Verifying Concurrent Stacks by Divergence-Sensitive Bisimulation

The verification of linearizability -- a key correctness criterion for concurrent objects -- is based on trace refinement whose checking is PSPACE-complete. This paper suggests to use \emph{branching} bisimulation instead. Our approach is based on comparing an abstract specification in which object methods are executed atomically to a real object program. Exploiting divergence sensitivity, this also applies to progress properties such as lock-freedom. These results enable the use of \emph{polynomial-time} divergence-sensitive branching bisimulation checking techniques for verifying linearizability and progress. We conducted the experiment on concurrent lock-free stacks to validate the efficiency and effectiveness of our methods

Urban-Rural Mobility through the Lens of Food Documentary: A Case Study of “A Bite of China: Season Two”

The combination of traditional Chinese food processing techniques and contemporary commercial food production forms the main topics of “A Bite of China: Season Two” (ABOC-2). Built upon the success of its first reason, ABOC-2 has achieved a record high TV rating among domestic audience and also made history by becoming the best-selling Chinese documentary overseas. By examining the individual experiences of migrant workers shown in ABOC-2, this paper discusses the important role of rural Chinese cuisine in maintaining the urban-rural mobility of contemporary China. We argue that one major storyline of ABOC-2, in which migrant workers maintain their inherent economic and social connections with their native countryside through cooking and consuming hometown dishes, sheds light upon migrant workers’ active resistance to the partially rational yet overly standardized ways of urban living. By showing respect to rural traditions and culture, ABOC-2 has successfully promoted the dignity of Chinese migrant workers and depicted their spiritual plight in contemporary China’s drive toward modernization

One-Shot Relational Learning for Knowledge Graphs

Knowledge graphs (KGs) are the key components of various natural language processing applications. To further expand KGs' coverage, previous studies on knowledge graph completion usually require a large number of training instances for each relation. However, we observe that long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge extracted by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.Comment: EMNLP 201