Abstract: Methods and systems are provided for automatic classification of information in an adversarial machine learning setting. For example, a learner that includes multiple classifiers may perform data classification automatically. Each classifier may be trained with adversarial data of a different strength. For a given query to be classified, the learner is configured to intelligently select a classifier that is commensurate with the adversarial strength of the data inside the query, without explicit knowledge of that data or its adversarial strength.

Abstract: Methods and systems are provided for automatic classification of information in an adversarial machine learning setting. For example, a learner that includes multiple classifiers may perform data classification automatically. Each classifier may be trained with adversarial data of a different strength. For a given query to be classified, the learner is configured to intelligently select a classifier that is commensurate with the adversarial strength of the data inside the query, without explicit knowledge of that data or its adversarial strength.

Abstract: We describe an efficient algorithm for evaluating the (weighted bipartite graph of) associations between two sets of data with gaussian error, e.g., between a set of measured state vectors and a set of estimated state vectors. First a general method is developed for determining, from the covariance matrix, minimal d-dimensional error ellipsoids for the state vectors which always overlap when a gating criterion is satisfied. Circumscribing boxes, or d-ranges, for the data ellipsoids are then found and whenever they overlap the association probability is computed. For efficiently determining the intersections of the d-ranges a multidimensional search tree method is used to reduce the overall scaling of the evaluation of associations. Very few associations that lie outside the predetermined error threshold or gate are evaluated. Empirical testing for variously distributed data in both three and eight dimensions indicate that the scaling is significantly reduced from N2, where N is the size of the data set.

Abstract: A fuel transfer device for transferring fuel from the fuel supply line of an internal combustion engine to, for example, a fuel receiving vessel, with reduced loss of vapor to the environment. A fuel supply conduit includes a fuel flow control valve and a vapor recovery hood, and is connected to the fuel supply line of an internal combustion engine. Vapor is returned to the air intake manifold of the engine by way of a vapor return conduit.

Abstract: A high capacity storage unit comprising a frame, a first plurality of storage locations semi-permanently fixed on the frame, at least one movable panel overlying at least a portion of a vertical elevation of the frame and pivotally movable with respect to the frame between a closed position and an open position, and a second plurality of storage locations semi-permanently fixed on the panel. Preferably, the panel is substantially parallel to the vertical elevation of the frame when the panel is in the open position, so that it lies flat against the frame in that position. When the panel is in its closed position, the panel covers or prevents access to the first plurality of storage locations. The frame includes a top frame portion.