Wenqi Liu

Language is a tool for cultural communication, and diffusion is influenced by many factors. However, many studies have highlighted the importance of language status, while the critical factor of minority identity has been neglected. Minority identity is a sociological factor reflecting individual preferences for minority languages. Here, we introduce a framework for characterizing the language diffusion within minority groups, leading to the emergence of new ethnolinguistic phenomena: language segregation and coexistence. This finding challenges the previous assumption that language status alone determines language dynamics. Furthermore, we add a self-minority identity transmission mechanism to understand how language diffusion occurs. Monte Carlo simulations and theoretical analyses reveal that self-minority identity transmission significantly fosters minority language diffusion in both heterogeneous and homogeneous networks, especially in heterogeneous networks, and that increasing the average degree of the network promotes minority language diffusion. Finally, we apply a real-world social network in the Wa minority region of Yunnan, China, to validate that minority language diffusion exhibits a phase transition and that the critical threshold depends on the network structure and the diffusion of self-minority identity. Moreover, we deepen the theoretical understanding of sociolinguistics and provide a theoretical basis and policy recommendations for protecting and promoting minority languages.

SUMMARY: The scientific process of earthquake forecasting involves estimating the probability and intensity of earthquakes in a specific area within a certain timeframe, based on seismic activity features and observational data. Among the various methodologies, epidemic-type aftershock sequence (ETAS) models, rooted in seismic empirical laws, stand as widely used tools for earthquake forecasting. In this study, we introduce the CL-ETAS model, a novel approach that integrates convolutional long short-term memory (ConvLSTM), a deep learning model, with the ETAS model. Specifically, we leverage the forecasting outputs of ETAS to enhance both the training and forecasting processes within the ConvLSTM framework. Through forecasting tests, our findings illustrate the effectiveness of the CL-ETAS model in capturing the trends observed in earthquake numbers ($M \ge 3$) in Southern California following three main shocks. Overall, our model outperforms both a simple ETAS model and ConvLSTM in this context.

By constructing a correlation network between global sea surface temperature anomalies (SSTAs) and summer precipitation anomalies in the Yunnan–Guizhou Plateau, key SST regions influencing summer precipitation anomalies in the Yunnan–Guizhou Plateau were selected. It was found that spring SSTAs in the Bay of Bengal, southwestern Atlantic, and eastern Pacific are crucial for influencing summer precipitation anomalies in the Yunnan–Guizhou Plateau. Setting SSTAs from these three regions as predictor variables 3 months in advance, we constructed multiple linear regression (MLR), ridge regression (RR), and lasso regression (LR) models to predict summer precipitation anomalies over the Yunnan–Guizhou region. The training phase involved data spanning from 1961 to 2005, which aimed to predict precipitation anomalies in the Yunnan–Guizhou Plateau for the period extending from 2006 to 2022. Based on MLR, RR, and LR models, the correlations between predicted values and observed summer precipitation anomalies in Yunnan–Guizhou were 0.48, 0.46, and 0.46, respectively. These values were all higher than the correlation coefficients of the NCC_CSM model’s predicted and observed values. Additionally, its performance in predicting summer precipitation anomalies over the Yunnan–Guizhou region, based on key SST regions, was assessed using performance metrics such as anomaly correlation coefficient (ACC), anomaly sign consistency rate (PC), and trend anomaly comprehensive score (PS score). The average ACC of MLR, RR, and LR models was higher than that of the NCC_CSM model’s predictions. For MLR, RR, LR, and NCC_CSM models, the PCs exceeding 50% of the year were 14, 14, 11, and 10, respectively. Furthermore, the average PS score for predicting summer precipitation anomalies over the Yunnan–Guizhou region using MLR, RR, and LR was approximately 73 points; 8 higher than the average PS score of the NCC_CSM model. Therefore, predicting summer precipitation anomalies over the Yunnan–Guizhou region based on key SST regions is of great significance for improving the prediction skills of precipitation anomalies in this region.

