Abstract: A display device includes a first display area and a second display area; a first pixel circuit part positioned on the first display area; a first light-emitting element electrically connected to the first pixel circuit part; a second pixel circuit part positioned in the second display area; second light-emitting elements electrically connected to the second pixel circuit part; and a driving circuit part electrically connected to the first pixel circuit part and the second pixel circuit part and overlapping the second light-emitting elements in a plan view.

Abstract: The present invention relates to an image inpainting apparatus and an image inpainting method, the image inpainting apparatus including: a background inpainting part configured to generate a background-inpainted image by carrying out inpainting on a background with respect to an input image in which a region to be inpainted is set up; an object inpainting part configured to generate an object image by carrying out inpainting on an object; and an image overlapping part configured to generate an output image by causing the background-inpainted image and the object image, which are generated, to overlap each other.

Abstract: A multilayer electronic component includes a body including a dielectric layer and an internal electrode alternately disposed with the dielectric layer, an external electrode disposed on the body and connected to the internal electrode, and a region containing Ni, Al and Cu. The internal electrode includes Ni, and the external electrode contains Al and Cu.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a pull-over location using machine learning. One of the methods includes obtaining data specifying a target pull-over location for an autonomous vehicle travelling on a roadway. A plurality of candidate pull-over locations in a vicinity of the target pull-over location are identified. For each candidate pull-over location, an input that includes features of the candidate pull-over location is processed using a machine learning model to generate a respective likelihood score representing a predicted likelihood that the candidate pull-over location is an optimal location. The features of the candidate pull-over location include one or more features that compare the candidate pull-over location to the target pull-over location. Using the respective likelihood scores, one of the candidate pull-over locations is selected as an actual pull-over location for the autonomous vehicle.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a pull-over location using machine learning. One of the methods includes obtaining data specifying a target pull-over location for an autonomous vehicle travelling on a roadway. A plurality of candidate pull-over locations in a vicinity of the target pull-over location are identified. For each candidate pull-over location, an input that includes features of the candidate pull-over location is processed using a machine learning model to generate a respective likelihood score representing a predicted likelihood that the candidate pull-over location is an optimal location. The features of the candidate pull-over location include one or more features that compare the candidate pull-over location to the target pull-over location. Using the respective likelihood scores, one of the candidate pull-over locations is selected as an actual pull-over location for the autonomous vehicle.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a pull-over location using machine learning. One of the methods includes obtaining data specifying a target pull-over location for an autonomous vehicle travelling on a roadway. A plurality of candidate pull-over locations in a vicinity of the target pull-over location are identified. For each candidate pull-over location, an input that includes features of the candidate pull-over location is processed using a machine learning model to generate a respective likelihood score representing a predicted likelihood that the candidate pull-over location is an optimal location. The features of the candidate pull-over location include one or more features that compare the candidate pull-over location to the target pull-over location. Using the respective likelihood scores, one of the candidate pull-over locations is selected as an actual pull-over location for the autonomous vehicle.