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Typical And Atypical Development Of The Brain’S Functional Network Architecture, Ashley Nicole Nielsen
Typical And Atypical Development Of The Brain’S Functional Network Architecture, Ashley Nicole Nielsen
Arts & Sciences Electronic Theses and Dissertations
The human brain is a complex organ that gives rise to many behaviors. Specialized neural regions cooperate as functional networks that form an intricate functional architecture. Development provides a unique window into how brain functioning and human thinking are affected if the necessary neural features and connections are not fully formed. Similarly, developmental disorders can shed light on atypical trajectories of neural systems that may lead to or be a consequence of symptomatic behavior. A description of the typical and atypical development of functional networks is essential to identify the features of brain organization critical for mature human thinking and …
Recent Advances In Low-Cost Particulate Matter Sensor: Calibration And Application, Jiayu Li
Recent Advances In Low-Cost Particulate Matter Sensor: Calibration And Application, Jiayu Li
McKelvey School of Engineering Theses & Dissertations
Particulate matter (PM) has been monitored routinely due to its negative effects on human health and atmospheric visibility. Standard gravimetric measurements and current commercial instruments for field measurements are still expensive and laborious. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with insufficient spatial resolution. The new trends of PM concentration measurement are personalized portable devices for individual customers and networking of large quantity sensors to meet the demand of Big Data. Therefore, low-cost PM sensors have been studied …
Machine Learning And Empirical Asset Pricing, Yingnan Yi
Machine Learning And Empirical Asset Pricing, Yingnan Yi
Doctor of Business Administration Dissertations
In this paper, I conduct a comprehensive study of using machine learning tools to forecast the U.S. stock returns. I use three sets of predictors: the past history summarized by 120 lagged returns, the technical indicators measured by 120 moving average trading signals, and the 79 firm fundamentals, which helps to understand the weak-form market efficiency, algorithm trading and fundamental analysis. I find each set independently has strong predictive power, and buying the top 20% stocks with the greatest predicted returns and shorting bottom 20% with the lowest earns economically significant profits, and the profitability is robust to a number …