Results 1  10
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11
Protocols and impossibility results for gossipbased communication mechanisms
, 2002
"... In recent years, gossipbased algorithms have gained prominence as a methodology for designing robust and scalable communication schemes in large distributed systems. The premise underlying distributed gossip is very simple: in each time step, each node v in the system selects some other node w as a ..."
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Cited by 55 (3 self)
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In recent years, gossipbased algorithms have gained prominence as a methodology for designing robust and scalable communication schemes in large distributed systems. The premise underlying distributed gossip is very simple: in each time step, each node v in the system selects some other node w as a communication partner — generally by a simple randomized rule — and exchanges information with w; over a period of time, information spreads through the system in an “epidemic fashion”. A fundamental issue which is not well understood is the following: how does the underlying lowlevel gossip mechanism — the means by which communication partners are chosen — affect one’s ability to design efficient highlevel gossipbased protocols? We establish one of the first concrete results addressing this question, by showing a fundamental limitation on the power of the commonly used uniform gossip mechanism for solving nearestresource location problems. In contrast, very efficient protocols for this problem can be designed using a nonuniform spatial gossip mechanism, as established in earlier work with Alan Demers. We go on to consider the design of protocols for more complex problems, providing an efficient distributed gossipbased protocol for a set of nodes in Euclidean space to construct an approximate minimum spanning tree. Here too, we establish a contrasting limitation on the power of uniform gossip for solving this problem. Finally, we investigate gossipbased packet routing as a primitive that underpins the communication patterns in many protocols, and as a way to understand the capabilities of different gossip mechanisms at a general level.
Engineering an External Memory Minimum Spanning Tree Algorithm
 IN PROC. 3RD IFIP INTL. CONF. ON THEORETICAL COMPUTER SCIENCE
, 2004
"... We develop an external memory algorithm for computing minimum spanning trees. The algorithm is considerably simpler than previously known external memory algorithms for this problem and needs a factor of at least four less I/Os for realistic inputs. Our implementation indicates that this algorithm ..."
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Cited by 14 (3 self)
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We develop an external memory algorithm for computing minimum spanning trees. The algorithm is considerably simpler than previously known external memory algorithms for this problem and needs a factor of at least four less I/Os for realistic inputs. Our implementation indicates that this algorithm processes graphs only limited by the disk capacity of most current machines in time no more than a factor 2–5 of a good internal algorithm with sufficient memory space.
Hierarchical Image Partitioning with Dual Graph Contraction
 Proc. of 25th DAGM Symposium LNCS
, 2003
"... We present a hierarchical partitioning of images using a pairwise similarity function on a graphbased representation of an image. This function measures the difference along the boundary of two components relative to a measure of differences of the components' internal differences. This definition ..."
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Cited by 12 (4 self)
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We present a hierarchical partitioning of images using a pairwise similarity function on a graphbased representation of an image. This function measures the difference along the boundary of two components relative to a measure of differences of the components' internal differences. This definition tries to encapsulate the intuitive notion of contrast. Two components are merged if there is a lowcost connection between them. Each component's internal difference is represented by the maximum edge weight of its minimum spanning tree. External differences are the smallest weight of edges connecting components. We use this idea for building a minimum spanning tree to find region borders quickly and effortlessly in a bottomup way, based on local differences in a specific feature.
Segmentation Graph Hierarchies
 In: Proceedings of Joint Workshops on Structural, Syntactic, and Statistical Pattern Recognition S+SSPR. Volume 3138 of Lecture Notes in Computer Science
, 2004
"... The region's internal properties (color, texture, ...) help to identify them and their external relations (adjacency, inclusion, ...) are used to build groups of regions having a particular consistent meaning in a more abstract context. Lowlevel cue image segmentation in a bottomup way, cannot ..."
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Cited by 12 (5 self)
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The region's internal properties (color, texture, ...) help to identify them and their external relations (adjacency, inclusion, ...) are used to build groups of regions having a particular consistent meaning in a more abstract context. Lowlevel cue image segmentation in a bottomup way, cannot and should not produce a complete final "good" segmentation. We present a hierarchical partitioning of images using a pairwise similarity function on a graphbased representation of an image.
