Abstract: A method is provided that includes predicting, using a language model, one or more words from a first set of words expected to be difficult for a reader, and providing the first set of words for display to the reader. The predicted one or more words in the first set of words are displayed differently from non-predicted words in the first set of words.

Abstract: A generative network may be learned in an adversarial setting with a goal of modifying synthetic data such that a discriminative network may not be able to reliably tell the difference between refined synthetic data and real data. The generative network and discriminative network may work together to learn how to produce more realistic synthetic data with reduced computational cost. The generative network may iteratively learn a function that synthetic data with a goal of generating refined synthetic data that is more difficult for the discriminative network to differentiate from real data, while the discriminative network may be configured to iteratively learn a function that classifies data as either synthetic or real. Over multiple iterations, the generative network may learn to refine the synthetic data to produce refined synthetic data on which other machine learning models may be trained.

Abstract: A generative network may be learned in an adversarial setting with a goal of modifying synthetic data such that a discriminative network may not be able to reliably tell the difference between refined synthetic data and real data. The generative network and discriminative network may work together to learn how to produce more realistic synthetic data with reduced computational cost. The generative network may iteratively learn a function that synthetic data with a goal of generating refined synthetic data that is more difficult for the discriminative network to differentiate from real data, while the discriminative network may be configured to iteratively learn a function that classifies data as either synthetic or real. Over multiple iterations, the generative network may learn to refine the synthetic data to produce refined synthetic data on which other machine learning models may be trained.

Abstract: Modifying operation of an intelligent agent in response to facial expressions and/or emotions.

Abstract: A method includes, while displaying a first set of text content via a display device, determining an engagement value that characterizes a level of user engagement with respect to the first set of text content. The method includes, in accordance with a determination that the engagement value satisfies a threshold, replacing the first set of text content with a second set of text content via the display device. The first set of text content is different from the second set of text content. The method includes in accordance with a determination that the engagement value does not satisfy the threshold, maintaining display of the first set of text content via the display device.

Abstract: A method includes displaying a first set of text content characterized by a first difficulty level. The method includes obtaining speech data associated with the first set of text content. The method includes determining linguistic feature(s) within the speech data. The method includes in response to completion of the speech data, determining a reading proficiency value associated with the first set of text content and based on the linguistic feature(s). The method includes in accordance with determining the reading proficiency value satisfies change criteria, changing a difficulty level for a second set of text content. After changing the difficulty level, the second set of text content corresponds to a second difficulty level different from the first difficulty level. The method includes in accordance with determining the reading proficiency value does not satisfy the change criteria, maintaining the second set of text content at the first difficulty level.

Abstract: A vehicle may have optical structures such as windows and mirrors that have the potential to allow glare from external objects to shine into the eyes of a driver or other vehicle occupant. A control circuit may gather information on where the eyes of the driver are located using a camera mounted in the vehicle and may gather information on where the sun or other source of glare are located outside of the vehicle. Based on this information, the control circuit may direct a light modulator on a window or mirror to selectively darken an area that prevents the glare from reaching the eyes of the driver. The light modulator may have a photochromic layer that is adjusted by shining light onto the photochromic layer, may be a liquid crystal modulator, an electrochromic modulator, or other light modulator layer.

Abstract: Modifying operation of an intelligent agent in response to facial expressions and/or emotions.

Abstract: Some embodiments provide a novel user interface (UI) tool that is a unified slider control, which includes multiple sliders that slide along a region. The region is a straight line in some embodiments, while it is an angular arc in other embodiments. In some embodiments, the unified slider control is used in a media editing application to allow a user to modify several different properties of the image by moving several different sliders along the region. Each slider is associated with a property of the image. A position of the slider in the region corresponds to a value of the property associated with the slider.

Abstract: Modifying operation of an intelligent agent in response to facial expressions and/or emotions.

Abstract: A method for detecting objects in an environment includes obtaining, from one or more sensors, distance measurements from the one or more sensors to portions of the environment, generating a representation of a three-dimensional space using the distance measurements, identifying object features in the representation of the three-dimensional space using a neural network, comparing the object features to pre-defined three-dimensional templates to generate scores that represent correspondence of the object features to the pre-defined three-dimensional templates, and determining a location and a rotational orientation for a three-dimensional object based on the scores.

Abstract: A method includes processing an input image using convolution layers to define image features and processing the image features to define feature statistics. Processing the image features includes applying an activation function in a feature dimension of the image features. The method also includes processing the feature statistics using fully connected layers to produce a binary output regarding a characteristic of the input image.

