Abstract: Systems and methods for publishing content are provided. One method includes generating a content item template for a content item configured for publication within one or more resources. The method further includes storing the content item template within a memory for later publication. The method further includes receiving input data from the content provider and detecting a trigger signal associated with the content item. The method further includes entering the content item into an auction in response to detecting the trigger signal. The method further includes adding the input data provided by the content provider via the interface to the content item template to generate the content item prior to publication of the content item within the one or more resources.

Abstract: A method may include in response to a user request for a playlist, identifying, by a processing device, a plurality of media items from the playlist, for each of the plurality of media items, determining whether a respective media item is available for a playback on a user device, in response to a determination that the respective media item is not available for the playback on the user device, causing the use device to advance to a next media item on the playlist without user interaction, and in response to a determination that the respective media item is available for the playback on the user device, causing the user device to play the respective media item.

Abstract: Techniques and apparatuses are described that implement a smart-device-based radar system capable of detecting user gestures in the presence of saturation. In particular, a radar system employs machine learning to compensate for distortions resulting from saturation. This enables gesture recognition to be performed while the radar system's receiver is saturated. As such, the radar system can forgo integrating an automatic gain control circuit to prevent the receiver from becoming saturated. Furthermore, the radar system can operate with higher gains to increasing sensitivity without adding additional antennas. By using machine learning, the radar system's dynamic range increases, which enables the radar system to detect a variety of different types of gestures having small or large radar cross sections, and performed at various distances from the radar system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for brightness control for under-display fingerprint sensor are disclosed. A method includes receiving, at a computing device, an indication to activate an under-display sensor that is located underneath a display of the computing device; activating a collection of LEDs of the display to provide illumination for the under-display sensor, including activating an LED in the collection of LEDs by: establishing, by drive circuitry of the LED, a first overdriven voltage across an LED-driving transistor that is arranged to energize the LED; establishing a second overdriven voltage across the LED-driving transistor; and establishing a steady state voltage across the LED-driving transistor; and activating the under-display sensor by reading a signal from the under-display sensor once the collection of LEDs has activated, including once the steady state voltage has been programmed across the LED-driving transistor.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a packet from a client, the packet having header information including a destination Internet Protocol (IP) address, a destination port, a source IP address, and a source port, and wherein the source IP address and source port are associated with the client; selecting a destination virtual machine based on the destination port; modifying the packet by replacing the destination IP address in the header information with an IP address of the selected destination virtual machine; and sending the modified packet to the destination virtual machine.

Abstract: An example method includes determining, by a controller of an image capture system, a plurality of sets of exposure parameter values for one or more exposure parameters. The plurality of sets of exposure parameter values are determined at an exposure determination rate. The method further includes capturing, by an image capture device of the image capture system, a plurality of images. Each image of the plurality of images is captured according to a set of exposure parameter values of the plurality of sets of exposure parameter values. The method also includes sending, by the controller of the image capture system to an image processing unit, a subset of the plurality of images. Each subset of images is sent at a sampling rate, and the sampling rate is less than the exposure determination rate.

Abstract: A display includes subpixel emissive areas of first, second, and third colors arranged in an array that includes rows and columns. The display also includes scan lines, column lines, and electronic subpixel circuits arranged in the array, with each subpixel circuit in a column of the array being electrically connected to a same column line and each electronic subpixel circuit configured for receiving electronic signals from a scan line and from a column line and for converting the received signals into a current signal provided to one of the subpixel emissive areas to drive light emission from the subpixel emissive area. The display further includes demultiplexer (DEMUX) switches, where every other column line of the columns lines is configured to be connected to at least two outputs from a column line driver through the DEMUX switches.

Abstract: Techniques for implementing an uplink control channel in Non-Orthogonal Multiple Access (NOMA) asynchronous transmissions are disclosed. The techniques described herein provide an additional control channel in NOMA transmissions for a user equipment (UE) (102) to indicate certain information to a base station (106) to enable the base station (106) to detect and decode the data transmitted by the UE (102). For example, uplink control information (UCI) is transmitted (406) via an uplink control channel in connection with a group user equipment identifier (group UE-ID) to enable the base station (106) to detect and decode the UCI. After decoding (506) the UCI based on the group UE-ID, the base station (106) obtains information usable to distinguish (508) the UE (102), such as a UE-specific ID, as well as to detect (510) and decode (512) a data transmission from the UE (102) on an uplink data channel.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium. In one aspect, a method includes receiving a text prompt describing a scene; processing the text prompt using a text encoder neural network to generate a contextual embedding of the text prompt; and processing the contextual embedding using a sequence of generative neural networks to generate a final video depicting the scene.

Abstract: A method for proactive notifications in a voice interface device includes: receiving a first user voice request for an action with an future performance time; assigning the first user voice request to a voice assistant service for performance; subsequent to the receiving, receiving a second user voice request and in response to the second user voice request initiating a conversation with the user; and during the conversation: receiving a notification from the voice assistant service of performance of the action; triggering a first audible announcement to the user to indicate a transition from the conversation and interrupting the conversation; triggering a second audible announcement to the user to indicate performance of the action; and triggering a third audible announcement to the user to indicate a transition back to the conversation and rejoining the conversation.

