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113
Characterization of complex networks: A survey of measurements
 ADVANCES IN PHYSICS
, 2005
"... Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics and function of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of mea ..."
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Cited by 94 (7 self)
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Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics and function of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements organized into classes. Special attention is given to relating complex network analysis with the areas of pattern recognition and feature selection, as well as on surveying some concepts and measurements from traditional graph theory which are potentially useful for complex network research. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the
A Multilevel Relaxation Algorithm for Simultaneous Localisation and Mapping
, 2004
"... This paper addresses the problem of simultaneous localisation and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation meth ..."
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Cited by 90 (5 self)
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This paper addresses the problem of simultaneous localisation and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared to other SLAM algorithms. Experimental comparisons with alternative algorithms using two wellknown data sets and mapping results on a real robot are also presented.
Formulation and Preliminary Test of an Empirical Theory of Coordination in Software Engineering
 In 2003 International Conference on Foundations of Software Engineering
, 2003
"... Motivated by evidence that coordination and dependencies among engineering decisions in a software project are key to better understanding and better methods of software creation, we set out to create empirically testable theory to characterize and make predictions about coordination of engineering ..."
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Cited by 39 (10 self)
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Motivated by evidence that coordination and dependencies among engineering decisions in a software project are key to better understanding and better methods of software creation, we set out to create empirically testable theory to characterize and make predictions about coordination of engineering decisions. We demonstrate that our theory is capable of expressing some of the main ideas about coordination in software engineering, such as Conway's law and the effects of information hiding in modular design. We then used software project data to create measures and test two hypotheses derived from our theory. Our results provide preliminary support for our formulations.
AN INTRODUCTION TO NUMERICAL TRANSFORM INVERSION AND ITS APPLICATION TO PROBABILITY MODELS
, 1999
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Waterquality simulation modeling and uncertainty analysis for risk assessment and decision making
 Ecol Model
, 1994
"... The usefulness of water quality simulation models for environmental management is explored with a focus on prediction uncertainty. Ecological risk and environmental analysis often involve scientific assessments that are highly uncertain. Still, environmental management decisions are being made, ofte ..."
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Cited by 20 (8 self)
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The usefulness of water quality simulation models for environmental management is explored with a focus on prediction uncertainty. Ecological risk and environmental analysis often involve scientific assessments that are highly uncertain. Still, environmental management decisions are being made, often with the support of a mathematical simulation model. In the area of pollutant transport and fate in surface waters, few of the extant simulation models have been rigorously evaluated. Limited observational data and limited scientific knowledge are often incompatible with the highlydetailed model structures of the large pollutant transport and fate models. Two examples are presented to illustrate data and knowledge weaknesses that are likely to undermine these large models for decision support. An alternative to comprehensive structured simulation models is proposed as a flexible approach to introduce science into the environmental risk assessment and decision making process, 1.
Hyperparameters: optimize, or integrate out?
 IN MAXIMUM ENTROPY AND BAYESIAN METHODS, SANTA BARBARA
, 1996
"... I examine two approximate methods for computational implementation of Bayesian hierarchical models, that is, models which include unknown hyperparameters such as regularization constants. In the `evidence framework' the model parameters are integrated over, and the resulting evidence is maximi ..."
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Cited by 18 (4 self)
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I examine two approximate methods for computational implementation of Bayesian hierarchical models, that is, models which include unknown hyperparameters such as regularization constants. In the `evidence framework' the model parameters are integrated over, and the resulting evidence is maximized over the hyperparameters. The optimized hyperparameters are used to define a Gaussian approximation to the posterior distribution. In the alternative `MAP' method, the true posterior probability is found by integrating over the hyperparameters. The true posterior is then maximized over the model parameters, and a Gaussian approximation is made. The similarities of the two approaches, and their relative merits, are discussed, and comparisons are made with the ideal hierarchical Bayesian solution. In moderately illposed problems, integration over hyperparameters yields a probability distribution with a skew peak which causes significant biases to arise in the MAP method. In contrast, the evidence framework is shown to introduce negligible predictive error, under straightforward conditions. General lessons are drawn concerning the distinctive properties of inference in many dimensions.
Emergent Cooperative GoalSatisfaction in Large Scale AutomatedAgent Systems
 Artificial Intelligence
, 1999
"... Cooperation among autonomous agents has been discussed in the DAI community for several years. Papers about cooperation [6, 45], negotiation [33], distributed planning [5], and coalition formation [28, 48], have provided a variety of approaches and several algorithms and solutions to situations wher ..."
