Human ability to simultaneously track multiple items declines with set size. This effect is commonly attributed to a fixed limit on the number of items that can be attended to, a notion that is formalized in limited-capacity and slot models. Instead, we propose that observers are constrained by stimulus uncertainty that increases with the number of items but use Bayesian inference to achieve optimal performance. We model five data sets from published deviation discrimination experiments that varied set size, number of deviations, and magnitude of deviation. A constrained Bayesian observer better explains each data set than do the traditional limited-capacity model, the recently proposed slots-plus-averaging model, a fixed-uncertainty Bayesian model, a Bayesian model with capacity limit, and a simple averaging model. This indicates that the notion of limited capacity in attentional tracking needs to be revised. Moreover, it supports the idea that Bayesian optimality of human perception extends to high-level perceptual computations.

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Dual bipolar resistive switching characteristics were observed in the Pt/DyMn2O5/TiN memory devices. The typical switching effect could be attributed to the formation and rupture of the conducting filament in DyMn2O5 films. The parasitic switching behavior can be observed in the specific operation condition. Dual bipolar resistance switching behaviors of filament-type and interface-type can coexist in the devices by appropriate voltage operation. The operating current can be significantly decreased (100 times) by parasitic switching operation for portable electronic product application. In addition, the relationship between filament-type and interface-type switching behaviors were studied in this paper.

In this paper, we are concerned with the computation of a few eigenpairs with smallest eigenvalues in absolute value of quadratic eigenvalue problems. We first develop a semiorthogonal generalized Arnoldi method where the name comes from the application of a pseudo inner product in the construction of a generalized Arnoldi reduction for a generalized eigenvalue problem. The method applies the Rayleigh-Ritz orthogonal projection technique on the quadratic eigenvalue problem. Consequently, it preserves the spectral properties of the original quadratic eigenvalue problem. Furthermore, we propose a refinement scheme to improve the accuracy of the Ritz vectors for the quadratic eigenvalue problem. Given shifts, we also show how to restart the method by implicitly updating the starting vector and constructing better projection subspace. We combine the ideas of the refinement and the restart by selecting shifts upon the information of refined Ritz vectors. Finally, an implicitly restarted refined semiorthogonal generalized Arnoldi method is developed. Numerical examples demonstrate that the implicitly restarted semiorthogonal generalized Arnoldi method with or without refinement has superior convergence behaviors than the implicitly restarted Arnoldi method applied to the linearized quadratic eigenvalue problem.The SGA method applies the Rayleigh-Ritz subspace projection technique on the subspace Q m Á spanfQ m g with the Galerkin condition . 2 M C ÂD C K/ ? Q m , and the conclusion of Theorem 3.1 follows directly from (33) and (34).To prove (34), it suffices to show that p i 2 spanf MQ m , p 1 g, 1 6 i 6 m. We prove this by induction. Clearly, p 1 2 spanf MQ m , p 1 g. Suppose that p 1 , : : : , p i 2 spanf MQ m , p 1 g for 1 < i 6 m 1. From the equality (32), we have MQ m D P m Hm C pme > m . Thus,