StateoftheArt Algorithms for Minimum Spanning Trees  A Tutorial Discussion
, 1997
"... The classic “easy” optimization problem is to find the minimum spanning tree (MST) of a connected, undirected graph. Good polynomialtime algorithms have been known since 1930. Over the last 10 years, however, the standard O(mlogn) results of Kruskal and Prim have been improved to linear or nearli ..."
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Cited by 7 (0 self)
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The classic “easy” optimization problem is to find the minimum spanning tree (MST) of a connected, undirected graph. Good polynomialtime algorithms have been known since 1930. Over the last 10 years, however, the standard O(mlogn) results of Kruskal and Prim have been improved to linear or nearlinear time. The new methods use several tricks of general interest in order to reduce the number of edge weight comparisons and the amount of other work. This tutorial reviews those methods, building up strategies step by step so as to expose the insights behind the algorithms. Implementation details are clarified, and some generalizations are given. Specifically, the paper attempts to shed light on the classical algorithms of Kruskal, of Prim, and of Bor˙uvka; the improved approach of Gabow, Galil, and Spencer, which takes time only O(mlog(log*n−log * m n)); and the randomized O(m) algorithm of Karger, Klein, and Tarjan,
Hierarchy of Partitions with Dual Graph Contraction
 In Proceedings of the DAGM conference
, 2003
"... We present a hierarchical partitioning of images using a pairwise similarity function on a graphbased representation of an image. ..."
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Cited by 6 (4 self)
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We present a hierarchical partitioning of images using a pairwise similarity function on a graphbased representation of an image.
The filterkruskal minimum spanning tree algorithm
, 2009
"... We present FilterKruskal – a simple modification of Kruskal’s algorithm that avoids sorting edges that are “obviously” not in the MST. For arbitrary graphs with random edge weights FilterKruskal runs in time O (m + n lognlog m n, i.e. in linear time for not too sparse graphs. Experiments indicate ..."
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Cited by 6 (0 self)
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We present FilterKruskal – a simple modification of Kruskal’s algorithm that avoids sorting edges that are “obviously” not in the MST. For arbitrary graphs with random edge weights FilterKruskal runs in time O (m + n lognlog m n, i.e. in linear time for not too sparse graphs. Experiments indicate that the algorithm has very good practical performance over the entire range of edge densities. An equally simple parallelization seems to be the currently best practical algorithm on multicore machines.
The CP(Graph) Computation Domain in Constraint Programming
, 2006
"... Dissertation présentée en vue de l’obtention du titre de Docteur en ..."
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Cited by 4 (0 self)
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Dissertation présentée en vue de l’obtention du titre de Docteur en
Pyramid segmentation algorithms revisited
, 2006
"... The main goal of this work is to compare pyramidal structures proposed to solve segmentation tasks. Segmentation algorithms based on regular and irregular pyramids are described, together with the data structures and decimation procedures which encode and manage the information in the pyramid. In or ..."
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Cited by 2 (0 self)
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The main goal of this work is to compare pyramidal structures proposed to solve segmentation tasks. Segmentation algorithms based on regular and irregular pyramids are described, together with the data structures and decimation procedures which encode and manage the information in the pyramid. In order to compare the different segmentation algorithms, we have employed three types of quality measurements: the shift variance measure, the F function and the Q function.
Wisdom of the Crowds in Minimum Spanning Tree Problems
"... The „wisdom of the crowds ‟ effect describes the finding that combining responses across a number of individuals in a group leads to aggregate performance that is as good as or better than the performance of the best individuals in the group. Here, we look at the wisdom of the crowds effect in the M ..."
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Cited by 1 (0 self)
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The „wisdom of the crowds ‟ effect describes the finding that combining responses across a number of individuals in a group leads to aggregate performance that is as good as or better than the performance of the best individuals in the group. Here, we look at the wisdom of the crowds effect in the Minimum Spanning Tree Problem (MSTP). The MSTP is an optimization problem where observers must connect a set of nodes into a network with the shortest path length possible. A method is developed that creates aggregate solutions based only on the nodes connected in individuals ‟ solutions, without access to spatial information about the nodes. Across the three problems analyzed, the solutions produced by the aggregation method perform better than even the best individual, leading to a strong wisdom of the crowds effect. We show this effect can be observed even with sample sizes as small as 6 individuals.