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27
Domain adaptation for statistical classifiers
 J. Artif. Int. Res
, 2006
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Speed scaling to manage energy and temperature
 Journal of the ACM
"... We first consider online speed scaling algorithms to minimize the energy used subject to the constraint that every job finishes by its deadline. We assume that the power required to run at speed s is P s s. We provide a tight bound on the competitive ratio of the previously proposed Optimal A ..."
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Cited by 169 (17 self)
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We first consider online speed scaling algorithms to minimize the energy used subject to the constraint that every job finishes by its deadline. We assume that the power required to run at speed s is P s s. We provide a tight bound on the competitive ratio of the previously proposed Optimal Available algorithm. This improves the best known competitive ratio by a factor of . We then introduce a new online algorithm, and show that this algorithm’s competitive ratio is at most e. This competitive ratio is significantly better and is approximately e for large . Our result is essentially tight for large . In particular, as approaches infinity, we show that any algorithm must have competitive ratio e (up to lower order terms). We then turn to the problem of dynamic speed scaling to minimize the maximum temperature that the device ever reaches, again subject to the constraint that all jobs finish by their deadlines. We assume that the device cools according to Fourier’s law. We show how to solve this problem in polynomial time, within any error bound, using the Ellipsoid algorithm. 1.
Optimal boundary triangulations of an interpolating ruled surface
 Journal of Computing and Information Science in Engineering 5
, 2005
"... We investigate how to define a triangulated ruled surface interpolating two polygonal directrices that will meet a variety of optimization objectives which originate from many CAD/CAM and geometric modeling applications. This optimal triangulation problem is formulated as a combinatorial search prob ..."
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Cited by 8 (3 self)
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We investigate how to define a triangulated ruled surface interpolating two polygonal directrices that will meet a variety of optimization objectives which originate from many CAD/CAM and geometric modeling applications. This optimal triangulation problem is formulated as a combinatorial search problem whose search space however has the size tightly factorial to the numbers of points on the two directrices. To tackle this bound, we introduce a novel computational tool called multilayer directed graph and establish an equivalence between the optimal triangulation and the singlesource shortest path problem on the graph. Well known graph search algorithms such as the Dijkstra’s are then employed to solve the singlesource shortest path problem, which effectively solves the optimal triangulation problem in O(mn) time, where n and m are the numbers of vertices on the two directrices respectively. Numerous experimental examples are provided to demonstrate the usefulness of the proposed optimal triangulation problem in a variety of engineering applications.
Constraints And Their Satisfaction In The Recovery Of Local Surface Structure
, 1997
"... Abstract This thesis deals with the problem of recovering the local structure of surfaces from discrete range data. It is assumed that this recovery is done mostly in a bottomup fashion, that is, without the help of a priori knowledge about the viewed surface. Because the problem is illposed, we ..."
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Cited by 2 (0 self)
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Abstract This thesis deals with the problem of recovering the local structure of surfaces from discrete range data. It is assumed that this recovery is done mostly in a bottomup fashion, that is, without the help of a priori knowledge about the viewed surface. Because the problem is illposed, we nevertheless need to place constraints on the recovered structure to get a unique solution. In a bottomup approach, these constraints must come from generic assumptions that apply to all surfaces. Many methods of bottomup surface reconstruction have been proposed up to now, some of them dealing with intensity surfaces, some with range surfaces. Each of these methods either explicitly or implicitly applies a set of constraints on the data. The way in which the constraints are applied also varies from method to method. The main contribution of this thesis is some success at unifying a number of those methods under a common formalism of energy minimization, which will permit to better compare the choice of constraints between methods. We also show that the most successful surface reconstruction methods form idempotent operators, which we argue is to be expected. One method, Sander's curvature consistency, is studied in more detail than the others because it has not been studied much elsewhere yet. ii TABLE OF CONTENTS TABLE OF CONTENTS Abstract : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 LIST OF FIGURES : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : iv Abstract : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 Sommaire : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 Acknowledgements : : :...
Joint Reconstruction of Image and Motion in Gated PositronEmissionTomography
"... Abstract—We present a novel method for joint reconstruction of both image and motion in positronemissiontomography (PET). Most other methods separate image from motion estimation: They use deformable image registration/optical flow techniques in order to estimate the motion from individually recon ..."
