Abstract: Image data is processed for noise reduction before encoding and subsequent decoding. For an input image in a spatial domain, two-dimensional (2-D) wavelet coefficients at multiple levels are generated. Each level includes multiple subbands, each associated with a respective subband type in a wavelet domain. For respective levels, a flat region of a subband is identified, which flat region includes blocks of the subband having a variance no higher than a first threshold variance. A flat block set for the subband type associated with the subband is identified, which includes blocks common to respective flat regions of the subband. A second threshold variance is determined using variances of the flat block set, and is then used for thresholding at least some of the 2-D wavelet coefficients to remove noise. After thresholding, a denoised image is generated in the spatial domain using the levels.

Abstract: Embodiments according to examples aspects of the present disclosure provide for an example computer-implemented method. The example method can include obtaining a data structure configured for rendering a plurality of content containers on a user device, the plurality of content containers providing a collapsed configuration and an expanded configuration. The example method can include rendering, based on a first rate parameter descriptive of a user input associated with the user device, one or more of the plurality of content containers according to the collapsed configuration. The example method can include rendering, based on a second rate parameter, at least one of the plurality of content containers according to the expanded configuration.

Abstract: Bitrate-adaptive segmentation is performed for transcoding a video stream uploaded to an online video platform for hosting and later playback to platform users. The video stream is segmented into chunks based on prediction-based bit costs determined for frames of the video stream rather than based on scene changes detected within the video stream. The bitrate-adaptive segmentation includes determining inter-prediction bit costs and intra-prediction bit costs for frames of the video stream based on information indicated within a pass log based on a first pass encoding of the video stream, determining chunk boundaries for segmenting the video stream into a chunk based on the inter-prediction bit costs and the intra-prediction bit costs for the frames, and transcoding the chunk to produce a transcoded video stream.

Abstract: Implementations are directed to updating a trained voice bot that is deployed for conducting conversations on behalf of a third-party. A third-party developer can interact with a voice bot development system that enables the third-party developer to train, update, validate, and monitor performance of the trained voice bot. In various implementations, the trained voice bot can be updated by updating a corpus of training instances that was initially utilized to train the voice bot, and updating the trained voice bot based on the updated corpus. In some implementations, the corpus of training instances may be updated in response to identifying occurrence(s) of behavioral error(s) of the trained voice bot while the conversations are being conducted on behalf of the third-party. In additional or alternative implementations, the corpus of training instances may be updated in response to determining the trained voice bot does not include a desired behavior.

Abstract: A display system employs multiple micro-electromechanical system (MEMS) mirrors in series to receive collimated light and direct the light to provide light having input angles corresponding to a desired field of view at a point or line at an incoupler (IC) of a waveguide without an optical relay. An initial one or more MEMS mirrors accepts collimated light and generates the scan angles. A last MEMS mirror in the series scans at a range of angles proportional to the scan angles generated by the initial MEMS mirror(s) and directs the scanned light back to a spot or a line at the IC.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for providing context specific schema files that allow an automated assistant to broker human-to-computer dialogs between a user and an application that is separate from the automated assistant. The context specific schema file can provide the automated assistant with sufficient data to be responsive to user queries without necessarily communicating with a remote device, such as a server. Multiple different context specific schema files can be made available to the automated assistant according to a context in which a user is interacting with the automated assistant. In this way, latency otherwise exhibited by the automated assistant can be mitigated by providing the automated assistant with the information needed to respond to a user without continually retrieving the information over a network.

Abstract: Techniques are described herein for detecting and handling failures in other automated assistants. A method includes: executing a first automated assistant in an inactive state at least in part on a computing device operated by a user; while in the inactive state, determining, by the first automated assistant, that a second automated assistant failed to fulfill a request of the user; in response to determining that the second automated assistant failed to fulfill the request of the user, the first automated assistant processing cached audio data that captures a spoken utterance of the user comprising the request that the second automated assistant failed to fulfill, or features of the cached audio data, to determine a response that fulfills the request of the user; and providing, by the first automated assistant to the user, the response that fulfills the request of the user.

Abstract: A method, in a user device configured to communicate with a first base station via a radio link, for managing communication of a segmented radio resource control (RRC) message that includes N segments includes transmitting (212) a first M segments of the segmented RRC message to the first base station, M being an integer greater than zero and less than N, detecting (220), by processing hardware of the user device and before transmitting an (M+1)-th segment of the segmented RRC message, a failure of the radio link, and, after detecting the failure of the radio link, transmitting (264) at least a last N?M+1 segments of the segmented RRC message to either the first base station or a second base station.

Abstract: Systems, methods, and computer program products for performing semi-supervised contrastive learning of visual representations are provided. For example, the present disclosure provides systems and methods that leverage particular data augmentation schemes and a learnable nonlinear transformation between the representation and the contrastive loss to provide improved visual representations. Further, the present disclosure also provides improvements for semi-supervised contrastive learning.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an automated calling system are disclosed. In one aspect, a method includes the actions of receiving audio data of an utterance spoken by a user who is having a telephone conversation with a bot. The actions further include determining a context of the telephone conversation. The actions further include determining a user intent of a first previous portion of the telephone conversation spoken by the user and a bot intent of a second previous portion of the telephone conversation outputted by a speech synthesizer of the bot. The actions further include, based on the audio data of the utterance, the context of the telephone conversation, the user intent, and the bot intent, generating synthesized speech of a reply by the bot to the utterance. The actions further include, providing, for output, the synthesized speech.