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Cited by 14 (3 self)
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Cooperation among autonomous agents has been discussed in the DAI community for several years. Papers about cooperation [6, 45], negotiation [33], distributed planning [5], and coalition formation [28, 48], have provided a variety of approaches and several algorithms and solutions to situations wherein cooperation is possible. However, the case of cooperation in largescale multiagent systems (MAS) has not been thoroughly examined. Therefore, in this paper we present a framework for cooperative goalsatisfaction in largescale environments focusing on a low complexity physicsoriented approach. The multiagent systems with which we deal are modeled by a physicsoriented model. According to the model, MAS inherit physical properties, and therefore the evolution of the computational systems is similar to the evolution of physical systems. To enable implementation of the model, we provide a detailed algorithm to be used by a single agent within the system. The model and the algorithm are a...
Calculating Normalization Constants of Closed Queueing Networks by Numerically Inverting Their Generating Functions
 J. ACM
, 1995
"... A new algorithm is developed for calculating normalization constants (partition functions) and moments of productform steadystate distributions of closed queueing networks and related models. The essential idea is to numerically invert the generating function of the normalization constant and rela ..."
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Cited by 11 (7 self)
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A new algorithm is developed for calculating normalization constants (partition functions) and moments of productform steadystate distributions of closed queueing networks and related models. The essential idea is to numerically invert the generating function of the normalization constant and related generating functions appearing in expressions for the moments. It is known that the generating function of the normalization constant often has a remarkably simple form, but numerical inversion evidently has not been considered before. For pdimensional transforms, as occur with queueing networks having p closed chains, the algorithm recursively performs p onedimensional inversions. The required computation grows exponentially in the dimension, but the dimension can often be reduced by exploiting conditional decomposition based on special structure. For large populations, the inversion algorithm is made more efficient by computing large sums using Euler summation. The inversion algorithm also has a very low storage requirement. A key ingredient in the inversion algorithm is scaling. An effective static scaling is developed for multichain closed queueing networks with only singleserver and (optionally) infiniteserver queues. An important feature of the inversion algorithm is a selfcontained accuracy check, which allows the results to be verified in the absence of alternative algorithms. Key words and phrases: performance analysis, closed queueing networks, productform model, normalization constant, partition function, generating function, numerical transform inversion, scaling, dimension reduction, Euler summation. 1.
Densitybased Clustering of Time Series Subsequences
"... Doubts have been raised that time series subsequences can be clustered in a meaningful way. This paper introduces a kerneldensitybased algorithm that detects meaningful patterns in the presence of a vast number of randomwalklike subsequences. The value of densitybased algorithms for noise elimi ..."
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Cited by 10 (0 self)
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Doubts have been raised that time series subsequences can be clustered in a meaningful way. This paper introduces a kerneldensitybased algorithm that detects meaningful patterns in the presence of a vast number of randomwalklike subsequences. The value of densitybased algorithms for noise elimination in general has long been demonstrated. The challenge of applying such techniques to timeseries data consists in first specifying uninteresting sequences that are to be considered as noise, and secondly ensuring that those uninteresting sequences will not a#ect the clustering result. Both problems are addressed in this paper and the success of the technique is demonstrated on several standard data sets.
Dynamic: decision behavior and optimal guidance through information services: Models and experiments
 In Schreckenberg, A. and Selten, R. edits, Human Behaviour and Traffic Networks, Springer,Berlin Heidelberg
"... Abstract. In this contribution, dynamical models for decision making with and without temporal constraints are developed and applied to opinion formation, migration, game theory, the selforganization of behavioral conventions, etc. These models take into account the nontransitive and probabilistic ..."
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Cited by 9 (1 self)
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Abstract. In this contribution, dynamical models for decision making with and without temporal constraints are developed and applied to opinion formation, migration, game theory, the selforganization of behavioral conventions, etc. These models take into account the nontransitive and probabilistic aspects of decisions, i.e. they reflect the observation that individuals do not always take the decision with the highest utility or payoff. We will also discuss issues like the freedom of decision making, the redbusbluebus problem, and effects of pair interactions such as the transition from individual to mass behavior. In the second part, the theory is compared with recent results of experimental games relevant to the route choice behavior of drivers. The adaptivity (“group intelligence”) with respect to changing environmental conditions and unreliable information is very astonishing. Nevertheless, we find an intermittent dynamical reaction to aggregate information similar to volatility clustering in stock market data, which leads to considerable losses in the average payoffs. It turns out that the decision behavior is not just driven by the potential gains in payoffs. To understand these findings, one has to consider reinforcement learning, which can also explain the empirically observed emergence of individual response patterns. Our results are highly significant for predicting decision behavior and reaching the optimal distribution of behaviors by means of decision support systems. These results are practically relevant for any information service provider. 1