This research work proposes a quantum traffic flow prediction system based on the difficulty of traffic congestion in cities. The system uses real-time traffic data, details on weather condition, and the behavioral pa...
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The delineation of urban functional zones is essential for urban planning and management. Most current methods rely on points of interest data, which tend to be concentrated in city centers, resulting in poor performa...
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This paper presents a new perspective on framing through the lens of speech acts and investigates how politicians make use of different pragmatic speech act functions in political debates. To that end, we create a new...
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With the expansion of pre-trained language model usage in recent years, the importance of datasets for performing tasks in specialized domains has significantly increased. Therefore, we have built a Korean dataset cal...
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Due to the growing prevalence of chronic diseases stemming from unhealthy lifestyles, a personalized approach to patient care is crucial. This paper delves into a system that utilizes cosine similarity and Pearson cor...
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Face, a non-intrusive recognition modality, is an ideal candidate for identifying criminals or performing general-purpose person identification. On top of that, faces are not only related to identity but other essenti...
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Large Language Model (LLM) Retrieval-Augmented Generation (RAG) chatbots hold immense potential for enhancing user engagement and information access. However, bringing such a system to life on a real-world platform pr...
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With the breakthroughs in deep learning technology, human pose estimation has emerged as a critical research area in computer vision. Its wide-ranging applications include posture assessment, posture correction, sport...
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Sparse phase retrieval aims to reconstruct an ndimensional k-sparse signal from its phaseless measurements. For most of the existing reconstruction algorithms, their sampling complexity is known to be dominated by the...
Sparse phase retrieval aims to reconstruct an ndimensional k-sparse signal from its phaseless measurements. For most of the existing reconstruction algorithms, their sampling complexity is known to be dominated by the initialization stage. In this paper, in order to improve the sampling complexity for initialization, we propose a novel method termed exponential spectral pursuit (ESP). Theoretically, our method offers a tighter bound of sampling complexity compared to the state-of-the-art ones, such as the truncated power method. Moreover, it empirically outperforms the existing initialization methods for sparse phase retrieval. Copyright 2024 by the author(s)
In this study, we focus on estimating financial crashes within a network of small and medium enterprises (SMEs) that are customers of Yapi Kredi Bank. These SMEs have complex financial relationships involving receivab...
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