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64
Design of an Optimal Loosely Coupled Heterogeneous Multiprocessor System
 Proc. ED&TC
, 1996
"... This paper presents an approach for mapping tasks optimal to hardware and software components in order to design a realtime system. The tasks are derived from an algorithm and are represented by a taskgraph. The performance of the algorithm on the resulting realtime system will meet the specified ..."
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Cited by 24 (0 self)
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This paper presents an approach for mapping tasks optimal to hardware and software components in order to design a realtime system. The tasks are derived from an algorithm and are represented by a taskgraph. The performance of the algorithm on the resulting realtime system will meet the specified timing constraints. Some of the hardware components are programmable and others are application specific hardware processors. We propose a powerful MILP (Mixed Integer Linear Program) model with and without functional pipelining. The efficiency of the method is demonstrated with practical examples. 1 Introduction One important task of a hardware/software codesign is to map different tasks of an algorithm onto hardware or software components. Some components are programmable and others are application specific hardware processors. The aim of the codesign is to design a heterogeneous and loosely coupled multiprocessor system, which violates no timing constraints while performing the underly...
Workflow Scheduling Algorithms for Grid Computing
"... Workflow scheduling is one of the key issues in the management of workflow execution. Scheduling is a process that maps and manages execution of interdependent tasks on distributed resources. It introduces allocating suitable resources to workflow tasks so that the execution can be completed to sat ..."
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Cited by 21 (4 self)
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Workflow scheduling is one of the key issues in the management of workflow execution. Scheduling is a process that maps and manages execution of interdependent tasks on distributed resources. It introduces allocating suitable resources to workflow tasks so that the execution can be completed to satisfy objective functions specified by users. Proper scheduling can have significant impact on the performance of the system. In this chapter, we investigate existing workflow scheduling algorithms developed and deployed by various Grid projects.
Population Markov Chain Monte Carlo
 Machine Learning
, 2003
"... Stochastic search algorithms inspired by physical and biological systems are applied to the problem of learning directed graphical probability models in the presence of missing observations and hidden variables. For this class of problems, deterministic search algorithms tend to halt at local optima ..."
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Cited by 12 (2 self)
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Stochastic search algorithms inspired by physical and biological systems are applied to the problem of learning directed graphical probability models in the presence of missing observations and hidden variables. For this class of problems, deterministic search algorithms tend to halt at local optima, requiring random restarts to obtain solutions of acceptable quality. We compare three stochastic search algorithms: a MetropolisHastings Sampler (MHS), an Evolutionary Algorithm (EA), and a new hybrid algorithm called Population Markov Chain Monte Carlo, or popMCMC. PopMCMC uses statistical information from a population of MHSs to inform the proposal distributions for individual samplers in the population. Experimental results show that popMCMC and EAs learn more efficiently than the MHS with no information exchange. Populations of MCMC samplers exhibit more diversity than populations evolving according to EAs not satisfying physicsinspired local reversibility conditions. KEY WORDS: Markov Chain Monte Carlo, MetropolisHastings Algorithm, Graphical Probabilistic Models, Bayesian Networks, Bayesian Learning, Evolutionary Algorithms Machine Learning MCMC Issue 1 5/16/01 1.
Minimum Message Length Clustering Using Gibbs Sampling
 16 TH INTERNATIONAL CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, (UAI
, 2000
"... The KMeans and EM algorithms are popular in clustering and mixture modeling due to their simplicity and ease of implementation. However, they have several significant limitations. Both converge to a local optimum of their respective objective functions (ignoring the uncertainty in the model s ..."
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Cited by 7 (3 self)
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The KMeans and EM algorithms are popular in clustering and mixture modeling due to their simplicity and ease of implementation. However, they have several significant limitations. Both converge to a local optimum of their respective objective functions (ignoring the uncertainty in the model space), require the apriori specification of the number of classes/clusters, and are inconsistent. In this work we overcome these limitations by using the Minimum Message Length (MML) principle and a variation to the KMeans /EM observation assignment and parameter calculation scheme. We maintain the simplicity of these approaches while constructing a Bayesian mixture modeling tool that samples/searches the model space using a Markov Chain Monte Carlo (MCMC) sampler known as a Gibbs sampler. Gibbs
The Monte Carlo Method in Science and Engineering
, 2006
"... Since 1953, researchers have applied the Monte Carlo method to a wide range of areas. Specialized algorithms have also been developed to extend the method’s applicability and efficiency. The author describes some of the algorithms that have been developed to ..."
