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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Ioannis N. Melas Aikaterini D. Chairakaki Alexander Mitsos Zoe Dailiana Christopher G. Provatidis Leonidas G. Alexopoulos National Technical University of Athens Mechanical Engineering Department Massachusetts Institute of Technology Mechanical Engineering Department University Hospital of Larissa Department of Orthopedic Surgery

ISBN: (纸本)9781424441211

Chondrocytes, the only cell type in articular cartilage, are responsible for maintaining the composition of cartilage extracellular matrix (ECM) through a complex interplay of anabolic and catabolic stimuli. Although understanding the way chondrocytes respond to stimuli is of utmost importance for shedding light into the etiology of joint diseases, an integrative approach to studying their signaling transduction mechanisms is yet to be introduced. Herein, we propose an approach that combines high throughput proteomic measurements and state of the art optimization algorithms to construct a predictive model of chondrocyte signaling network, downstream of 78 receptors of interest.

关键词： Articular Cartilages Chondrocyte cell type Stimuli Signal Pathways state of the art anabolics Prediction models optimization algorithms signalling network

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Multiple Instance Learning on Structured Data 11

Multiple Instance Learning on Structured Data

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Annual Conference on Neural Information Processing Systems

作者： Dan Zhang Yan Liu Luo Si Jian Zhang Richard D. Lawrence Computer Science Department Purdue University West Lafayette IN 47906 Computer Science Department University of Southern California Los Angeles CA 90089 Statistics Department Purdue University West Lafayette IN 47906 Machine Learning Group IBM T.J. Watson Research Center Yorktown Heights NY 10598

ISBN: (纸本)9781618395993

Most existing Multiple-Instance Learning (MIL) algorithms assume data instances and/or data bags are independently and identically distributed. But there often exists rich additional dependency/structure information between instances/bags within many applications of MIL. Ignoring this structure information limits the performance of existing MIL algorithms. This paper explores the research problem as multiple instance learning on structured data (MILSD) and formulates a novel framework that considers additional structure information. In particular, an effective and efficient optimization algorithm has been proposed to solve the original non-convex optimization problem by using a combination of Concave-Convex Constraint Programming (CCCP) method and an adapted Cutting Plane method, which deals with two sets of constraints caused by learning on instances within individual bags and learning on structured data. Our method has the nice convergence property, with specified precision on each set of constraints. Experimental results on three different applications, i.e., webpage classification, market targeting, and protein fold identification, clearly demonstrate the advantages of the proposed method over state-of-the-art methods.

关键词： Learning optimization algorithms Research Problems cutting plane method

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Ranking of Stopping Criteria for Log Domain Diffeomorphic Demons Application in Clinical Radiation Therapy

Ranking of Stopping Criteria for Log Domain Diffeomorphic De...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： M. Peroni P. Golland G. C. Sharp G. Baroni Department of Bioengineering at Politecnico di Milano Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Department of Radiation Oncology and Harvard Medical School

ISBN: (纸本)9781424441211

Deformable Image Registration is a complex optimization algorithm with the goal of modeling a non-rigid transformation between two images. A crucial issue in this field is guaranteeing the user a robust but computationally reasonable algorithm. We rank the performances of four stopping criteria and six stopping value computation strategies for a log domain deformable registration. The stopping criteria we test are: (a) velocity field update magnitude, (b) vector field Jacobian, (c) mean squared error, and (d) harmonic energy. Experiments demonstrate that comparing the metric value over the last three iterations with the metric minimum of between four and six previous iterations is a robust and appropriate strategy. The harmonic energy and vector field update magnitude metrics give the best results in terms of robustness and speed of convergence.

关键词： Radiotherapy stopping image registration harmonic energy optimization algorithms Metronidazole Vector field Robustness logs logarithm (log) iterative methods

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Alternating Direction Method of Multipliers Applied to 3D Light Sheet Fluorescence Microscopy Image Deblurring Using GPU Hardware

Alternating Direction Method of Multipliers Applied to 3D Li...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： F. de Vieilleville P. Weiss V. Lobjois D. Kouame Universite de Toulouse ITAV

ISBN: (纸本)9781424441211

This paper focuses on the deblurring and denoising of Poisson noise contaminated images acquired with a new imaging technique producing large 3D data sets: Light Sheet Fluorescence Microscopy. This paper details the optimization algorithm used, which is based on the Alternating Direction Method of Multipliers, and its efficient implementation using GPU hardware. In practice, a 3D 100 million voxel image is deconvolved in five minutes, which is at least 25 times faster than a state-of-the-art MATLAB implementation.

