Abstract: A storage application is disclosed herein that employs a method for managing read and write requests associated with a database distributed across multiple replicas in multiple data centers. The storage application may be implemented on one or more computing devices in the context of a data center, cloud environment, or the like. The method includes the storage application receiving a client request to perform an operation with respect to an object at an any-local-quorum consistency level. In response to client request, the storage application iteratively generates and submits coordinator requests to the multiple data centers at a local-quorum consistency level, until one of the multiple data centers successfully replies to one of the coordinator requests with a result. The storage application then replies to the client request based on the result provided by the one of the multiple data centers.

Abstract: A storage application is disclosed herein that employs a method for managing read and write requests associated with a database distributed across multiple replicas in multiple data centers. The storage application may be implemented on one or more computing devices in the context of a data center, cloud environment, or the like. The method includes the storage application receiving a client request to perform an operation with respect to an object at an any-local-quorum consistency level. In response to client request, the storage application iteratively generates and submits coordinator requests to the multiple data centers at a local-quorum consistency level, until one of the multiple data centers successfully replies to one of the coordinator requests with a result. The storage application then replies to the client request based on the result provided by the one of the multiple data centers.

Abstract: In various examples, detected object data representative of locations of detected objects in a field of view may be determined. One or more clusters of the detected objects may be generated based at least in part on the locations and features of the cluster may be determined for use as inputs to a machine learning model(s). A confidence score, computed by the machine learning model(s) based at least in part on the inputs, may be received, where the confidence score may be representative of a probability that the cluster corresponds to an object depicted at least partially in the field of view. Further examples provide approaches for determining ground truth data for training object detectors, such as for determining coverage values for ground truth objects using associated shapes, and for determining soft coverage values for ground truth objects.

Abstract: Determining a language for speech recognition of a spoken utterance received via an automated assistant interface for interacting with an automated assistant. Implementations can enable multilingual interaction with the automated assistant, without necessitating a user explicitly designate a language to be utilized for each interaction. Implementations determine a user profile that corresponds to audio data that captures a spoken utterance, and utilize language(s), and optionally corresponding probabilities, assigned to the user profile in determining a language for speech recognition of the spoken utterance. Some implementations select only a subset of languages, assigned to the user profile, to utilize in speech recognition of a given spoken utterance of the user.

Abstract: In various examples, detected object data representative of locations of detected objects in a field of view may be determined. One or more clusters of the detected objects may be generated based at least in part on the locations and features of the cluster may be determined for use as inputs to a machine learning model(s). A confidence score, computed by the machine learning model(s) based at least in part on the inputs, may be received, where the confidence score may be representative of a probability that the cluster corresponds to an object depicted at least partially in the field of view. Further examples provide approaches for determining ground truth data for training object detectors, such as for determining coverage values for ground truth objects using associated shapes, and for determining soft coverage values for ground truth objects.

Abstract: Determining a language for speech recognition of a spoken utterance received via an automated assistant interface for interacting with an automated assistant. Implementations can enable multilingual interaction with the automated assistant, without necessitating a user explicitly designate a language to be utilized for each interaction. Implementations determine a user profile that corresponds to audio data that captures a spoken utterance, and utilize language(s), and optionally corresponding probabilities, assigned to the user profile in determining a language for speech recognition of the spoken utterance. Some implementations select only a subset of languages, assigned to the user profile, to utilize in speech recognition of a given spoken utterance of the user.

Abstract: Determining a language for speech recognition of a spoken utterance received via an automated assistant interface for interacting with an automated assistant. Implementations can enable multilingual interaction with the automated assistant, without necessitating a user explicitly designate a language to be utilized for each interaction. Implementations determine a user profile that corresponds to audio data that captures a spoken utterance, and utilize language(s), and optionally corresponding probabilities, assigned to the user profile in determining a language for speech recognition of the spoken utterance. Some implementations select only a subset of languages, assigned to the user profile, to utilize in speech recognition of a given spoken utterance of the user.

Abstract: In various examples, detected object data representative of locations of detected objects in a field of view may be determined. One or more clusters of the detected objects may be generated based at least in part on the locations and features of the cluster may be determined for use as inputs to a machine learning model(s). A confidence score, computed by the machine learning model(s) based at least in part on the inputs, may be received, where the confidence score may be representative of a probability that the cluster corresponds to an object depicted at least partially in the field of view. Further examples provide approaches for determining ground truth data for training object detectors, such as for determining coverage values for ground truth objects using associated shapes, and for determining soft coverage values for ground truth objects.

Abstract: In various examples, detected object data representative of locations of detected objects in a field of view may be determined. One or more clusters of the detected objects may be generated based at least in part on the locations and features of the cluster may be determined for use as inputs to a machine learning model(s). A confidence score, computed by the machine learning model(s) based at least in part on the inputs, may be received, where the confidence score may be representative of a probability that the cluster corresponds to an object depicted at least partially in the field of view. Further examples provide approaches for determining ground truth data for training object detectors, such as for determining coverage values for ground truth objects using associated shapes, and for determining soft coverage values for ground truth objects.

Abstract: Determining a language for speech recognition of a spoken utterance received via an automated assistant interface for interacting with an automated assistant. Implementations can enable multilingual interaction with the automated assistant, without necessitating a user explicitly designate a language to be utilized for each interaction. Implementations determine a user profile that corresponds to audio data that captures a spoken utterance, and utilize language(s), and optionally corresponding probabilities, assigned to the user profile in determining a language for speech recognition of the spoken utterance. Some implementations select only a subset of languages, assigned to the user profile, to utilize in speech recognition of a given spoken utterance of the user.

