The reload cost concept refers to the cost that occurs at a vertex along a path on an edge-colored graph when it traverses an internal vertex between two edges of different colors. This cost depends only on the colors...
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The reload cost concept refers to the cost that occurs at a vertex along a path on an edge-colored graph when it traverses an internal vertex between two edges of different colors. This cost depends only on the colors of the traversed edges. Reload costs arise in various applications such as transportation networks, energy distribution networks, and telecommunications. Previous work on reload costs focuses on two problems of finding a spanning tree with minimum cost with respect to two different cost measures. In both problems the cost is associated with a set of paths from a given vertex r to all the leaves of the constructed tree. The first cost measure is the sum of the reload costs of all paths from r to the leaves. The second cost measure is the changeover cost, in which the cost of traversing a vertex by using two specific incident edges is paid only once regardless of the number of paths traversing it. The first problem is inapproximable within any polynomial time computable function of the input size [1], and the second problem is inapproximable within n(1-epsilon) for any epsilon > 0 [2]. In this paper we show that the first hardness result holds also for the second problem. Given this strong inapproximability result, we study the complexity and approximability properties of numerous special cases of this second problem. We mainly focus on bounded costs, and consider both directed and undirected graphs, bounded and unbounded number of colors, and both bounded and unbounded degree graphs. We also present polynomial time exact algorithms and an approximation algorithm for some special case. To the best of our knowledge, these are the first algorithms with a provable performance guarantee for the problem. Moreover, our approximation algorithm shows a tight bound on the approximability of the problem for a specific family of instances. (C) 2014 Elsevier B.V. All rights reserved.
Minimizing total energy to keep an ad hoc wireless network symmetrically connected is an NP-hard problem. Recently, several greedy approximations have been proposed, based on k-restricted decompositions of the network...
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Minimizing total energy to keep an ad hoc wireless network symmetrically connected is an NP-hard problem. Recently, several greedy approximations have been proposed, based on k-restricted decompositions of the network. Their performance ratios are established through estimations of the least upper bound rho(k) for the ratio between total powers of best possible k-restricted decomposition and the optimal solution. In this paper, we determine the exact value Of rho(k) for all k. (C) 2004 Elsevier B.V. All rights reserved.
Discovering frequent patterns over event sequences is an important data mining problem. Existing methods typically require multiple passes over the data, rendering them unsuitable for streaming contexts. We present th...
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Discovering frequent patterns over event sequences is an important data mining problem. Existing methods typically require multiple passes over the data, rendering them unsuitable for streaming contexts. We present the first streaming algorithm for mining frequent patterns over a window of recent events in the stream. We derive approximation guarantees for our algorithm in terms of: (i) the separation of frequent patterns from the infrequent ones, and (ii) the rate of change of stream characteristics. Our parameterization of the problem provides a new sweet spot in the tradeoff between making distributional assumptions over the stream and algorithmic efficiencies of mining. We illustrate how this yields significant benefits when mining practical streams from neuroscience and telecommunications logs.
Allocation of bandwidth among components is a fundamental problem in compositional real-time systems. State-of-the-art algorithms for bandwidth allocation use either exponential-time or pseudo-polynomial-time techniqu...
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Allocation of bandwidth among components is a fundamental problem in compositional real-time systems. State-of-the-art algorithms for bandwidth allocation use either exponential-time or pseudo-polynomial-time techniques for exact allocation, or linear-time, utilization-based techniques which may over-provision bandwidth. In this paper, we propose research into a third possible approach: parametric approximation algorithms for bandwidth allocation in compositional real-time systems. We develop a fully-polynomial-time approximation scheme (FPTAS) for allocating bandwidth for sporadic task systems scheduled by earliest-deadline first (EDF) upon an Explicit-Deadline Periodic (EDP) resource. Our algorithm takes, as parameters, the task system and an accuracy parameter I mu > 0, and returns a bandwidth which is guaranteed to be at most a factor (1+I mu) more than the optimal minimum bandwidth required to successfully schedule the task system. Furthermore, the algorithm has time complexity that is polynomial in the number of tasks and 1/I mu.
We consider large-scale Markov decision processes (MDPs) with a time-consistent risk measure of variability in cost under the risk-aware MDP paradigm. Previous studies showed that risk-aware MDPs, based on a minimax a...
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We consider large-scale Markov decision processes (MDPs) with a time-consistent risk measure of variability in cost under the risk-aware MDP paradigm. Previous studies showed that risk-aware MDPs, based on a minimax approach to handling risk, can be solved using dynamic programming for small-to medium-sized problems. However, due to the "curse of dimensionality," MDPs that model real-life problems are typically prohibitively large for such approaches. In this technical note, we employ an approximate dynamic programming approach and develop a family of simulation-based algorithms to approximately solve large-scale risk-aware MDPs with time-consistent risk measures. In parallel, we develop a unified convergence analysis technique to derive sample complexity bounds for this new family of algorithms.
In this paper, we propose an automatic tool to search for optimal differential and linear trails in ARX ciphers. It's shown that a modulo addition can be divided into sequential small modulo additions with carry b...
