Abstract: A method for detecting network anomalies includes receiving a control message from a cellular network and extracting one or more features from the control message. The method also includes predicting a potential label for the control message using a predictive model configured to receive the one or more extracted features from the control message as feature inputs. Here, the predictive model is trained on a set of training control messages where each training control message includes one or more corresponding features and an actual label. The method further includes determining that a probability of the potential label satisfies a confidence threshold. The method also includes analyzing the control message to determine whether the control message corresponds to a respective network performance issue. When the control message impacts network performance, the method includes communicating the network performance issue to a network entity responsible for the network performance issue.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for increasing dimensionality of data structures associated with filling positions. In some implementations, a prediction of desired experience for a given position to be filled may be incorporated into a searchable field of the data structure. Among other things, increasing the dimensionality of the data structure may facilitate more granular searching of positions and guided creation of new positions to be filled. In some implementations, a predicted desired experience may be used to notify a user posting a new position whether a specified desired experience corresponds to a predicted desired experience.

Abstract: Implementations set forth herein relate to providing selectable autofill suggestions, which correspond to application actions that are at least partially fulfilled using server command data—prior to a user selecting a particular selectable autofill suggestion. Proactively fulfilling command data in this way mitigates latency between user selection of a suggestion and fulfillment of a particular action. Initially, a partial input can be processed to generate autofill suggestions, which can be communicated to a server device for further processing. The autofill suggestions can also be rendered for selection at a touch display interface, thereby allowing a user to select one of the autofill suggestions. As command fulfillment data is provided by the server, the command fulfillment data can be available to a corresponding application(s) in order that any corresponding actions can be at least partially fulfilled prior to user selection.

Abstract: A wearable heads-up display includes a power source, laser sources, and a lightguide. A photodetector is positioned to detect an intensity of a test light emitted at a perimeter of the lightguide from an optical path within the lightguide. A laser safety circuit provides a control to reduce or shut off a supply of electrical power from the power source to the laser sources in response to an output signal from the photodetector indicating that the detected intensity is below a threshold.

Abstract: Systems and methods are described that include in response to detecting a copy operation or a cut operation associated with a content item, triggering a search of data associated with the content item, the search configured to determine a content type and triggering, based on a detected paste operation generation of a representation of the content item based on the content type and rendering of a clipboard history user interface with the content item rendered in the clipboard history user interface according to the representation, the content item being rendered with other content items rendered in the clipboard history user interface.

Abstract: Coordinating processing of audio queries is provided. A system receives a query. The system provides the query to a first digital assistant component and a second digital assistant component for processing. The system receives a first response to the query from the first digital assistant component, and a second response to the query from the second digital assistant component. The first digital assistant component can be authorized to access a database the second digital assistant component is prohibited from accessing. The system determines, based on a ranking decision function, to select the second response to the query from the second digital assistant component. The system provides, responsive to the selection, the second response from the second digital assistant to a computing device.

Abstract: Using a natural language (NL) latent presentation in the automated conversion of source code from a base programming language (e.g., C++) to a target programming language (e.g., Python). A base-to-NL model can be used to generate an NL latent representation by processing a base source code snippet in the base programming language. Further, an NL-to-target model can be used to generate a target source code snippet in the target programming language (that is functionally equivalent to the base source code snippet), by processing the NL latent representation. In some implementations, output(s) from the NL-to-target model indicate canonical representation(s) of variables, and in generating the target source code snippet, technique(s) are used to match those canonical representation(s) to variable(s) of the base source code snippet. In some implementations, multiple candidate target source code snippets are generated, and a subset (e.g., one) is selected based on evaluation(s).

Abstract: Implementations set forth herein relate to interactions, between vehicle computing devices and mobile computing devices, that reduce duplicative processes from occurring at either device. Reduction of such processes can be performed, in some instances, via communications between a vehicle computing device and a mobile computing device in order to determine, for example, how to uniquely render content at an interface of each respective computing device while the user is driving the vehicle. These communications can occur before a user has entered a vehicle, while the user is in the vehicle, and/or after a user has left the vehicle. For instance, just before a user enters a vehicle, a vehicle computing device can be primed for certain automated assistant interactions between the user and their mobile computing device. Alternatively, or additionally, the user can authorize the vehicle computing device to perform certain processes immediately after leaving the vehicle.

