Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: The present invention extends to methods, systems, and computer program products for classifying time series image data. Aspects of the invention include encoding motion information from video frames in an eccentricity map. An eccentricity map is essentially a static image that aggregates apparent motion of objects, surfaces, and edges, from a plurality of video frames. In general, eccentricity reflects how different a data point is from the past readings of the same set of variables. Neural networks can be trained to detect and classify actions in videos from eccentricity maps. Eccentricity maps can be provided to a neural network as input. Output from the neural network can indicate if detected motion in a video is or is not classified as an action, such as, for example, a hand gesture.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: An aspect of the present disclosure facilitates mobility of user applications across cloud infrastructures. In one embodiment, a mobility server maintains a setup data which indicates that a user application is to be operative based on a specific set of application components when the user application is to be hosted on a cloud infrastructure. Upon receiving a request to move the user application currently executing in a source cloud infrastructure to the cloud infrastructure, mobility server provisions the specific set of application components in the cloud infrastructure in view of the setup data and then executes the user application in the cloud infrastructure based on the specific set of application components.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: The present invention extends to methods, systems, and computer program products for classifying time series image data. Aspects of the invention include encoding motion information from video frames in an eccentricity map. An eccentricity map is essentially a static image that aggregates apparent motion of objects, surfaces, and edges, from a plurality of video frames. In general, eccentricity reflects how different a data point is from the past readings of the same set of variables. Neural networks can be trained to detect and classify actions in videos from eccentricity maps. Eccentricity maps can be provided to a neural network as input. Output from the neural network can indicate if detected motion in a video is or is not classified as an action, such as, for example, a hand gesture.

Abstract: A computing system can determine moving objects in a sequence of images based on recursively calculating red-green-blue (RGB) eccentricity 249 k based on a video data stream. A vehicle can be operated based on the determined moving objects. The video data stream can be acquired by a color video sensor included in the vehicle or a traffic infrastructure system.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: The present invention extends to methods, systems, and computer program products for classifying time series image data. Aspects of the invention include encoding motion information from video frames in an eccentricity map. An eccentricity map is essentially a static image that aggregates apparent motion of objects, surfaces, and edges, from a plurality of video frames. In general, eccentricity reflects how different a data point is from the past readings of the same set of variables. Neural networks can be trained to detect and classify actions in videos from eccentricity maps. Eccentricity maps can be provided to a neural network as input. Output from the neural network can indicate if detected motion in a video is or is not classified as an action, such as, for example, a hand gesture.

Abstract: In one embodiment, an apparatus includes a host controller to implement one or more layers of a Universal Serial Bus (USB)-based protocol to provide an interconnect for a plurality of devices. The host controller is to monitor control plane messages on the interconnect, detect, in the control plane messages, a power state change command for a device coupled to the interconnect, wherein the devices utilizes a tunneled protocol on the interconnect, and modify power distribution for one or more other devices of the interconnect based on detecting the power state change command.

Abstract: An aspect of the present disclosure facilitates mobility of user applications across cloud infrastructures. In one embodiment, a mobility server maintains a setup data which indicates that a user application is to be operative based on a specific set of application components when the user application is to be hosted on a cloud infrastructure. Upon receiving a request to move the user application currently executing in a source cloud infrastructure to the cloud infrastructure, mobility server provisions the specific set of application components in the cloud infrastructure in view of the setup data and then executes the user application in the cloud infrastructure based on the specific set of application components.

Abstract: The present invention extends to methods, systems, and computer program products for classifying time series image data. Aspects of the invention include encoding motion information from video frames in an eccentricity map. An eccentricity map is essentially a static image that aggregates apparent motion of objects, surfaces, and edges, from a plurality of video frames. In general, eccentricity reflects how different a data point is from the past readings of the same set of variables. Neural networks can be trained to detect and classify actions in videos from eccentricity maps. Eccentricity maps can be provided to a neural network as input. Output from the neural network can indicate if detected motion in a video is or is not classified as an action, such as, for example, a hand gesture.

Abstract: A computing system can determine moving objects in a sequence of images based on recursively calculating red-green-blue (RGB) eccentricity 249 k based on a video data stream. A vehicle can be operated based on the determined moving objects. The video data stream can be acquired by a color video sensor included in the vehicle or a traffic infrastructure system.

Abstract: Techniques and examples pertaining to objection detection and trajectory prediction for autonomous vehicles are described. A processor receives an input stream of image frames and fuses a spatiotemporal input stream of the image frames and an appearance-based stream of the image frames using a deep neural network (DNN) to generate an augmented stream of the image frames. The processor performs object detection and trajectory prediction of one or more objects in the image frames based on the augmented stream.

Abstract: Techniques and examples pertaining to objection detection and trajectory prediction for autonomous vehicles are described. A processor receives an input stream of image frames and fuses a spatiotemporal input stream of the image frames and an appearance-based stream of the image frames using a deep neural network (DNN) to generate an augmented stream of the image frames. The processor performs object detection and trajectory prediction of one or more objects in the image frames based on the augmented stream.