Abstract: A method performed by a node for predicting a behavior of users of a communications network is described. The node manages an artificial neural network. The node merges a first pre-existing predictive model of the behavior in a first group of users with a second model of the behavior in a second group of users. The merging comprises establishing connections between the first model and the second model. Each of the connections has a respective weight. The respective weights of the connections are learned by respective connections of neurons in the artificial neural network based on data from a third group of users. The node also obtains a third model for predicting the behavior in the third group of users, based on the merged models and the data from the third group of users.

Abstract: It is provided a method for adjusting alignment for microwave transmissions from a microwave transmitter to a microwave receiver based on a reinforcement learning, RL, model. The method comprises the steps of: obtaining state space comprising external state space and internal state space, the external state space comprising at least one value of a parameter related to environmental conditions, and the internal state space relates to alignment of the microwave transmitter; determining an action in an action space, the action space comprising actions to adjust alignment of the microwave transmitter; obtaining a measurement of path loss for a transmission from the microwave transmitter to the microwave receiver; determining a reward value based on the path loss, wherein an increase in path loss results in a reduced reward value; and adjusting the RL model based on the obtained state space, the determined action and the determined reward value.

Abstract: A data storage system in which different copies of a data object (e.g., a file) can be compressed using different compression processes (e.g. different compression algorithms/processes and/or compression parameters), with some favoring faster decompression, while others favoring storage space savings. When a data object needs to be accessed, the copy of the data object that can be decompressed using minimal resource (computing and/or time) can be located and retrieved.

Abstract: (57) Abstract: A method, performed by a first node (111), for handling parameters to configure a second node (112). The first node (111) determines (207) parameters to configure the second node (112). The determining (207) is based on an analysis by a Generative Adversarial Network comprising performing iteratively: i) generating a set of parameters estimated to be linked to a performance of a first group of nodes (121) being above a threshold, and ii) discriminating between the generated set of parameters and a first set of parameters observed to be linked to the performance of the first group of nodes (121) being above the threshold to obtain a score for every parameter. The score indicates how different the generated parameters and the first set of parameters are. The determined parameters have the score resulting from the discriminating being lower than anotherthreshold. The first node (111) also outputs an indication comprising the determined parameters.

Abstract: Resource allocation problems involve identification of resource, selection by certain criteria and offering of resources to the requester. Identification of required resources may involve matching the type of resource, selecting based on user requirements and policy criteria, and offering the resource through an assignment system. An apparatus and a method are provided that enable identification and selection of resources. The method includes receiving a resource allocation request for the allocation of a resource, the resource allocation request specifying a set of user requirements. The method includes receiving an operator policy associated with the resource, the operator policy including one or more policy requirements. The method includes synthesizing a resource request based on the resource allocation request and the operator policy.

Abstract: An automatic rule generation and evaluation system enables a user to generate rules and evaluate the rules even as the rules are generated. The user employs a rules workbench user interface (UI) to provide input regarding the rules to be implemented on data obtained from a plurality of data sources. The rules workbench UI generates scripts from the user input. The scripts are employed to build a model and generate queries. Displaying the result sets obtained from executing the queries enables the user to evaluate the rules in real-time. If the user is satisfied, the user may finalize the rules, else the user can further modify the rules and the steps of rule modification and rule evaluation may be iterated until the rules are finalized.

Abstract: The disclosure provides methods, apparatus and machine-readable mediums for mitigating power failures in base stations in wireless communication networks. In one aspect of the disclosure, a method for mitigating power failures in a wireless communication network is provided. The method comprises, responsive to a determination that a primary power supply for a base station in the wireless communication network has failed or is predicted to fail, initiating deployment of an unmanned aerial vehicle to the base station to charge one or more batteries providing a secondary power supply for the base station.

Abstract: A method (100) for managing a group of Unmanned Aerial Vehicles (UAVs) operable to fly in a formation is disclosed, each UAV being programmed with a task to be performed by the UAV. The method is performed in a controller UAV of the group and comprises receiving UAV status information from UAVs in the group (110), obtaining information on a current formation of the group (120) and combining the received UAV status information with the information on current group formation to form a representation of a first state of the group (130). The method further comprises using a machine learning model to predict, on the basis of the first state of the group, an optimal formation transition to a new formation (140) and to instruct the UAVs in the group to perform the predicted optimal formation transition (150). An optimal formation transition is a transition to a formation that will minimise predicted total energy consumption for all UAVs in the group to complete their tasks.

Abstract: A data storage system in which different copies of a data object (e.g., a file) can be compressed using different compression processes (e.g. different compression algorithms/processes and/or compression parameters), with some favoring faster decompression, while others favoring storage space savings. When a data object needs to be accessed, the copy of the data object that can be decompressed using minimal resource (computing and/or time) can be located and retrieved.

Abstract: Resource allocation problems involve identification of resource, selection by certain criteria and offering of resources to the requester. Identification of required resources may involve matching the type of resource, selecting based on user requirements and policy criteria, and offering the resource through an assignment system. An apparatus and a method are provided that enable identification and selection of resources. The method includes receiving a resource allocation request for the allocation of a resource, the resource allocation request specifying a set of user requirements. The method includes receiving an operator policy associated with the resource, the operator policy including one or more policy requirements. The method includes synthesizing a resource request based on the resource allocation request and the operator policy.

Abstract: Data slices of historical time series are leveraged to facilitate in more accurately predicting like data slices of future time series. Different predictive models are employed to detect outliers in different data slices to enhance the accuracy of the predictions. The data slices can be temporal and/or non-temporal attributes of a data set represented by the historical time series. In this manner, for example, a historical time series for a network location can be sliced temporally into one hour time periods as a function of a day, a week, a month, a year, etc. Outliers detected in these data slices can then be mitigated utilizing the predictive time series model by replacing the outlier with the expected value. The mitigated historical time series can then be employed in a predictive model to predict future web traffic for the network location (and advertising revenue values) with a substantial increase in accuracy.