Abstract: A prediction system for simulating effects of a real-world event can be used for autonomous driving. In operation, the system receives input data regarding a complex system (e.g., roadways) and various real-world events. A full-scale network is constructed of the complex system, such that nodes represent road intersections and edges between nodes represent road segments linking the road intersections. The network is reduced is scaled down to generate a multi-layer model of the complex system. Each layer in the model is simulated to identify equilibrium flows, with the model thereafter destabilized by applying stimuli to reflect the real-world event. An autonomous vehicle can then be caused to chart and traverse a road path based on road segments and intersections that are least affected by the real-world event.

Abstract: Described is a system for predicting an occurrence of large-scale events using social media data. A collection of time series is acquired from social media data related to an event of interest. The collection of time series is partitioned into time intervals and semantic features are extracted from the time intervals as a set of semantic intervals. The semantic features are encoded into a multilayer network. Subgraphs of the multilayer network are transformed into a state transition network. A prediction of a future event of interest is generated by analyzing the encoded network using the state transition network. Using the analyzed encoded network, a device is controlled based on the prediction of the future event of interest.

Abstract: A method and apparatus for maintaining an aircraft. Real-time event information indicating faults in systems on the aircraft and aircraft condition monitoring system data indicating conditions of the systems on the aircraft are stored during a plurality of legs of flights of the aircraft. A feature table comprising the real-time event information and the aircraft condition monitoring system data is built. Feature vectors are extracted from the feature table. A machine learning algorithm is applied to the extracted feature vectors to generate a predicted maintenance event message that identifies a predicted maintenance event. The predicted maintenance event message is used to perform a maintenance operation on the aircraft.

Abstract: Described is a system for extracting multi-scale hierarchical clustering on customer observables (COs) data in a vehicle. The system selects a parameter for a set of incident data of COs data. Simplicial complexes are generated from the COs data based on the selected parameter. Face networks are generated from the simplicial complexes. For each face network, a set of connected components is extracted. Each connected component is transformed to a cluster of related COs, resulting in a first extracted relation between COs. The first extracted relation is used to automatically generate an alert at a client device when a second extracted relation different from the first extracted relation results from the transformation.

Abstract: Described is a system for extracting multi-scale hierarchical clustering on customer observables (COs) data in a vehicle. The system selects a parameter for a set of incident data of COs data. Simplicial complexes are generated from the COs data based on the selected parameter. Face networks are generated from the simplicial complexes. For each face network, a set of connected components is extracted. Each connected component is transformed to a cluster of related COs, resulting in a first extracted relation between COs. The first extracted relation is used to automatically generate an alert at a client device when a second extracted relation different from the first extracted relation results from the transformation.

Abstract: Described is system for accurate user alignment across multiple online social media platforms. Out of textual messages from multiple user accounts of a first social media platform, the system identifies a set of textual messages from a first user account and a second user account of the first social media platform, each textual message in the set of textual messages comprising a set of specific character strings. The set of specific character strings represents a link to a post on a second social media platform, resulting in linked messages, the post originating from a linked account of the second social media platform. Either the first user account or the second user account is selected as an associated account by determining which originated the greater number of messages in the set of textual messages. A map component associated with a user identity that includes the associated account and the linked account is generated.

Abstract: A method and apparatus for maintaining an aircraft. Real-time event information indicating faults in systems on the aircraft and aircraft condition monitoring system data indicating conditions of the systems on the aircraft are stored during a plurality of legs of flights of the aircraft. A feature table comprising the real-time event information and the aircraft condition monitoring system data is built. Feature vectors are extracted from the feature table. A machine learning algorithm is applied to the extracted feature vectors to generate a predicted maintenance event message that identifies a predicted maintenance event. The predicted maintenance event message is used to perform a maintenance operation on the aircraft.