Joon Shik Kim

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OBJECTIVE The functional strategic mechanisms in the brain during performing visuospatial working memory tasks, especially tasks with heavy load, are controversial. We conducted the functional magnetic resonance imaging (fMRI) while sixteen subjects were performing face- and location-matching n-back tasks to examine causal relations within the(More)
Many DNA-based technologies, such as DNA computing, DNA nanoassembly and DNA biochips, rely on DNA hybridization reactions. Previous hybridization models have focused on macroscopic reactions between two DNA strands at the sequence level. Here, we propose a novel population-based Monte Carlo algorithm that simulates a microscopic model of reacting DNA(More)
We report an in-situ neutron diffraction study of a large format pouch battery cell. The succession of Li-Graphite intercalation phases was fully captured under an 1C charge-discharge condition (i.e., charge to full capacity in 1 hour). However, the lithiation and dilithiation pathways are distinctively different and, unlike in slowing charging experiments(More)
Understanding episodic memory formation of real-world events is essential for the investigation of human cognition. Most studies have stressed on delimiting the upper boundaries of this memory by using memorization tasks with conditional experimental paradigms, rather than the performance of everyday tasks. However, naturally occurring sensory stimuli are(More)
Dialogues are linguistic interactions between people and can provide hints on social relationship and personality. We aim to analyze the social relationships of the characters in TV dramas based on dialogues. In addition to knowing just who talks to whom, the analysis of dialogue content gives more detailed information on the types of interaction, e.g.(More)
The global minimum search problem is important in neural networks because the error cost involved is formed as multiminima potential in weight parametric space. Therefore , parameters that produce a global minimum in a cost function are the best values for enhancing the performance of neural networks. Previously, a global minimum search based on a damped(More)
Convolutional neural networks are known to be effective in learning complex image classification tasks. However, how to design the architecture or complexity of the network structure requires a more quantitative analysis of the architecture design. In this paper, we study the effect of model complexity on generalization capability of the convolutional(More)
—We describe a " molecular " evolutionary algorithm that can be implemented in DNA computing in vitro to learn the recently-proposed hypernetwork model of cognitive memory. The molecular learning process is designed to make it possible to perform wet-lab experiments using DNA molecules and bio-lab tools. We present the bio-experimental protocols for(More)
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