Abstract: This document describes techniques and systems enabling functionality associated with disparate software applications through one or more widgets within a user interface. The techniques and systems enable users to interact with a widget that enables functions of multiple disparate software applications. By so doing, users are enabled to easily see information from, and interact with, these disparate software applications. The described techniques and systems thereby enable users to quickly access, alter, or interact with multiple disparate software applications through one or more widgets within a user interface.

Abstract: Techniques, systems, and apparatuses directed to audio distortion mitigation on a computing device associated with a physical blockage of a speaker port of the device are disclosed. In one example, a computer-implemented method includes generating a sound output corresponding to an audio signal, emitting the sound output from a speaker port defined in a housing of a computing device, receiving first sensor data from at least one first sensor, determining a physical blockage of the speaker port based on the first sensor data, receiving second sensor data from at least one second sensor, and determining a cause of the determined physical blockage of the speaker port. Responsive to determining a cause of the physical blockage of the speaker port, the computing device performs one or more actions to mitigate audio distortion of the sound output from the speaker port.

Abstract: A method executed on data processing hardware and including displaying, on a display in communication with the data processing hardware, a user interface for a chat session between a user and a chatbot, the user interface including a plurality of entries input by the user and a plurality of corresponding responses from the chatbot. The method also includes receiving, in the user interface, a user input indication indicating selection of a particular entry of the plurality of entries, the particular entry being other than a last entry of the plurality of entries. The method further includes receiving, in the user interface, a new entry input by the user for an additional entry associated with the particular entry. The method additionally includes based on the additional entry associated with the particular entry, causing the chatbot to update one or more of the plurality of corresponding responses from the chatbot.

Abstract: Systems and methods for transporting live insects are disclosed. An example method of packaging insects for transportation involves providing a shipping box, the shipping box lined with insulation on a portion of inner surfaces of the shipping box. The method further involves placing an insect transportation container into a recess of a container tray, the container tray sized to fit within the shipping box, the recess shaped to receive the insect transportation container. The method further involves placing the container tray into the interior of the shipping box. The method further involves sealing the shipping box with the container tray therein.

Abstract: This document describes systems and techniques directed at virtual batches in non-causal large language model (LLM) inferences. An LLM generates a plurality of tokens. The plurality of tokens includes a prefix sequence. The LLM generates a dependency map. The dependency map includes one or more index markers for each of the plurality of tokens and a correlation marker for each of the one or more index markers. The LLM generates a plurality of virtual batches based on the dependency map. Generating the plurality of virtual batches may include applying an attention mask having a non-causal mask portion corresponding to the prefix sequence. Each of the plurality of virtual batches includes a discrete inference. The LLM selects one or more of the plurality of virtual batches as a final inference.

Abstract: A method (500) includes, for each training sample (410) of a plurality of training samples: processing, using a sequence transduction model (200), corresponding training input features (415) to obtain one or more output token sequence hypotheses (432) each including one or more predicted common tokens (204); and determining a token-level loss (462) based on, for each hypothesis: a number of special token insertions each associated with a corresponding predicted special token that appears in the hypothesis but does not appear in a corresponding sequence of ground-truth output tokens; and a number of special token deletions each associated with a corresponding ground-truth special token in the set of ground-truth special tokens that does not appear in hypothesis. The method also includes training the sequence transduction model to minimize additive error rate based on the token-level losses determined for the plurality of training samples.

Abstract: The present document describes techniques for an image-denoising preprocess for robust contrast-detection autofocus with low-light and low-contrast subjects. The techniques described herein provide a software preprocessing approach for denoising image data for contrast-detection autofocus, particularly with low-light conditions and low-contrast subjects. This software autofocus technique utilizes a Gaussian pyramid to reduce noise and downscale the image to reduce computation costs. In aspects, a spatially adaptive denoise preprocessing technique (e.g., Wiener filter, bilateral filter) is then applied to further reduce noise prior to contrast-detection autofocus measurement generation. The denoise preprocessing technique provides a denoised output that is used as input to a focus-value filter for multizone focus-value measurements usable for focusing a camera.

