In this paper, a Loop Tightness Algorithm (LTA) is proposed. First, it finds the network loops and calculates it tightness value quickly. Then, it obtains the communities of the networks based on the tightness values....
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In this paper, a Loop Tightness Algorithm (LTA) is proposed. First, it finds the network loops and calculates it tightness value quickly. Then, it obtains the communities of the networks based on the tightness values. Finally, it reveals the relationship between the network loops and the community structure. The LTA is tested and validated by means of synthetic networks and real networks.
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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We consider a discrete model that describes a linear chain of particles coupled to an isolated ring composed of N defects. This simple system can be regarded as a generalization of the familiar Fano Anderson model. It...
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We consider a discrete model that describes a linear chain of particles coupled to an isolated ring composed of N defects. This simple system can be regarded as a generalization of the familiar Fano Anderson model. It can be used to model discrete networks of coupled defect modes in photonic crystals and simple waveguide arrays in two-dimensional lattices. The analytical result of the transmission coefficient is obtained, along with the conditions for perfect reflections and transmissions due to either destructive or constructive interferences. Using a simple example, we further investigate the relationship between the resonant frequencies and the number of defects N, and study how to affect the numbers of perfect reflections and transmissions. In addition, we demonstrate how these resonance transmissions and refections can be tuned by one nonlinear defect of the network that possesses a nonlinear Kerr-like response.
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.
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.
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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In this paper, one-dimensional (1.) 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 (1.) 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.
In this paper, we investigate belief revision in possibilistic logic, which is a weighted logic proposed to deal with incomplete and uncertain information. Existing revision operators in possibilistic logic are restri...
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Time interrupts play an important role in the system. When time interrupts occur, system will inspect process running state, providing an opportunity to schedule, which is important for improving system real time perf...
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