Abstract: A display may include illumination optics, a ferroelectric liquid crystal on silicon (fLCOS) panel, and a waveguide. The illumination optics may produce illumination that is modulated by the fLCOS panel to produce image light. The waveguide may direct the image light towards an eye box. The fLCOS panel may include a ferroelectric liquid crystal (fLC) layer and a backplane. In order to maximize the reflectance of the fLCOS panel and thus the optical performance of the display, the backplane may be a silver backplane or a dielectric mirror backplane. In addition, the backplane may have a cell gap that is equal to a wavelength divided by four times the birefringence of the fLC layer. In order to further optimize the optical performance of the display module, the wavelength used in determining the cell gap may be a green wavelength between 500 nm and 565 nm.

Abstract: In an embodiment, an electronic device includes a display and processing circuitry. The display includes a plurality of pixels arranged in a plurality of rows, wherein a first grouping of the plurality of rows displays image content during a first portion of an image frame, and wherein a second grouping of the plurality of rows displays image content during a second portion of the image frame. The processing circuitry is operatively coupled to the display and determines a velocity associated with the image content displayed by the first grouping of the plurality of rows moving across the display and adjusts a position of the image content displayed by the second grouping of the plurality of rows during the second portion of the image frame.

Abstract: This application relates to a method for constructing a multi-task learning model, an electronic device, and a computer-readable storage medium. The method includes: constructing a search space formed between an input node and a plurality of task nodes by arranging a plurality of subnetwork layers and a plurality of search layers in a staggered manner. A search layer in the plurality of search layers is arranged between two subnetwork layers of the plurality of subnetwork layers. The method includes sampling a path from the input node to each task node of the plurality of task nodes through the search space to obtain a candidate path as a candidate network structure; and training a parameter of the candidate network structure according to sample data, to generate the multi-task learning model for performing a multi-task prediction.