Misinformation spans various domains, but detection methods trained on specific domains often perform poorly when applied to others. With the rapid development of Large Language Models (LLMs), researchers have begun to utilize LLMs for cross-domain misinformation detection. However, existing LLM-based methods often fail to adequately analyze news in the target domain, limiting their detection capabilities. More importantly, these methods typically rely on manually designed decision rules, which are limited by domain knowledge and expert experience, thus limiting the generalizability of decision rules to different domains. To address these issues, we propose a MultiAgent Framework for cross-domain misinformation detection with Automated Decision Rule Optimization (MARO). Under this framework, we first employs multiple expert agents to analyze target-domain news. Subsequently, we introduce a question-reflection mechanism that guides expert agents to facilitate higherquality analysis. Furthermore, we propose a decision rule optimization approach based on carefully-designed cross-domain validation tasks to iteratively enhance the effectiveness of decision rules in different domains. Experimental results and in-depth analysis on commonlyused datasets demonstrate that MARO achieves significant improvements over existing methods.

The reliability of substation equipment is crucial to the stability of power systems, but traditional fault analysis methods heavily rely on manual expertise, limiting their effectiveness in handling complex and large-scale data. This paper proposes a substation equipment fault analysis method based on a multimodal large language model (MLLM). We developed a database containing 40,000 entries, including images, defect labels, and analysis reports, and used an image-to-video generation model for data augmentation. Detailed fault analysis reports were generated using GPT-4. Based on this database, we developed SubstationAI, the first model dedicated to substation fault analysis, and designed a fault diagnosis knowledge base along with knowledge enhancement methods. Experimental results show that SubstationAI significantly outperforms existing models, such as GPT-4, across various evaluation metrics, demonstrating higher accuracy and practicality in fault cause analysis, repair suggestions, and preventive measures, providing a more advanced solution for substation equipment fault analysis.

In this study, we propose a novel asset pricing approach, LLM Agent-based Asset Pricing Models (AAPM), which fuses qualitative discretionary investment analysis from LLM agents and quantitative manual financial economic factors to predict excess asset returns. The experimental results show that our approach outperforms machine learning-based asset pricing baselines in portfolio optimization and asset pricing errors. Specifically, the Sharpe ratio and average $|\alpha|$ for anomaly portfolios improved significantly by 9.6\% and 10.8\% respectively. In addition, we conducted extensive ablation studies on our model and analysis of the data to reveal further insights into the proposed method.

Classifying brain signals collected by wearable Internet of Things (IoT) sensors, especially brain-computer interfaces (BCIs), is one of the fastest-growing areas of research. However, research has mostly ignored the secure storage and privacy protection issues of collected personal neurophysiological data. Therefore, in this article, we try to bridge this gap and propose a secure privacy-preserving protocol for implementing BCI applications. We first transformed brain signals into images and used generative adversarial network to generate synthetic signals to protect data privacy. Subsequently, we applied the paradigm of transfer learning for signal classification. The proposed method was evaluated by a case study and results indicate that real electroencephalogram data augmented with artificially generated samples provide superior classification performance. In addition, we proposed a blockchain-based scheme and developed a prototype on Ethereum, which aims to make storing, querying and sharing personal neurophysiological data and analysis reports secure and privacy-aware. The rights of three main transaction bodies - construction workers, BCI service providers and project managers - are described and the advantages of the proposed system are discussed. We believe this paper provides a well-rounded solution to safeguard private data against cyber-attacks, level the playing field for BCI application developers, and to the end improve professional well-being in the industry.

The Global Artificial Intelligence Chip Market report gives a holistic analysis of the marketplace for the forecast interval. The report includes numerous segments in addition to an evaluation of the trends and elements which might be taking part in a considerable position out there. These factors; the market dynamics contain the drivers, restraints, alternatives, and challenges through which the influence of those elements out there is printed. The drivers and restraints are intrinsic elements whereas opportunities and challenges are extrinsic elements of the market. The Global Artificial Intelligence Chip Market study gives an outlook on the event of the market when it comes to revenue throughout the prognosis interval.

During the last two years, several cloud service providers, including Alibaba, Amazon, Facebook, Google, Huawei, and Tencent, have been busy designing their own in-house chipsets for handling Artificial Intelligence (AI) workloads in their data centers. ABI Research, a global tech market advisory firm, estimates that cloud service providers commanded 3.3% market share of the total AI Cloud chip shipments in the first half of 2019. These players will increasingly rely on their own in-house AI chips and will be producing a total of 300,000 cloud AI chips by 2024, representing 18% of the global cloud AI chipsets shipped in 2024. The increasing requirements for intelligent services by many enterprise verticals are pushing cloud service providers to rapidly upgrade their data centers with AI capabilities, which has already created an enormous demand for cloud AI chipsets in recent years. ABI Research expects revenues from these chipset shipments to increase significantly in the next five years, from US$4.2 billion in 2019 to US$10 billion in 2024.