time and that country performance orientation and country labour cost moderate the training and organisational performance relationship. We found no evidence for the moderating effects of the three organisational context moderators we examined (i.e. industry sector, organisational size and technology intensity). Finally, our results reveal that training type (i.e. general or firm‐specific) does not moderate the training and organisational performance relationship.     

优化营商环境,推动发展高质量经济

促进中国经济高质量发展的必由之路是优化营商环境,提升区域竞争力。目前我国面临着国际内外的复杂局势,经济未来的发展趋势不容乐观,为此我们要深入落实新的发展理念,建立新的发展思路,构建新的发展格局。优化营商环境应实现建立服务型政府,实现企业经营环节的全覆盖,降低制度性交易成本、建立完善的商业系统生态链、打造具有中国特色的营商环境。     

基于MBP算法和深度学习的人脸识别

为了解决在深度学习提取人脸图像特征时,易忽略其局部结构特征和缺乏对其旋转不变性学习的问题,提出了一种基于单演局部二值模式(MBP)与深度学习相结合的高效率人脸识别方法。首先,用多尺度单演滤波器对图像进行滤波,得到幅值和方向信息;其次,用LBP算法和象限比特的方法进行编码,分块计算组合其直方图特征;然后,将提取的单演特征作为深度信念网络(DBN)的输入,逐层训练优化网络参数,得到优异的网络模型;最后,将训练好的DBN网络在ORL人脸数据库上进行人脸识别实验,进行识别率计算,其识别率为98.75%。所提出的方法使用无监督的贪婪算法,隐藏层设定为2层,使用反向传播算法优化网络。相较于已知的人脸识别方法,MBP+DBN算法对光照、表情和部分遮挡变化具有较好的鲁棒性,在人脸识别中识别率较高,具有一定的优势,为图像特征提供了一种新的识别方法。