Browsing by Author "Chang, Yu-Kai"

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Effects of Exercise Modes on Neural Processing of Working Memory in Late Middle-Aged Adults: An fMRI Study
(2019-09-04)
Chen, Feng-Tzu
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Chen, Ya-Ping
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Schneider, Stefan
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Kao, Shih-Chun
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Huang, Chih-Mao
Recent studies have highlighted the importance of regular exercise on cognitive function in aging populations, with aerobic exercise and cardiovascular fitness having received the largest amount of research attention. However, the relationship between exercise mode and cognitive function underlying behavioral modification and neural activation remains unknown. The present study, therefore, sought to examine the associations between different exercise modes and the working memory (WM) aspect of executive function as well as its task-evoked brain activation in the late middle-aged population. Seventy late middle-aged adults were classified into open-skill, closed-skill, or irregular exercise groups based on their participation in exercise activities prior to the study and then performed a spatial working memory (SWM) task while undergoing functional magnetic resonance imaging (fMRI) scanning. The results revealed that exercise groups, regardless of exercise modes, showed better SWM and physical fitness performance. Additionally, the open-skill group exhibited greater brain activation in the prefrontal lobe, anterior cingulate cortex/supplementary motor area (ACC/SMA), and hippocampus than those in the closed-skill group, suggesting a mode-sensitive compensatory mechanism in late middle-aged adults. These findings indicate that exercise promotes cognitive health, improves WM, and enhances neurocognitive scaffolding in late middle-aged adults and further suggest that various exercise modes can effectively modulate frontal and hippocampal function in the face of age-related neurocognitive declines, implications that may inform the development of exercise programs for the elderly.
Enhancing the Realism of Sketch and Painted Portraits With Adaptable Patches
(2018-02-01)
Lee, Yin-Hsuan
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Chang, Yu-Kai
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Chang, Yu-Lun
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Lin, I-Chen
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Wang, Yu-Shuen
Realizing unrealistic faces is a complicated task that requires a rich imagination and comprehension of facial structures. When face matching, warping or stitching techniques are applied, existing methods are generally incapable of capturing detailed personal characteristics, are disturbed by block boundary artefacts, or require painting-photo pairs for training. This paper presents a data-driven framework to enhance the realism of sketch and portrait paintings based only on photo samples. It retrieves the optimal patches of adaptable shapes and numbers according to the content of the input portrait and collected photos. These patches are then seamlessly stitched by chromatic gain and offset compensation and multi-level blending. Experiments and user evaluations show that the proposed method is able to generate realistic and novel results for a moderately sized photo collection.