Abstract: A method can perform a process with a method including capturing an image, determining an environment that a user is operating a computing device, detecting a hand gesture based on an object in the image, determining, using a machine learned model, an intent of a user based on the hand gesture and the environment, and executing a task based at least on the determined intent.

Abstract: Systems and methods for detecting objects in a video are provided. A method can include inputting a video comprising a plurality of frames into an interleaved object detection model comprising a plurality of feature extractor networks and a shared memory layer. For each of one or more frames, the operations can include selecting one of the plurality of feature extractor networks to analyze the one or more frames, analyzing the one or more frames by the selected feature extractor network to determine one or more features of the one or more frames, determining an updated set of features based at least in part on the one or more features and one or more previously extracted features extracted from a previous frame stored in the shared memory layer, and detecting an object in the one or more frames based at least in part on the updated set of features.

Abstract: Image denoising includes obtaining a saliency map for an image. The saliency map includes respective saliency scores for pixels of the image. Respective noise levels are assigned to the pixels using the respective saliency scores to obtain a noise level map. The image is denoised using the noise level map to obtain a denoised image. The denoised image is output, such as to a display or a storage device.

Abstract: Providing at least one contextually relevant suggestion to one or more users of an ongoing message exchange thread between the users. The suggestion is provided for presentation to the user(s) via user interface output device(s) of computing device(s) of the user(s). The suggestion indicates a query that can be submitted to an automated assistant to cause the automated assistant to incorporate, into the message exchange thread, content that is responsive to the query. In some implementations, the suggestion is a selectable suggestion and content that is responsive to the query is incorporated into the message exchange thread in response to user interface input that is directed to the selectable suggestion. In some implementations, the suggestion is determined based on one or more messages that have already been communicated between users of the message exchange thread.

Abstract: Methods, systems, and apparatus for controlling smart devices are described. In one aspect a method includes capturing, by a camera on a user device, a plurality of successive images for display in an application environment of an application executing on the user device, performing an object recognition process on the images, the object recognition process including determining that a plurality of images, each depicting a particular object, are required to perform object recognition on the particular object, and in response to the determination, generating a user interface element that indicates a camera operation to be performed, the camera option capturing two or more images, determining that a user, in response to the user interface element, has caused the indicated camera operation to be performed to capture the two or more images, and in response, determining whether a particular object is positively identified from the plurality of images.

Abstract: The present disclosure is directed to systems and methods that include and/or leverage one or more machine-learned language models that generate intermediate textual analysis (e.g., including usage of structural tools such as APIs) in service of contextual text generation. For example, a computing system can obtain a contextual text string that includes one or more contextual text tokens. The computing system can process the contextual text string with the machine-learned language model to generate one or more intermediate text strings that include one or more intermediate text tokens. The computing system can process the one or more intermediate text strings with the machine-learned language model to generate an output text string comprising one or more output text tokens. The one or more intermediate text strings can include textual analysis of the contextual text string that supports the output text string.

Abstract: Computer-implemented techniques can include obtaining, by a client computing device, a digital media item and a request for a processing task on the digital item and determining a set of operating parameters based on (i) available computing resources at the client computing device and (ii) a condition of a network. Based on the set of operating parameters, the client computing device or a server computing device can select one of a plurality of artificial neural networks (ANNs), each ANN defining which portions of the processing task are to be performed by the client and server computing devices. The client and server computing devices can coordinate processing of the processing task according to the selected ANN. The client computing device can also obtain final processing results corresponding to a final evaluation of the processing task and generate an output based on the final processing results.

Abstract: A User Equipment 210 (UE) determines 215 a maximum UL/DL duty cycle to utilize for a connection with a base station 202 based on a content of a UE capabilities request 212 sent by the base station, where the maximum UL/DL duty cycle is typically is less than a most restrictive, possible UL/DL duty cycle of the UE, yet allows the UE to remain SAR compliant. The UE may determine 215 the maximum UL/DL duty cycle further based on, e.g., requested frequency bands, operational UE transceivers, carrier components, UE power class, stored power data, etc. The UE communicates 218 the maximum UL/DL duty cycle for the connection to the base station, thereby resulting in more efficient downlink data delivery during the connection as well as increasing cell site coverage and overall system efficiency. The UE may store a global parameter whose value is indicative of the maximum UL/DL duty cycle.