Abstract: A solar cell includes a first photoelectric conversion part, a second photoelectric conversion part, a side insulating layer, a first electrode, and a second electrode. The first photoelectric conversion part includes a photoelectric conversion layer composed of a perovskite compound, a first transport layer on one side of the photoelectric conversion layer, and a second transport layer on the other side of the photoelectric conversion layer. The second photoelectric conversion part is arranged below the second transport layer and has a different material or structure from the first photoelectric conversion part. The side insulating layer is formed as a side surface surrounding the first photoelectric conversion part. The first electrode is electrically connected to the first photoelectric conversion part on one surface of the first photoelectric conversion part serving as a light-receiving surface. The second electrode is electrically connected to a bottom of the second photoelectric conversion part.

Abstract: Novel techniques are described for automated transition classification for binge watching of content. For example, a number of frame images is extracted from a candidate segment time window of content. The frame images can automatically be classified by a trained machine learning model into segment and non-segment classifications, and the classification results can be represented by a two-dimensional (2D) image. The 2D image can be run through a multi-level convolutional conversion to output a set of output images, and a serialized representation of the output images can be run through a trained computational neural network to generate a transition array, from which a candidate transition time can be derived (indicating a precise time at which the content transitions to the classified segment).

Abstract: Novel techniques are described for automated transition classification for binge watching of content. For example, a number of frame images is extracted from a candidate segment time window of content. The frame images can automatically be classified by a trained machine learning model into segment and non-segment classifications, and the classification results can be represented by a two-dimensional (2D) image. The 2D image can be run through a multi-level convolutional conversion to output a set of output images, and a serialized representation of the output images can be run through a trained computational neural network to generate a transition array, from which a candidate transition time can be derived (indicating a precise time at which the content transitions to the classified segment).

Abstract: Novel techniques are described for automated transition classification for binge watching of content. For example, a number of frame images is extracted from a candidate segment time window of content. The frame images can automatically be classified by a trained machine learning model into segment and non-segment classifications, and the classification results can be represented by a two-dimensional (2D) image. The 2D image can be run through a multi-level convolutional conversion to output a set of output images, and a serialized representation of the output images can be run through a trained computational neural network to generate a transition array, from which a candidate transition time can be derived (indicating a precise time at which the content transitions to the classified segment).

Abstract: Novel techniques are described for automated transition classification for binge watching of content. For example, a number of frame images is extracted from a candidate segment time window of content. The frame images can automatically be classified by a trained machine learning model into segment and non-segment classifications, and the classification results can be represented by a two-dimensional (2D) image. The 2D image can be run through a multi-level convolutional conversion to output a set of output images, and a serialized representation of the output images can be run through a trained computational neural network to generate a transition array, from which a candidate transition time can be derived (indicating a precise time at which the content transitions to the classified segment).