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In this paper, we propose an automatic tool to search for optimal differential and linear trails in ARX ciphers. It's shown that a modulo addition can be divided into sequential small modulo additions with carry bit, which turns an ARX cipher into an S-box-like cipher. From this insight, we introduce the concepts of carry-bit-dependent difference distribution table (CDDT) and carry-bit-dependent linear approximation table (CLAT). Based on them, we give efficient methods to trace all possible output differences and linear masks of a big modulo addition, with returning their differential probabilities and linear correlations simultaneously. Then an adapted Matsui's algorithm is introduced, which can find the optimal differential and linear trails in ARX ciphers. Besides, the superiority of our tool's potency is also confirmed by experimental results for round-reduced versions of HIGHT and SPECK. More specifically, we find the optimal differential trails for up to 10 rounds of HIGHT, reported for the first time. We also find the optimal differential trails for 10, 12, 16, 8 and 8 rounds of SPECK32/48/64/96/128, and report the provably optimal differential trails for SPECK48 and SPECK64 for the first time. The optimal linear trails for up to 9 rounds of HIGHT are reported for the first time, and the optimal linear trails for 22, 13, 15, 9 and 9 rounds of SPECK32/48/64/96/128 are also found respectively. These results evaluate the security of HIGHT and SPECK against differential and linear cryptanalysis. Also, our tool is useful to estimate the security in the design of ARX ciphers.
作者：
SHAN, LPEYROT, AHGrad. Student
Dept. of Civ. Engrg. Univ. of Wisconsin Madison WI 53706 Prof.
Dept. of Civ. Engrg. 1415 Johnson Dr. Univ. of Wisconsin Madison WI 53706
Angle members in steel lattice towers are currently individually designed by approximate formulas that take into account geometric and material properties, as well as conditions of eccentricity and restraint at end jo...
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Angle members in steel lattice towers are currently individually designed by approximate formulas that take into account geometric and material properties, as well as conditions of eccentricity and restraint at end joints. Full‐scale tests of towers show that current design formulas are not always appropriate and that failures often involve a group of members (subassembly) rather than individual members. An alternate is proposed herein to full‐scale testing for determining the ultimate strength of angle members or subassemblies. It proposes to model angle members by nonlinear plate elements located in the legs of the angles. Such a detailed modeling allows for the occurrence of local buckling, lateral‐torsional buckling, eccentricity of connections, localized plasticity, etc., A plate element appropriate for modeling steel angles is discussed in detail, and several examples are presented to demonstrate the usefulness of the concept.
We present a study of the Within-Strip Discrete Unit Disk Cover (WSDUDC) problem, which is a restricted version of the Discrete Unit Disk Cover (DUDC) problem. For the WSDUDC problem, there exist a set of points and a...
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We present a study of the Within-Strip Discrete Unit Disk Cover (WSDUDC) problem, which is a restricted version of the Discrete Unit Disk Cover (DUDC) problem. For the WSDUDC problem, there exist a set of points and a set of unit disks in the plane, and the points and disk centres are confined to a strip of fixed width. An optimal solution to the WSDUDC problem is a subset of the disks of minimum cardinality that covers all points in the input set. We describe two approximation algorithms for the problem: a 3-approximate algorithm which applies for strips of width at most 0.8 units, and a general scheme for any strip with less than unit width. We prove that the WSDUDC problem is NP-hard on strips of any fixed width, which is our most interesting result from a theoretical standpoint. The result is also quite surprising, since a number of similar problems are tractable on strips of fixed width. Finally, we discuss how these results may be applied to known DUDC approximation algorithms. (C) 2017 Elsevier B.V. All rights reserved.
Multi-UAV cooperative mission assignment is an important research direction in the field of UAV research. In the planning process, the assignment conflict between UAVs and mission points and the solution efficiency ar...
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Multi-UAV cooperative mission assignment is an important research direction in the field of UAV research. In the planning process, the assignment conflict between UAVs and mission points and the solution efficiency are the key difficulties in multi-UAV cooperative mission assignment. Based on the Weighted Approximate Flight Cost (WAFC) method and the Reduced Redundant Assignment Scheme (RRAS) algorithm, a multi-UAV cooperative mission problem considering different assignment models is proposed. The contributions of this study are: firstly, the approximate flight cost matrix is constructed based on the vertical distance between the UAV and the mission point, and the matrix is applied to calculate the initial flight distance of the UAV, which fully takes into account the effects of mountain type and radar threat on the mission assignment. Secondly, the auxiliary flight cost method and the spatial mapping matrix are constructed, which maps the discrete approximation of the flight cost to the continuous space and solves the UAV's local conflict problem in the assignment process. Finally, an adaptive selection mutation strategy based on iterative individual fitness values is proposed to reduce the redundancy of candidate assignment schemes and improve the efficiency of the planning system. The simulation results verify that the algorithm has high cooperative capability, good robustness and fast solution speed when dealing with cooperative multi-UAV mission assignment planning.
5G communication network is vital to the intelligent and unmanned technology of open-pit mine, but the high cost of 5G base station deployment will bring huge cost increases to mining enterprises. Thus, this article p...
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5G communication network is vital to the intelligent and unmanned technology of open-pit mine, but the high cost of 5G base station deployment will bring huge cost increases to mining enterprises. Thus, this article proposes an improved sparrow search algorithm (SSA) using random walk strategy (RWSSA) to optimization the distribution and signal coverage of 5G base stations in open-pit mine. In order to verify the performance of the algorithm, the SSA, the modified ant lion optimizer, the particle swarm optimization, and the RWSSA were used to compare and analyze the accuracy and speed of the algorithm in solving high dimensionality benchmark functions, respectively. The dimensions used in the test are 30, 100, and 500 dimensions. After that, four algorithms were applied to a real example to optimize the deployment of 5G base stations, including macrobase stations and microbase stations. RWSSA has very good performance in terms of performance and practical application. Therefore, RWSSA is more suitable for the application of 5G base station distribution optimization in open-pit mines.
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