Abstract: Techniques for masking images based on a particular task are described. A system masks portions of an image that are not relevant to a particular task, thus, reducing the amount of data used by applications for image processing tasks. For example, images to be processed using a hair color classification model are masked so that only portions that show the person's hair are available for the model to analyze. The system configures different masker components to mask images for different tasks. A masker component can be implemented at a user device to mask images prior to sending to an application/task-specific model.

Abstract: Devices and techniques are generally described for class-agnostic object detection. In some examples, a first frame of image data comprising a first plurality of pixels may be received. First class-agnostic feature data representing the first plurality of pixels may be generated. A first object detection component may be used to determine that the first plurality of pixels corresponds to an arbitrary object represented in the first frame of image data based at least in part on the first class-agnostic feature data. Class-agnostic data indicating that the first plurality of pixels in the first frame of image data corresponds to the arbitrary object may be generated.

Abstract: The invention features methods that are useful for treatment of a patient at increased risk for infection and for selecting a patient for treatment for an infection. In various embodiments, the infection is coronavirus disease 2019 (COVID-19), sepsis, or other respiratory infections.

Abstract: An image processing system may receive image data from a camera of a user device and perform gaze prediction processing of the image data to predict one or more gaze patterns. The gaze prediction processing may include processing the image data using a neural network to detect faces and/or objects and generate an image feature map. The gaze prediction processing may include performing gaze direction prediction operations using the feature map and detected faces and/or objects to determine gaze direction probability data. The gaze prediction processing may include predicting a gaze pattern based on the gaze direction probability data and the image feature map. The gaze pattern may be short-term (e.g., atomic-level) or long-term (e.g., event-level).

Abstract: Devices and techniques are generally described for inference reduction in natural language processing using semantic similarity-based caching. In various examples, first automatic speech recognition (ASR) data representing a first natural language input may be determined. A cache may be searched using the first ASR data. A first skill associated with the first ASR data may be determined from the cache. In some examples, first intent data representing a semantic interpretation of the first natural language input data may be determined by using a first natural language process associated with the first skill.

Abstract: A system can operate a speech-controlled device to perform active speaker detection to detect an utterance using image data showing a user speaking the utterance. This enables the device to perform utterance detection using the image data and/or determine which user is speaking the utterance. To perform active speaker detection, the device processes the image data to determine expression parameters associated with the user's face and generates facial measurements based on the expression parameters. For example, the device can use the expression parameters to generate a 3D model including an agnostic facial representation and determine a mouth aspect ratio by measuring a mouth height and a mouth width of the agnostic facial representation. As the mouth aspect ratio changes when the user is speaking, the device can determine that the user is speaking and/or detect an utterance based on an amount of variation of the mouth aspect ratio.

Abstract: Techniques for masking images based on a particular task are described. A system masks portions of an image that are not relevant to a particular task, thus, reducing the amount of data used by applications for image processing tasks. For example, images to be processed using a hair color classification model are masked so that only portions that show the person's hair are available for the model to analyze. The system configures different masker components to mask images for different tasks. A masker component can be implemented at a user device to mask images prior to sending to an application/task-specific model.

Abstract: Devices and techniques are generally described for inference reduction in natural language processing using semantic similarity-based caching. In various examples, first automatic speech recognition (ASR) data representing a first natural language input may be determined. A cache may be searched using the first ASR data. A first skill associated with the first ASR data may be determined from the cache. In some examples, first intent data representing a semantic interpretation of the first natural language input data may be determined by using a first natural language process associated with the first skill.

Abstract: This application provides for methods of treatment for liver disease, especially in subjects with a DNMT3A, TET2, JAK2, and/or ASXL1. The application also provides for methods of diagnosing liver disease, as well as kits for use in the claimed methods.

Abstract: Devices and techniques are generally described for three dimensional mesh generation. In various examples, first two-dimensional (2D) image data representing a human may be received. In various further examples, bounding box data identifying a location of the human in the first 2D image data and joint data identifying locations of joints of the human may be received. Second 2D image data representing a cropped portion of the human may be generated using the bounding box data and the joint data. A three-dimensional (3D) mesh prediction model may be used to determine a pose, a shape, and a projection matrix for the human. The 3D mesh prediction model may be used to determine a transformed projection matrix for the portion of the human represented in the second 2D image data.

Abstract: Techniques for masking images based on a particular task are described. A system masks portions of an image that are not relevant to a particular task, thus, reducing the amount of data used by applications for image processing tasks. For example, images to be processed using a hair color classification model are masked so that only portions that show the person's hair are available for the model to analyze. The system configures different masker components to mask images for different tasks. A masker component can be implemented at a user device to mask images prior to sending to an application/task-specific model.

