The present paper is motivated by a recent result which is obtained by Liu and Xiao, the exact distribution of roots for a quadratic polynomial will be considered. For completeness, the proof of the Liu-Xiao's lemma will firstly be supplemented, however, the condition of the unit complex roots will be improved. In this case, the computations of the Neimark-Sacker bifurcation or the Hopf bifurcation can be simplified. Furthermore, some new results are also established by using the relationship of the roots and coefficients which is very important in the annals of history, through the discoveries of Abel, Galois and Gauss in the nineteenth century. The obtained results can be used for the bifurcations of the two dimensional discrete-time dynamical systems.
The true potential of human-AI collaboration lies in exploiting the complementary capabilities of humans and AI to achieve a joint performance superior to that of the individual AI or human, i.e., to achieve complementary team performance (CTP). To realize this complementarity potential, humans need to exercise discretion in following AI 's advice, i.e., appropriately relying on the AI's advice. While previous work has focused on building a mental model of the AI to assess AI recommendations, recent research has shown that the mental model alone cannot explain appropriate reliance. We hypothesize that, in addition to the mental model, human learning is a key mediator of appropriate reliance and, thus, CTP. In this study, we demonstrate the relationship between learning and appropriate reliance in an experiment with 100 participants. This work provides fundamental concepts for analyzing reliance and derives implications for the effective design of human-AI decision-making.
Although pervasive spread of misinformation on social media platforms has become a pressing challenge, existing platform interventions have shown limited success in curbing its dissemination. In this study, we propose a stance-aware graph neural network (stance-aware GNN) that leverages users' stances to proactively predict misinformation spread. As different user stances can form unique echo chambers, we customize four information passing paths in stance-aware GNN, while the trainable attention weights provide explainability by highlighting each structure's importance. Evaluated on a real-world dataset, stance-aware GNN outperforms benchmarks by 32.65% and exceeds advanced GNNs without user stance by over 4.69%. Furthermore, the attention weights indicate that users' opposition stances have a higher impact on their neighbors' behaviors than supportive ones, which function as social correction to halt misinformation propagation. Overall, our study provides an effective predictive model for platforms to combat misinformation, and highlights the impact of user stances in the misinformation propagation.
With the booming development of China's ocean shipping and tourism business, the demand for safety and economy when ships are sailing at sea is increasing. Accurate meteorological and hydrographic forecasts can provide meteorological navigation for ships to avoid typhoons and bad weather areas as much as possible and reduce the damage to the ship's hull from wind and waves. To obtain accurate and reliable wind speed prediction results, this paper combines the advantages of convolutional neural network and gated recurrent unit network to form a deep convolutional gated recurrent unit network model (CNN-GRU). For multiple locations, the CNN-GRU algorithm is used to extract the characteristic meteorological elements, and then the convolutional neural network is used to establish the spatial characteristic relationship between each location, and the gated recurrent unit network is used to establish the temporal characteristic relationship between historical time points, and the final wind speed prediction results are obtained based on the spatio-temporal correlation analysis. In this paper, the CNN-GRU model was established using meteorological data from 2019 to 2021, and the prediction results were compared with CNN model and GRU model and the accuracy was verified. The results show that the experimental results obtained by the CNN-GRU model are more accurate and prove the effectiveness of the proposed method.
Cotton frequently experiences substantial yield reduction due to disease afflictions. This paper introduces a proof-of-concept mobile application that employs transfer learning with pre-trained MobileNetV3 and NASNetMobile models to detect cotton plant diseases. The system addresses a four-class problem, identifying the health status of cotton plants or leaves. Using TensorFlow, these models are refined with publicly available data and deployed on an iOS app. This study also provides a performance assessment of both models, previously unexplored in this specific context. The methodology, showcasing significant potential for real-world implementations, recorded overall accuracies of 97.7% for NASNetMobile and 96.7% for MobileNetV3.
