Embodiments are disclosed for performing content linting in a graphic design system. A method of content linting includes receiving a selection of a content type to be generated, receiving a selection of a location in a digital canvas to place content of the content type, determining a placement context associated with the location in the digital canvas, identifying one or more content rules to the content based on a static analysis of the placement context, and generating, using one or more machine learning models, content of the selected content type at the location in the digital canvas using the one or more content rules.

Embodiments are disclosed for performing content linting in a graphic design system. A method of content linting includes receiving a selection of a content type to be generated, receiving a selection of a location in a digital canvas to place content of the content type, determining a placement context associated with the location in the digital canvas, identifying one or more content rules to the content based on a static analysis of the placement context, and generating, using one or more machine learning models, content of the selected content type at the location in the digital canvas using the one or more content rules.

Computer-implemented machines, systems and methods for managing missing values in a dataset for a machine learning model. The method may comprise importing a dataset with missing values; computing data statistics and identifying the missing values; verifying the missing values; updating the missing values; imputing missing values; encoding reasons for why values are missing; combining imputed missing values and the encoded reasons; and recommending models and hyperparameters to handle special or missing values.

Computer-implemented machines, systems and methods for managing missing values in a dataset for a machine learning model. The method may comprise importing a dataset with missing values; computing data statistics and identifying the missing values; verifying the missing values; updating the missing values; imputing missing values; encoding reasons for why values are missing; combining imputed missing values and the encoded reasons; and recommending models and hyperparameters to handle special or missing values.

Detections in video frames of a video signal, which are output from a machine learning model, are associated to generate a detection chain. Display of a detection in the video signal is caused based on a position of the detection in the detection chain, the confidence value of the detection and the location of the detection.

Described are examples for detecting objects in an image on a device including setting, based on a condition, a number of sparse proposals to use in performing object detection in the image, performing object detection in the image based on providing the sparse proposals as input to an object detection process to infer object location and classification of one or more objects in the image, and indicating, to an application and based on an output of the object detection process, the object location and classification of the one or more objects.

An image processing system with scalable models is provided. The image processing system comprises computing devices having a graphic analysis environment that includes instructions to execute an analysis process on a first image having a native resolution. The analysis process causes the one or more computing devices to perform operations includes: resampling the first image to generate a second image, wherein the second image has a resampled resolution greater than the native resolution in pixel number; detecting a plurality of first patches and a plurality of second patches in the first image and the second image, respectively, wherein the first patches and the second patches are detected by different detection models of a first scalable model collection according to sizes of the first image and the second image; and aggregating the first patches and the second patches. A method for processing an image with scalable models is also provided.

A videoconferencing system includes a camera acquiring image data and a microphone array acquiring audio data. Image data is used in conjunction with sound source localization (SSL) data to locate a talker depicted in the image data. SSL processes the audio data and determines SSL pan angle values indicative of an estimated direction of a sound. Columns of pixels in an image are associated with bins. A bin count is incremented for each SSL pan angle value of the audio data that falls within a given bin. A bounding box in the image data is determined that encompasses a face depicted in the image data. A range of pixels is determined for the bounding box, such as extending from a leftmost column to a rightmost column. The bin with the highest bin count that also overlaps a range of pixels for a bounding box is deemed to contain the talker.

A videoconferencing system includes a camera acquiring image data and a microphone array acquiring audio data. Image data is used in conjunction with sound source localization (SSL) data to locate a talker depicted in the image data. SSL processes the audio data and determines SSL pan angle values indicative of an estimated direction of a sound. Columns of pixels in an image are associated with bins. A bin count is incremented for each SSL pan angle value of the audio data that falls within a given bin. A bounding box in the image data is determined that encompasses a face depicted in the image data. A range of pixels is determined for the bounding box, such as extending from a leftmost column to a rightmost column. The bin with the highest bin count that also overlaps a range of pixels for a bounding box is deemed to contain the talker.

A particle measurement system and method of operation thereof are described. The system and method render a characteristic for a set of particles measured while passing through a measurement volume. The system includes a source that generates a particle-laden field containing the set of particles. The system further includes a sensor that generates a raw particle data corresponding to the set particles passing through the measurement volume of the particle measurement system, where the raw particle data comprises a set of raw particle records and each of one of the raw particle records includes a particle data content. A preconditioning stage carries out a preconditioning operation on the particle data content of the set of raw particle records to render a conditioned input data. A machine learning stage processes the conditioned input data to render an output characteristic parameter value for the set of particles.