关键词： Multiplying circuits THIN PLATES optimization algorithms GRAPPER PICK UP Graphics Processing Unit Fluorescence microscopy Poisson noise deblurring image reconstruction Computer hardware Noise reduction

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Discrete Parameter Simulation optimization algorithms with Applications to Admission Control with Dependent Service Times

Discrete Parameter Simulation Optimization Algorithms with A...

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Proceedings of the 46th IEEE Conference on Decision and Control, Pages 919-1382

作者： Vivek Mishra Shalabh Bhatnagar N. Hemachandra Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Industrial Engineering and Operations Research Indian Institute of Technology Mumbai India

ISBN: (纸本)9781424414970;1424414970

We propose certain discrete parameter variants of well known simulation optimization algorithms. Two of these algorithms are based on the smoothed functional (SF) technique while two others are based on the simultaneous perturbation stochastic approximation (SPSA) method. They differ from each other in the way perturbations are obtained and also the manner in which projections and parameter updates are performed. All our algorithms use two simulations and two-timescale stochastic approximation. As an application setting, we consider the important problem of admission control of packets in communication networks under dependent service times. We consider a discrete time slotted queueing model of the system and consider two different scenarios - one where the service times have a dependence on the system state and the other where they depend on the number of arrivals in a time slot. Under our settings, the simulated objective function appears ill-behaved with multiple local minima and a unique global minimum characterized by a sharp dip in the objective function in a small region of the parameter space. We compare the performance of our algorithms on these settings and observe that the two SF algorithms show the best results overall. In fact, in many cases studied, SF algorithms converge to the global minimum.

关键词： Admission control parameter space methods algorithms Global Minimum optimization algorithms Service time simulating Objective function stochastic approximation networks(communications) arrivals discrete-time

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Calibration and optimization of the Pumping and Disinfection of a Real Water Supply System

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE 2010年第4期136卷 493-501页

作者： Gibbs, Matthew S. Dandy, Graeme C. Maier, Holger R. Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia

Maintaining a disinfectant residual in water distribution systems (WDSs) is generally considered paramount to ensuring a safe drinking water supply. This objective can be assisted by the use of booster stations to increase disinfectant concentrations throughout the network. However, identifying the appropriate dose at each station is an optimization problem. The aim is to minimize the total mass of disinfectant dosed and reduce the cost of disinfection along with potential taste, odor, or by-product problems, while maintaining a certain minimum residual in the network. The residual present in the water at any location is not only dependent on the amount of disinfectant added to the water, but also the hydraulics of the system and the resulting detention times. A number of previous studies have tackled this optimization problem, however, a review of current literature suggests that in many cases the hydraulics of the system have been held constant, or the WDSs considered were hypothetical systems with relatively few constraints. This study considers the booster disinfection dosing problem, including daily pump scheduling, for a real system in Sydney, Australia. Before the system can be optimized, a representative model is required to ensure useful results, and the many constraints on the daily operation system must be accounted for in the fitness function considered. The results from the optimization study indicate it is necessary to consider the hydraulics as well as the dosing regime in the optimization process, as cycling reservoir levels minimizes detention times, and hence, disinfectant residuals are maintained at the extremities of the network. Also, significant energy cost savings of up to 30% can be made by scheduling the pumping in the system in line with the off-peak electricity costs.

关键词： Genetic algorithms Evolutionary computation optimization algorithms Water distribution systems Pipe networks Water quality

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Combinatorial optimization Solutions for the Maximum Quartet Consistency Problem

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FUNDAMENTA INFORMATICAE 2010年第3-4期102卷 363-389页

作者： Morgado, Antonio Marques-Silva, Joao Univ Coll Dublin Complex & Adapt Syst Lab Sch Comp Sci & Informat Dublin Ireland

