Yuelin Zou

In this study, we explore the application of sentiment analysis on financial news headlines to understand investor sentiment. By leveraging Natural Language Processing (NLP) and Large Language Models (LLM), we analyze sentiment from the perspective of retail investors. The FinancialPhraseBank dataset, which contains categorized sentiments of financial news headlines, serves as the basis for our analysis. We fine-tuned several models, including distilbert-base-uncased, Llama, and gemma-7b, to evaluate their effectiveness in sentiment classification. Our experiments demonstrate that the fine-tuned gemma7b model outperforms others, achieving the highest precision, recall, and F1-score. Specifically, the gemma-7b model showed significant improvements in accuracy after fine-tuning, indicating its robustness in capturing the nuances of financial sentiment. This model can be instrumental in providing market insights, risk management, and aiding investment decisions by accurately predicting the sentiment of financial news. The results highlight the potential of advanced LLMs in transforming how we analyze and interpret financial information, offering a powerful tool for stakeholders in the financial industry.

In order to solve the problem of frequent deceleration of unmanned vehicles when approaching obstacles, this article uses a Deep Q-Network (DQN) and its extension, the Double Deep Q-Network (DDQN), to develop a local navigation system that adapts to obstacles while maintaining optimal speed planning. By integrating improved reward functions and obstacle angle determination methods, the system demonstrates significant enhancements in maneuvering capabilities without frequent decelerations. Experiments conducted in simulated environments with varying obstacle densities confirm the effectiveness of the proposed method in achieving more stable and efficient path planning.

This paper proposes new methods to enhance click-through rate (CTR) prediction models using the Deep Interest Network (DIN) model, specifically applied to the advertising system of Alibaba’s Taobao platform. Unlike traditional deep learning approaches, this research focuses on localized user behavior activation for tailored ad targeting by leveraging extensive user behavior data. Compared to traditional models, this method demonstrates superior ability to handle diverse and dynamic user data, thereby improving the efficiency of ad systems and increasing revenue.

In this study, we explore the application of sentiment analysis on financial news headlines to understand investor sentiment. By leveraging Natural Language Processing (NLP) and Large Language Models (LLM), we analyze sentiment from the perspective of retail investors. The FinancialPhraseBank dataset, which contains categorized sentiments of financial news headlines, serves as the basis for our analysis. We fine-tuned several models, including distilbert-base-uncased, Llama, and gemma-7b, to evaluate their effectiveness in sentiment classification. Our experiments demonstrate that the fine-tuned gemma-7b model outperforms others, achieving the highest precision, recall, and F1 score. Specifically, the gemma-7b model showed significant improvements in accuracy after fine-tuning, indicating its robustness in capturing the nuances of financial sentiment. This model can be instrumental in providing market insights, risk management, and aiding investment decisions by accurately predicting the sentiment of financial news. The results highlight the potential of advanced LLMs in transforming how we analyze and interpret financial information, offering a powerful tool for stakeholders in the financial industry.

The rapid urbanization of cities has exacerbated traffic congestion, resulting in significant environmental impacts, including elevated greenhouse gas emissions and deteriorating air quality. Traffic management systems, while effective in many contexts, often fail to consider the ecological and dynamic complexities of modern urban environments. This paper introduces MM-STMAP, a framework for urban traffic management that integrates multi modal perception with deep reinforcement learning. The approach utilizes a spatio temporal graph convolutional network to model intricate traffic patterns across diverse urban environments, while incorporating real-time environmental data, including meteorological factors, to address the ecological limitations of traditional traffic systems. A linear attention mechanism is employed to optimize computational efficiency in processing large-scale, dynamic traffic data, thereby enhancing both operational performance and energy consumption. The multi agent reinforcement learning structure governs the coordination of traffic signals across intersections, achieving a dual optimization of reduced vehicular delays and minimized emissions. Empirical evaluations on major metropolitan datasets demonstrate that MM-STMAP outperforms existing traffic management methods and significantly enhances traffic flow efficiency. The model's ability to integrate heterogeneous data streams spanning traffic sensors and environmental reports enables a comprehensive and adaptive approach to urban mobility, supporting the development of sustainable smart city infrastructure.