Abstract: A method includes displaying a first set of text content characterized by a first difficulty level. The method includes obtaining speech data associated with the first set of text content. The method includes determining linguistic feature(s) within the speech data. The method includes in response to completion of the speech data, determining a reading proficiency value associated with the first set of text content and based on the linguistic feature(s). The method includes in accordance with determining the reading proficiency value satisfies change criteria, changing a difficulty level for a second set of text content. After changing the difficulty level, the second set of text content corresponds to a second difficulty level different from the first difficulty level. The method includes in accordance with determining the reading proficiency value does not satisfy the change criteria, maintaining the second set of text content at the first difficulty level.

Abstract: A method includes obtaining user input interaction data. The user input interaction data includes one or more user interaction input values respectively obtained from the corresponding one or more input devices. The user input interaction data includes a word combination. The method includes generating a user interaction-style indicator value corresponding to the word combination in the user input interaction data. The user interaction-style indicator value is a function of the word combination and a portion of the one or more user interaction input values. The method includes determining, using a semantic text analyzer, a semantic assessment of the word combination in the user input interaction data based on the user interaction-style indicator value and a natural language assessment of the word combination. The method includes generating a response to the user input interaction data according to the user interaction-style indicator value and the semantic assessment of the word combination.

Abstract: Modifying operation of an intelligent agent in response to facial expressions and/or emotions.

Abstract: Some embodiments provide a warning prompt control module which adjustably controls the display of warning prompts for vehicle elements in a vehicle, based on a determined profile with which the occupant is associated. An occupant detected in the vehicle interior can be associated with a profile based on a sensor data representation of the occupant correlating with a sensor data representation included in the profile, accessing a profile from a device supporting the occupant, etc. A profile can include interaction history data which indicate historical interactions with warning prompts for a vehicle element. Displaying a warning prompt for a vehicle element can be adjustably controlled based on the interaction history data, included in a profile, which is associated with the vehicle element.

Abstract: A vehicle may have optical structures such as windows and mirrors that have the potential to allow glare from external objects to shine into the eyes of a driver or other vehicle occupant. A control circuit may gather information on where the eyes of the driver are located using a camera mounted in the vehicle and may gather information on where the sun or other source of glare are located outside of the vehicle. Based on this information, the control circuit may direct a light modulator on a window or mirror to selectively darken an area that prevents the glare from reaching the eyes of the driver. The light modulator may have a photochromic layer that is adjusted by shining light onto the photochromic layer, may be a liquid crystal modulator, an electrochromic modulator, or other light modulator layer.

Abstract: Described is an image editing application with a novel color modification slider tool. This tool adjusts one or more color attributes of different pixels differently when moved. This tool in some embodiments is a novel saturation tool that produces non-photorealistic effects by treating differently the highly saturated and lowly saturated pixels of an image. The saturation tool performs either (1) a positive de-saturation effect that de-saturates the low saturated pixels of an image while enhancing the saturation of the high saturated pixels, or (2) a negative de-saturation effect that de-saturates the high saturated pixels of the image while leaving intact or slightly modifying the saturation level of the lowly saturated pixels.

Abstract: A method and system for providing a dynamic grain effect tool for a media-editing application that generates a grain effect and applies the grain effect to a digital image. The application first generates a random pixel field for the image based on a seed value. The application then generates a film grain pattern for the image by consecutively applying a blurring function and an unsharp masking function, based on an ISO value, to the randomly generated pixel field. The application then blends the grain field with the original image by adjusting each pixel based on the value of the corresponding pixel location in the grain field. The application then adjusts the grain amount in the previously generated full-grain image by receiving a grain amount value from a user and applying this value to the full-grain image.

Abstract: Some embodiments provide a novel method for tempering an adjustment of an image to account for prior adjustments to the image. The adjustment in some embodiments is an automatic exposure adjustment. The method performs an operation for a first adjustment on a first set of parameters (e.g., saturation, sharpness, luminance). The method compares the first set of parameters to a second set of parameters to produce a third set of parameters that expresses the difference between the first adjustment and a second adjustment. The method performs a third operation to produce an adjusted image. The first set of parameters quantify a set of prior adjustments to the image by an image capturing device when the image was captured in some embodiments. The second set of parameters is a set of target parameters. The third set of parameters specify the tempered adjustment of the image.