Abstract: A system includes servers, each server including a baseboard management controller (BMC) and operating environments including motherboards and processors. One of the BMCs includes circuitry to determine that the other server is in a standby mode. In a standby mode, the operating environment including the motherboard and processor is powered down and the BMC is powered only through a connection between the BMCs. The first BMC determines that additional resources for execution by the system from the other server are to be activated, and send a wake-up signal to the other BMC. The other BMC includes circuitry configured to receive the wake-up signal, wake the operating environment, and provision the operating environment, without power from the operating environment.

Abstract: A merged infrastructure for manufacturing and lifecycle management of both hardware and software is disclosed. In various embodiments, a library comprising a superset of device drivers is stored, the superset including for each of a plurality of supported systems a corresponding set of device drivers for devices comprising that supported system. A context in which a processor is deployed is determined, the context being associated with a specific corresponding one of the plurality of supported systems. The library is used to provision based on the determined context at least a subset of devices accessible by the processor in the context in which the processor is deployed.

Abstract: A wireless management controller device is disclosed. In various embodiments, a board management controller device includes a wireless communication interface and a processor coupled to the wireless communication interface and configured to control the wireless communication interface independently of a main circuit board with which the management controller device is associated, and communicate via the wireless communication interface independently of the main circuit board with which the management controller device is associated.

Abstract: A method of determining, one or more damage states of one or more auxiliary parts of a damaged vehicle, the vehicle comprising a plurality of normalized parts and at least some of the normalized parts further comprising one or more auxiliary parts. The method includes receiving one or more images of the vehicle, using a plurality of classifiers, each determining at least one classification of damage to the vehicle, each said classification being determined for each of a plurality of normalized parts of the vehicle, determining one or more classifications for the plurality of auxiliary parts using one or more trained models, wherein each classification comprises at least one indication of damage to at least one auxiliary part and outputting the determined damage states of the one or more auxiliary parts.

Abstract: A merged infrastructure for manufacturing and lifecycle management of both hardware and software is disclosed. In various embodiments, a library comprising a superset of device drivers is stored, the superset including for each of a plurality of supported systems a corresponding set of device drivers for devices comprising that supported system. A context in which a processor is deployed is determined, the context being associated with a specific corresponding one of the plurality of supported systems. The library is used to provision based on the determined context at least a subset of devices accessible by the processor in the context in which the processor is deployed.

Abstract: A merged infrastructure for manufacturing and lifecycle management of both hardware and software is disclosed. In various embodiments, a library comprising a superset of device drivers is stored, the superset including for each of a plurality of supported systems a corresponding set of device drivers for devices comprising that supported system. A context in which a processor is deployed is determined, the context being associated with a specific corresponding one of the plurality of supported systems. The library is used to provision based on the determined context at least a subset of devices accessible by the processor in the context in which the processor is deployed.

Abstract: An apparatus includes a communications interface and a management server. The management server is configured to access servers through the interface, determine that additional resources are needed for execution by a system, and determine that one of the servers is in a standby mode. In the standby mode, the server is powered down and a baseboard management controller (BMC) therein is only powered through a connection from the apparatus or another server of the network. The management server is configured to determine that additional resources for execution by the system from the server are to be activated and cause a wake-up signal to be sent to the BMC, wherein the wake-up signal is configured to cause the BMC to wake and provision the operating environment of the server.

Abstract: An apparatus includes an operating environment including a motherboard and a processor, and a baseboard management controller (BMC) including circuitry configured to determine that another server is in a standby mode. The other server includes its own BMC and operating environment, and, in the standby mode, the second operating environment is powered down and the second BMC is powered only through a connection to the BMC of the apparatus. The BMC of the apparatus is further configured to determine that additional resources for execution by a system including the apparatus are to be activated. The BMC is further configured to send a control signal to the other BMC, wherein the control signal is configured to issue a wake-up signal to the other BMC to wake at least a portion of the other BMC's operating environment, and to provision the other BMC's operating environment.

Abstract: An apparatus includes an interface for an electronic device and a baseboard management controller (BMC). The BMC includes circuitry configured to, through the interface, monitor current usage by the electronic device, determine a security status of the electronic device based upon the current usage, and take a corrective action based upon the security status.

Abstract: A method (100) of constructing a probabilistic graphical model (10) of a system from data that includes both normal and anomalous data includes the step of learning parameters of a structure for the probabilistic graphical model (10). The structure includes at least one latent variable (26) on which other variables (12, 14, 16, 18, 20, 22, 24) are conditional, and has a plurality of components. The method further includes the steps of: iteratively associating one or more of the plurality of components of the latent variable (26) with normal data; constructing a matrix of the associations; detecting abnormal components of the latent variable (26) based on one of a low association with the normal data or the matrix of associations; and deleting the abnormal components of the latent variable (26) from the probabilistic graphical model (10).

Abstract: The present disclosure relates to systems and methods for monitoring data recorded from systems over time. The techniques described herein include the ability to detect and classify system events, and to provide indicators of normal system operation and anomaly detection. The systems and methods of the present disclosure can represent events occurring in the system being monitored in such a way that the temporal characteristics of the events can be captured and utilized for detection, classification and/or anomaly detection, which can be particularly useful when dealing with complex systems and/or events.

Abstract: A method (100) of constructing a probabilistic graphical model (10) of a system from data that includes both normal and anomalous data includes the step of learning parameters of a structure for the probabilistic graphical model (10). The structure includes at least one latent variable (26) on which other variables (12, 14, 16, 18, 20, 22, 24) are conditional, and has a plurality of components. The method further includes the steps of: iteratively associating one or more of the plurality of components of the latent variable (26) with normal data; constructing a matrix of the associations; detecting abnormal components of the latent variable (26) based on one of a low association with the normal data or the matrix of associations; and deleting the abnormal components of the latent variable (26) from the probabilistic graphical model (10).