Abstract: A device for measuring magnetism of a permanent magnet material at a high temperature includes a laser device, a power controller, a light beam controller, a temperature controller, a magnetism measurement unit, temperature sensors, and electromagnet pole heads. The electromagnet pole heads are divided into an upper piece and a lower piece for clamping upper and lower surfaces of a sample. Heat absorbing sheets are respectively fixed on front and rear surfaces of the sample. Temperatures of the heat absorbing sheets are measured by the temperature sensors. The sample is heated by laser, and the temperature controller is used to adjust a ratio of light beams of the power controller and the light beam controller irradiating the heat absorbing sheets on the front and rear surfaces of the sample, thus adjusting the temperatures of the heat absorbing sheets. The magnetism of the sample is measured using the magnetism measurement unit.

Abstract: A head-mounted display may include a display system and an optical system in a housing. The display system may have displays that produce images. Positioners may be used to move the displays relative to the eye positions of a user's eyes. An adjustable optical system may include tunable lenses such as tunable cylindrical liquid crystal lenses. The displays may be viewed through the lenses when the user's eyes are at the eye positions. A sensor may be incorporated into the head-mounted display to measure refractive errors in the user's eyes. The sensor may include waveguides and volume holograms, and a camera for gathering light that has reflected from the retinas of the user's eyes. Viewing comfort may be enhanced by adjusting display positions relative to the eye positions and/or by adjusting lens settings based on the content being presented on the display and/or measured refractive errors.

Abstract: A compound of Formula (I), or a pharmaceutically acceptable salt thereof, is provided that has been shown to be useful for treating a PRC2-mediated disease or disorder: wherein R1, R2, R3, R4, R5, and n are as defined herein.

Abstract: A head-mounted display may include a display system and an optical system in a housing. The display system may have displays that produce images. Positioners may be used to move the displays relative to the eye positions of a user's eyes. An adjustable optical system may include tunable lenses such as tunable cylindrical liquid crystal lenses. The displays may be viewed through the lenses when the user's eyes are at the eye positions. A sensor may be incorporated into the head-mounted display to measure refractive errors in the user's eyes. The sensor may include waveguides and volume holograms, and a camera for gathering light that has reflected from the retinas of the user's eyes. Viewing comfort may be enhanced by adjusting display positions relative to the eye positions and/or by adjusting lens settings based on the content being presented on the display and/or measured refractive errors.

Abstract: Methods and apparatus for using polarized light (e.g., infrared (IR) light) to improve eye-related functions such as iris recognition. An eye camera system includes one or more IR cameras that capture images of the user's eyes that are processed to perform iris recognition, gaze tracking, or other functions. At least one polarizing element may be located in the path of the light which is used to capture images of the user's eye. The user's eye may be illuminated by IR light emitted by one or more LEDs. At least one of the LEDs may be an LED with a polarizing filter. Instead or in addition, at least one polarizer may be located at the eye camera sensor, in the eye camera optics, or as or in an additional optical element located on the light path between the eye camera and the user's eye.

Abstract: Techniques related to generating holographic images for a holographic heads up display are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via an iterative propagation feedback model. The iterative propagation feedback model is used to generate a feedback strength value, which is then used to generate a phase diffraction pattern for presentation at a holographic plane of the heads up display.

Abstract: A method for file encryption includes: determining, in response to obtaining a file to be encrypted, a first secret key for encrypting a file name according to the file name of the file to be encrypted; encrypting the file name according to the first secret key to obtain file name encryption information; and sending the file to be encrypted and the file name encryption information to a local service layer, in order that the local service layer encrypts a file content of the file to be encrypted based on the file to be encrypted and the file name encryption information. The local service layer is high in security and is not prone to be decompiled and cracked. Moreover, the encryption operation of the file content is mainly performed in the local service layer, so that the security protection of the encrypted file may be guaranteed.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a suitable propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.

Abstract: Hot word free adaptation, of function(s) of an automated assistant, responsive to determining, based on gaze measure(s) and/or active speech measure(s), that a user is engaging with the automated assistant. Implementations relate to techniques for mitigating false positive occurrences of and/or false negative occurrences, of hot word free adaptation, through utilization of a permissive parameter set in some situation(s) and a restrictive parameter set in other situation(s). For example, utilizing the restrictive parameter set when it is determined that a user is engaged in conversation with additional user(s). The permissive parameter set includes permissive parameter(s) that are more permissive than counterpart(s) in the restrictive parameter set.

Abstract: Hot word free adaptation, of one or more function(s) of an automated assistant, responsive to determining, based on gaze measure(s) and/or active speech measure(s), that a user is engaging with the automated assistant. Implementations relate to various techniques for mitigating false positive occurrences of and/or false negative occurrences, of hot word free adaptation, through utilization of personalized parameter(s) for at least some user(s) of an assistant device. The personalized parameter(s) are utilized in determining whether condition(s) are satisfied, where those condition(s), if satisfied, indicate that the user is engaging in hot word free interaction with the automated assistant and result in adaptation of function(s) of the automated assistant.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via an iterative propagation feedback model. The iterative propagation feedback model is used to generate a feedback strength value, which is then used to generate a phase diffraction pattern for presentation at a holographic plane.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via a wave propagation model. The wave propagation model is used to generate holographic data, which is then adjusted according to one or more constraints associated with the holographic display that will be used to generate a holographic image based on the adjusted holographic data.

Abstract: Disclosed herein are systems, apparatus, methods, and articles of manufacture to present three dimensional images without glasses. An example apparatus includes a micro lens array and at least one processor. The at least one processor is to: determine a first position of a first pupil of a viewer; determine a second position of a second pupil of the viewer; align a first eye box with the first position of the first pupil; align a second eye box with the second position of the second pupil; render, for presentation on a display, at least one of a color plus depth image or a light field image based on the first position of the first pupil and the second position of the second pupil; and cause backlight to be steered through the micro lens array and alternatingly through the first eye box and the second eye box.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a suitable propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a sui table propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.

Abstract: Disclosed herein are systems, apparatus, methods, and articles of manufacture to present three dimensional images without glasses. An example apparatus includes a micro lens array and at least one processor. The at least one processor is to: determine a first position of a first pupil of a viewer; determine a second position of a second pupil of the viewer; align a first eye box with the first position of the first pupil; align a second eye box with the second position of the second pupil; render, for presentation on a display, at least one of a color plus depth image or a light field image based on the first position of the first pupil and the second position of the second pupil; and cause backlight to be steered through the micro lens array and alternatingly through the first eye box and the second eye box.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via an iterative propagation feedback model. The iterative propagation feedback model is used to generate a feedback strength value, which is then used to generate a phase diffraction pattern for presentation at a holographic plane.

Abstract: In one implementation, an apparatus includes: a display to emit light in a first wavelength range; one or more light sources to emit light in a second wavelength range; a camera to detect the light in the second wavelength range; and an eyepiece to reflect and refract the light in the first wavelength range while passing, without substantial distortion, the light in the second wavelength range, wherein the eyepiece includes two lens halves separated by a retarder that changes light in the first wavelength range from a first polarization to a second polarization different from the first polarization.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a pre-trained deep neural network to a target holographic image to generate a feedback strength value for error feedback in an iterative propagation feedback model and generating a diffraction pattern image corresponding to the target holographic image by applying the iterative propagation feedback model based on the target holographic image and using the feedback strength value.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via a wave propagation model. The wave propagation model is used to generate holographic data, which is then adjusted according to one or more constraints associated with the holographic display that will be used to generate a holographic image based on the adjusted holographic data.