Abstract: A computer-implemented method for determining auction prices of vehicles may include obtaining wholesale auction price data indicative of wholesale auction prices and vehicle attribute values of a plurality of vehicles; grouping the plurality of vehicles into a plurality of vehicle groups respectively associated with a plurality of characteristics; determining a plurality of regression models respectively based on the plurality of vehicle groups; obtaining information describing a target vehicle; based on the target-vehicle values and values for the one or more economic indicators, determining an estimated wholesale auction price of the target vehicle using an identified regression model of the plurality of regression models; determining a discount factor representing an expected discount of a post-repossession auction price of the target vehicle from the estimated wholesale auction price; and determining an estimated post-repossession auction price of the target vehicle based on the estimated wholesale aucti

Abstract: Devices and techniques are generally described for an edge computing system. In various examples, a first application executed by a first virtual machine may generate a configuration file specifying a first machine learning task and first data. The first virtual machine may send the configuration file to a second virtual machine. The second virtual machine may generate first data effective to cause the first machine learning task to be executed by a third virtual machine. In various examples, the second virtual machine may retrieve the first data. In at least some examples, the second virtual machine may send the first data and the second data to a third virtual machine configured with access to a graphical processing unit.

Abstract: A computer-implemented method for determining auction prices of vehicles may include obtaining wholesale auction price data indicative of wholesale auction prices and vehicle attribute values of a plurality of vehicles; grouping the plurality of vehicles into a plurality of vehicle groups respectively associated with a plurality of characteristics; determining a plurality of regression models respectively based on the plurality of vehicle groups; obtaining information describing a target vehicle; based on the target-vehicle values and values for the one or more economic indicators, determining an estimated wholesale auction price of the target vehicle using an identified regression model of the plurality of regression models; determining a discount factor representing an expected discount of a post-repossession auction price of the target vehicle from the estimated wholesale auction price; and determining an estimated post-repossession auction price of the target vehicle based on the estimated wholesale aucti

Abstract: A computer-implemented method for determining auction prices of vehicles may include obtaining wholesale auction price data indicative of wholesale auction prices and vehicle attribute values of a plurality of vehicles; grouping the plurality of vehicles into a plurality of vehicle groups respectively associated with a plurality of characteristics; determining a plurality of regression models respectively based on the plurality of vehicle groups; obtaining information describing a target vehicle; based on the target-vehicle values and values for the one or more economic indicators, determining an estimated wholesale auction price of the target vehicle using an identified regression model of the plurality of regression models; determining a discount factor representing an expected discount of a post-repossession auction price of the target vehicle from the estimated wholesale auction price; and determining an estimated post-repossession auction price of the target vehicle based on the estimated wholesale aucti

Abstract: A computer-implemented method for determining auction prices of vehicles may include obtaining wholesale auction price data indicative of wholesale auction prices and vehicle attribute values of a plurality of vehicles; grouping the plurality of vehicles into a plurality of vehicle groups respectively associated with a plurality of characteristics; determining a plurality of regression models respectively based on the plurality of vehicle groups; obtaining information describing a target vehicle; based on the target-vehicle values and values for the one or more economic indicators, determining an estimated wholesale auction price of the target vehicle using an identified regression model of the plurality of regression models; determining a discount factor representing an expected discount of a post-repossession auction price of the target vehicle from the estimated wholesale auction price; and determining an estimated post-repossession auction price of the target vehicle based on the estimated wholesale aucti

Abstract: Disclosed herein are platforms and methods for collecting migraine associated data. Migraine associated data is collected automatically, such as via a sensor, or is self-reported manually.

Abstract: Wearable systems and methods to comprehensively analyze physical activity of a user for training and/or therapeutic purposes, by analyzing multiple channels of data about both muscle activity, using non-invasive surface electromyography (sEMG), and associated motion from that muscle activity, using inertial measurement units (IMU), are disclosed.

Abstract: Disclosed herein are platforms and methods for collecting migraine associated data. Migraine associated data is collected automatically, such as via a sensor, or is self-reported manually.

Abstract: The disclosure is generally directed to systems and methods for HTTP header-based authentication. For example, the systems and methods include receiving, at a mobile platform server, a first request message from a client device, the first request message requesting to download an application from the mobile platform server, sending, to the client device, a first response message having a first authentication query within header portions of the first response message, receiving, at the mobile platform server, a second request message having first authentication credentials within header portions of the second request message, sending, to the client device, a second response message having a second authentication query within header portions of the second response message, receiving, at the mobile platform server first device, a third request message having second authentication credentials within header portions of the third request message, and sending, to the client device, the application.

Abstract: The disclosure is generally directed to systems and methods for HTTP header-based authentication. For example, the systems and methods include receiving, at a mobile platform server, a first request message from a client device, the first request message requesting to download an application from the mobile platform server, sending, to the client device, a first response message having a first authentication query within header portions of the first response message, receiving, at the mobile platform server, a second request message having first authentication credentials within header portions of the second request message, sending, to the client device, a second response message having a second authentication query within header portions of the second response message, receiving, at the mobile platform server first device, a third request message having second authentication credentials within header portions of the third request message, and sending, to the client device, the application.