Abstract: The presently claimed invention relates to a novel, highly efficient and general process for the preparation of UV absorbers.

Abstract: A first video stream comprising a first image of a first participant of a virtual meeting, a second image of a second participant, and a third image of a third participant are received from a first client device connected to a virtual meeting platform. It is determined whether an image combining condition is satisfied. Responsive to determining that the image combining condition is satisfied with respect to the first image and the second image, a first screen tile comprising the first image and the second image is generated. A first size of the first screen tile is defined based on a number of images comprised by the first screen tile. A second screen tile comprising the third image is generated. A virtual meeting user interface comprising the first screen tile and the second screen tile is provided for presentation on a second client device connected to the virtual meeting platform.

Abstract: A domestic cooking stove comprises a frame that mounts a primary tube, one or more of nozzles for fuel injection, knobs for controlling the fuel injection, mixing tubes for modulating pressure gradient, burner tops for combustion of the air fuel mixture, and pan supports to support the vessel, heat reflectors to reflect heat, and legs to support the frame. Each heat reflector is positioned above burner top to provide secondary air entrainment for combustion by reducing a gap between an inner circumference of heat reflector and an outer circumference of the burner top to minimize heat losses. The heat reflector has a curved orientation to reduce heat transfer in a downward direction and generates eddies that increases heat transfer towards vessel bottom. The legs are positioned below the frame and has a predetermined height to maintain a gap between table top and the frame bottom surface for entrainment of air.

Abstract: Methods and systems for real-time video enhancement are provided herein. A current frame of a video stream generated by a client device of a plurality of client devices participating in the video conference is identified during a video conference. An enhanced previous frame corresponding to an enhanced version of a previous frame in the video stream is identified. At least the current frame and the enhanced previous frame are provided as input to a machine-learning model. An output of the machine learning model is obtained. The output of the machine learning model indicates an enhanced current frame corresponding to an enhanced version of the current frame. The current frame is replaced with the enhanced current frame in the video stream.

Abstract: A method includes obtaining split-pixel image data including a first sub-image and a second sub-image. The method also includes determining, for each respective pixel of the split-pixel image data, a corresponding position of a scene feature represented by the respective pixel relative to a depth of field, and identifying, based on the corresponding positions, out-of-focus pixels. The method additionally includes determining, for each respective out-of-focus pixel, a corresponding pixel value based on the corresponding position, a location of the respective out-of-focus pixel within the split-pixel image data, and at least one of: a first value of a corresponding first pixel in the first sub-image or a second value of a corresponding second pixel in the second sub-image. The method further includes generating, based on the corresponding pixel values, an enhanced image having an extended depth of field.

Abstract: A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.

Abstract: Techniques for testing semiconductor devices include a semiconductor device having a plurality of components, a test bus, and a test data transfer unit. The test data transfer unit receives, from a host computer, configuration information for performing a test of the semiconductor device, reads, via a high-speed data transfer link, test data associated with the test from memory of the host computer using direct memory access, sends the test data to the plurality of components via the test bus, causes one or more operations to be performed on the semiconductor device to effect at least a portion of the test, and after the one or more operations have completed, retrieves test results of the at least a portion of the test from the test bus and stores, via the high-speed data transfer link, the test results in the memory of the host computer using direct memory access.

Abstract: A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.

Abstract: A method includes obtaining dual-pixel image data that includes a first sub-image and a second sub-image, and generating an in-focus image, a first kernel corresponding to the first sub-image, and a second kernel corresponding to the second sub-image. A loss value may be determined using a loss function that determines a difference between (i) a convolution of the first sub-image with the second kernel and (ii) a convolution of the second sub-image with the first kernel, and/or a sum of (i) a difference between the first sub-image and a convolution of the in-focus image with the first kernel and (ii) a difference between the second sub-image and a convolution of the in-focus image with the second kernel. Based on the loss value and the loss function, the in-focus image, the first kernel, and/or the second kernel, may be updated and displayed.

Abstract: Disclosed herein are systems and methods for providing and using a decentralized network using a blockchain. A provider (and/or miner) may provide network coverage to one or more devices in return for tokens on the blockchain. The blockchain may employ a proof-of-coverage scheme to verify (and even guarantee) that the miners are honestly representing the wireless network coverage they are providing. In some instances, the proof of coverage may require the providers to prove coverage periodically, upon demand, and/or at random intervals.

Abstract: Techniques for isolating interfaces while testing a semiconductor device include a semiconductor device having a link interface that couples the semiconductor device to a high-speed data transfer link, a clock control unit that transmits one or more clock signals to the link interface; and a protection module. The protection module asserts a clock stop request to the clock control unit and, in response to receiving a clock stop acknowledgement from the clock control unit, asserts a clamp enable to cause the link interface to be isolated from portions of the semiconductor device. After waiting for a first predetermined period of time to expire, the protection module de-asserts the clock stop request.

