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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Xinyue Lan, Liyue Wang, Cong Wang, Gang Sun, Jinzhang Feng and Miao Zhang
In this research, we introduce a deep-learning-based framework designed for the prediction of transonic flow through a linear cascade utilizing large-scale point-cloud data. In our experimental cases, the predictions demonstrate a nearly four-fold speed ...
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Michail Salampasis, Alkiviadis Katsalis, Theodosios Siomos, Marina Delianidi, Dimitrios Tektonidis, Konstantinos Christantonis, Pantelis Kaplanoglou, Ifigeneia Karaveli, Chrysostomos Bourlis and Konstantinos Diamantaras
Research into session-based recommendation systems (SBSR) has attracted a lot of attention, but each study focuses on a specific class of methods. This work examines and evaluates a large range of methods, from simpler statistical co-occurrence methods t...
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Sewon Kim, Won C. Bae, Koichi Masuda, Christine B. Chung and Dosik Hwang
We propose a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance (MR) images of the human lumbar spine. Quantitative analysis of spine MR images often necessitate segmentation of the image into specific regions represe...
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