Parasocial relationships with transgender characters and attitudes toward transgender individuals

The transgender population lives in a condition of serious discrimination, poverty and violence (NCTE, 2014). Few studies, however, have been conducted to understand people’s attitudes toward this population and factors that affect the responses. Applying the Parasocial Contact Hypothesis (Schiappa, Gregg & Hewes, 2005), this thesis investigated the relationship between audiences’ positive and negative parasocial relationships with transgender characters in TV and their attitudes toward transgender people in real life. A survey method was employed to address research questions and test hypothesis. Results show a significant association between parasocial relationship (positive or negative) and attitudes toward trans people in real life. Parasocial relationship was also found to have mediating and interactive effects on the relationship between perceived realism of characters and attitudes toward transgender population. Finally, it was also suggested that positive parasocial relationship with comedy characters is a stronger predictor of attitudes than with non-comedy characters. Contribution, limitation and implications were also discussed

Selling rural China: The construction and commodification of rurality in Chinese promotional livestreaming

With promotional livestreaming transforming the digital culture and e-commerce landscape in China, rural streamers take this opportunity to not only harvest economic rewards but also construct rural identities and associated imagery. Employing a digital ethnographic approach, this article closely explored how rural spaces and rural labor activities are constructed and commodified in Chinese promotional livestreaming. I argue that although rural streamers’ creative use of platform-afforded liveness and interactivity enriches Chinese digital culture by making everyday life in rural spaces visible, this constructed rurality is, however, flattened, decontextualized, and romanticized – thus, ready to be commodified and sold to the audience. In addition, agricultural labor is made hyper-visible, generating the possibility for demystifying said labor process, while other forms of labor, mainly affective labor and labor for negotiation with the platforms, are made invisible, undervalued, and exploited, deepening the precarious condition of such platform-dependent labor

On Power Law Scaling Dynamics for Time-fractional Phase Field Models during Coarsening

In this paper, we study the phase field models with fractional-order in time. The phase field models have been widely used to study coarsening dynamics of material systems with microstructures. It is known that phase field models are usually derived from energy variation so that they obey some energy dissipation laws intrinsically. Recently, many works have been published on investigating fractional-order phase field models, but little is known of the corresponding energy dissipation laws. We focus on the time-fractional phase field models and report that the effective free energy and roughness obey a universal power-law scaling dynamics during coarsening. Mainly, the effective free energy and roughness in the time-fractional phase field models scale by following a similar power law as the integer phase field models, where the power is linearly proportional to the fractional order. This universal scaling law is verified numerically against several phase field models, including the Cahn-Hilliard equations with different variable mobilities and molecular beam epitaxy models. This new finding sheds light on potential applications of time fractional phase field models in studying coarsening dynamics and crystal growths

Continuous Input Embedding Size Search For Recommender Systems

Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance. Latent factor models represent users and items as real-valued embedding vectors for pairwise similarity computation, and all embeddings are traditionally restricted to a uniform size that is relatively large (e.g., 256-dimensional). With the exponentially expanding user base and item catalog in contemporary e-commerce, this design is admittedly becoming memory-inefficient. To facilitate lightweight recommendation, reinforcement learning (RL) has recently opened up opportunities for identifying varying embedding sizes for different users/items. However, challenged by search efficiency and learning an optimal RL policy, existing RL-based methods are restricted to highly discrete, predefined embedding size choices. This leads to a largely overlooked potential of introducing finer granularity into embedding sizes to obtain better recommendation effectiveness under a given memory budget. In this paper, we propose continuous input embedding size search (CIESS), a novel RL-based method that operates on a continuous search space with arbitrary embedding sizes to choose from. In CIESS, we further present an innovative random walk-based exploration strategy to allow the RL policy to efficiently explore more candidate embedding sizes and converge to a better decision. CIESS is also model-agnostic and hence generalizable to a variety of latent factor RSs, whilst experiments on two real-world datasets have shown state-of-the-art performance of CIESS under different memory budgets when paired with three popular recommendation models.Comment: To appear in SIGIR'2