The Experts below are selected from a list of 83739 Experts worldwide ranked by ideXlab platform
S Nill - One of the best experts on this subject based on the ideXlab platform.
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from physical dose Constraints to equivalent uniform dose Constraints in inverse radiotherapy planning
Medical Physics, 2003Co-Authors: Christian Thieke, Thomas Bortfeld, Andrzej Niemierko, S NillAbstract:Optimization algorithms in inverse radiotherapy planning need information about the desired dose distribution. Usually the planner defines physical dose Constraints for each structure of the treatment plan, either in form of minimum and maximum doses or as dose-volume Constraints. The concept of equivalent uniform dose (EUD) was designed to describe dose distributions with a higher clinical relevance. In this paper, we present a method to consider the EUD as an Optimization Constraint by using the method of projections onto convex sets (POCS). In each iteration of the Optimization loop, for the actual dose distribution of an organ that violates an EUD Constraint a new dose distribution is calculated that satisfies the EUD Constraint, leading to voxel-based physical dose Constraints. The new dose distribution is found by projecting the current one onto the convex set of all dose distributions fulfilling the EUD Constraint. The algorithm is easy to integrate into existing inverse planning systems, and it allows the planner to choose between physical and EUD Constraints separately for each structure. A clinical case of a head and neck tumor is optimized using three different sets of Constraints: physical Constraints for all structures, physical Constraints for the target and EUD Constraints for the organs at risk, and EUD Constraints for all structures. The results show that the POCS method converges stable and given EUD Constraints are reached closely.
Louismartin Rousseau - One of the best experts on this subject based on the ideXlab platform.
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a Constraint programming approach for a batch processing problem with non identical job sizes
European Journal of Operational Research, 2012Co-Authors: Arnaud Malapert, Christelle Gueret, Louismartin RousseauAbstract:This paper presents a Constraint programming approach for a batch processing machine on which a finite number of jobs of non-identical sizes must be scheduled. A parallel batch processing machine can process several jobs simultaneously and the objective is to minimize the maximal lateness. The Constraint programming formulation proposed relies on the decomposition of the problem into finding an assignment of the jobs to the batches, and then minimizing the lateness of the batches on a single machine. This formulation is enhanced by a new Optimization Constraint which is based on a relaxed problem and applies cost-based domain filtering techniques. Experimental results demonstrate the efficiency of cost-based domain filtering techniques. Comparisons to other exact approaches clearly show the benefits of the proposed approach: it can optimally solve problems that are one order of magnitude greater than those solved by a mathematical formulation or by a branch-and-price.
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A Cost-Regular based Hybrid Column Generation Approach
Constraints, 2006Co-Authors: Sophie Demassey, Gilles Pesant, Louismartin RousseauAbstract:Constraint Programming (CP) offers a rich modeling language of Constraints embedding efficient algorithms to handle complex and heterogeneous combinatorial problems. To solve hard combinatorial Optimization problems using CP alone or hybrid CP-ILP decomposition methods, costs also have to be taken into account within the propagation process. Optimization Constraints, with their cost-based filtering algorithms, aim to apply inference based on optimality rather than feasibility. This paper introduces a new Optimization Constraint: cost-regular. Its filtering algorithm is based on the computation of shortest and longest paths in a layered directed graph. The support information is also used to guide the search for solutions. We believe this Constraint to be particularly useful in modeling and solving Column Generation subproblems and evaluate its behaviour on complex Employee Timetabling Problems through a flexible CP-based column generation approach. Computational results on generated benchmark sets and on a complex real-world instance are given.
Christian Thieke - One of the best experts on this subject based on the ideXlab platform.
