7,897 research outputs found
Giant magnetoimpedance in crystalline Mumetal
We studied giant magnetoimpedance (GMI) effect in commercial crystalline
Mumetal, with the emphasis to sample thickness dependence and annealing
effects. By using appropriate heat treatment one can achieve GMI ratios as high
as 310%, and field sensitivity of about 20%/Oe, which is comparable to the best
GMI characteristics obtained for amorphous and nanocrystalline soft magnetic
materials.Comment: 8 pages, 3 figure
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1458 EMT-inhibiting transcription factor Ovol2 regulates directional cell migration and proliferation in adult skin epithelia
Quantum state engineering with flux-biased Josephson phase qubits by Stark-chirped rapid adiabatic passages
In this paper, the scheme of quantum computing based on Stark chirped rapid
adiabatic passage (SCRAP) technique [L. F. Wei et al., Phys. Rev. Lett. 100,
113601 (2008)] is extensively applied to implement the quantum-state
manipulations in the flux-biased Josephson phase qubits. The broken-parity
symmetries of bound states in flux-biased Josephson junctions are utilized to
conveniently generate the desirable Stark-shifts. Then, assisted by various
transition pulses universal quantum logic gates as well as arbitrary
quantum-state preparations could be implemented. Compared with the usual
PI-pulses operations widely used in the experiments, the adiabatic population
passage proposed here is insensitive the details of the applied pulses and thus
the desirable population transfers could be satisfyingly implemented. The
experimental feasibility of the proposal is also discussed.Comment: 9 pages, 4 figure
Unsupervised Feature Selection with Adaptive Structure Learning
The problem of feature selection has raised considerable interests in the
past decade. Traditional unsupervised methods select the features which can
faithfully preserve the intrinsic structures of data, where the intrinsic
structures are estimated using all the input features of data. However, the
estimated intrinsic structures are unreliable/inaccurate when the redundant and
noisy features are not removed. Therefore, we face a dilemma here: one need the
true structures of data to identify the informative features, and one need the
informative features to accurately estimate the true structures of data. To
address this, we propose a unified learning framework which performs structure
learning and feature selection simultaneously. The structures are adaptively
learned from the results of feature selection, and the informative features are
reselected to preserve the refined structures of data. By leveraging the
interactions between these two essential tasks, we are able to capture accurate
structures and select more informative features. Experimental results on many
benchmark data sets demonstrate that the proposed method outperforms many state
of the art unsupervised feature selection methods
A matrix-based approach to solving the inverse Frobenius–Perron problem using sequences of density functions of stochastically perturbed dynamical systems
The paper introduces a matrix-based approach to estimate the unique one-dimensional discrete-time dynamical system that generated a given sequence of probability density functions whilst subjected to an additive stochastic perturbation with known density
Differential quadrature method for space-fractional diffusion equations on 2D irregular domains
In mathematical physics, the space-fractional diffusion equations are of
particular interest in the studies of physical phenomena modelled by L\'{e}vy
processes, which are sometimes called super-diffusion equations. In this
article, we develop the differential quadrature (DQ) methods for solving the 2D
space-fractional diffusion equations on irregular domains. The methods in
presence reduce the original equation into a set of ordinary differential
equations (ODEs) by introducing valid DQ formulations to fractional directional
derivatives based on the functional values at scattered nodal points on problem
domain. The required weighted coefficients are calculated by using radial basis
functions (RBFs) as trial functions, and the resultant ODEs are discretized by
the Crank-Nicolson scheme. The main advantages of our methods lie in their
flexibility and applicability to arbitrary domains. A series of illustrated
examples are finally provided to support these points.Comment: 25 pages, 25 figures, 7 table
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