Highly entangled states called cluster states are a universal resource for measurement-based quantum computing (QC). Here we propose an efficient method for producing large cluster states using superconducting quantum...
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Highly entangled states called cluster states are a universal resource for measurement-based quantum computing (QC). Here we propose an efficient method for producing large cluster states using superconducting quantum circuits. We show that a large cluster state can be efficiently generated in just one step by turning on the interqubit coupling for a short time. Because the interqubit coupling is only switched on during the time interval for generating the cluster state, our approach is also convenient for preparing the initial state for each qubit and for implementing one-way QC via single-qubit measurements. Moreover, the cluster state is robust against parameter variations.
Backlight usually uses CCFL as light sources for the traditional LCD display. With the growing development of LED, it replaces CCFL gradually because of its advantage, such as durability, reliability, low-power and no...
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作者：
Chang-Woo ShinSeunghwan KimAsia Pacific Center for Theoretical Physics
National Core Research Center for System Biodynamics and Nonlinear and Complex Systems Laboratory Department of Physics Pohang University of Science and Technology San 31 Hyoja-dong Nam-gu Pohang Gyungbuk 790-784 Korea
To found the suitable models to describe the behavior of biochemistry systems, the dynamic Ε -SVM method was proposed on the basis of SVM. Each training sample uses different error. The existed methods for selecting ...
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Epilepsy is a dynamic disease in which self-organization and emergent structures occur dynamically at multiple levels of neuronal integration. Therefore, the transient relationship within multichannel electroencephalo...
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Epilepsy is a dynamic disease in which self-organization and emergent structures occur dynamically at multiple levels of neuronal integration. Therefore, the transient relationship within multichannel electroencephalograms (EEGs) is crucial for understanding epileptic processes. In this paper, we show that the global relationship within multichannel EEGs provides us with more useful information in classifying two different epilepsy types than pairwise relationships such as cross correlation. To demonstrate this, we determine the global network structure within channels of the scalp EEG based on the minimum spanning tree method. The topological dissimilarity of the network structures from different types of temporal lobe epilepsy is described in the form of the divergence rate and is computed for 11 patients with left (LTLE) and right temporal lobe epilepsy (RTLE). We find that patients with LTLE and RTLE exhibit different large scale network structures, which emerge at the epoch immediately before the seizure onset, not in the preceding epochs. Our results suggest that patients with the two different epilepsy types display distinct large scale dynamical networks with characteristic epileptic network structures.
Nervous systems under periodic stimuli display rich dynamical states including mode-locking and chaotic responses, which have been a subject of intense studies in neurodynamics. The bifurcation structure of the Hodgki...
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Nervous systems under periodic stimuli display rich dynamical states including mode-locking and chaotic responses, which have been a subject of intense studies in neurodynamics. The bifurcation structure of the Hodgkin-Huxley neuron under sinusoidal stimulus is studied in detail. The mechanisms of the firing onset and rich firing dynamics are studied with the help of the codimension-2 bifurcations, which play the role of the organizing center for myriads of saddle-node, period-doubling, and inverse-flip bifurcations forming the boundaries of the complex mode-locking structure. This study provides a useful insight into the organization of similar bifurcation structures in excitable systems such as neurons under periodic forcing.
To found the suitable models to describe the behavior of biochemistry systems, the dynamic epsiv-SVM method was proposed on the basis of SVM. Each training sample uses different error. The existed methods for selectin...
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To found the suitable models to describe the behavior of biochemistry systems, the dynamic epsiv-SVM method was proposed on the basis of SVM. Each training sample uses different error. The existed methods for selecting the parameters of SVM not only consume time, but also are difficult to find the optimal parameters. The optimal parameters were automatically decided by using multi-object genetic algorithm (MOGA). A new modeling method that combined MOGA with the dynamic epsiv-SVM was presented. The model for penicillin titer pre-estimate was developed by it in Matlab 6.5 with data collected from real plant. The model possesses the strong capability of fitting and generalization. Experiments show that the dynamic epsiv-SVM is superior to the standard SVM modeling method. MOGA is very feasible and efficient too
This paper addresses the issues related to the design of robust controller using genetic algorithms (GA) for lightweight, one-link flexible manipulators working under dynamic environments and other uncertain influence...
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This paper addresses the issues related to the design of robust controller using genetic algorithms (GA) for lightweight, one-link flexible manipulators working under dynamic environments and other uncertain influences. By selecting sensitivity weight functions properly using the GA method, a mixed sensitivity H infin controller is developed to ensure robustness of manipulator control systems for varying payloads and other modeling uncertainties. Numeric simulation has been conducted and the results have demonstrated the effectiveness of the proposed method
A variety of problems in operations research, performance analysis, manufacturing, and communication networks, etc., can be modelled as discrete event systems with minimum and maximum constraints. When such systems re...
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A variety of problems in operations research, performance analysis, manufacturing, and communication networks, etc., can be modelled as discrete event systems with minimum and maximum constraints. When such systems require only maximum constraints （or dually, only minimum constraints）, they can be studied using linear methods based on max-plus algebra. systems with mixed constraints are called min-max systems in which rain, max and addition operations appear simultaneously. A significant amount of work on such systems can be seen in literature. In this paper we provide some new results with regard to the balance problem of min-max functions; these are the structure properties of min-max systems. We use these results in the structural stabilization. Our main results are two sufficient conditions for the balance and one sufficient condition for the structural stabilization. The block technique is used to analyse the structure of the systems. The proposed methods, based on directed graph and max-plus algebra are constructive in nature. We provide several examples to demonstrate how the methods work in practice.
作者：
Chang-Woo ShinSeunghwan Kim[]Asia Pacific Center for Theoretical Physics
National Core Research Center for System Biodynamics and Nonlinear and Complex Systems Laboratory Department of Physics Pohang University of Science and Technology San 31 Hyoja-dong Nam-gu Pohang Gyungbuk Korea 790-784
Recent studies on complexsystems have shown that the synchronization of oscillators, including neuronal ones, is faster, stronger, and more efficient in small-world networks than in regular or random networks. We sho...
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Recent studies on complexsystems have shown that the synchronization of oscillators, including neuronal ones, is faster, stronger, and more efficient in small-world networks than in regular or random networks. We show that the functional structures in the brain can be self-organized to both small-world and scale-free networks by synaptic reorganization via spike timing dependent synaptic plasticity instead of conventional Hebbian learning rules. We show that the balance between the excitatory and the inhibitory synaptic inputs is critical in the formation of the functional structure, which is found to lie in a self-organized critical state.
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