Abstract: Example aspects of the present disclosure are directed to systems and methods for generation of improved language embeddings (e.g., entity embeddings for natural language tokens) which provide improved model performance. In addition, the proposed techniques require less computational consumption relative to previous approaches.

Abstract: Techniques are described herein for multi-factor audio watermarking. A method includes: receiving audio data; processing the audio data to generate predicted output that indicates a probability of one or more hotwords being present in the audio data; determining that the predicted output satisfies a threshold that is indicative of the one or more hotwords being present in the audio data; in response to determining that the predicted output satisfies the threshold, processing the audio data using automatic speech recognition to generate a speech transcription feature; detecting a watermark that is embedded in the audio data; and in response to detecting the watermark: determining that the speech transcription feature corresponds to one of a plurality of stored speech transcription features; and in response to determining that the speech transcription feature corresponds to one of the plurality of stored speech transcription features, suppressing processing of a query included in the audio data.

Abstract: An automated speech recognition (ASR) transcript of at least a portion of a media content is obtained from an ASR tool. Suggested words are received for corrected words of the ASR transcript of the media content. Features are obtained using at least the suggested words or the corrected words. The features include features relating to sound similarities between the suggested words and the corrected words. The features are input into a machine learning (ML) model to obtain a determination regarding a validity of the suggested words. Responsive to the suggested words constituting a valid suggestion, the suggested words are incorporated into the ASR transcript. At least a portion of the ASR transcript is transmitted to a user device in conjunction with at least a portion of the media content.

Abstract: Processing of acoustic features of audio data to generate one or more revised versions of the acoustic features, where each of the revised versions of the acoustic features isolates one or more utterances of a single respective human speaker. Various implementations generate the acoustic features by processing audio data using portion(s) of an automatic speech recognition system. Various implementations generate the revised acoustic features by processing the acoustic features using a mask generated by processing the acoustic features and a speaker embedding for the single human speaker using a trained voice filter model. Output generated over the trained voice filter model is processed using the automatic speech recognition system to generate a predicted text representation of the utterance(s) of the single human speaker without reconstructing the audio data.

Abstract: A navigation system can identify locations of interest at a route destination. Those locations of interest at the destination can fall within a predetermined distance of the destination or those that are responsive to a query that includes the destination as a parameter. The navigation system can receive a selection of an identified location of interest near the destination, and update a route to terminate at the identified location of interest rather than the original destination. Information identifying the origin and destination for the route can be collected at a first user device and the route can be displayed at a second user device. Additional information is then received from the first user device (such as a selection of an identified location of interest near the destination). This additional information received at the first user device is used to update the route that is displayed on the second user device.

Abstract: Implementations described herein relate to receiving user input directed to an automated assistant, processing the user input to determine whether data from a server and/or third-party application is needed to perform certain fulfillment of an assistant command included in the user input, and generating a prompt that requests a user consent to transmitting of a request to the server and/or the third-party application to obtain the data needed to perform the certain fulfillment. In implementations where the user consents, the data can be obtained and utilized to perform the certain fulfillment. In implementations where the user does not consent, client data can be generated locally at a client device and utilized to perform alternate fulfillment of the assistant command. In various implementations, the request transmitted to the server and/or third-party application can be modified based on ambient noise captured when the user input is received.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for tailoring composite graphical assistant interfaces for interacting with multiple different connected devices. The composite graphical assistant interfaces can be generated in response to a user providing a request for an automated assistant to cause a connected device to perform a particular function. In response to the automated assistant receiving the request, the automated assistant can identify other connected devices, and other functions capable of being performed by the other connected devices. The other functions can then be mapped to various graphical control elements in order to provide a composite graphical assistant interface from which the user can interact with different connected devices. Each graphical control element can be arranged to reflect how each connected device is operating simultaneous to the presentation of the composite graphical assistant interface.

Abstract: Techniques of providing continual augmented reality navigation include combining first and second 6DoF poses from, respectively, a localization from a map in a vicinity of a location to an image and from predicted 6DoF poses based on IMU data. The first 6DoF pose is based on a visual positioning system that uses map data from a vicinity of a location combined with an image from the world-facing camera of the AR smartglasses that provides 6DoF pose in world coordinates independent of the IMU. The second 6DoF pose is based on compensated rotational velocity and acceleration measurements derived from IMU intrinsic values.

Abstract: Systems and methods for attributing a scroll event are described herein. The system can provide, to a client device, an infinite scroll attribution script. The script can cause the client device to set a dimension of an inline frame, embedded with a content document, of an page to a dimension corresponding to a viewport of an application and determine, responsive to detecting a scroll event, that a first offset between a first content document end and a first viewport end is less than or equal to a first predetermined threshold. The script can further cause the client device to determine, responsive to detecting the scroll event, that a second offset between a second content document end and a second viewport end is greater than or equal to a second threshold and assign the scroll event to the inline frame responsive to the determinations of the first and second offsets.