Abstract: Examples are disclosed herein that are related to gaze tracking via image data. One example provides, on a gaze tracking system comprising an image sensor, a method of determining a gaze direction, the method comprising acquiring image data via the image sensor, detecting in the image data facial features of a human subject, determining an eye rotation center based upon the facial features using a calibrated face model, determining an estimated position of a center of a lens of an eye from the image data, determining an optical axis based upon the eye rotation center and the estimated position of the center of the lens, determining a visual axis by applying an adjustment to the optical axis, determining the gaze direction based upon the visual axis, and providing an output based upon the gaze direction.

Abstract: Examples are disclosed herein that relate to gaze tracking. One example provides a computing device including an eye-tracking system including an image sensor, a logic device, and a storage device comprising instructions executable by the logic device to track an eye gaze direction by acquiring an image of the eye via the eye-tracking system, and determining a determined location of a center of a lens of the eye from the image of the eye. The instructions are further executable to adjust the determined location of the center of the lens on a sub-pixel scale by applying a predetermined sub-pixel offset to the determined location of the center of the lens to produce an adjusted location of the center of the lens, to determine a gaze direction from the adjusted location of the center of the lens, and perform an action on a computing device based on the gaze direction.

Abstract: Machine learning may be personalized to individual users of computing devices, and can be used to increase machine learning prediction accuracy and speed, and/or reduce memory footprint. Personalizing machine learning can include hosting, by a computing device, a consensus machine learning model and collecting information, locally by the computing device, associated with an application executed by the client device. Personalizing machine learning can also include modifying the consensus machine learning model accessible by the application based, at least in part, on the information collected locally by the client device. Modifying the consensus machine learning model can generate a personalized machine learning model. Personalizing machine learning can also include transmitting the personalized machine learning model to a server that updates the consensus machine learning model.

Abstract: Machine learning may be personalized to individual users of personal computing devices, and can be used to increase machine learning prediction accuracy and speed, and/or reduce memory footprint. Personalizing machine learning can include selecting a subset of a machine learning model to load into memory. Such selecting is based, at least in part, on information collected locally by the personal computing device. Personalizing machine learning can additionally or alternatively include adjusting a classification threshold of the machine learning model based, at least in part, on the information collected locally by the personal computing device. Moreover, personalizing machine learning can additionally or alternatively include normalizing a feature output of the machine learning model accessible by an application based, at least in part, on the information collected locally by the personal computing device.

Abstract: Methods, compositions, and systems for detecting gamma radiation is disclosed and described. A compound for detecting gamma radiation can comprise a conjugated imidazole having the following structure: [Formula I] where at least one of R1, R2, and R3 are conjugated organic groups. Additionally, the conjugated imidazole can be capable of reacting with a radical or ion formed by the reaction of gamma radiation with a radical generating component such as a halogen solvent to decrease a molar extinction coefficient of the conjugated imidazole in the visible light region or to quench fluorescence of the conjugated imidazole. As a sensor (100), a radiation detection indicator (108) can indicate the change in molar extinction coefficient or fluorescence of the conjugated imidazole material (120) upon exposure to gamma radiation.