Reflection and Review of General Education Localisation Research in China

Abstract: With the reform of the general education curriculum in China’s higher education, scholars have universally recognized the importance of general education. The exploration of general education has evolved from the study of theory to attention to practice. General education aims to promote the comprehensive development of the quality of college students. However, the foundation of general education during its integration process is relatively weak in varying degrees of ‘the climate does not suit one’ phenomenon. Thus, combining localisation theory and practice of general education and analysing the factors that hinder the localisation process for the development of general education in China should be re-examined.With the reform of the general education curriculum in China’s higher education, scholars have universally recognized the importance of general education. The exploration of general education has evolved from the study of theory of attention to practice. General education aims to promote the comprehensive development of the quality of college students. However, the foundation of general education during its integration process is relatively weak in varying degrees of ‘the climate does not suit one’ phenomenon. Thus, combining localisation theory and practice of general education and analysing the factors that hinder the localisation process for the development of general education in China should be re-examined

Multilabel Consensus Classification

In the era of big data, a large amount of noisy and incomplete data can be collected from multiple sources for prediction tasks. Combining multiple models or data sources helps to counteract the effects of low data quality and the bias of any single model or data source, and thus can improve the robustness and the performance of predictive models. Out of privacy, storage and bandwidth considerations, in certain circumstances one has to combine the predictions from multiple models or data sources to obtain the final predictions without accessing the raw data. Consensus-based prediction combination algorithms are effective for such situations. However, current research on prediction combination focuses on the single label setting, where an instance can have one and only one label. Nonetheless, data nowadays are usually multilabeled, such that more than one label have to be predicted at the same time. Direct applications of existing prediction combination methods to multilabel settings can lead to degenerated performance. In this paper, we address the challenges of combining predictions from multiple multilabel classifiers and propose two novel algorithms, MLCM-r (MultiLabel Consensus Maximization for ranking) and MLCM-a (MLCM for microAUC). These algorithms can capture label correlations that are common in multilabel classifications, and optimize corresponding performance metrics. Experimental results on popular multilabel classification tasks verify the theoretical analysis and effectiveness of the proposed methods

Thermodynamics of rotating Bose gases in a trap

Novel ground state properties of rotating Bose gases have been intensively studied in the context of neutral cold atoms. We investigate the rotating Bose gas in a trap from a thermodynamic perspective, taking the charged ideal Bose gas in magnetic field (which is equivalent to a neutral gas in a synthetic magnetic field) as an example. It is indicated that the Bose-Einstein condensation temperature is irrelevant to the magnetic field, conflicting with established intuition that the critical temperature decreases with the field increasing. The specific heat and Landau diamagnetization also exhibit intriguing behaviors. In contrast, we demonstrate that the condensation temperature for neutral Bose gases in a rotating frame drops to zero in the fast rotation limit, signaling a non-condensed quantum phase in the ground state.Comment: 4 pages, 1 figur

Online auction-based relay selection for cooperative communication in CR networks

Cognitive radio and cooperative communication are two new network technologies. So, the combination of these two new technologies is a novel solution to solve the problem of spectrum scarcity. Two main challenges exist in the integration of cognitive radio and cooperative communication. First, there is a lack of incentives for the participating wireless devices to serve as relay nodes. Second, there is not an effective relay selection policy. In this paper, we propose an online auction-based relay selection scheme for cooperative communication in cognitive radio (CR) networks. Specifically, we design an auction scheme through adopting stopping theory. The proposed scheme ensures that the primary user (PU) can effectively select a CR relay to transmit its packets in a given time bound. In addition, we have analytically proven the truthfulness and the individual rationality of our online auction scheme. Extensive simulations demonstrate that the proposed relay selection scheme can always successfully and efficiently select a proper relay for a PU and can achieve a higher cooperative communication throughput compared with the conventional schemes