联合国际学院

How does the housing market respond to a newly imposed value-based property tax? Based on individual property transaction data spanning seven years, we investigate the repercussions of a new property tax on the local housing market in Chongqing, China. In particular, we adopt a fuzzy regression discontinuity approach to examine the impact of the tax policy on house prices, transaction volumes, and the broader social welfare. We find that the property tax in Chongqing has not caused a reduction in house prices, but it has made a non-trivial contribution to local public finance for public housing programs. Our findings suggest that the property-tax-capitalization hypothesis is not supported in Chongqing's context, although the tax improves wealth redistribution.

Music perception, a multi-sensory process based on the synesthesia effect, is an essential component of music aesthetic education. Understanding music structure helps both perception and aesthetic education. Music structure incorporates a range of information, the coordination of which forms the melody, just as different military actions cooperate to produce a military strategy. However, there are a few ways for assessing music perception from the perspectives of system operation and information management. In this paper, we explore the similarities between music structure and military strategy while creating the Music Clips Correlation Network (MCCN) based on Mel-frequency Cepstral Coefficients (MFCCs). The inspiration comes from the comparison between a concert conductor's musical score and a military war commander's sand table exercise. Specifically, we create MCCNs for various kinds of war movie soundtracks, then relate military tactics (Sun Tzu's Art of War, etc.) and political institutions to military operations networks. Our primary findings suggest a few similarities, implying that music perception and aesthetic education can be approached from a military strategy and management perspective through this interdisciplinary research. Similarly, we can discover similarities between the art of military scheming and the art of musical structure based on network analysis in order to facilitate the understanding of the relationship between technology and art.

This review explores the role of prompt engineering in unleashing the capabilities of large language models (LLMs). Prompt engineering is the process of structuring inputs, and it has emerged as a crucial technique for maximizing the utility and accuracy of these models. Both foundational and advanced prompt engineering methodologies-including techniques such as self-consistency, chain of thought, and generated knowledge, which can significantly enhance the performance of models-are explored in this paper. Additionally, the prompt methods for vision language models (VLMs) are examined in detail. Prompt methods are evaluated with subjective and objective metrics, ensuring a robust analysis of their efficacy. Critical to this discussion is the role of prompt engineering in artificial intelligence (AI) security, particularly in terms of defending against adversarial attacks that exploit vulnerabilities in LLMs. Strategies for minimizing these risks and improving the robustness of models are thoroughly reviewed. Finally, we provide a perspective for future research and applications.

The complex relationship between diet, the gut microbiota, and mental health, particularly depression, has become a focal point of contemporary research. This critical review examines how specific dietary components, such as fiber, proteins, fats, vitamins, minerals, and bioactive compounds, shape the gut microbiome and influence microbial metabolism in order to regulate depressive outcomes. These dietary-induced changes in the gut microbiota can modulate the production of microbial metabolites, which play vital roles in gut–brain communication. The gut–brain axis facilitates this communication through neural, immune, and endocrine pathways. Alterations in microbial metabolites can influence central nervous system (CNS) functions by impacting neuroplasticity, inflammatory responses, and neurotransmitter levels—all of which are linked to the onset and course of depression. This review highlights recent findings linking dietary components with beneficial changes in gut microbiota composition and reduced depressive symptoms. We also explore the challenges of individual variability in responses to dietary interventions and the long-term sustainability of these strategies. The review underscores the necessity for further longitudinal and mechanistic studies to elucidate the precise mechanisms through which diet and gut microbiota interactions can be leveraged to mitigate depression, paving the way for personalized nutritional therapies.

The endeavor of stock trend forecasting is principally focused on predicting the future trajectory of the stock market, utilizing either manual or technical methodologies to optimize profitability. Recent advancements in machine learning technologies have showcased their efficacy in discerning authentic profit signals within the realm of stock trend forecasting, predominantly employing temporal data derived from historical stock price patterns. Nevertheless, the inherently volatile and dynamic characteristics of the stock market render the learning and capture of multi-scale temporal dependencies and stable trading opportunities a formidable challenge. This predicament is primarily attributed to the difficulty in distinguishing real profit signal patterns amidst a plethora of mixed, noisy data. In response to these complexities, we propose a Multi-Scale Temporal Memory Learning and Efficient Debiasing (MTMD) model. This innovative approach encompasses the creation of a learnable embedding coupled with external attention, serving as a memory module through self-similarity. It aims to mitigate noise interference and bolster temporal consistency within the model. The MTMD model adeptly amalgamates comprehensive local data at each timestamp while concurrently focusing on salient historical patterns on a global scale. Furthermore, the incorporation of a graph network, tailored to assimilate global and local information, facilitates the adaptive fusion of heterogeneous multi-scale data. Rigorous ablation studies and experimental evaluations affirm that the MTMD model surpasses contemporary state-of-the-art methodologies by a substantial margin in benchmark datasets.