Abstract: In one embodiment, a method includes accessing, by one or more computing devices, user sensor data from one or more wearable sensors on one or more players and optical sensor data from one or more cameras, where the user sensor data includes location data of the player and acceleration data, and where the optical sensor data includes several frames portraying the players and several scenes from an athletic event. The one or more computing devices analyzes, using a machine-learning model, the optical sensor data to identify the players and one or more actions during the athletic event and calculates one or more player metrics for the players based on the user sensor data and the identified actions. The one or more computing devices normalizes the player metrics for the players based on one or more weighted parameters and provides a report to one or more users.

Abstract: A method includes receiving an input image. The input image corresponds to one or more masked regions to be inpainted. The method includes providing the input image to a first neural network. The first neural network outputs a first inpainted image at a first resolution, and the one or more masked regions are inpainted in the first inpainted image. The method includes creating a second inpainted image by increasing a resolution of the first inpainted image from the first resolution to a second resolution. The second resolution is greater than the first resolution such that the one or more inpainted masked regions have an increased resolution. The method includes providing the second inpainted image to a second neural network. The second neural network outputs a first refined inpainted image at the second resolution, and the first refined inpainted image is a refined version of the second inpainted image.

Abstract: A method includes obtaining split-pixel image data including a first sub-image and a second sub-image. The method also includes determining, for each respective pixel of the split-pixel image data, a corresponding position of a scene feature represented by the respective pixel relative to a depth of field, and identifying, based on the corresponding positions, out-of-focus pixels. The method additionally includes determining, for each respective out-of-focus pixel, a corresponding pixel value based on the corresponding position, a location of the respective out-of-focus pixel within the split-pixel image data, and at least one of: a first value of a corresponding first pixel in the first sub-image or a second value of a corresponding second pixel in the second sub-image. The method further includes generating, based on the corresponding pixel values, an enhanced image having an extended depth of field.

Abstract: Apparatus and methods related to using machine learning to determine depth maps for dual pixel images of objects are provided. A computing device can receive a dual pixel image of at least a foreground object. The dual pixel image can include a plurality of dual pixels. A dual pixel of the plurality of dual pixels can include a left-side pixel and a right-side pixel that both represent light incident on a single dual pixel element used to capture the dual pixel image. The computing device can be used to train a machine learning system to determine a depth map associated with the dual pixel image. The computing device can provide the trained machine learning system.

Abstract: Systems and methods provide for execution of different provisioning engines within a resource provider environment. A user may submit a request to provision one or more resources using a particular provisioning engine, which may include a provisioning engine that is non-native to the resource provider environment. A control plane may evaluate and transmit requests to the provisioning engine executing within the resource provider environment. Operations associated with the provisioning engine may be executed and stored within a data store, which may be processed upon completion and made accessible.

Abstract: A system includes a computing device. The computing device is configured to perform a set of functions. The set of functions includes receiving an image, wherein the image comprises a two-dimensional array of data. The set of functions includes extracting, by a two-dimensional neural network, a plurality of two-dimensional features from the two-dimensional array of data. The set of functions includes generating a linear combination of the plurality of two-dimensional features to form a single three-dimensional input feature. The set of functions includes extracting, by a three-dimensional neural network, a plurality of three-dimensional features from the single three-dimensional input feature. The set of functions includes determining a two-dimensional depth map. The two-dimensional depth map contains depth information corresponding to the plurality of three-dimensional features.

Abstract: A domestic cooking stove comprises a frame that mounts a primary tube, one or more of nozzles for fuel injection, knobs for controlling the fuel injection, mixing tubes for modulating pressure gradient, burner tops for combustion of the air fuel mixture, and pan supports to support the vessel, heat reflectors to reflect heat, and legs to support the frame. Each heat reflector is positioned above burner top to provide secondary air entrainment for combustion by reducing a gap between an inner circumference of heat reflector and an outer circumference of the burner top to minimize heat losses. The heat reflector has a curved orientation to reduce heat transfer in a downward direction and generates eddies that increases heat transfer towards vessel bottom. The legs are positioned below the frame and has a predetermined height to maintain a gap between table top and the frame bottom surface for entrainment of air.

Abstract: A method includes obtaining dual-pixel image data that includes a first sub-image and a second sub-image, and generating an in-focus image, a first kernel corresponding to the first sub-image, and a second kernel corresponding to the second sub-image. A loss value may be determined using a loss function that determines a difference between (i) a convolution of the first sub-image with the second kernel and (ii) a convolution of the second sub-image with the first kernel, and/or a sum of (i) a difference between the first sub-image and a convolution of the in-focus image with the first kernel and (ii) a difference between the second sub-image and a convolution of the in-focus image with the second kernel. Based on the loss value and the loss function, the in-focus image, the first kernel, and/or the second kernel, may be updated and displayed.

Abstract: A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.