Abstract: A hoist system may be used to raise and mount an interchangeable vehicle body onto a vehicle chassis. The hoist system may include a hoist frame having a roller, a subframe, a pivotal connection between the hoist frame and the subframe, a hinge joint member interconnected between the hoist frame and the subframe and a winch assembly connected to the hoist system. The subframe may have a bolt-on assembly for selectively, fixedly connecting the subframe to the vehicle chassis and the winch assembly may be selectively operable to raise the interchangeable body onto the hoist frame as the interchangeable body engages the roller.

Abstract: Described is a system for detecting adversarial activities. During operation, the system generates a multi-layer temporal graph tensor (MTGT) representation based on an input tag stream of activities. The MTGT representation is decomposed to identify normal activities and abnormal activities, with the abnormal activities being designated as adversarial activities. A device can then be controlled based on the designation of the adversarial activities.

Abstract: Described is a system for feature selection that extends supervised hierarchical clustering to neural activity signals. The system generates, using a hierarchical clustering process, a hierarchical dendrogram representing a set of neural activity data comprising individual neural data elements having neural activity patterns. The hierarchical dendrogram is searched for an optimal cluster parcellation using a stochastic supervised search process. An optimal cluster parcellation of the hierarchical dendrogram is determined that provides a classification of the set of neural activity data with respect to a supervised classifier, resulting in a reduced neural activity feature set. The set of neural activity data is classified using the reduced neural activity feature set, and the classified set of neural activity data is decoded.

Abstract: Described is a system for neural decoding of neural activity. Using at least one neural feature extraction method, neural data that is correlated with a set of behavioral data is transformed into sparse neural representations. Semantic features are extracted from a set of semantic data. Using a combination of distinct classification modes, the set of semantic data is mapped to the sparse neural representations, and new input neural data can be interpreted.