Seizure prediction are necessary for epileptic patients. The global spatial interactions among channels, and long-range temporal dependencies play a crucial role in seizure onset prediction. In addition, it is necessary to search for seizure prediction features in a vast space to learn new generalized feature representations. Many previous deep learning algorithms have achieved some results in automatic seizure prediction. However, most of them do not consider global spatial interactions among channels and long-range temporal dependencies together, and only learn the feature representation in the deep space. To tackle these issues, in this study, an novel bi-level programming seizure prediction model, B2-ViT Net, is proposed for learning the new generalized spatio-temporal long-range correlation features, which can characterize the global interactions among channels in spatial, and long-range dependencies in temporal required for seizure prediction. In addition, the proposed model can comprehensively learn generalized seizure prediction features in a vast space due to its strong deep and broad feature search capabilities. Sufficient experiments are conducted on two public datasets, CHB-MIT and Kaggle datasets. Compared with other existing methods, our proposed model has shown promising results in automatic seizure prediction tasks, and provides a certain degree of interpretability.

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Interpretable distributed group intelligence techniques have emerged as an essential topic in artificial intelligence. The mathematical interpretability of prediction outcomes is critical for improving the reliability of machine learning, especially in random scenes. Although some experimental results published so far show that the prediction of group intelligence is better than individual intelligence, establishing a mathematical foundation for the superiority of distributed group intelligence is still a challenging problem for enhancing the interpretability of learning systems. Through the Radermacher complexity principle, we proved mathematically that the learning quality of group machine intelligence is better than its subset machine intelligence with a high probability, significantly better than any individual among them if the number of individuals in the group is large enough. We proposed a multi-agent distributed learning method for time series forecasting by incorporating multi-agent cooperation in cognitive processes into machine learning. In addition, since the way of cooperative interaction between multi-agent affects the training effect of the model, we provide a generalized interaction approach and prove its convergence. We conduct sufficient experiments on predicting time series for classically chaotic systems, and the results indicate that distributed group intelligence significantly improves the prediction accuracy of individual intelligence. The experiments result shows that the prediction error reduces substantially as the number of agents increases, confirming the theoretical accuracy and the model's validity. This work provides new ideas for theoretically exploring how group intelligence emerges.

There are thousands of languages in the world, many of which are in danger of extinction due to language competition and evolution. Language is an aspect of culture, the rise, and fall of a language directly affects its corresponding culture. To preserve languages and prevent their mass extinction, it is crucial to develop a mathematical model of language coexistence. In this paper, we use a qualitative theory of ordinary differential equations to analyze the bilingual competition model, and obtain the trivial and non-trivial solutions of the bilingual competition model without sliding mode control, then analyze the stability of solutions and prove that solutions of the model have positive invariance. In addition, to maintain linguistic diversity and prevent mass extinction of languages, we propose a novel bilingual competition model with sliding control. The bilingual competition model is analyzed by proposing a sliding control policy to obtain a pseudo-equilibrium point. Meanwhile, numerical simulations clearly illustrate the effectiveness of the sliding mode control strategy. The results show that the likelihood of successful language coexistence can be increased by changing the status of languages and the value of monolingual-bilingual interaction, provides theoretical analysis for the development of policies to prevent language extinction.

Anthropogenic climate change poses a significant threat to both natural and social systems worldwide. In this study, we aim to identify regions most impacted by climate change using the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP-NCAR) reanalysis of near-surface daily air temperature data spanning 73 years (1948–2020). We develop a novel climate network framework to identify “hot spots”, regions that exhibit significant impact or impacted characteristics. Specifically, we use the node degree, a fundamental feature of the network, to measure the influence of each region and analyze its trend over time using the Mann–Kendall test. Our findings reveal that the majority of land areas experiencing increasing degrees are more closely connected to other regions, while the ocean shows the opposite trend due to weakened oceanic circulations. In particular, the degree in the central Pacific Ocean’s El Niño region is significantly reduced. Notably, we identify three “hot spots” in East Asia, South America, and North Africa, respectively, with intensive increasing network degree fields. Additionally, we find that the hot spot in East Asia is teleconnected to remote regions, such as the South Pacific, Siberia, and North America, with stronger teleconnections in recent years. This provides a new perspective for assessing the planetary impacts of anthropogenic global warming. By using a novel climate network framework, our study highlights regions that are most vulnerable to the effects of climate change and emphasizes the importance of understanding network structures to assess the global impacts of anthropogenic climate change.