The Human Relationships from Conflict and Contradiction to Reconciliation and Harmony in Amy Tan’s Novels

The conflict and ultimately reaching reconciliation between mother and daughter is one of the recurring themes in Amy Tan's novels. The sources of conflicts and confrontation between mother and daughter are mainly due to three sources, the cultural differences between mother and daughter, the influence of mother's original family as well as mother’s traumatic experiences, and the influence of Orientalism in the mainstream society. Cultural differences will be analyzed from several aspects, such as ethnic values, language and communication context, living styles, Through the textual analysis of the four novels, this research shows the complex emotional entanglement between mother and daughter

Waterfront Development: A Case Study of Dalian, China

Waterfront development has been an issue of wide concern and extensive discussion since the 1970s. This study provides more insight into the phenomenon by applying the existing knowledge to a different political and economic context. A ?phased model? is proposed as a ?prototype? of waterfront development. It analyzes waterfront development as a series of five temporal stages, i. e. pre-start-up, start-up, early development, mega projects and maturity. Each of the phases is characterized by the involvement of specific stakeholders, issues and events, and outcomes. A trend in the input of stakeholders is discerned: Trends in the inputs of three stakeholders, the municipal government, the special purpose agencies and the private sector, are discerned whereby initial government investments are complemented and eventually exceeded by investments from the private sector. Analyses of three sites in Dalian?s (in Northeast China) waterfront development suggest that all three sites fit appropriately into the phase model. This suggests the applicability of western theory to cases elsewhere in a different political and economic context. The hypothesis on the evolutionary trend of stakeholders? inputs is tested, rendering generally confirmative results that: (1) the municipal government?s input followed a first increasing and then decreasing trend; (2) special purpose agencies? input intensity showed a rising pattern, exceeding that of the municipal government in later phases; (3) The private sector was seldom involved in waterfront development until the mega projects phase but their investments rose sharply after that. Finally, applying criteria for success gleaned from the literature, the overall evaluation of Dalian?s waterfront development can be viewed as being a success. However, the seasonality of tourism, the lifecycle of tourism products and the insufficient respect paid to local residents? interests are pointed out as possible deficiencies

NPCL: Neural Processes for Uncertainty-Aware Continual Learning

Continual learning (CL) aims to train deep neural networks efficiently on streaming data while limiting the forgetting caused by new tasks. However, learning transferable knowledge with less interference between tasks is difficult, and real-world deployment of CL models is limited by their inability to measure predictive uncertainties. To address these issues, we propose handling CL tasks with neural processes (NPs), a class of meta-learners that encode different tasks into probabilistic distributions over functions all while providing reliable uncertainty estimates. Specifically, we propose an NP-based CL approach (NPCL) with task-specific modules arranged in a hierarchical latent variable model. We tailor regularizers on the learned latent distributions to alleviate forgetting. The uncertainty estimation capabilities of the NPCL can also be used to handle the task head/module inference challenge in CL. Our experiments show that the NPCL outperforms previous CL approaches. We validate the effectiveness of uncertainty estimation in the NPCL for identifying novel data and evaluating instance-level model confidence. Code is available at \url{https://github.com/srvCodes/NPCL}.Comment: Accepted as a poster at NeurIPS 202