QQ~Q\widetilde{Q}-systems for twisted quantum affine algebras

We establish the $Q \widetilde{Q}$-systems for the twisted quantum affine algebras that were conjectured in arXiv:1606.05301. We develop the representation theory of Borel subalgebra of twisted quantum affine algebras and we construct their prefundamental representations. We also propose a general conjecture on the relations between twisted and non-twisted types. We prove this conjecture for some particular classes of representations, including prefundamental representations.Comment: 39 page

Factors for Chinese students choosing Australian higher education and motivation for returning: a systematic review

Under the third wave of international student mobility, Australia has become the third largest country receiving international students. Compared with the United States and the United Kingdom, Australia can still maintain a stable increase in terms of hosting Chinese students. For Australia, attracting international students becomes an important part of Australian universities’ business and cultural diversity. This paper reports the Chinese students’ initiations of choosing Australian higher education and motivations for returning, aiming at contributing to a more accurate and comprehensive understanding of Chinese students’ international flows. By retrieving all relevant literature published from 2000 to 2017, this paper engages with a systematic review to provide an overview of what exactly motivates Chinese students choosing Australian higher education and returning. Based on the robust assessment criteria, we selected 68 articles for analysis, and according to the coding results, we developed four themes influencing Chinese students’ choice of Australia, including academic requirement and attainment, employment and future career prospects, host country environment, and social connections and three themes for returning: emotional needs, culture and integration in Australia, and career opportunities in China. The research results contribute to policy implications for Australian international higher education development

An Embedding-based Approach to Inconsistency-tolerant Reasoning with Inconsistent Ontologies

Inconsistency handling is an important issue in knowledge management. Especially in ontology engineering, logical inconsistencies may occur during ontology construction. A natural way to reason with an inconsistent ontology is to utilize the maximal consistent subsets of the ontology. However, previous studies on selecting maximum consistent subsets have rarely considered the semantics of the axioms, which may result in irrational inference. In this paper, we propose a novel approach to reasoning with inconsistent ontologies in description logics based on the embeddings of axioms. We first give a method for turning axioms into distributed semantic vectors to compute the semantic connections between the axioms. We then define an embedding-based method for selecting the maximum consistent subsets and use it to define an inconsistency-tolerant inference relation. We show the rationality of our inference relation by considering some logical properties. Finally, we conduct experiments on several ontologies to evaluate the reasoning power of our inference relation. The experimental results show that our embedding-based method can outperform existing inconsistency-tolerant reasoning methods based on maximal consistent subsets.Comment: 9 pages,1 figur

Generative Input: Towards Next-Generation Input Methods Paradigm

Since the release of ChatGPT, generative models have achieved tremendous success and become the de facto approach for various NLP tasks. However, its application in the field of input methods remains under-explored. Many neural network approaches have been applied to the construction of Chinese input method engines(IMEs).Previous research often assumed that the input pinyin was correct and focused on Pinyin-to-character(P2C) task, which significantly falls short of meeting users' demands. Moreover, previous research could not leverage user feedback to optimize the model and provide personalized results. In this study, we propose a novel Generative Input paradigm named GeneInput. It uses prompts to handle all input scenarios and other intelligent auxiliary input functions, optimizing the model with user feedback to deliver personalized results. The results demonstrate that we have achieved state-of-the-art performance for the first time in the Full-mode Key-sequence to Characters(FK2C) task. We propose a novel reward model training method that eliminates the need for additional manual annotations and the performance surpasses GPT-4 in tasks involving intelligent association and conversational assistance. Compared to traditional paradigms, GeneInput not only demonstrates superior performance but also exhibits enhanced robustness, scalability, and online learning capabilities