A weak order is a way to rank n objects where ties are allowed. In this paper, we extend the prefer-larger and the prefer-opposite algorithms for de Bruijn sequences to provide the first known greedy universal cycle c...
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ISBN:
(纸本)9783030392185;9783030392192
A weak order is a way to rank n objects where ties are allowed. In this paper, we extend the prefer-larger and the prefer-opposite algorithms for de Bruijn sequences to provide the first known greedy universal cycle constructions for weak orders.
Regularization methods for the Cox proportional hazards regression with high-dimensional survival data have been studied extensively in the literature. However, if the model is mis-specified, this would result in misl...
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Regularization methods for the Cox proportional hazards regression with high-dimensional survival data have been studied extensively in the literature. However, if the model is mis-specified, this would result in misleading statistical inference and prediction. To enhance the prediction accuracy for the relative risk and the survival probability, we propose three model averaging approaches for the high-dimensional Cox proportional hazards regression. Based on the martingale residual process, we define the delete-one cross-validation (CV) process, and further propose three novel CV functionals, including the end-time CV, integrated CV, and supremum CV, to achieve more accurate prediction for the risk quantities of clinical interest. The optimal weights for candidate models, without the constraint of summing up to one, can be obtained by minimizing these functionals, respectively. The proposed model averaging approach can attain the lowest possible prediction loss asymptotically. Furthermore, we develop a greedy model averaging algorithm to overcome the computational obstacle when the dimension is high. The performances of the proposed model averaging procedures are evaluated via extensive simulation studies, demonstrating that our methods achieve superior prediction accuracy over the existing regularization methods. As an illustration, we apply the proposed methods to the mantle cell lymphoma study.
We suggest a nonlinear model reduction approach for transient electro-quasistatic field simulations of high-voltage devices that comprise strongly nonlinear electric field stress grading material. The singular value d...
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We suggest a nonlinear model reduction approach for transient electro-quasistatic field simulations of high-voltage devices that comprise strongly nonlinear electric field stress grading material. The singular value decomposition is employed to obtain the proper orthogonal decomposition modes, while nodes at which interpolation constraints are imposed, are sampled according to a greedy approach and to information criteria. More precisely, in addition to the greedy approach, at each node of the computational mesh the spectral Shannon entropy of the electric potential is computed and interpolation constraints at high-entropy nodes are introduced. Numerical experiments validate that this maximal information node sampling strategy results in improved reduced models, in terms of nonlinear iterations and, in cases, also in terms of accuracy. (C) 2019 Elsevier Inc. All rights reserved.
greedy algorithms, which are employed to solve the sparse reconstruction based on l0 minimization, always present two main shortcomings. One is that they are easy to fall into sub-optimal solutions by utilizing fast s...
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greedy algorithms, which are employed to solve the sparse reconstruction based on l0 minimization, always present two main shortcomings. One is that they are easy to fall into sub-optimal solutions by utilizing fast searching strategies and perform relatively bad on reconstruction accuracy. The other is that they need a large number of iterations to be convergent by setting negative gradient as the searching direction. To improve the two shortcomings, this paper proposes a novel fast global matching pursuit algorithm (FGMP) for sparse reconstruction. Firstly, we design global matching pursuit strategies to solve the l0 minimization essentially, which is more likely to find the global optimal solution accurately. Then, the global searching direction is designed based on Quasi-Newton projection to replace the negative gradient, which is efficient to reduce the iterations of convergence and avoid the long time-consuming least square implementation to accelerate the reconstruction speed. The proposed FGMP algorithm is as simple as greedy algorithms, while it has better performance on both reconstruction accuracy and reconstruction speed. Simulated experiments on signal reconstruction and image reconstruction demonstrate that FGMP outperforms the state-of-the-art greedy algorithms especially when the sparsity level is relatively large.
The smart city is a concept of utilizing digital technologies to improve and enhance the lives of a city's inhabitants. This concept has been the subject of increasing interest over the past few years. However, mo...
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The smart city is a concept of utilizing digital technologies to improve and enhance the lives of a city's inhabitants. This concept has been the subject of increasing interest over the past few years. However, most studies address improving aspects of a city's infrastructure, such as information security, privacy, communication networks, government, and transportation. Noticeably absent from the subject matter of these studies are social problems, such as poverty and homelessness. In this paper, we explore how technology can be harnessed to mitigate homelessness. We introduce eight novel heuristic algorithms that create a desirable homeless-to-housing assignment with regards to homeless individuals' characteristics and the nature of services. We discuss the efficiency of each of the algorithms through simulations. Our best performing algorithm obtains 92% accuracy in comparison to the optimal solution and 99.7% fairness. The algorithms are compared in terms of execution time, solution accuracy, fairness, and the relative difference with the optimal solution of this NP-hard problem.
Firewalls are a fundamental element of network security systems with the ability to block network data traffic flows according to pre-defined rules. Software-defined networking (SDN) technology, which can provide flex...