Abstract: Determining a language for speech recognition of a spoken utterance received via an automated assistant interface for interacting with an automated assistant. Implementations can enable multilingual interaction with the automated assistant, without necessitating a user explicitly designate a language to be utilized for each interaction. Implementations determine a user profile that corresponds to audio data that captures a spoken utterance, and utilize language(s), and optionally corresponding probabilities, assigned to the user profile in determining a language for speech recognition of the spoken utterance. Some implementations select only a subset of languages, assigned to the user profile, to utilize in speech recognition of a given spoken utterance of the user.

Abstract: Determining a language for speech recognition of a spoken utterance received via an automated assistant interface for interacting with an automated assistant. Implementations can enable multilingual interaction with the automated assistant, without necessitating a user explicitly designate a language to be utilized for each interaction. Implementations determine a user profile that corresponds to audio data that captures a spoken utterance, and utilize language(s), and optionally corresponding probabilities, assigned to the user profile in determining a language for speech recognition of the spoken utterance. Some implementations select only a subset of languages, assigned to the user profile, to utilize in speech recognition of a given spoken utterance of the user.

Abstract: In various examples, detected object data representative of locations of detected objects in a field of view may be determined. One or more clusters of the detected objects may be generated based at least in part on the locations and features of the cluster may be determined for use as inputs to a machine learning model(s). A confidence score, computed by the machine learning model(s) based at least in part on the inputs, may be received, where the confidence score may be representative of a probability that the cluster corresponds to an object depicted at least partially in the field of view. Further examples provide approaches for determining ground truth data for training object detectors, such as for determining coverage values for ground truth objects using associated shapes, and for determining soft coverage values for ground truth objects.

Abstract: A device includes a software agent that discovers a controller. Current state information indicating upgradeable files currently installed on the device is maintained. The agent sends the current state information to the controller, and the controller sends the current state information to a repository, via the Internet. The repository determines upgradeable files currently installed on the device based on the current state information, and checks for file updates for the upgradeable files. The repository sends file update information to the controller. The file update information includes file update locations. The controller sends the file update information to the agent, and the agent requests the controller to retrieve file updates. The controller retrieves each file update from its file update location on the Internet. The controller sends a notification to the agent when the file updates are retrieved, and the agent retrieves the file updates from the controller.

Abstract: The present disclosure relates to the field of microelectronic substrate fabrication and, more particularly, to alignment inspection for vias formed in the microelectronic substrates. The alignment inspection may be achieved by determining the relative positions of fluorescing and non-fluorescing elements in a microelectronic substrate.

Abstract: A device includes a software agent that discovers a controller. Current state information indicating upgradeable files currently installed on the device is maintained. The agent sends the current state information to the controller, and the controller sends the current state information to a repository, via the Internet. The repository determines upgradeable files currently installed on the device based on the current state information, and checks for file updates for the upgradeable files. The repository sends file update information to the controller. The file update information includes file update locations. The controller sends the file update information to the agent, and the agent requests the controller to retrieve file updates. The controller retrieves each file update from its file update location on the Internet. The controller sends a notification to the agent when the file updates are retrieved, and the agent retrieves the file updates from the controller.

Abstract: Securely storing a public key for encryption of data in a computing device by using a user-specific key pair which is securely stored in the computing device, including receiving a target public key corresponding to a target device, obtaining a user-specific key pair from a secure registry, using a user-specific private key from the user-specific key pair to create a target key verifier based on the target public key, storing the target key verifier and the target public key in a storage area, retrieving the target key verifier and the target public key from the storage area, applying a user-specific public key from the user-specific key pair to the target key verifier for verifying the authenticity of the target public key, and encrypting data with the target public key, if authenticity of the target public key is verified, thereby creating encrypted data for transmission to the target device.

Abstract: A device (such as a printer or a network device that may be connected to the printer) that is connected to a network and which performs secure operations using an existing encryption keypair maintained within the device, generates a new encryption keypair within the device by receiving a request from another device on the network to provide an encryption key of the existing encryption keypair to the another device. In response to the request, the device determines whether an encryption key of the existing encryption keypair within the device is valid. In a case where it is determined that the encryption key of the existing encryption keypair is invalid, the device automatically deletes each key of the existing encryption keypair from the device, generates a new encryption keypair within the device and stores the new encryption keypair in the device. The device then provides a new encryption key corresponding to the requested encryption key of the new encryption keypair to another device.

Abstract: The present invention provides a system for the contactless testing and configuring of electronic assemblies during the manufacturing process. The system includes an onboard optical transceiver, a system controller, and a controller optical transceiver. The onboard optical transceiver is located on the electronic assembly. The onboard optical transceiver is connected to an integrated circuit which is capable of performing functional tests or storing programs on the assembly. The controller optical transceiver is connected to the system controller and located adjacent to the electronic assembly. The onboard transceiver and the controller optical transceiver are used to establish a contactless communication link between the system controller and the electronic assembly. The contactless nature of the communication link allows the assembly to be transported past the controller optical transceiver by a simple conveyor while the system controller is communicating with the electronic assembly.

Abstract: The present invention addresses the foregoing problems and concerns a print driver executable on a user's personal computer and responsive to a print option selection from any application program, generates output that conforms to a standardized markup language format.