Abstract: A equipment (UE) communicates with a RAN in a first cell in accordance with a first configuration. The UE receives, from the RAN, a message including a configuration for performing a serving cell change to a second cell subsequent to an activation command; detects a communication failure with the RAN, prior to receiving the activation command; and in response to the detecting, performing at least one of the following: (i) performing an RRC reestablishment procedure, or releasing the second configuration.

Abstract: A media application receives an input panning video that includes image frames captured with a camera that pans at least once from a first side to a second side. The media application includes reprojecting the input panning video onto a panoramic canvas, wherein the panoramic canvas includes the image frames and missing regions. The media application further includes outputting, with a generative machine-learning model, a panoramic video that includes synthesized pixels for the missing regions of the panoramic canvas by: generating a base video at a coarse temporal scale that includes the synthesized pixels for the missing regions and refining the base video by performing temporal upsampling, merging the temporally upsampled base video with the panoramic canvas, and resynthesizing the synthesized pixels for the missing regions in the base video.

Abstract: A method includes receiving, at a server, first requests to register with a first peer-to-peer network from a first node, a second node, and a third node, the first peer-to-peer network providing transmission of live media data. The method further includes generating a first configuration for the first peer-to-peer network comprising at least the first node, the second node, and the third node, the first configuration defining a first set of one or more media data routes to provide the live media data to one or more peer nodes of the first peer-to-peer network upon receipt of the live media data by a respective one of the first node, the second node, and the third node. The method further includes transmitting one or more portions of the first configuration to each of the first node, the second node, and the third node.

Abstract: In a mobile cellular network, a mobile network operator (MNO) or other entity can specify whether a user equipment (UE) is to utilize either a concealed identifier (e.g., a subscription concealed identifier (SUCI)) calculated by a universal subscriber identity module (USIM) of a universal integrated circuit card (UICC) or a concealed identifier calculated by mobile equipment (ME) of the UE that is separate from the UICC and USIM. In the event that a USIM-calculated concealed identifier is specified for use but the USIM fails in generation of a concealed identifier, the UE can utilize one or more failure recovery procedures to attempt completion of attachment of the UE to the same network or a different network to help ensure that the UE does not remain attached to a network without access to services of the network due to authentication failure.

Abstract: Methods, systems, and apparatus for parallel decoding for quantum error correction codes. In one aspect, a classical computer system is configured to implement a decoding process on measurement data received from a quantum computing system to determine errors in a quantum computation. The classical computing system implements a main thread, multiple worker threads, and a data structure common to each worker thread. The data structure stores data of a dynamic system of disjoint clusters of nodes of a detector graph for the decoding process, where the data includes compressed logical flip information of child nodes in each cluster of nodes. During execution of the decoding process, the multiple worker threads are configured to, in parallel: obtain clusters of nodes and modify the clusters of nodes, where, for each modification, the worker thread updates data in the data structure that corresponds to the cluster under an atomicity primitive.

Abstract: Systems and methods for training a machine-learned model are disclosed herein. The method can include obtaining, by a processor, a plurality of images, each image having a set of parameter values comprising values for a plurality of camera parameters and determining a covariance matrix for the plurality of camera parameters with respect to a plurality of projected points generated via evaluation of a projection function. The method can also include performing a whitening algorithm to identify a preconditioning matrix that, when applied to the sets of parameter values, results in the covariance matrix being approximately equal to an identity matrix and performing an optimization algorithm on the plurality of sets of parameter values, Performing the optimization algorithm can include applying an inverse of the preconditioning matrix to the plurality of sets of parameters in a forward prediction pass and applying the preconditioning matrix in a backward gradient pass.

