CROSS-CULTURAL EXAMINATION OF SAMSUNG’S MARKETING STRATEGIES ON DOUYIN AND TIKTOK

This thesis project aims to investigate the use and effects of tech companies’ content marketing strategies on Douyin and TikTok through a cross-cultural case study on Samsung. Qualitative content analysis is used to analyze Samsung’s 60 high-performing and lowperforming Douyin and TikTok posts to examine the use of content gratification factors and the executions of the hashtag, influencer and metanarrative strategies. Quantitative sentiment analysis is used on 1,400 comments to evaluate public feedback through engagement and sentiment scores. Based on a modified internet gratification framework, results revealed a consistent focus on information-seeking, personal status and aesthetic experience gratifications among marketers and audiences across cultures with failed content motivations of monetary compensation and diversion. Qualitative content examinations also indicated a potentially culture-based strategy use and effects on Douyin and TikTok. As metanarrative outweighs other strategies with culturally consistent central and supportive narrative patterns, hashtag and influencer strategies attained moderately supportive engagement and sentiment with successful challenge-focused hashtags on TikTok and effective influencer and brand partnership tactics on Douyin. While experience sharing and group recognition remain part of the primary focuses in user comments, user-driven organic sharing was also a significant comment function for Douyin users. While future research is required for more generalizable results among tech brands, all the preliminary results and analyses shed light on the existing marketing patterns and user interactions for the current tech marketing strategies on TikTok and Douyin while offering exploratory culture-based perspectives for cross-cultural TikTok marketing campaigns.Master of Art

Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans

The aging population of the U.S. drives the prevalence of Alzheimer's disease. Brookmeyer et al. forecasts approximately 15 million Americans will have either clinical AD or mild cognitive impairment by 2060. In response to this urgent call, methods for early detection of Alzheimer's disease have been developed for prevention and pre-treatment. Notably, literature on the application of deep learning in the automatic detection of the disease has been proliferating. This study builds upon previous literature and maintains a focus on leveraging multi-modal information to enhance automatic detection. We aim to predict the stage of the disease - Cognitively Normal (CN), Mildly Cognitive Impairment (MCI), and Alzheimer's Disease (AD), based on two different types of brain MRI scans. We design an AlexNet-based deep learning model that learns the synergy of complementary information from both T1 and FLAIR MRI scans

明初文化格局中的地方儒家與臺閣文風

明初文學中一個引人注目的現象是永樂至宣德間臺閣文風的盛行。以朝廷爲主導的臺閣文風，如何成功地覆蓋了在野的、地方的、下層的廣泛社會文化場域？追溯其來龍去脈，地方儒學官是值得關注的群體。相較於明中後期學校的荒廢，明初是地方儒學發展的興盛期。地方儒學官在政治、文化生活中作用顯著，永宣時代的臺閣要員就多有任教地方的仕宦經歷；經由考竅選拔、親族鄉誼等聯結因素，地方儒學官與臺閣要員往來密切，交流頻繁，大量酬赠詩序體現出受臺閣影響的旨趣和文風。臺閣體的長期風行，是廣泛的士人群體參與的結果，體現了不同社會階層之間的文化整合。臺閣文風可視爲明初打破朝與野、地方與中央二元對立從而實現以皇權爲中心的政治大一统格局的文化操誌

Electron Density Dependence of in-plane Spin Relaxation Anisotropy in GaAs/AlGaAs Two-Dimensional Electron Gas

We investigated the spin dynamics of two-dimensional electrons in (001) GaAs/AlGaAs heterostructure using the time resolved Kerr rotation technique under a transverse magnetic field. The in-plane spin lifetime is found to be anisotropic below 150k due to the interference of Rashba and Dresselhaus spin-orbit coupling and D'yakonov-Perel' spin relaxation. The ratio of in-plane spin lifetimes is measured directly as a function of temperature and pump power, showing that the electron density in 2DEG channel strongly affects the Rashba spin-orbit coupling.Comment: 3 pages, 2 figure

PEGA: Personality-Guided Preference Aggregator for Ephemeral Group Recommendation

Recently, making recommendations for ephemeral groups which contain dynamic users and few historic interactions have received an increasing number of attention. The main challenge of ephemeral group recommender is how to aggregate individual preferences to represent the group's overall preference. Score aggregation and preference aggregation are two commonly-used methods that adopt hand-craft predefined strategies and data-driven strategies, respectively. However, they neglect to take into account the importance of the individual inherent factors such as personality in the group. In addition, they fail to work well due to a small number of interactive records. To address these issues, we propose a Personality-Guided Preference Aggregator (PEGA) for ephemeral group recommendation. Concretely, we first adopt hyper-rectangle to define the concept of Group Personality. We then use the personality attention mechanism to aggregate group preferences. The role of personality in our approach is twofold: (1) To estimate individual users' importance in a group and provide explainability; (2) to alleviate the data sparsity issue that occurred in ephemeral groups. The experimental results demonstrate that our model significantly outperforms the state-of-the-art methods w.r.t. the score of both Recall and NDCG on Amazon and Yelp datasets