Abstract: The technology of this application relates to a multi-task network model-based communication method, apparatus, and system. The method may include a first communication apparatus processing an input signal by using a first backbone network model, to obtain a fusion feature, where the fusion feature is obtained by fusing a plurality of first features which are obtained by performing feature extraction on the input signal. The method may further include performing compression and channel coding on the fusion feature to obtain first information, and sending the first information to a second communication apparatus.

Abstract: Aspects described herein relate to securely performing cross-boundary backup operations. A service of a computing resource service provider may enable backup operations between a source account and a destination account of an organization based at least in part on a security policy allowing such operations.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages migration of an application between a primary compute infrastructure and a secondary compute infrastructure. The secondary compute infrastructure may be a failover environment for the primary compute infrastructure. Primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines. Secondary snapshots of the virtual machines are generated, where the secondary snapshots are incremental snapshots of the primary snapshots. In failback, the secondary snapshots are provided to the primary compute infrastructure, where they are combined with the primary snapshots into construct a current state of the application, and the application is deployed in the current state by deploying virtual machines on the primary compute infrastructure.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages migration of an application between a primary compute infrastructure and a secondary compute infrastructure. The secondary compute infrastructure may be a failover environment for the primary compute infrastructure. Primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines. Secondary snapshots of the virtual machines are generated, where the secondary snapshots are incremental snapshots of the primary snapshots. In failback, the secondary snapshots are provided to the primary compute infrastructure, where they are combined with the primary snapshots into construct a current state of the application, and the application is deployed in the current state by deploying virtual machines on the primary compute infrastructure.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages migration of an application between a primary compute infrastructure and a secondary compute infrastructure. The secondary compute infrastructure may be a failover environment for the primary compute infrastructure. Primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines. Secondary snapshots of the virtual machines are generated, where the secondary snapshots are incremental snapshots of the primary snapshots. In failback, the secondary snapshots are provided to the primary compute infrastructure, where they are combined with the primary snapshots into construct a current state of the application, and the application is deployed in the current state by deploying virtual machines on the primary compute infrastructure.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages data of an application distributed across a set of machines of a compute infrastructure. A DMS node associates a set of machines with the application, and generates data fetch jobs for the set of machines for execution by multiple peer DMS nodes. The DMS node determining whether each of the data fetch jobs for the set of machines is ready for execution by the peer DMS nodes. In response to determining that each of the data fetch jobs is ready for execution, the peer DMS nodes execute the data fetch jobs to generate snapshots of the set of machines. The snapshots may be full or incremental snapshots, and collectively form a snapshot of the application.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages migration of an application between a primary compute infrastructure and a secondary compute infrastructure. The secondary compute infrastructure may be a failover environment for the primary compute infrastructure. Primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines. Secondary snapshots of the virtual machines are generated, where the secondary snapshots are incremental snapshots of the primary snapshots. In failback, the secondary snapshots are provided to the primary compute infrastructure, where they are combined with the primary snapshots into construct a current state of the application, and the application is deployed in the current state by deploying virtual machines on the primary compute infrastructure.

Abstract: In certain aspects, a leakage compensation method and system for a headphone are disclosed. An audio reference signal is obtained responsive to an audio signal to be played by a speaker of the headphone. An audio feedback signal is obtained based on a microphone signal acquired by a microphone of the headphone responsive to the audio signal being played by the speaker. One or more compensation parameters of a compensation filter are determined based on the audio reference signal and the audio feedback signal. The compensation filter is configured using the one or more compensation parameters. A music signal is processed using the compensation filter to generate a leakage-compensated music signal to be played by the speaker.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages data of a compute infrastructure by generating snapshots of partitions of a fileset of the compute infrastructure and providing a passthrough for storing the snapshots in a data storage separate from the DMS cluster, such as a cloud computing system. In one approach, the DMS nodes determine partitions of a fileset using fileset metadata, generates snapshots of the partitions, and stores the snapshots in the data storage. Each DMS node may include a local storage which is used to facilitate creation of a snapshot of a partition. The snapshot may be removed from the local storage after being sent to the data storage. Rather than storing the snapshot, the DMS cluster stores fileset metadata that is referenced to retrieve the snapshot from the data storage. The snapshot is deployed to retrieve a file stored in the partition.

Abstract: In certain aspects, an active noise control (ANC) method and system for a headphone are disclosed. It is determined whether a music signal is played by a speaker of the headphone. Responsive to the music signal not being played by the speaker and a noise level of an ambient noise signal being greater than a noise threshold, a set of noise feedforward (FF) signals is obtained based on a set of FF microphone signals acquired by a set of FF microphones of the headphone. A noise feedback (FB) signal is obtained based on a first FB microphone signal acquired by a FB microphone of the headphone. A set of leakage monitoring parameters is obtained based on the set of noise FF signals and the noise FB signal. A set of FF filter parameters for a set of FF filters is adjusted based on the set of leakage monitoring parameters.