Abstract: Example embodiments of the present disclosure provide for an example method. The example method includes obtaining session data descriptive of a plurality of user sessions, the plurality of user sessions respectively including an interaction with an input element rendered at a user device and a request for a resource associated with the input element. The example method includes obtaining, using a first machine-learned model, a plurality of weights associated with the plurality of user sessions by, for a respective user session of the plurality of user sessions: inputting, to the first machine-learned model, data descriptive of one or more characteristics of the respective user session; and obtaining, from the first machine-learned model, a respective weight of the plurality of weights, the respective weight indicative of an incremental probability of the request conditioned on rendering of the input element.

Abstract: The subject matter of this specification generally relates to preventing the distribution of forbidden network content. In one aspect, a system includes a front-end server that receives content for distribution over a data communication network. The back-end server identifies, in the query log, a set of received queries for which a given forbidden term was used to identify a search result in response to the received query even though the given forbidden term was not included in queries included in the set of received queries. The back-end server classifies, as variants of the given forbidden term, a term from one or more queries in the set of received queries that caused a search engine to use the given forbidden term to identify one or more search results in response to the one or more queries and prevents distribution of content that includes a variant.

Abstract: Implementations relate to depersonalizing and/or anonymizing generative model inputs and/or outputs to remove sensitive and/or private information, such as PII (personally identifiable information). In various implementations, a generative model input prompt may be retrieved from local memory of the edge computing device and further processed using one or more generative models to generate generative model output. The generative model input prompt may be submitted to an edge-based anonymization process including assembling, as an anonymization prompt and processing the anonymization prompt using one or more on-device generative models of the edge computing device to generate an anonymized version of the portion of the generative model input prompt that is stripped of PII. The anonymized version may further be inspected of any remaining PII and the fully anonymized version may be surfaced without concern of leaking PII.

Abstract: One example aspect of the present disclosure is directed to a device including a superconducting tunnel junction. The superconducting tunnel junction includes a first layer. The first layer includes a superconducting material. The superconducting tunnel junction includes a second layer at least partially overlapping the first layer. The second layer includes the superconducting material. The superconducting tunnel junction includes a barrier layer between the first layer and the second layer and a junction area including an overlapping area of the first layer and the second layer. The junction area has a surface area of about 6400 nm2 or less.

Abstract: Example embodiments of the present disclosure relate to systems and methods for compressing attributes of volumetric and hypervolumetric datasets. An example system performs operations including obtaining a reference dataset comprising attributes indexed by a domain of multidimensional coordinates; subdividing the domain into a plurality of blocks respectively associated with a plurality of attribute subsets; inputting, to a local nonlinear operator, a latent representation for an attribute subset associated with at least one block of the plurality of blocks; obtaining, using the local nonlinear operator and based on the latent representation, an attribute representation of one or more attributes of the attribute subset; and updating the latent representation based on a comparison of the attribute representation and the reference dataset.

Abstract: Adaptive howling suppression is provided for active noise control (ANC) systems of wearable audio components (WACs), such as earbuds, based on detecting whether the WACs are presently being worn. Upon first detecting howling, embodiments can pre-suppress the howling audio signature by reducing ANC output. Then, embodiments detect whether the WAC is presently on-ear (worn) or off-ear and attempt to fully suppress the howling using different parameters, based on this detection. For example, if on-ear, embodiments use on-ear tuning settings to cycle through a set of suppression stages until ANC output can be restored without howling; if off-ear, embodiments use off-ear tuning settings to cycle through the set of suppression stages until ANC output can be restored without howling. Once ANC output can be restored without howling, the system can be released to a normal idle state.

Abstract: Implementations relate to receiving natural language input that requests an automated assistant to provide information and processing the natural language input to identify the requested information and to identify one or more predicted actions. Those implementations further cause a computing device, at which the natural language input is received, to render the requested information and the one or more predicted actions in response to the natural language input. Yet further, those implementations, in response to the user confirming a rendered predicted action, cause the automated assistant to initialize the predicted action.