Abstract: A system for machine-learning for prediction grid carbon emissions includes a computing device configured to receive, from a first local grid monitoring device monitoring a first local grid, a plurality of first power output quantities of a plurality of power generators in the first local grid, train an emission projection machine-learning process, wherein training further includes compiling a plurality of training data entries, each training data entry correlating a plurality of past power output quantities with at least a reported carbon emission datum and training the emission projection machine-learning process using the plurality of training data entries, and generate, using the emission projection machine-learning process, a projected carbon emission rate as a function of the plurality of power output quantities.

Abstract: An electronic device includes an input assembly and an output assembly, to be respectively used for data input and data output, a support assembly, to at least support the input assembly or the output assembly and to enable either of the input assembly or the output assembly to stand on a base, and a connector mechanism, to connect the input assembly and/or the output assembly to the support assembly, and to enable the input assembly and/or the output assembly to rotate relative the support assembly in various dimensions. The electronic device according to embodiments of the present disclosure is rotatable in multiple dimensions to meet user's needs in variable position adjustments.

Abstract: A method for generating an image of a scene with a corresponding depth map is disclosed herein. The method comprises collecting a plurality of copies of a two-dimensional image of the scene, generating an intermediate image by concatenating the plurality of copies of the two-dimensional image along a first direction, generating an intermediate depth map corresponding to the intermediate image by applying a neural network to the intermediate image, and generating, from the intermediate image and the intermediate depth map, the image of the scene with the corresponding depth map.

Abstract: A method for generating an image of a scene with a corresponding depth map is disclosed herein. The method comprises collecting a plurality of copies of a two-dimensional image of the scene, generating an intermediate image by concatenating the plurality of copies of the two-dimensional image along a first direction, generating an intermediate depth map corresponding to the intermediate image by applying a neural network to the intermediate image, and generating, from the intermediate image and the intermediate depth map, the image of the scene with the corresponding depth map.

Abstract: A method for generating an image of a scene with a corresponding depth map is disclosed herein. The method comprises collecting a plurality of copies of a two-dimensional image of the scene, generating an intermediate image by concatenating the plurality of copies of the two-dimensional image along a first direction, generating an intermediate depth map corresponding to the intermediate image by applying a neural network to the intermediate image, and generating, from the intermediate image and the intermediate depth map, the image of the scene with the corresponding depth map.

Abstract: An electronic device includes an input assembly and an output assembly, to be respectively used for data input and data output, a support assembly, to at least support the input assembly or the output assembly and to enable either of the input assembly or the output assembly to stand on a base, and a connector mechanism, to connect the input assembly and/or the output assembly to the support assembly, and to enable the input assembly and/or the output assembly to rotate relative the support assembly in various dimensions. The electronic device according to embodiments of the present disclosure is rotatable in multiple dimensions to meet user's needs in variable position adjustments.

Abstract: A system for machine-learning for prediction grid carbon emissions includes a computing device configured to receive, from a first local grid monitoring device monitoring a first local grid, a plurality of first power output quantities of a plurality of power generators in the first local grid, train an emission projection machine-learning process, wherein training further includes compiling a plurality of training data entries, each training data entry correlating a plurality of past power output quantities with at least a reported carbon emission datum and training the emission projection machine-learning process using the plurality of training data entries, and generate, using the emission projection machine-learning process, a projected carbon emission rate as a function of the plurality of power output quantities.

Abstract: It is provided according to embodiments of the application a connecting device and an electronic device including the connecting device. The connecting device includes a first mounting plate, a second mounting plate, a hinge assembly and a filler. The hinge assembly rotatably connects the first mounting plate and the second mounting plate, and the filler fills a gap inside the hinge assembly.

Abstract: Disclosed are electronic devices and connectors, including: a first main body, a second main body; the first magnet being arranged at a first end of the first main body and the second magnet being arranged at a second end of the second main body; the first magnet having a magnetic property opposite that of the second magnet; the first main body being pluggably connected to the second main body through the interaction between the first magnet and the second magnet; the first number of spacers is arranged at a preset position of the first magnet, and the first number of spacers is arranged at a preset positions of the second magnet; and the spacers are non-magnetic. Other embodiments, including manufacturing methods, are described and claimed.

Abstract: It is provided according to embodiments of the application a connecting device and an electronic device including the connecting device. The connecting device includes a first mounting plate, a second mounting plate, a hinge assembly and a filler. The hinge assembly rotatably connects the first mounting plate and the second mounting plate, and the filler fills a gap inside the hinge assembly.