Abstract: A system can operate a speech-controlled device in a mode where the speech-controlled device determines that an utterance is directed at the speech-controlled device using image data showing the user speaking the utterance. If the user is directing the user's gaze at the speech-controlled device while speaking, the system may determine the utterance is system directed and thus may perform further speech processing based on the utterance. If the user's gaze is directed elsewhere, the system may determine the utterance is not system directed (for example directed at another user) and thus the system may not perform further speech processing based on the utterance and may take other actions, for example discarding audio data of the utterance.

Abstract: Techniques for masking images based on a particular task are described. A system masks portions of an image that are not relevant to a particular task, thus, reducing the amount of data used by applications for image processing tasks. For example, images to be processed using a hair color classification model are masked so that only portions that show the person's hair are available for the model to analyze. The system configures different masker components to mask images for different tasks. A masker component can be implemented at a user device to mask images prior to sending to an application/task-specific model.

Abstract: An approach is provided for sending a notification of change(s) in a database in a multi-client database environment. Event data in a database satisfying criteria is identified by examining a log that is updated after the event data is committed in the database. The criteria indicate change(s) in data in the database. The event data is grouped into data sets specified by subscriptions of client computer(s) in the multi-client database environment. The grouped event data is pushed to the client computer(s) for consumption by application(s) being executed by the client computer(s), respectively. A notification is sent to the client computer(s) of the change(s) to the data in the database.

Abstract: Disclosed are methods of treating venous thromboembolism (VTE) in a subject, the method comprising administering a venous thrombus size lowering therapy to the subject, wherein the venous thrombus size lowering therapy is a plasminogen activator inhibitor 1 (PAI-1) inhibitor. Disclosed are methods of diagnosing and VTE, the method comprising diagnosing the subject as being at greater risk of developing VTE; and administering a venous thrombus size lowering therapy to the subject, wherein the venous thrombus size lowering therapy is a PAI-1 inhibitor. Disclosed are methods of determining a PRS for developing VTE in a subject comprising identifying the presence of one or more of the 297 SNPs identified in FIG. 19 are present in a biological sample from the subject; and calculating the PRS by summing the weighted risk score associated with each SNP identified.

Abstract: An approach is provided for sending a notification of change(s) in a database in a multi-client database environment. Event data in a database satisfying criteria is identified by examining a log that is updated after the event data is committed in the database. The criteria indicate change(s) in data in the database. The event data is grouped into data sets specified by subscriptions of client computer(s) in the multi-client database environment. The grouped event data is pushed to the client computer(s) for consumption by application(s) being executed by the client computer(s), respectively. A notification is sent to the client computer(s) of the change(s) to the data in the database.

Abstract: A system to recognize text or symbols contained in a captured image using machine learning models leverages context information about the image to improve accuracy. Contextual information is determined for the entire image, or spatial regions of the images, and is provided to a machine learning model when a determination is made as to whether a region does or does not contain text or symbols. Associating features related to the larger context with features extracted from regions potentially containing text or symbolic content provides an incremental improvement of results obtained using machine learning techniques.

Abstract: A system to select video frames for optical character recognition (OCR) based on feature metrics associated with blur and sharpness. A device captures a video frame including text characters. An edge detection filter is applied to the frame to determine gradient features in perpendicular directions. An “edge map” is created from the gradient features, and points along edges in the edge map are identified. Edge transition widths are determined at each of the edge points based in local intensity minimum and maximum on opposite sides of the respective edge point in the frame. Sharper edges have smaller edge transition widths than blurry images. Statistics are determined from the edge transition widths, and the statistics are processed by a trained classifier to determine if the frame is or is not sufficiently sharp for text processing.

Abstract: A method for feature transformation of a data set includes: receiving a data set including original feature samples with corresponding class labels; splitting the data set into a direction optimization set and a training set; using the direction optimization set to calculate an optimum transformation vector that maximizes inter-class separability and minimizes intra-class variance of the feature samples with respect to corresponding class labels; using the optimum transformation vector to transform the rest of the original feature samples of the data set to new feature samples with enhanced discriminative characteristics; and training a classifier using the new feature samples, wherein the method is performed by one or more processors.

Abstract: A system to recognize text, objects, or symbols in a captured image using machine learning models reduces computational overhead by generating a plurality of thumbnail versions of the image at different downscaled resolutions and aspect ratios, and then processing the downscaled images instead of the entire image, or sections of the entire image. The downscaled images are processed to produce a combine feature vector characterizing the overall image. The combined feature vector is processed using the machine learning model.