The authors provide data structures that maintain a graph as edges are inserted and deleted, and keep track of the following properties: minimum spanning forests, best swap, graph connectivity, and graph 2-edge-connec...
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The authors provide data structures that maintain a graph as edges are inserted and deleted, and keep track of the following properties: minimum spanning forests, best swap, graph connectivity, and graph 2-edge-connectivity, in time O(n/sup 1/2/log(m/n)) per change; 3-edge-connectivity, in time O(n/sup 2/3/) per change; 4-edge-connectivity, in time O(n alpha (n)) per change; k-edge-connectivity, in time O(n log n) per change; bipartiteness, 2-vertex-connectivity, and 3-vertex-connectivity, in time O(n log(m/n)) per change; and 4-vertex-connectivity, in time O(n log(m/n)+n alpha (n)) per change. Further results speed up the insertion times to match the bounds of known partially dynamic algorithms. The algorithms are based on a technique that transforms algorithms for sparse graphs into ones that work on any graph, which they call sparsification.< >
K-means algorithm is widely used in spatial clustering. It takes the mean value of each cluster centroid as the Heuristic information, so it has some disadvantages: sensitive to the initial centroid and ins...
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K-means algorithm is widely used in spatial clustering. It takes the mean value of each cluster centroid as the Heuristic information, so it has some disadvantages: sensitive to the initial centroid and instability. The improved clustering algorithm referred to the best clustering centriod which is searched during the optimization of clustering centroid. That increased the searching probability around the best centroid and improved the stability of the algorithm. The experiment on two groups of representative dataset proved that the improved K-means algorithm performs better in global searching and is less sensitive to the initial centroid.
Grid computing provides a platform for users to access worldwide distributed resources. To meet the timing constrains and quality requirements imposed by the tasks running on a grid, the resources assigned to the task...
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Grid computing provides a platform for users to access worldwide distributed resources. To meet the timing constrains and quality requirements imposed by the tasks running on a grid, the resources assigned to the tasks may need to be periodically reallocated. Thus, an effective strategy for reinforcing resources to or reclaiming resources from the tasks is needed. In this paper, a novel scheduling algorithm is proposed for grid computing with periodical resource reallocation. It migrates the ongoing tasks from a set of computing nodes to another set so as to fully employ the newly available computing power. To achieve high performance computing, the algorithm also balances the workload of the grid in the meanwhile of task migration. In this paper, the simulation results show the usefulness and effectiveness of our scheduling algorithm.
To deal with the problem of too many answers returned from a Web database in response to a user query, this paper proposes a novel categorization approach which takes advantages of the user contextual preferences to c...
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To deal with the problem of too many answers returned from a Web database in response to a user query, this paper proposes a novel categorization approach which takes advantages of the user contextual preferences to construct a navigational tree in order to reduce the information overload. Based on the user original query, we first speculate how much the user cares about each attribute in the specified context and assign a corresponding weight to it. Then, the categorizing attribute in each level of the tree can be determined according to the weight of the attribute. The categorizing attribute for the first level of the tree is the attribute with the maximum weight. Next, we use the histogram construction algorithm to partition the values of each categories of the tree, the category with the larger exploring probability will be provided earlier to the user. Finally, the navigational tree is generated automatically and presented to the user, such that the user can easily select the relevant tuples matching his needs. Results of a preliminary user study demonstrate that our categorization method can capture the user's preferences effectively.
We present a computational framework for automatic deployment of a robot from a temporal logic specification over a set of properties of interest satisfied at the regions of a partitioned environment. We assume that, ...
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We present a computational framework for automatic deployment of a robot from a temporal logic specification over a set of properties of interest satisfied at the regions of a partitioned environment. We assume that, during the motion of the robot in the environment, the current region can be precisely determined, while due to sensor and actuation noise, the outcome of a control action can only be predicted probabilistically. Under these assumptions, the deployment problem translates to generating a control strategy for a Markov Decision Process (MDP) from a temporal logic *** propose an algorithm inspired from probabilistic Computation Tree Logic (PCTL) model checking to find a control strategy that maximizes the probability of satisfying the specification. We illustrate our method with simulation and experimental results.
We introduce a greedy algorithm that works from coarse to fine by iteratively applying localized principal component analysis (PCA). The decision where and when to split or add new components is based on two antagonis...
