A Study on Taiwanese International Students and Taiwanese American Students: The Interface between Naming and Identity

In this study, I analyze how Chinese and English naming practices influence name adoption and explicate how the use and choice of names by Taiwanese international students and Taiwanese American students at San José State University, California, are structured through social interaction and cultural context. The data were collected from in-depth interviews with 10 Taiwanese international students and 10 Taiwanese American students. The interviews focused on how they construct their identities and produce social relations with others through their choice and use of ethnic and/or English names. The study findings help to illuminate areas that until now have not received much scholarly attention. Certain traditional practices, such as generation names, are used by both Taiwanese parents and first generation Taiwanese parents to solidify the kindred relationships among siblings and collateral relatives, thus showing continuity even when parents have immigrated to the United States. Furthermore, the use of ethnic and English names by Taiwanese international students, which appeared at first to be governed by personal choice, is often constrained by linguistic and social factors. Their use of English names begins in Taiwan and then continues in the United States, not only helping them to transform themselves from outsiders to insiders, but also greatly influencing their acculturation

Child Health and the Income Gradient: Evidence from China

Though the positive income gradient of child health is well documented in developed countries, evidence from developing countries is rare. Few studies attempt to identify a causal link between family income and child health. Utilizing unique longitudinal data from the China Health and Nutrition Survey, we have found a positive, age-enhancing income gradient of child health, measured by height-for-age z scores. The gradient is robust to alternative specifications and a comprehensive set of controls. Using the fact that the rural tax reform implemented since 2000 created an exogenous variation in family income across regions and over time, we explore a causal explanation for the income gradient, and find that it has a very strong independent causal effect on child health.child health, income gradient, rural tax reform

Path diversity improves the identification of influential spreaders

Identifying influential spreaders in complex networks is a crucial problem which relates to wide applications. Many methods based on the global information such as $k$-shell and PageRank have been applied to rank spreaders. However, most of related previous works overwhelmingly focus on the number of paths for propagation, while whether the paths are diverse enough is usually overlooked. Generally, the spreading ability of a node might not be strong if its propagation depends on one or two paths while the other paths are dead ends. In this Letter, we introduced the concept of path diversity and find that it can largely improve the ranking accuracy. We further propose a local method combining the information of path number and path diversity to identify influential nodes in complex networks. This method is shown to outperform many well-known methods in both undirected and directed networks. Moreover, the efficiency of our method makes it possible to be applied to very large systems.Comment: 6 pages, 6 figure

Revisiting the problem of audio-based hit song prediction using convolutional neural networks

Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by exter- nal factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own. Motivated by the recent success of deep learning techniques, we attempt to ex- tend previous work on hit song prediction by jointly learning the audio features and prediction models using deep learning. Specifically, we experiment with a convolutional neural net- work model that takes the primitive mel-spectrogram as the input for feature learning, a more advanced JYnet model that uses an external song dataset for supervised pre-training and auto-tagging, and the combination of these two models. We also consider the inception model to characterize audio infor- mation in different scales. Our experiments suggest that deep structures are indeed more accurate than shallow structures in predicting the popularity of either Chinese or Western Pop songs in Taiwan. We also use the tags predicted by JYnet to gain insights into the result of different models.Comment: To appear in the proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP