The advantage of Chinese-as-a-heritage-language (CHL) learners in acquiring Chinese has been widely recognized. However, it is still unclear whether the effect of CHL background on Chinese receptive vocabulary breadth varies across different countries. To address this gap, the present study recruited 232 Chinese language learners (half were CHL learners) from Indonesia and Thailand and administered a Chinese vocabulary proficiency test. The results of regression analysis revealed an interaction effect between country and CHL background on vocabulary breadth, with the contribution of CHL background to vocabulary breadth more robust in the Indonesian group than that in the Thai group. Interviews were then conducted to explore the factors that might influence such an interaction effect. Analysis of the interview data found that the influencing factors could be categorized into four themes, including individual differences, family background, Chinese language education and socio-cultural factors. The overall results were discussed within the framework of ecological system theory, and pedagogical implications for CHL learners were proposed.
Worldwide, humanities and social sciences (HSS) scholars produce and disseminate knowledge in an unequal global knowledge space, which can be caused by various structural, epistemological, and individual-level factors. Although global epistemic injustice receives much attention, the factors contributing to it, including the extraverted mindsets and practices of non-Euro-American scholars, remain less discussed. This article draws on semibiographical interviews with 30 high-achieving ethnic Chinese HSS scholars in mainland China, in Hong Kong, and overseas. It explores how these scholars display intellectual extraversion and why and how they are reflexive about and confronting it. The findings reveal three manifestations of intellectual extraversion, four sources of reflexivity regarding such extraversion, and three ways to confront it. The research uncovers the continuous reflexivity and efforts of ethnic Chinese HSS scholars in dealing with lingering epistemic discontinuities and exclusions and sheds light on new possible approaches to challenging global epistemic injustice in HSS research.
As a judicious correspondence to the classical maxcut, the anti-Cheeger cut has more balanced structure, but few numerical results on it have been reported so far. In this paper, we propose a continuous iterative algorithm (CIA) for the anti-Cheeger cut problem through fully using an equivalent continuous formulation. It does not need rounding at all and has advantages that all subproblems have explicit analytic solutions, the objective function values are monotonically updated and the iteration points converge to a local optimum in finite steps via an appropriate subgradient selection. It can also be easily combined with the maxcut iterations for breaking out of local optima and improving the solution quality thanks to the similarity between the anti-Cheeger cut problem and the maxcut problem. The performance of CIAs is fully demonstrated through numerical experiments on G-set from two aspects: one is on the solution quality where we find that the approximate solutions obtained by CIAs are of comparable quality to those by the multiple search operator heuristic method; the other is on the computational cost where we show that CIAs always run faster than the often-used continuous iterative algorithm based on the rank-two relaxation.
Mineral precipitation is ubiquitous in natural and engineered environments, such as carbon mineralization, contaminant remediation, and oil recovery in unconventional reservoirs. The precipitation process continuously alters the medium permeability, thereby influencing fluid transport and subsequent reaction kinetics. The diversity of preferential precipitation zones controls flow and transport efficiency as well as the capacity of mineral sequestration and immobilization. Taking barite precipitation as an example, previous studies have examined this process in porous and/or fractured media, but pore-scale mechanisms under varying flowing and geochemical conditions remain unexplored. In this study, we conducted real-rock microfluidic experiments to investigate the precipitation dynamics within a fractured porous system. Direct observations of the evolution of the porous structure and flow channel and quantifications of barite precipitation dynamics using X-ray diffraction (XRD) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), revealed two distinct precipitation regimes: precipitation on the fracture surface (regime I) and precipitation in the alteration zone (regime II). Through theoretical analysis of the rate of advection and nucleation, we defined a dimensionless number Da above which regime I occurs and regime II prevails otherwise. At the large Da number, when the precipitation rate is large compared with the flow rate, precipitation on the fracture surface is favored. As the precipitation regimes are expected to impact differently the permeability of the fractured porous media, the mass transfer across matrix and fractures, and the spatial distributions of coprecipitated contaminants, our work sheds light on accurately modeling reactive transport in fractured porous media across diverse applications.
The Transformer model, particularly its cross-attention module, is widely used for feature fusion in target sound extraction which extracts the signal of interest based on given clues. Despite its effectiveness, this approach suffers from low computational efficiency. Recent advancements in state space models, notably the latest work Mamba, have shown comparable performance to Transformer-based methods while significantly reducing computational complexity in various tasks. However, Mamba’s applicability in target sound extraction is limited due to its inability to capture dependencies between different sequences as the cross-attention does. In this paper, we propose CrossMamba for target sound extraction, which leverages the hidden attention mechanism of Mamba to compute dependencies between the given clues and the audio mixture. The calculation of Mamba can be divided to the query, key and value. We utilize the clue to generate the query and the audio mixture to derive the key and value, adhering to the principle of the cross-attention mechanism in Transformers. Experimental results from two representative target sound extraction methods validate the efficacy of the proposed CrossMamba
ABSTRACT An increasing body of research has investigated the role of enjoyment in second language acquisition (SLA); however, few studies have explored whether learners of Chinese as a second/foreign language (CS/FL) experience enjoyment in learning Hanzi (Chinese characters) and how enjoyment impacts Hanzi recognition performance. To address this gap, a Hanzi Learning Enjoyment Scale was developed and administered to 446 Arabic CS/FL learners, 144 of whom also completed a Hanzi recognition test. Two key findings emerged. First, the results of factor analysis revealed four factors underlying Hanzi learning enjoyment: Hanzi culture, personal attitudes, teacher support, and personal fulfillment. Second, enjoyment did not emerge as a significant predictor of Hanzi recognition performance. Notably, the variance in Hanzi recognition scores explained by enjoyment ranked among the top three explanatory variables, comparable to the predictive power of years spent learning Chinese. This study concludes with theoretical insights into the construct of foreign language enjoyment (FLE) across different languages and language components, as well as practical recommendations for enhancing Hanzi instruction.