1,395 research outputs found

    Multi-Scale Information, Network, Causality, and Dynamics: Mathematical Computation and Bayesian Inference to Cognitive Neuroscience and Aging

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    The human brain is estimated to contain 100 billion or so neurons and 10 thousand times as many connections. Neurons never function in isolation: each of them is connected to 10, 000 others and they interact extensively every millisecond. Brain cells are organized into neural circuits often in a dynamic way, processing specific types of information and providing th

    Vortex signal detection method with stochastic resonance based on adaptive coupled feedback control

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    The control of stochastic resonance is the key to its application. A feedback method is proposed to control the generation of stochastic resonance with coupling, and then enhance resonance effect with the optimization of control parameters. The method is applied to detect vortex signal. Artificial fish swarm algorithm is used to adjust the control variables adaptively, thus the optimal control of the coupled bistable stochastic resonance is realized. Numerical simulation and experimental results manifest that by this means the resonance effect can be enhanced effectively, the signal-to-noise ratio (SNR) of vortex signal can be improved, and the vortex shedding frequency can be obtained accurately

    Group-Based Asynchronous Distributed Alternating Direction Method of Multipliers in Multicore Cluster

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    The distributed alternating direction method of multipliers (ADMM) algorithm is one of the effective methods to solve the global consensus optimization problem. Considering the differences between the communication of intra-nodes and inter-nodes in multicore cluster, we propose a group-based asynchronous distributed ADMM (GAD-ADMM) algorithm: based on the traditional star topology network, the grouping layer is added. The workers are grouped according to the process allocation in nodes and model similarity of datasets, and the group local variables are used to replace the local variables to compute the global variable. The algorithm improves the communication efficiency of the system by reducing communication between nodes and accelerates the convergence speed by relaxing the global consistency constraint. Finally, the algorithm is used to solve the logistic regression problem in a multicore cluster. The experiments on the Ziqiang 4000 showed that the GAD-ADMM reduces the system time cost by 35 % compared with the AD-ADMM
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