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words constitute nearly half of the human lexicon and are critically associated with human abstract thoughts, yet little is known about how they are represented in the brain. We tested the neural basis of 2 classical cognitive notions of abstract meaning representation: by linguistic contexts and by semantic features. We collected fMRI BOLD responses for 360 abstract words and built theoretical representational models from state-of-the-art corpus-based natural language processing models and behavioral ratings of semantic features. Representational similarity analyses revealed that both linguistic contextual and semantic feature similarity affected the representation of abstract concepts, but in distinct neural levels. The corpus-based similarity was coded in the high-level linguistic processing system, whereas semantic feature information was reflected in distributed brain regions and in the principal component space derived from whole-brain activation patterns. These findings highlight the multidimensional organization and the neural dissociation between linguistic contextual and featural aspects of abstract concepts.

Summary This paper considers event‐triggering controller design for directly observable discrete‐time linear systems subject to bounded disturbances. The main control objective is diminishing the influence aroused by the disturbances despite a reduction of the communication. Criteria are given to design feedback controllers in order to guarantee that systems are uniformly ultimately bounded in an ellipsoidal‐positive invariant set, which is used as an estimate of control performance for disturbance rejection. An optimization for minimizing the ellipsoidal‐positive invariant set is achieved by synthesizing the feedback control gain and the given event‐triggering conditions in LMIs. The effectiveness and applicability of the controller are illustrated by simulations and experimental implementations. Copyright © 2015 John Wiley & Sons, Ltd.

Online gradient methods are widely used for training feedforward neural networks. We prove in this paper a convergence theorem for an online gradient method with variable step size for backward propagation (BP) neural networks with a hidden layer. Unlike most of the convergence results that are of probabilistic and nonmonotone nature, the convergence result that we establish here has a deterministic and monotone nature.

This technical note considers event-triggering conditions and controller synthesis approaches for delayed linear systems. Optimization problems for minimizing the upper bound of quadratic cost functions are formulated in the form of linear matrix inequalities (LMIs). By solving the optimization problems a unique control gain can be obtained. The performance considered in this technical note includes a linear quadratic cost function for quantifying the control performance and average event times at which the control input must be updated for quantifying the transmission reductions. Comparisons with other approaches in the literature are given to demonstrate the advantages with respect to the two performance indices. Furthermore, an experimental implementation of the proposed methods in an inverted pendulum system shows the applicability and effectiveness in real world.

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