Abstract: Disclosed are a vehicle including an electric motor and a method of controlling the same for providing a notification function to an occupant of the vehicle by controlling a pitching motion of a vehicle body. The method includes calculating a first torque value for providing the pitching motion based on driving state information including a vehicle speed, a driving mode, and an environment of a driving road; calculating a second torque value based on a request of a driver; and calculating a final torque value for controlling the electric motor based on the first torque value and the second torque value.

Abstract: Disclosed is a learning method and device for alzheimer prediction model based on domain adaptation performed by at least one processor including extracting a point related to an object in a learning image from the learning image for a 3D reconstruction model, obtaining a gradient map including surrounding context information in three dimensions of the point from a 3D model of the object, determining a weight of the point based on the learning image and the gradient map, and learning the 3D reconstruction model by using the weight such that the 3D model of the object is output from the 3D reconstruction model into which the learning image is input.

Abstract: A domain adaptation device for longitudinal data includes a first module that generates first transformation data using a projection matrix for domain transformation and a graph matrix for data filtering, a second module that determines a domain of the first transformation data, and a third module that determines a label of the first transformation data.

Abstract: A prospective classification device for predicting dementia that predicts a risk of a patient with mild cognitive impairment being converted to a dementia patient based on the characteristics of prognostic brain imaging data converted from a diagnostic brain imaging data and a method of operating the same are disclosed. The prospective classification device is configured to convert features of the diagnostic brain imaging data obtained at the time of diagnosis of a patient with mild cognitive impairment into features of prognostic brain imaging data corresponding to the prognostic time after the time of diagnosis using a prospective classification model.

Abstract: Provided is a method of identifying a candidate gene for a genetic disease includes obtaining a disease network, disease-gene association information, and a gene network, obtaining a single nucleotide polymorphism (SNP) network based on intra-relation data between a plurality of SNPs, and inter-relation data between genes and SNPs, creating a disease-gene-SNP multilayered network based on the disease network, the disease-gene association information, the gene network, the SNP network, and the interrelation data between genes and SNPs, and identifying a candidate gene for a genetic disease using the multilayered network.

Abstract: Provided is a method of visualizing a plurality of nodes respectively including a plurality of variable values for a data object. The method includes: allocating a predetermined upper limit value and a predetermined lower limit value for each of a plurality of variables to vertices of a three-dimensional polygon facing each other; respectively determining partial positions related to the variables for the nodes based on the upper limit value and the lower limit value for each of the variables, a maximum variable value and a minimum variable value for each variable from among variable values of the nodes, and the variable values of the nodes; respectively determining final positions of the nodes in the three-dimensional polygon based on the determined partial positions; and arranging the nodes in the three-dimensional polygon according to the determined final positions.

Abstract: Provided is a method of visualizing a plurality of nodes respectively including a plurality of variable values for a data object. The method includes: allocating a predetermined upper limit value and a predetermined lower limit value for each of a plurality of variables to vertices of a three-dimensional polygon facing each other; respectively determining partial positions related to the variables for the nodes based on the upper limit value and the lower limit value for each of the variables, a maximum variable value and a minimum variable value for each variable from among variable values of the nodes, and the variable values of the nodes; respectively determining final positions of the nodes in the three-dimensional polygon based on the determined partial positions; and arranging the nodes in the three-dimensional polygon according to the determined final positions.