Chinese Center-Bridge: East and West Cultural and Education for Everyone

The Chinese Community Center is a multi-ethnic organization serving all resident Chinese Americans. The purpose is to bridge the East and West worlds by providing families with educational, cultural, and social service programs to help them achieve the language and cultural crossover.https://digitalscholarship.unlv.edu/educ_fys_103/1031/thumbnail.jp

Theory of Mind and its Relation to Psychopathy

Psychopathy is a personality dysfunction wherein an individual is unemotional and has a deficit in empathy (Dolan & Fullam, 2004). Theory of mind is the ability to perceive other’s thoughts, beliefs, emotions, etc. (Vonk, Hill, Mercer & Noser, 2015). This is similar to empathy, and as such is likely to have a relationship with psychopathy, although no such research has been undertaken to date. In this study, I correlated measures of psychopathy with theory of mind, sampling from Butler’s undergraduate population. Due to my combined major in Psychology and Criminology, I then researched and discuss the similarities theory of mind has with the sociological term, role taking, which is the process of viewing oneself from another perspective (Crawford & Novak, 2014). Rather than being an aptitude that varies per person, as is theory of mind, role taking is seen as an innate ability and rather is looked at in terms of the propensity in which one engages in it. While there were no significant relationships between theory of mind and psychopathy detected, the results suggested that a study with more statistical power may be able to find such a relationship. If a relationship does not exist between theory of mind and psychopathy, this can be explained by role taking theory

An examination of the keyboard technique of Bach, Haydn, Chopin, Scriabin and Prokofiev

Master's Project (M.Mu.) University of Alaska Fairbanks, 2016In this research paper, I will explore the keyboard technique of each composer presented in my recital: J.S. Bach, Franz Joseph Haydn, Frederic Chopin, Alexander Scriabin and Sergei Prokofiev. I hope to elucidate the physical approach used by each composer, and show in turn how that same approach influenced the music of each composer by analyzing the pieces performed in my recital. To understand the distinct technique of the composers, it is important to know some context. The instrument each composer wrote for necessarily influenced their technique and resulting composition. However, the instrument cannot explain every facet of technique, and it becomes necessary to understand the underlying aesthetics of technique. Moving chronologically from Bach to Prokofiev, a general trend of expansion in the use of the hand and arm will be seen throughout. Keyboards became louder and heavier in touch and the hand faced greater reaches in every generation. The technique of Bach and Haydn was largely focused on compact and relaxed hands with distinct finger movements, while Scriabin and Prokofiev at the other end require sweeping gestures that occupy the entire arm. However, it would be too easy to present this progression as a story that technique is only getting better and better, implying that the older composers were inferior to the later. That is simply false. Instead, extended study of each composer shows that many technical principles are universal. The baroque keyboardists were likely playing with more weight than popularly imagined and one cannot play Scriabin with mittens on the hands

Space languages

Applications of linguistic principles to potential problems of human and machine communication in space settings are discussed. Variations in language among speakers of different backgrounds and change in language forms resulting from new experiences or reduced contact with other groups need to be considered in the design of intelligent machine systems

DeepNav: Learning to Navigate Large Cities

We present DeepNav, a Convolutional Neural Network (CNN) based algorithm for navigating large cities using locally visible street-view images. The DeepNav agent learns to reach its destination quickly by making the correct navigation decisions at intersections. We collect a large-scale dataset of street-view images organized in a graph where nodes are connected by roads. This dataset contains 10 city graphs and more than 1 million street-view images. We propose 3 supervised learning approaches for the navigation task and show how A* search in the city graph can be used to generate supervision for the learning. Our annotation process is fully automated using publicly available mapping services and requires no human input. We evaluate the proposed DeepNav models on 4 held-out cities for navigating to 5 different types of destinations. Our algorithms outperform previous work that uses hand-crafted features and Support Vector Regression (SVR)[19].Comment: CVPR 2017 camera ready versio