Abstract: An example method of managing and storing data includes pulling and saving a first snapshot of a first state of a target machine running on a cloud service and creating a first virtual machine (VM) package associated with the first snapshot. The first VM package sufficient to instantiate a VM emulating the target machine with the first state on an on-premise destination VM platform. The method includes pulling and saving a second snapshot of a second state of the target machine, wherein the second state occurs after the first state. A second VM package is created and associated with the second snapshot, the second VM package sufficient to instantiate the VM emulating the target machine with the second state on the destination VM platform. The second VM package is created by updating the first VM package according to differences between the first snapshot and the second snapshot.

Abstract: In one aspect, a computer system automatically identifies style issues in a source code base. A reference set for a known style issue includes source code examples that exhibit the style issue. The source code examples in the reference set are compared to the source code base, for example using string convolution. Based on the comparison, locations in the source code base that are likely to exhibit the style issue are identified. Various steps in the processing may be implemented using machine learning models, clustering or other automated data science techniques.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages data backup and disaster recovery using a service level agreement. The backup data can be replicated from a primary compute infrastructure to the secondary compute infrastructure. For example, primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. If the primary compute infrastructure is lost, the secondary compute infrastructure is initialized to replicate the primary compute infrastructure. Applications that were running in the primary compute infrastructure can failover to run in the secondary compute infrastructure. The replicated backup data is used to facilitate the migration. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines.

Abstract: In one aspect, a computer system automatically identifies style issues in a source code base. A reference set for a known style issue includes source code examples that exhibit the style issue. The source code examples in the reference set are compared to the source code base, for example using string convolution. Based on the comparison, locations in the source code base that are likely to exhibit the style issue are identified. Various steps in the processing may be implemented using machine learning models, clustering or other automated data science techniques.

Abstract: In one aspect, a computer system automatically identifies style issues in a source code base. A reference set for a known style issue includes source code examples that exhibit the style issue. The source code examples in the reference set are compared to the source code base, for example using string convolution. Based on the comparison, locations in the source code base that are likely to exhibit the style issue are identified. Various steps in the processing may be implemented using machine learning models, clustering or other automated data science techniques.

Abstract: In one aspect, a computer system automatically identifies style issues in a source code base. A reference set for a known style issue includes source code examples that exhibit the style issue. The source code examples in the reference set are compared to the source code base, for example using string convolution. Based on the comparison, locations in the source code base that are likely to exhibit the style issue are identified. Various steps in the processing may be implemented using machine learning models, clustering or other automated data science techniques.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages data backup and disaster recovery using a service level agreement. The backup data can be replicated from a primary compute infrastructure to the secondary compute infrastructure. For example, primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. If the primary compute infrastructure is lost, the secondary compute infrastructure is initialized to replicate the primary compute infrastructure. Applications that were running in the primary compute infrastructure can failover to run in the secondary compute infrastructure. The replicated backup data is used to facilitate the migration. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages data of a compute infrastructure by generating snapshots of partitions of a fileset of the compute infrastructure and providing a passthrough for storing the snapshots in a data storage separate from the DMS cluster, such as a cloud computing system. In one approach, the DMS nodes determine partitions of a fileset using fileset metadata, generates snapshots of the partititons, and stores the snapshots in the data storage. Each DMS node may include a local storage which is used to facilitate creation of a snapshot of a partition. The snapshot may be removed from the local storage after being sent to the data storage. Rather than storing the snapshot, the DMS cluster stores fileset metadata that is referenced to retrieve the snapshot from the data storage. The snapshot is deployed to retrieve a file stored in the partition.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages migration of an application between a primary compute infrastructure and a secondary compute infrastructure. The secondary compute infrastructure may be a failover environment for the primary compute infrastructure. Primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines. Secondary snapshots of the virtual machines are generated, where the secondary snapshots are incremental snapshots of the primary snapshots. In failback, the secondary snapshots are provided to the primary compute infrastructure, where they are combined with the primary snapshots into construct a current state of the application, and the application is deployed in the current state by deploying virtual machines on the primary compute infrastructure.

Abstract: An example method of managing and storing data includes pulling and saving a first snapshot of a first state of a target machine running on a cloud service and creating a first virtual machine (VM) package associated with the first snapshot. The first VM package sufficient to instantiate a VM emulating the target machine with the first state on an on-premise destination VM platform. The method includes pulling and saving a second snapshot of a second state of the target machine, wherein the second state occurs after the first state. A second VM package is created and associated with the second snapshot, the second VM package sufficient to instantiate the VM emulating the target machine with the second state on the destination VM platform. The second VM package is created by updating the first VM package according to differences between the first snapshot and the second snapshot.