We have investigated theoretically the field-driven electron transport through a single-quantum-well semiconductor heterostructure with spin-orbit coupling. The splitting of the asymmetric Fano-type resonance peaks du...
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We have investigated theoretically the field-driven electron transport through a single-quantum-well semiconductor heterostructure with spin-orbit coupling. The splitting of the asymmetric Fano-type resonance peaks due to the Dresselhaus spin-orbit coupling is found to be highly sensitive to the direction of the incident electron. The splitting of the Fano-type resonance induces the spin-polarization dependent electron current. The location and the line shape of the Fano-type resonance can be controlled by adjusting the energy and the direction of the incident electron, the oscillation frequency, and the amplitude of the external field. These interesting features may be used to devise tunable spin filters and realize pure spin transmission currents.
This paper focuses on a new color-gray image fusion algorithm based on Morphological Component Analysis (MCA) which is a novel decomposition (separation) method based on sparse representation of signals and images. Th...
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In this paper, we propose a non-interior path following algorithm for solving the optimal power flow problem. By using the new approach, we can choose initial guesses arbitrarily in the whole space of n dimensional Eu...
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A new image resizing algorithm was proposed based on a color space and an energy updating mechanism. First, the concept of color space was introduced into image resizing algorithm and the pixel's energy function w...
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A new image resizing algorithm was proposed based on a color space and an energy updating mechanism. First, the concept of color space was introduced into image resizing algorithm and the pixel's energy function was re-built based on the gradient of gray space and color space. Three kinds of color energy functions were tested and the energy function in HSI space was finally chosen because of its excellent performance. Additionally, since image resizing might badly corrupt the contours of main areas in an original image and might further affect the understanding of the image, an energy updating mechanism was proposed. The energy updating mechanism could increase the neighborhood energy values where a pixel had been deleted. By doing so, it could decrease the probability that seams cross this area again, thus the contour of this area could be protected well. In experiments, the degree of Scale Invariant Feature Transform (SIFT) matching between original image and resized image with different λ values was calculated. The best performance shows that the proportion between the color and gray energy functions was 7:3. By comparing the proposed method with other two state of arts algorithms, it indicates that the shape of main area will be better preserved with the proposed algorithm without introducing any more complexity.
A Bloom filter is a space-efficient data structure used for concisely representing a set as well as membership queries at the expense of introducing false positive. In this paper, we propose the L-priorities Bloom fil...
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A Bloom filter is a space-efficient data structure used for concisely representing a set as well as membership queries at the expense of introducing false positive. In this paper, we propose the L-priorities Bloom filter （LPBF） as a new member of the Bloom filter （BF） family, it uses a limited multidimensional bit space matrix to replace the bit vector of standard bloom filters in order to support different priorities for the elements of a set. We demonstrate the time and space complexity, especially the false positive rate of LPBF. Furthermore, we also present a detailed practical evaluation of the false positive rate achieved by LPBF. The results show that LPBF performs better than standard BFs with respect to false positive rate.
In this paper, we introduce some new signature schemes and secret sharing schemes from the known Gröbner basis cryptosystems. Then, we discuss the issues related to the security of these schemes. Finally, we draw...
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The main challenge in the area of reinforcement learning is scaling up to larger and more complex problems. Aiming at the scaling problem of reinforcement learning, a scalable reinforcement learning method, DCS-SRL, i...
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The main challenge in the area of reinforcement learning is scaling up to larger and more complex problems. Aiming at the scaling problem of reinforcement learning, a scalable reinforcement learning method, DCS-SRL, is proposed on the basis of divide-and-conquer strategy, and its convergence is proved. In this method, the learning problem in large state space or continuous state space is decomposed into multiple smaller subproblems. Given a specific learning algorithm, each subproblem can be solved independently with limited available resources. In the end, component solutions can be recombined to obtain the desired result. To ad- dress the question of prioritizing subproblems in the scheduler, a weighted priority scheduling algorithm is proposed. This scheduling algorithm ensures that computation is focused on regions of the problem space which are expected to be maximally productive. To expedite the learning process, a new parallel method, called DCS-SPRL, is derived from combining DCS-SRL with a parallel scheduling architecture. In the DCS-SPRL method, the subproblems will be distributed among processors that have the capacity to work in parallel. The experimental results show that learning based on DCS-SPRL has fast convergence speed and good scalability.
Dynamic Bayesian Network (DBN) is a graphical model for representing temporal stochastic processes. Learning the structure of DBN is a fundamental step for parameter learning, inference and application. For large scal...
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We tackle the problem of improving the helpful actions technique for contingent planning. A revision of the heuristic computation that rearranges the relaxed planning graph is proposed, where the unreasonable conforma...
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In this paper, one-dimensional （1D） nonlinear beam equations of the form utt - uxx ＋ uxxxx ＋ mu = f （u） with Dirichlet boundary conditions are considered, where the nonlinearity f is an analytic, odd function an...
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In this paper, one-dimensional （1D） nonlinear beam equations of the form utt - uxx ＋ uxxxx ＋ mu = f （u） with Dirichlet boundary conditions are considered, where the nonlinearity f is an analytic, odd function and f（u） = O（u3）. It is proved that for all m ∈ （0, M＊] R （M＊ is a fixed large number）, but a set of small Lebesgue measure, the above equations admit small-amplitude quasi-periodic solutions corresponding to finite dimensional invariant tori for an associated infinite dimensional dynamical system. The proof is based on an infinite dimensional KAM theory and a partial Birkhoff normal form technique.
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