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Sarah A. Chauncey and H. Patricia McKenna
The purpose of this study is to advance conceptual understandings of the cognitive flexibility construct, in support of creativity and innovation in smart city civic spaces, employing the use of large language model artificial intelligence chatbots such ...
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Tamás Kegyes, Alex Kummer, Zoltán Süle and János Abonyi
We analyzed a special class of graph traversal problems, where the distances are stochastic, and the agent is restricted to take a limited range in one go. We showed that both constrained shortest Hamiltonian pathfinding problems and disassembly line bal...
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Ziyi Wang, Xinran Li, Luoyang Sun, Haifeng Zhang, Hualin Liu and Jun Wang
Efficient yet sufficient exploration remains a critical challenge in reinforcement learning (RL), especially for Markov Decision Processes (MDPs) with vast action spaces. Previous approaches have commonly involved projecting the original action space int...
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Jiawei Zhu, Bo Li, Hao Ouyang, Yuhan Wang and Ziyue Bai
Walking exercise is a prevalent physical activity in urban areas, with streetscapes playing a significant role in shaping preferences. Understanding this influence is essential for creating urban environments conducive to walking exercise and improving r...
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Lei Zhou, Weiye Xiao, Chen Wang, Haoran Wang
Pág. 143 - 161
Human mobility datasets, such as traffic flow data, reveal the connections between urban spaces. A novel framework is proposed to explore the spatial association between urban commercial and residential spaces via consumption travel flows in Shanghai. A ...
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Zhixin Li, Song Ji, Dazhao Fan, Zhen Yan, Fengyi Wang and Ren Wang
Accurate building geometry information is crucial for urban planning in constrained spaces, fueling the growing demand for large-scale, high-precision 3D city modeling. Traditional methods like oblique photogrammetry and LiDAR prove time consuming and ex...
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Bohdan Petryshyn, Serhii Postupaiev, Soufiane Ben Bari and Armantas Ostreika
The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is ...
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Qinna Feng, Heng Luo, Zijian Li, Jiarong Liang, Gege Li and Yan Yi
The last decade has witnessed the rapid development of immersive virtual reality (IVR) and its application in various contexts. However, its application in supporting real-time virtual collaboration has been quite rare due to technical barriers and the l...
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Sara Rajaram and Cassie S. Mitchell
The ability to translate Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) into different modalities and data types is essential to improve Deep Learning (DL) for predictive medicine. This work presents DACMVA, a novel framework ...
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Zahid Masood, Muhammad Usama, Shahroz Khan, Konstantinos Kostas and Panagiotis D. Kaklis
Generative models offer design diversity but tend to be computationally expensive, while non-generative models are computationally cost-effective but produce less diverse and often invalid designs. However, the limitations of non-generative models can be...
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