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Cited by 4 (0 self)
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Since 1953, researchers have applied the Monte Carlo method to a wide range of areas. Specialized algorithms have also been developed to extend the method’s applicability and efficiency. The author describes some of the algorithms that have been developed to
SemiSupervised Learning – A Statistical Physics Approach
 In “Learning with Partially Classified Training Data” – ICML05 workshop
, 2005
"... We present a novel approach to semisupervised learning which is based on statistical physics. Most of the former work in the field of semisupervised learning classifies the points by minimizing a certain energy function, which corresponds to a minimal kway cut solution. In contrast to these method ..."
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Cited by 4 (0 self)
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We present a novel approach to semisupervised learning which is based on statistical physics. Most of the former work in the field of semisupervised learning classifies the points by minimizing a certain energy function, which corresponds to a minimal kway cut solution. In contrast to these methods, we estimate the distribution of classifications, instead of the sole minimal kway cut, which yields more accurate and robust results. Our approach may be applied to all energy functions used for semisupervised learning. The method is based on sampling using a Multicanonical Markov chain MonteCarlo algorithm, and has a straightforward probabilistic interpretation, which allows for soft assignments of points to classes, and also to cope with yet unseen class types. The suggested approach is demonstrated on a toy data set and on two reallife data sets of gene expression. 1.
Dynamic System Evolution and Markov Chain Approximation
 Discrete Dynamics in NS, Gordon & Breach
, 1998
"... In this paper computational aspects of the mathematical modelling of dynamic system evolution have been considered as a problem in information theory. The construction of such models is treated as a decision making process with limited available information. The solution of the problem is associated ..."
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Cited by 4 (4 self)
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In this paper computational aspects of the mathematical modelling of dynamic system evolution have been considered as a problem in information theory. The construction of such models is treated as a decision making process with limited available information. The solution of the problem is associated with a computational model based on heuristics of a Markov Chain in a discrete spacetime of events. A stable approximation of the chain has been derived and the limiting cases are discussed. An intrinsic interconnection of constructive, sequential, and evolutionary approaches in related optimization problems provides new challenges for future work. Key words: decision making with limited information, optimal control theory, hyperbolicity of dynamic rules, generalized dynamic systems, Markov Chain approximation. 1 Introduction Many mathematical problems in information theory and optimal control related to dynamic system studies can be formulated in the following generic form. A decision...
A Note on the Finite Time Behaviour of Simulated Annealing
, 1999
"... Simulated Annealing has proven to be... In this paper we give a new proof of the convergence of Simulated Annealing by applying results about rapidly mixing Markov chains. With this proof technique it is possible to obtain better bounds for the finite time behaviour of Simulated Annealing than previ ..."
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Cited by 4 (1 self)
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Simulated Annealing has proven to be... In this paper we give a new proof of the convergence of Simulated Annealing by applying results about rapidly mixing Markov chains. With this proof technique it is possible to obtain better bounds for the finite time behaviour of Simulated Annealing than previously known.
Clustering Using the Minimum Message Length Criterion and Simulated Annealing
 in Proceedings of the 3 rd International A.I. Workshop
"... Clustering has many uses such as the generation of taxonomies and concept formation. It is essentially a search through a model space to maximise a given criterion. The criterion aims to guide the search to find models that are suitable for a purpose. The search's aim is to efficiently and c ..."
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Cited by 3 (2 self)
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Clustering has many uses such as the generation of taxonomies and concept formation. It is essentially a search through a model space to maximise a given criterion. The criterion aims to guide the search to find models that are suitable for a purpose. The search's aim is to efficiently and consistently find the model that gives the optimal criterion value. Considerable research has occurred into the criteria to use but minimal research has studied how to best search the model space. We describe how we have used simulated annealing to search the model space to optimise the minimum message length criterion.