Abstract: An electronic device can include a processor programmed to detect connection of an external power source to the electronic device, determine an estimated disconnection time at which the external power source is expected to be disconnected from the electronic device, analyze power grid data corresponding to the external power source to identify one or more desired background processing intervals and one or more undesired background processing intervals prior to the estimated disconnection time, and perform background processing during the identified one or more desired battery charging intervals and inhibit background processing during the one or more undesired battery charging intervals. The processor can be programmed to determine an estimated disconnection time using a machine learning model.

Abstract: An electronic device can include a power system including a battery and a processor programmed to detect connection of an external power source to the electronic device, determine an estimated disconnection time at which the external power source is expected to be disconnected from the electronic device, analyze power grid data corresponding to the external power source to identify one or more desired battery charging intervals and one or more undesired battery charging intervals prior to the estimated disconnection time, and operate the power system to charge the battery from the external power source during the identified one or more desired battery charging intervals and inhibit battery charging during the one or more undesired battery charging intervals. The processor can be programmed to inhibit battery charging by reducing the rate at which the battery charges or by preventing battery charging.

Abstract: An electronic device can include circuitry that measures power delivered to the device and one or more processors configured to calculate power or energy drawn from a power grid by the device over a time period based on one or more measurements of power delivered to the device. The one or more processors can include a model of one or more power adapters characterizes load versus efficiency for the adapter. The circuitry that measures power delivered to the device can measure power delivered to the device via a wired and/or a wireless interface. The one or more processors can be further configured to estimate a carbon footprint of the device from the calculated power or energy drawn from the power grid by the device over the time period. The one or more processors can be further configured to communicate the estimated carbon footprint to an external device.

Abstract: System and methods for determining network threats are disclosed. For each entity operating in a network being monitored for network security, an example method obtains an observed metric value for each metric that characterizes actions performed by the entity. Each observed metric value may be input into a machine learning model that is specific to the metric in order to determine an anomaly score for the observed metric value that represents how anomalous the observed metric value is relative to an expected metric value for the metric. A threat score may then be determined for each entity from the anomaly scores for each metric. A security threat presentation that identifies one or more high-scoring entities according to the threat scores may be generated and provided for display on a user device.

Abstract: System and methods for determining network threats are disclosed. For each entity operating in a network being monitored for network security, an example method obtains an observed metric value for each metric that characterizes actions performed by the entity. Each observed metric value may be input into a machine learning model that is specific to the metric in order to determine an anomaly score for the observed metric value that represents how anomalous the observed metric value is relative to an expected metric value for the metric. A threat score may then be determined for each entity from the anomaly scores for each metric. A security threat presentation that identifies one or more high-scoring entities according to the threat scores may be generated and provided for display on a user device.