임채영 교수

In NAFLD, the carnitine improved liver profile and peripheral blood mitochondrial DNA copy number. This results suggest that carnitine activate the mitochondria, thereby contributing to the improvement of NAFLD.

Summary Spatial regression is an important predictive tool in many scientific applications and an additive model provides a flexible regression relationship between predictors and a response variable. We develop a regularized variable selection technique for building a spatial additive model. We find that the methods developed for independent data do not work well for spatially dependent data. This motivates us to propose a spatially weighted l2-error norm with a group lasso type of penalty to s

Fingerprint individuality refers to the extent of uniqueness of fingerprints and is governed by the distribution of fingerprint features, termed minutiae, in a population. This article develops a flexible class of marked point processes for minutiae and associated methodology for assessing fingerprint individuality. Inference is carried out in a Bayesian MCMC framework. The flexibility of the model fit to different kinds of minutiae patterns is demonstrated using real fingerprints. Evidence of a

Multivariate analysis has been widely used and one of the popular multivariate analysis methods is canonical correlation analysis (CCA). CCA finds the linear combination in each group that maximizes the Pearson correlation. CCA has been extended to a kernel CCA for nonlinear relationships and generalized CCA that can consider more than two groups. We propose an extension of CCA that allows multi-group and nonlinear relationships in an additive fashion for a better interpretation, which we termed

We consider the problem of selecting covariates in a spatial regression model when the response is binary. Penalized likelihood-based approach is proved to be effective for both variable selection and estimation simultaneously. In the context of a spatially dependent binary variable, an uniquely interpretable likelihood is not available, rather a quasi-likelihood might be more suitable. We develop a penalized quasi-likelihood with spatial dependence for simultaneous variable selection and parame

Corrective feedback received on perceptual decisions is crucial for adjusting decision-making strategies to improve future choices. However, its complex interaction with other decision components, such as previous stimuli and choices, challenges a principled account of how it shapes subsequent decisions. One popular approach, based on animal behavior and extended to human perceptual decision-making, employs "reinforcement learning," a principle proven successful in reward-based decision-making.

Being able to monitor the heart will allow the diagnosis of heart diseases for patients during daily activities, and the detection of burden on the heart during strenuous exercise. Furthermore, with the help of U-health technology, immediate medical action can be taken, in the case of abnormal symptoms of the heart in daily life. Therefore, it appears to be necessary to develop the corresponding technology to monitor the condition of the heart daily. In this study, a novel wearable smart system

Appearance-based localization is a robot self-navigation technique that integrates visual appearance and kinematic information. To analyze the visual appearance, we need to build a regression model based on extracted visual features from raw images as predictors to estimate the robot's location in two-dimensional (2D) coordinates. Given the training data, our first problem is to find the optimal subset of the features that maximize the localization performance. To achieve appearance-based locali

The results recommend surgical intervention when a patient has an area_frac more than 60%. Although this recommendation should be considered with caution given the limited sample size, physicians can use the proposed model as a tool to find such recommendations with their own data.