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We introduce a greedy algorithm that works from coarse to fine by iteratively applying localized principal component analysis (PCA). The decision where and when to split or add new components is based on two antagonistic criteria. Firstly, the well known quadratic reconstruction error and secondly a measure for the homogeneity of the distribution. For the latter criterion, which we call “generation error”, we compared two different possible methods to assess if the data samples are distributed homogeneously. The proposed algorithm does not involve a costly multi-objective optimization to find a partition of the inputs. Further, the final number of local PCA units, as well as their individual dimensionality need not to be predefined. We demonstrate that the method can flexibly react to different intrinsic dimensionalities of the data.
Readers can know the subject of many document fields by reading only some specific words called field association (FA) terms. It is very important to construct these FA terms to decide correctly the document fields fr...
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Readers can know the subject of many document fields by reading only some specific words called field association (FA) terms. It is very important to construct these FA terms to decide correctly the document fields from few words information in part of file. The field can be decided efficiency if the number of these FA terms is many and the frequency rate is high. If the number of level I (words that direct connect to terminal fields) FA word is limited, old methods can not determine the documents tiled easily and fast, special when there is a small number of corpus documents. This paper proposes a new method for deciding FA terms using the weight of co-occurrence words and declinable words which related to a narrow association category with eliminating FA terms ambiguity. Moreover, efficient FA terms are difficult to be extracted only by the information of the frequency of them. This paper proposed a new efficient method using new cooccurrence words weight which makes precision and recall are higher than the case of degree of frequency.
This paper considers a multi-agent submodular set function maximization problem subject to partition matroid in which the utility is shared, but the agents’ policy choices are constrained locally. The paper’s main c...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
This paper considers a multi-agent submodular set function maximization problem subject to partition matroid in which the utility is shared, but the agents’ policy choices are constrained locally. The paper’s main contribution is a distributed algorithm that enables each agent to find a suboptimal policy locally with a guaranteed level of privacy. The submodular set function maximization problems are NP-hard. For agents communicating over a connected graph, this paper proposes a polynomial-time distributed algorithm to obtain a guaranteed near optimal solution. The proposed algorithm is based on a distributed randomized gradient ascent scheme on the multilinear extension of the submodular set function in the continuous domain. Our next contribution is the design of a distributed rounding algorithm that does not need any inter-agent communication. We base our algorithm’s privacy preservation characteristic on our proposed stochastic rounding method and tie the level of privacy to the variable γ ∈ [0, 1]. That is, the policy choice of an agent can be determined with the probability of at most γ. We show that our distributed algorithm results in a strategy set that when the team’s objective function is evaluated in the worst case, the objective function value is in 1 − (1/e) h(γ) − O(1/T ) of the optimal solution, highlighting the interplay between level of optimality gap and guaranteed level of privacy where T is the number of communication rounds between the agents.
Boolean functions are fundamental to synthesis and verification of digital logic, and compact representations of Boolean functions have great practical significance. Popular representations, such as CNF, DNF, circuits...
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Boolean functions are fundamental to synthesis and verification of digital logic, and compact representations of Boolean functions have great practical significance. Popular representations, such as CNF, DNF, circuits and ROBDDs [4], offer different advantages and are preferred for different tasks. Conversion between those representations is common, especially when one is used to represent the input and another speeds up relevant algorithms. Our work addresses the construction of ROBDDs that represent outputs of a given Boolean circuit. It is used in synthesis and verification. Earlier works (Fujita, Fujisawa, and Kawato, 1988. Malik et al., 1988.) proposed ordering circuit inputs and gates by graph traversals. We contribute orderings based on circuit partitioning and placement, leveraging the progress in recursive bisection and multi-level min-cut partitioning achieved in late 1990s. Our empirical results show that the proposed orderings based on circuit partitioning and placement are more successful than straightforward DFS and BFS, as well as related heuristics.
This paper presents an algorithm for disjointsupport decomposition of Boolean functions which combines functional and structural approaches. First, a set of proper cut points is identified in the circuit by using domi...
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This paper presents an algorithm for disjointsupport decomposition of Boolean functions which combines functional and structural approaches. First, a set of proper cut points is identified in the circuit by using dominator relations (structural method). Then, the circuit is partitioned along these cut points and a BDD-based decomposition is applied to the resulting smaller functions (functional method). Previous work on Boolean decomposition used only single methods and did not integrate a combined strategy. The experimental results show that the presented technique is more robust than a pure BDD-based approach and produces better-quality decompositions.
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