Scissor for finding outliers in RNA-seq

The impressive progress of high-throughput technologies has provided many interesting modern data types, which has tremendously increased the demand for Statistics. RNA-seq, in particular, allows a rich characterization of the genome with many exciting applications. This dissertation makes contributions to RNA-seq data analysis by addressing several statistical challenges especially characterized by high dimensionality. The dissertation is composed of two major parts. The first part concerns the issue of high dimensional outliers which are challenging to distinguish from inliers due to the special structure of high dimensional space. We introduce a new notion of high dimensional outliers that embraces various types and provides deep insights into understanding the behavior of these outliers based on several asymptotic regimes. Using this new framework, we develop an outlier detection method called Scissor that aims to identify sample outliers with distinct forms or patterns of transcripts across RNA-seq cohorts. Scissor offers a novel approach to unsupervised screening of a variety of shape changes that are possibly associated with important genetic events. Scissor has been implemented in R and is available online. The second part is motivated by a challenge raised by an application of PCA to RNA-seq data. A fundamental question using PCA is how many principal components are effective for reducing dimensions. Although several algorithms have been developed to address this question, it has been observed that these algorithms may not be appropriate for RNA-seq data due to its abnormal noise structure. We propose a new algorithm for determining an effective number of principal componentsin RNA-seq data assuming a flexible noise structure based on some fundamental results in random matrix theory. The proposed method also provides a visualization tool for assessing the noise assumption. This methodology has been successful in offering more reasonable numbers of principal components for RNA-seq data and implemented in Scissor.Doctor of Philosoph

The Mediating Effect of Creative Personality in the Relationship between Childcare Teacher’s Efficacy and Creative Teaching Behaviour

The purpose of this study is to identify the mediating effect of creative personality in the relationship between childcare teachers’ efficacy and creative teaching behavior. The participants of the study were 300 childcare teachers and selected the data between October 14 to 22, 2020. The study results were as follows. First, it evidenced positive correlations among efficacy, creative teaching behavior, and creative personality. Therefore, when childcare teachers show higher efficacy and creative personality levels, creative teaching behavior levels are likely to be higher. Second, childcare teachers’ efficacy directly affected creative teaching behaviors and creative personality, which also directly affected creative teaching behavior. Third, creative personality partially mediated between efficacy and creative teaching behaviors. As the childcare field continues to emphasize creative teaching behaviors, it must create an environment where childcare teachers can improve their efficacy and develop their creative personalities. Furthermore, educational programs should encourage teachers to enhance their efficacy and express their creative personalities

Korean Twitter Emotion Classification Using Automatically Built Emotion Lexicons and Fine-Grained Features

In recent years many people have begun to express their thoughts and opinions on Twit-ter. Naturally, Twitter has become an ef-fective source to investigate people’s emo-tions for numerous applications. Classifying only positive and negative tweets has been ex-ploited in depth, whereas analyzing finer emo-tions is still a difficult task. More elaborate emotion lexicons should be developed to deal with this problem, but existing lexicon sets are mostly in English. Moreover, building such lexicons is known to be extremely labor-intensive or resource-intensive. Finer-grained features need to be taken into account when determining finer-emotions, but many exist-ing works still utilize coarse features that have been widely used in analyzing only the po-larity of emotion. In this paper, we present a method to automatically build fine-grained emotion lexicon sets and suggest features that improve the performance of machine learning based emotion classification in Korean Twitter texts.

Hand Gesture Recognition Using Particle Swarm Movement

We present a gesture recognition method derived from particle swarm movement for free-air hand gesture recognition. Online gesture recognition remains a difficult problem due to uncertainty in vision-based gesture boundary detection methods. We suggest an automated process of segmenting meaningful gesture trajectories based on particle swarm movement. A subgesture detection and reasoning method is incorporated in the proposed recognizer to avoid premature gesture spotting. Evaluation of the proposed method shows promising recognition results: 97.6% on preisolated gestures, 94.9% on stream gestures with assistive boundary indicators, and 94.2% for blind gesture spotting on digit gesture vocabulary. The proposed recognizer requires fewer computation resources; thus it is a good candidate for real-time applications

How Does Artificial Intelligence Improve Human Decision-Making? Evidence from the AI-Powered Go Program

We study how humans learn from AI, exploiting an introduction of an AI-powered Go program (APG) that unexpectedly outperformed the best professional player. We compare the move quality of professional players to that of APG's superior solutions around its public release. Our analysis of 749,190 moves demonstrates significant improvements in players' move quality, accompanied by decreased number and magnitude of errors. The effect is pronounced in the early stages of the game where uncertainty is highest. In addition, younger players and those in AI-exposed countries experience greater improvement, suggesting potential inequality in learning from AI. Further, while players of all levels learn, less skilled players derive higher marginal benefits. These findings have implications for managers seeking to adopt and utilize AI effectively within their organizations