Phylogenetic analysis is a widely used technique, for example in biology and biomedical sciences. The construction of phylogenies can be computationally hard. A commonly used solution for construction of phylogenies is to start from a set of biological species and relations among those species. This work addresses the case where the relations among species are specified as quartet topologies. Moreover, the problem to be solved consists of computing a phylogeny that satisfies the maximum number of quartet topologies. This is referred to as the Maximum Quartet Consistency (MQC) problem, and represents an NP-hard optimization problem. MQC has been solved both heuristically and exactly. Exact solutions forMQC include those based on Constraint Programming, Answer Set Programming, Pseudo-Booleanoptimization (PBO), and Satisfiability Modulo Theories (SMT). This paper provides a comprehensive overview of the use of PBO and SMT for solving MQC, and builds on recent work in this area. Moreover, the paper provides new insights on how to use SMT for solving optimization problems, by focusing on the concrete case of MQC. The solutions based on PBO and SMT were experimentally compared with other exact solutions. The results show that for instances with small percentage of quartet errors, the models based on SMT can be competitive, whereas for instances with higher number of quartet errors the PBO models are more efficient.

关键词： maximum quartet consistency optimization algorithms pseudo-boolean optimization satisfiability modulo theories

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FAST ARRAY THINNING USING GLOBAL optimization METHODS

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2010年第16期24卷 2259-2271页

作者： Fernandez-Delgado, M. Rodriguez-Gonzalez, J. A. Iglesias, R. Barro, S. Ares-Pena, F. J. Univ Santiago de Compostela Radiating Syst Grp Fac Phys Dept Appl Phys Santiago De Compostela 15782 Spain Univ Santiago de Compostela Intelligent Syst Grp Dept Elect & Comp Sci Santiago De Compostela 15782 Spain

A simple and fast method to accelerate the global optimization approaches used in array thinning is described. This method tabulates the contribution of every array element to the far-field pattern in order to improve the numerical efficiency of the optimization algorithm employed. Experiments using our proposal alongside with a genetic algorithm reduce the search computation time about 90%. Simulation results for both linear and planar arrays are shown.

关键词： optimization methods computational time singling Array Element Shortcuts optimization algorithms Planar antennas Global optimization far-field optimization method arrays Genetic Algorithm (GA)

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Optimal Water Distribution Network Design with Honey-Bee Mating optimization

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2010年第1期24卷 117-126页

作者： Mohan, S. Babu, K. S. Jinesh Indian Inst Technol Dept Civil Engn Madras 600036 Tamil Nadu India

Water distribution network is a costly infrastructure and plays a crucial role in supplying water for the consumers especially for those who are living in the urban areas. The importance and huge capital cost of the system leads to considerable attention on seeking the optimal cost design. The necessity for such a sound research attention arises from the complexity associated with the problem. In the recent years, stochastic optimization algorithms like genetic algorithm, simulated annealing, ant colony optimization etc. are found to be successful in exploring the optimal combination of pipe diameters that can satisfy the hydraulic-head requirements with least cost. In this paper, the details on the optimal water distribution network design with a novel technique called honey-bee mating optimization and its validation with two benchmark water distribution networks are presented. From the results, it is observed that the proposed algorithm identifies the optimal solution with relatively less number of evaluations than the other well-established stochastic optimization algorithms.

关键词： optimization algorithms Stochastic models Water distribution systems Water supply

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optimization of forging processes using Finite Element simulations

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2010年第5期42卷 797-810页

作者： Bonte, Martijn H. A. Fourment, Lionel Do, Tien-tho van den Boogaard, A. H. Huetink, J. Univ Twente NL-7500 AE Enschede Netherlands Mines ParisTech CEMEF CNRS UMR 7635 F-06904 Sophia Antipolis France

During the last decades, simulation software based on the Finite Element Method (FEM) has significantly contributed to the design of feasible forming processes. Coupling FEM to mathematical optimization algorithms offers a promising opportunity to design optimal metal forming processes rather than just feasible ones. In this paper Sequential Approximate optimization (SAO) for optimizing forging processes is discussed. The algorithm incorporates time-consuming nonlinear FEM simulations. Three variants of the SAO algorithm-which differ by their sequential improvement strategies-have been investigated and compared to other optimization algorithms by application to two forging processes. The other algorithms taken into account are two iterative algorithms (BFGS and SCPIP) and a Metamodel Assisted Evolutionary Strategy (MAES). It is essential for sequential approximate optimization algorithms to implement an improvement strategy that uses as much information obtained during previous iterations as possible. If such a sequential improvement strategy is used, SAO provides a very efficient algorithm to optimize forging processes using time-consuming FEM simulations.

关键词： Sequential Approximate optimization optimization algorithms Forming processes Finite Element simulations Forging

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