Abstract: There are provided systems and methods for dynamic autoscaling of server resources using intelligent demand analytic systems. A service provider, such as an electronic transaction processor for digital transactions, may utilize different computing resource to provide computing resources to users. During use of such computing resources by end users and their computing devices, different demand and needs may be required by such users and devices. The service provider may utilize an intelligent machine learning system to predict computing resource needs and demands at different future time periods based on past usages over similar time periods, computing requests and demands, and network communications. The machine learning engines may identify one or more usages curves, which may be of one or more degrees of curvature, to determine potential future usage. Using these past analytics, the service provider may dynamically scale automatic provision of computing resources.

Abstract: Certain implementations of the disclosed technology may include computing systems and methods for associating computing devices. According to an example implementation, a computer-implemented method is provided. The method may include determining a first set of content items associated with a first computing device and determining a second set of content items associated with a second computing device. The method may also include determining whether the first and second computing devices constitute a candidate pair. The method may also include calculating s similarity metric between the first and second computing devices based on a common set of content items shared between the devices. Finally, the method may include associating the first and second computing devices with one another.

Abstract: Certain implementations of the disclosed technology may include computing systems and methods for associating computing devices. According to an example implementation, a computer-implemented method is provided. The method may include determining a first set of content items associated with a first computing device and determining a second set of content items associated with a second computing device. The method may also include determining whether the first and second computing devices constitute a candidate pair. The method may also include calculating s similarity metric between the first and second computing devices based on a common set of content items shared between the devices. Finally, the method may include associating the first and second computing devices with one another.

Abstract: An apparatus, system and method for performing a ventriculostomy using an ultrasound probe is disclosed. A head portion of the probe is connected to a handle portion at a proximal end of the head portion. A transducer is mounted in a distal end of the head portion to transmit ultrasound waves into the patient. The head portion includes a conduit portion arranged to accept and to accurately direct a catheter into the body of the patient in a direction parallel to the direction of the ultrasound waves. The head portion is sized to fit into a conventionally-sized craniotomy. An adjustment mechanism connects the head portion to the handle portion for selectively adjusting the angle between the handle portion and the head portion. A sterile sheath having an integral conduit assembly for mounting to the head is optionally provided. An illuminator is optionally provided on the probe.