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from physical dose Constraints to equivalent uniform dose Constraints in inverse radiotherapy planning
Medical Physics, 2003Co-Authors: Christian Thieke, Thomas Bortfeld, Andrzej Niemierko, S NillAbstract:Optimization algorithms in inverse radiotherapy planning need information about the desired dose distribution. Usually the planner defines physical dose Constraints for each structure of the treatment plan, either in form of minimum and maximum doses or as dose-volume Constraints. The concept of equivalent uniform dose (EUD) was designed to describe dose distributions with a higher clinical relevance. In this paper, we present a method to consider the EUD as an Optimization Constraint by using the method of projections onto convex sets (POCS). In each iteration of the Optimization loop, for the actual dose distribution of an organ that violates an EUD Constraint a new dose distribution is calculated that satisfies the EUD Constraint, leading to voxel-based physical dose Constraints. The new dose distribution is found by projecting the current one onto the convex set of all dose distributions fulfilling the EUD Constraint. The algorithm is easy to integrate into existing inverse planning systems, and it allows the planner to choose between physical and EUD Constraints separately for each structure. A clinical case of a head and neck tumor is optimized using three different sets of Constraints: physical Constraints for all structures, physical Constraints for the target and EUD Constraints for the organs at risk, and EUD Constraints for all structures. The results show that the POCS method converges stable and given EUD Constraints are reached closely.
Ning Liu - One of the best experts on this subject based on the ideXlab platform.
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universal physical camouflage attacks on object detectors
Computer Vision and Pattern Recognition, 2020Co-Authors: Lifeng Huang, Chengying Gao, Yuyin Zhou, Cihang Xie, Alan L Yuille, Changqing Zou, Ning LiuAbstract:In this paper, we study physical adversarial attacks on object detectors in the wild. Previous works mostly craft instance-dependent perturbations only for rigid or planar objects. To this end, we propose to learn an adversarial pattern to effectively attack all instances belonging to the same object category, referred to as Universal Physical Camouflage Attack (UPC). Concretely, UPC crafts camouflage by jointly fooling the region proposal network, as well as misleading the classifier and the regressor to output errors. In order to make UPC effective for non-rigid or non-planar objects, we introduce a set of transformations for mimicking deformable properties. We additionally impose Optimization Constraint to make generated patterns look natural to human observers. To fairly evaluate the effectiveness of different physical-world attacks, we present the first standardized virtual database, AttackScenes, which simulates the real 3D world in a controllable and reproducible environment. Extensive experiments suggest the superiority of our proposed UPC compared with existing physical adversarial attackers not only in virtual environments (AttackScenes), but also in real-world physical environments.
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universal physical camouflage attacks on object detectors
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Lifeng Huang, Chengying Gao, Yuyin Zhou, Cihang Xie, Alan L Yuille, Changqing Zou, Ning LiuAbstract:In this paper, we study physical adversarial attacks on object detectors in the wild. Previous works mostly craft instance-dependent perturbations only for rigid or planar objects. To this end, we propose to learn an adversarial pattern to effectively attack all instances belonging to the same object category, referred to as Universal Physical Camouflage Attack (UPC). Concretely, UPC crafts camouflage by jointly fooling the region proposal network, as well as misleading the classifier and the regressor to output errors. In order to make UPC effective for non-rigid or non-planar objects, we introduce a set of transformations for mimicking deformable properties. We additionally impose Optimization Constraint to make generated patterns look natural to human observers. To fairly evaluate the effectiveness of different physical-world attacks, we present the first standardized virtual database, AttackScenes, which simulates the real 3D world in a controllable and reproducible environment. Extensive experiments suggest the superiority of our proposed UPC compared with existing physical adversarial attackers not only in virtual environments (AttackScenes), but also in real-world physical environments. Code and dataset are available at this https URL.
Radoslaw Szymanek - One of the best experts on this subject based on the ideXlab platform.
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an efficient generic network flow Constraint
ACM Symposium on Applied Computing, 2011Co-Authors: Robin Steiger, Willemjan Van Hoeve, Radoslaw SzymanekAbstract:We propose a generic global Constraint that can be applied to model a wide range of network flow problems using Constraint programming. In our approach, all key aspects of a network flow can be represented by finite domain variables, making the Constraint very expressive. At the same time, we utilize a network simplex algorithm to design a highly efficient, and incremental, domain filtering algorithm. We thus integrate two powerful techniques for discrete Optimization: Constraint programming and the network simplex algorithm. Our generic Constraint can be applied to automatically implement effective and efficient domain filterng algorithms for ad-hoc networks, but also for existing global Constraints that rely on a network structure, including several soft global Constraints many of which are not yet supported by CP systems. Our experimental results demonstrate the efficiency of our Constraint, that can achieve speed-ups of several orders of magnitude with negligible overhead, when compared to a decomposition into primitive Constraints.