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Abstract—We present a novel method for joint reconstruction of both image and motion in positronemissiontomography (PET). Most other methods separate image from motion estimation: They use deformable image registration/optical flow techniques in order to estimate the motion from individually reconstructed gates. Then, the image is estimated based on this motion information. With these methods, a main problem lies in the motion estimation step, which is based on the noisy gated frames. The more noise is present, the more inaccurate the image registration becomes. As we show in a simulation study, our joint reconstruction approach overcomes these drawbacks and results in both visually and quantitatively better image quality. We attribute these results to the fact that for motion estimation always the currently best available image estimate is used and vice versa. Additionally, results for real dual respiratory and cardiac gated patient data are presented. Index Terms—PET, reconstruction, motion compensation, gating. I.
Quantum Algorithm for Continuous Global Optimization
, 2001
"... We investigate the entwined roles of information and quantum algorithms in reducing the complexity of the global optimization problem (GOP). We show that: (i) a modest amount of additional information is su±cient to map the general continuous GOP into the (discrete) Grover problem; (ii) while this a ..."
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We investigate the entwined roles of information and quantum algorithms in reducing the complexity of the global optimization problem (GOP). We show that: (i) a modest amount of additional information is su±cient to map the general continuous GOP into the (discrete) Grover problem; (ii) while this additional information is actually available in some classes of GOPs, it cannot be taken advantage of within classical optimization algorithms; (iii) on the contrary, quantum algorithms o®er a natural framework for the e±cient use of this information resulting in a speedup of the solution of the GOP. 1 1 Global Optimization Problem Optimization problems are ubiquitous and extremely consequential. The theoretical and practical interest they have generated has continued to grow from the ¯rst recorded instance of Queen Dido's problem [13] to present day forays in complexity theory or large scale logistics applications (see Refs. [14], [9], [8], [6], and references therein). The formulation
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"... Abstract. Speed scaling is a power management technique that involves dynamically changing the speed of a processor. We study policies for setting the speed of the processor for both of the goals of minimizing the energy used and the maximum temperature attained. The theoretical study of speed scali ..."
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Abstract. Speed scaling is a power management technique that involves dynamically changing the speed of a processor. We study policies for setting the speed of the processor for both of the goals of minimizing the energy used and the maximum temperature attained. The theoretical study of speed scaling policies to manage energy was initiated in a seminal paper by Yao et al. [1995], and we adopt their setting. We assume that the power required to run at speed s is P(s) = s α for some constant α>1. We assume a collection of tasks, each with a release time, a deadline, and an arbitrary amount of work that must be done between the release time and the deadline. Yao et al. [1995] gave an offline greedy algorithm YDS to compute the minimum energy schedule. They further proposed two online algorithms Average Rate (AVR) and Optimal Available (OA), and showed that AVR is 2 α−1 α αcompetitive with respect to energy. We provide a tight α α bound on the competitive ratio of OA with respect to energy. We initiate the study of speed scaling to manage temperature. We assume that the environment has a fixed ambient temperature and that the device cools according to Newton’s law of cooling. We observe that the maximum temperature can be approximated within a factor of two by the maximum energy used over any interval of length 1/b, where b is the cooling parameter of the
Multiobjective Optimisation of Active and SemiActive Suspension Systems with Application of Evolutionary Algorithm
"... Suspension system design is a challenging task with multiple control parameters, complex (often conflicting) objectives and stochastic disturbances. It is essential to develop a design environment, in the form of a mathematical model, which will help engineering efforts, not only in algorithm design ..."
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Suspension system design is a challenging task with multiple control parameters, complex (often conflicting) objectives and stochastic disturbances. It is essential to develop a design environment, in the form of a mathematical model, which will help engineering efforts, not only in algorithm design but also in the investigation of various research questions. This paper examines issues relevant to semiactive suspension control system design optimization. It also presents a numerical approach for such optimization. Evolutionary algorithms (EAs) are applied to the optimization of the control system parameters. EAs are computerbased techniques that mirror natural genetic evolution, and they have been