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Automated summarization of legal texts poses a significant challenge due to the complex and specialized nature of legal documentation. Despite the recent progress in reinforcement learning for natural language text summarization, its application in the legal domain has been less effective. This paper introduces SAC-VAE, a novel reinforcement learning framework specifically designed for legal text summarization. We leverage a Variational Autoencoder (VAE) to condense the high-dimensional state space into a more manageable lower-dimensional feature space. These compressed features are subsequently utilized by the Soft Actor-Critic (SAC) algorithm for policy learning, facilitating the automated generation of summaries from legal texts. Through comprehensive experimentation, we have empirically demonstrated the effectiveness and superior performance of the SAC-VAE framework in legal text summarization. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of reservation prices in all‐pay auctions based on the Bayesian Nash equilibrium of symmetric distributions with binary and correlated types. Our study finds that reservation prices affect players' behavior in two ways. Given the reservation price, some give up the original strategy of offering a bid lower than such minimum requirement. Therefore, the reservation prices may discourage the effort supply of weaker players. However, stronger innovators do not modify their strategies: They will start giving bids above the reservation price. Hence, setting a quality standard is effective in boosting effort supply. Our study contributes to the function of this particular pattern in the contests, thereby gaining more insight into designing and assessing such competition under different circumstances. [ABSTRACT FROM AUTHOR]

The decline of sea ice in the Arctic region is a critical indicator of rapid global warming and can also influence the feedback processes in the Arctic, so the prediction of sea ice extent and thickness plays an important role in climate modeling and prediction. This paper uses machine learning methods to predict the sea ice extent, and by adjusting the methods and factors, which include the climate variables, the past sea ice extent, and the simple linear-regression-simulated sea ice extent, then we found the best combination to give the result with the highest R2 score. We noticed that with longer periods of past sea ice extent data and shorter periods of climate data, the results appeared to be better. This might be related to the difference in climate and ocean memory. The sub-region sea ice extent prediction shows that the regions with whole-year ice cover are easier to predict and that those regions with sudden weather changes and significant seasonal variability appear to have lower R2 scores in the sea ice extent prediction. [ABSTRACT FROM AUTHOR]

Facial recognition technology (FRT) has emerged as a powerful tool for public governance and security, but its rapid adoption has also raised significant concerns about privacy, civil liberties, and ethical implications. This paper critically examines the current rules and policies governing FRT, highlighting the tensions between state and corporate interests on one hand, and individual rights and ethical considerations on the other. The study also investigates international legal frameworks aimed at protecting individual rights and privacy, arguing that current legislative measures often fall short of robust scholarly standards and international human rights norms. The paper concludes with recommendations for developing principled and adaptable governance frameworks that harness the benefits of FRT while mitigating its risks and negative impacts, underscoring the importance of placing human rights and ethics at the center of regulating this transformative technology., Competing Interests: XW was employed by Sage IT Consulting Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Wu, Zhou and Fu.)