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Firewalls are a fundamental element of network security systems with the ability to block network data traffic flows according to pre-defined rules. Software-defined networking (SDN) technology, which can provide flexibility, elasticity, and programmability for network management, has been applied to network security systems. We propose a software-defined firewall cyber-security system, which securely gathers the firewall rules of the host/network-based firewalls through the SDN control plane, converts the collected firewall rules in the form of SDN flow rules, and deploys them on OpenFlow (OF)-enabled switches. Furthermore, we formulate an optimization problem to find appropriate OF-enabled switches to which the SDN flow rules are to be sent. The proposed firewall system makes the traffic flows that are destined to be dropped by a firewall be dropped in advance at the OF-enabled switch with the corresponding SDN flow rules. The SDN-based testbed experiments demonstrate that the proposed firewall system reduces the aggregate network traffic volume and the resource utilization of end-hosts in the network.
We consider a compressed sensing problem to recover a sparse signal vector from a small number of one-bit quantized and noisy measurements. In this system, a probabilistic greedy algorithm, called bayesian matching pu...
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We consider a compressed sensing problem to recover a sparse signal vector from a small number of one-bit quantized and noisy measurements. In this system, a probabilistic greedy algorithm, called bayesian matching pursuit (BMP), has been recently proposed in which a new support index is identified for each iteration, via a local optimal strategy based on a Gaussian-approximated maximum a posteriori estimation. Although BMP can outperform the other existing methods as Quantized Compressive Sampling Matched Pursuit (QCoSaMP) and Quantized Iterative Shrinkage-Thresholding algorithm (QISTA), its accuracy is still far from the optimal, yielding a locally optimal solution. Motivated by this, we propose an advanced greedy algorithm by leveraging the idea of a stack algorithm, which is referred to as stacked BMP (StBMP). The key idea of the proposed algorithm is to store a number of candidate partial paths (i.e., the candidate support sets) in an ordered stack and tries to find the global optimal solution by searching along the best path in the stack. The proposed method can efficiently remove unnecessary paths having lower path metrics, which can provide a lower complexity. Simulation results demonstrate that the proposed StBMP can significantly improve the BMP by keeping a low computational complexity.
In this paper, we propose a practical and efficient methods to solve the vertex separator problem (VSP for short), based on branch-and-bound procedure, which uses linear programming, and a greedy algorithm. This probl...
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In this paper, we propose a practical and efficient methods to solve the vertex separator problem (VSP for short), based on branch-and-bound procedure, which uses linear programming, and a greedy algorithm. This problem arises in many areas of applications such as graph algorithms, communication networks, solving systems of equations, finite element and finite difference problems. We show, by computational experiments, that our approach is able to solve in short time large-scale instances of VSP from the literature to optimality or near optimality.
This paper proposes a quantum-inspired evolutionary algorithm (QiEA) to solve an optimal service-matching task-assignment problem. Our proposed algorithm comes with the advantage of generating always feasible populati...
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This paper proposes a quantum-inspired evolutionary algorithm (QiEA) to solve an optimal service-matching task-assignment problem. Our proposed algorithm comes with the advantage of generating always feasible population individuals and, thus, eliminating the necessity for a repair step. That is, with respect to other quantum-inspired evolutionary algorithms, our proposed QiEA algorithm presents a new way of collapsing the quantum state that integrates the problem constraints in order to avoid later adjusting operations of the system to make it feasible. This results in lower computations and also faster convergence. We compare our proposed QiEA algorithm with three commonly used benchmark methods: the greedy algorithm, Hungarian method and Simplex, in five different case studies. The results show that the quantum approach presents better scalability and interesting properties that can be used in a wider class of assignment problems where the matching is not perfect.
Lenstra-Lenstra-Lovasz (LLL) algorithm has been adopted as a lattice reduction (LR) technique for multiple-input multiple-output (MIMO) detection to improve error performance without exponential complexity. However, i...
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ISBN:
(纸本)9781479967704
Lenstra-Lenstra-Lovasz (LLL) algorithm has been adopted as a lattice reduction (LR) technique for multiple-input multiple-output (MIMO) detection to improve error performance without exponential complexity. However, implementing the LLL algorithm is still challenging. During the execution of each LLL iteration, the column swap operations may not happen in some cases, which is not efficient in terms of convergence speed. To address this issue, some greedy LLL variants have recently been proposed, which only select the iterations with column swap each time so that the number of LLL iterations can be reduced compared to the original LLL algorithm. In this paper, we propose an efficient greedy LLL algorithm, based on the relaxed Lovasz condition to search the candidate set of LLL iterations and the relaxed decrease of LLL potential to select an LLL iteration each time. Besides, we also present an efficient implementation of the proposed algorithm. Compared to the existing greedy LLL algorithms, simulations show that the proposed greedy LLL not only converges faster but also exhibits much lower complexity (save over 55% and 62% complexity in average for 4 x 4 and 8 x 8 MIMO systems) while maintaining similar error performance in LR-aided MIMO detectors.
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