Abstract: A method includes obtaining a source training dataset that includes a plurality of source training images and obtaining a target training dataset that includes a plurality of target training images. For each source training image, the method includes translating, using the forward generator neural network G, the source training image to a respective translated target image according to current values of forward generator parameters. For each target training image, the method includes translating, using a backward generator neural network F, the target training image to a respective translated source image according to current values of backward generator parameters. The method also includes training the forward generator neural network G jointly with the backward generator neural network F by adjusting the current values of the forward generator parameters and the backward generator parameters to optimize an objective function.

Abstract: An optical links diagnostic system (LDS) and its operation within an optical circuit switch (OCS) for measurement and diagnosis of fiber-optic network fiber performance and quality is disclosed. The LDS can contain two photodetectors, a laser source, and be coupled to an OCS. Optical circulators can further be linked to the OCS. The LDS can be used both as an optical time domain reflectometer (OTDR) or as an optical return loss (ORL) meter and can automate the diagnosis of the fiber optical network fiber insertion loss and return loss.

Abstract: A method for training a non-autoregressive TTS model includes receiving training data that includes a reference audio signal and a corresponding input text sequence. The method also includes encoding the reference audio signal into a variational embedding that disentangles the style/prosody information from the reference audio signal and encoding the input text sequence into an encoded text sequence. The method also includes predicting a phoneme duration for each phoneme in the input text sequence and determining a phoneme duration loss based on the predicted phoneme durations and a reference phoneme duration. The method also includes generating one or more predicted mel-frequency spectrogram sequences for the input text sequence and determining a final spectrogram loss based on the predicted mel-frequency spectrogram sequences and a reference mel-frequency spectrogram sequence. The method also includes training the TTS model based on the final spectrogram loss and the corresponding phoneme duration loss.

Abstract: A method for conserving resources in a distributed system includes receiving an event-criteria list from a resource controller. The event-criteria list includes one or more events watched by the resource controller and the resource controller controls at least one target resource and is configured to respond to events from the event-criteria list that occur. The method also includes determining whether the resource controller is idle. When the resource controller is idle, the method includes terminating the resource controller, determining whether any event from the event-criteria list occurs after terminating the resource controller, and, when at least one event from the event-criteria list occurs after terminating the resource controller, recreating the resource controller.

Abstract: A simulation test is run on a first machine learning model trained using first training data historically collected over a time period. The first training data includes a set of training inputs and a set of target outputs. In response to a determination that a result of the simulation test run on the first machine learning model satisfies one or more criteria, a size of the set of target outputs of the first training data is determined. Second training data for training a second machine learning model is obtained. A size of a set of target outputs of the second training data meets or exceeds the size of the target outputs of the first training data. The second machine learning model is trained using the second training data.

Abstract: A method includes receiving a scan request requesting to scan a set of network-connected assets designated for a network scan. For each respective network-connected asset, the method includes scanning, at a network security scanner using a first scanning privilege level, the respective network-connected asset. The method includes determining, based on the scan using the first scanning privilege level, whether the respective network-connected asset has a vulnerability. In response, the method includes scanning, at the network security scanner using a second scanning privilege level, the respective network-connected asset. The second scanning privilege level defines a lower level of access the network security scanner has than the first scanning privilege level. The method includes determining, based on the scans, an exposure level of the vulnerability. The method includes reporting the exposure level of the vulnerability to a user of the respective network-connected asset.

Abstract: This document generally describes systems and methods for dynamically adapting speech recognition for individual voice queries of a user using class-based language models. The method may include receiving a voice query from a user that includes audio data corresponding to an utterance of the user, and context data associated with the user. One or more class models are then generated that collectively identify a first set of terms determined based on the context data, and a respective class to which the respective term is assigned for each respective term in the first set of terms. A language model that includes a residual unigram may then be accessed and processed for each respective class to insert a respective class symbol at each instance of the residual unigram that occurs within the language model. A transcription of the utterance of the user is then generated using the modified language model.

Abstract: This document describes techniques and devices for a radar recognition-aided search. Through use of a radar-based recognition system, gestures made by, and physiological information about, persons can be determined. In the case of physiological information, the techniques can use this information to refine a search. For example, if a person requests a search for a coffee shop, the techniques may refine the search to coffee shops in the direction that the person is walking. In the case of a gesture, the techniques may refine or base a search solely on the gesture. Thus, a search for information about a store, car, or tree can be made responsive to a gesture pointing at the store, car, or tree with or without explicit entry of a search query.

Abstract: Features described herein generally relate to systems and methods for assembling an electronic device. Particularly, the electronic device can be assembled by holding a portion of the first component arranged in a first plane in a holding position with a stream of air output from a nozzle of an assembly system and aligning the portion of the first component with an opening in a portion of the second component arranged in a second plane parallel to the first plane while the portion of the first component is held in the holding position.

Abstract: A method includes receiving a source data set and a target data set and identifying a loss function for a deep learning model based on the source data set and the target data set. The loss function includes encoder weights, source classifier layer weights, target classifier layer weights, coefficients, and a policy weight. During a first phase of each of a plurality of learning iterations for a learning to transfer learn (L2TL) architecture, the method also includes: applying gradient decent-based optimization to learn the encoder weights, the source classifier layer weights, and the target classifier weights that minimize the loss function; and determining the coefficients by sampling actions of a policy model. During a second phase of each of the plurality of learning iterations, determining the policy weight that maximizes an evaluation metric.