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Title:

Intra-Firm Competition in Multinational Corporations: Towards a Political Framework

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2014-12-03

Source:

https://aib.msu.edu/events/2008/BestPapers/AIB2008-0070.pdf

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text

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en

Subjects:

Key words ; Intra-Firm Competition ; Intra-Firm Politics ; Micro-Political Strategies ; Integration of Pre-Existing Theoretical Approaches

Key words ; Intra-Firm Competition ; Intra-Firm Politics ; Micro-Political Strategies ; Integration of Pre-Existing Theoretical Approaches Minimize

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A Study of Non-Smooth Convex Flow Decomposition

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We present a mathematical and computational feasibility study of the variational convex decomposition of 2D vector fields into coherent structures and additively superposed flow textures. Such decompositions are of interest for the analysis of image sequences in experimental fluid dynamics and for highly non-rigid image flows in computer vision....

We present a mathematical and computational feasibility study of the variational convex decomposition of 2D vector fields into coherent structures and additively superposed flow textures. Such decompositions are of interest for the analysis of image sequences in experimental fluid dynamics and for highly non-rigid image flows in computer vision. Our work extends current research on image decomposition into structural and textural parts in a twofold way. Firstly, based on Gauss ’ integral theorem, we decompose flows into three components related to the flow’s divergence, curl, and the boundary flow. To this end, we use proper operator discretizations that yield exact analogs of the basic continuous relations of vector analysis. Secondly, we decompose simultaneously both the divergence and the curl component into respective structural and textural parts. We show that the variational problem to achieve this decomposition together with necessary compatibility constraints can be reliably solved using a single convex second-order conic program. 1. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://kiwi.math.uni-mannheim.de/PAPERS/vlsm05.pdf

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text

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en

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Title:

A Study of Non-Smooth Convex Flow Decomposition

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Abstract. We present a mathematical and computational feasibility study of the variational convex decomposition of 2D vector fields into coherent structures and additively superposed flow textures. Such decompositions are of interest for the analysis of image sequences in experimental fluid dynamics and for highly non-rigid image flows in comput...

Abstract. We present a mathematical and computational feasibility study of the variational convex decomposition of 2D vector fields into coherent structures and additively superposed flow textures. Such decompositions are of interest for the analysis of image sequences in experimental fluid dynamics and for highly non-rigid image flows in computer vision. Our work extends current research on image decomposition into structural and textural parts in a twofold way. Firstly, based on Gauss ’ integral theorem, we decompose flows into three components related to the flow’s divergence, curl, and the boundary flow. To this end, we use proper operator discretizations that yield exact analogs of the basic continuous relations of vector analysis. Secondly, we decompose simultaneously both the divergence and the curl component into respective structural and textural parts. We show that the variational problem to achieve this decomposition together with necessary compatibility constraints can be reliably solved using a single convex second-order conic program. 1 Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://www.cvgpr.uni-mannheim.de/Publications/vlsm05.pdf

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text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

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Title:

Matrix-Valued Filters as Convex Programs

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Abstract. Matrix-valued images gain increasing importance both as the output of new imaging techniques and as the result of image processing operations, bearing the need for robust and efficient filters for such images. Recently, a median filter for matrix-valued images has been introduced. We propose a new approach for the numerical computation...

Abstract. Matrix-valued images gain increasing importance both as the output of new imaging techniques and as the result of image processing operations, bearing the need for robust and efficient filters for such images. Recently, a median filter for matrix-valued images has been introduced. We propose a new approach for the numerical computation of matrix-valued median filters, and closely related mid-range filters, based on sound convex programming techniques. Matrix-valued medians are uniquely computed as global optima with interior point solvers. The robust performance is validated with experimental results for matrixvalued data including texture analysis and denoising. 1 Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-08-20

Source:

http://www.mia.uni-saarland.de/Publications/welk-bsw-ss05.pdf

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text

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en

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Title:

Median and Related Local Filters for Tensor-Valued Images

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We develop a concept for the median filtering of tensor data. The main part of this concept is the definition of median for symmetric matrices. This definition is based on the minimisation of a geometrically motivated objective function which measures the sum of distances of a variable matrix to the given data matrices. This theoretically wellfo...

We develop a concept for the median filtering of tensor data. The main part of this concept is the definition of median for symmetric matrices. This definition is based on the minimisation of a geometrically motivated objective function which measures the sum of distances of a variable matrix to the given data matrices. This theoretically wellfounded concept fits into a context of similarly defined median filters for other multivariate data. Unlike some other approaches, we do not require by definition that the median has to be one of the given data values. Nevertheless, it happens so in many cases, equipping the matrix-valued median even with root signals similar to the scalarvalued situation. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2010-09-11

Source:

http://www.math.uni-sb.de/service/preprints/preprint135.pdf

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text

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en

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Signal Processing 87:291–308, 2007 (special issue Tensor Signal Processing) – c Elsevier B. V. 2006 Median and Related Local Filters for Tensor-Valued Images

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We develop a concept for the median filtering of tensor data. The main part of this concept is the definition of median for symmetric matrices. This definition is based on the minimisation of a geometrically motivated objective function which measures the sum of distances of a variable matrix to the given data matrices. This theoretically well-f...

We develop a concept for the median filtering of tensor data. The main part of this concept is the definition of median for symmetric matrices. This definition is based on the minimisation of a geometrically motivated objective function which measures the sum of distances of a variable matrix to the given data matrices. This theoretically well-founded concept fits into a context of similarly defined median filters for other multivariate data. Unlike some other approaches, we do not require by definition that the median has to be one of the given data values. Nevertheless, it happens so in many cases, equipping the matrix-valued median even with root signals similar to the scalar-valued situation. Like their scalar-valued counterparts, matrix-valued median filters show excellent capabilities for structure-preserving denoising. Experiments on diffusion tensor imaging, fluid dynamics and orientation estimation data are shown to demonstrate this. The orientation estimation examples give rise to a new variant of a robust adaptive structure tensor which can be compared to existing concepts. For the efficient computation of matrix medians, we present a convex programming framework. By generalising the idea of the matrix median filters, we design a variety of other local matrix filters. These include matrix-valued mid-range filters and, more generally, M-smoothers but also weighted medians and α-quantiles. Mid-range filters and quantiles allow also interesting cross-links to fundamental concepts of matrix morphology. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://www.mia.uni-saarland.de/Publications/welk-sp07.pdf

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Document Type:

text

Language:

en

DDC:

518 Numerical analysis *(computed)*

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Title:

THE CASE OF EXPORT CARTEL EXEMPTIONS: BETWEEN COMPETITION AND PROTECTIONISM

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Most competition laws do not prohibit anticompetitive conduct that affects foreign target markets as long as there is no spill over effect on the home market. The U.S. in particular justifies this leniency towards export cartels by the aim of increasing efficiency in target markets that are suffering from high entrance barriers for importers. At...

Most competition laws do not prohibit anticompetitive conduct that affects foreign target markets as long as there is no spill over effect on the home market. The U.S. in particular justifies this leniency towards export cartels by the aim of increasing efficiency in target markets that are suffering from high entrance barriers for importers. Attempts to use the legal regime of the WTO to overcome private restrictions of competition are likely to fail, because of the fundamental differences between trade policy and competition policy. Although a multilateral competition policy would be best suited to challenge export cartels, the current state of the political debate makes it more likely that second-best solutions such as capacity building in lesser developed target states will have to be established. Minimize

Publisher:

Oxford University Press

Year of Publication:

2007-03-05 05:39:22.0

Document Type:

TEXT

Language:

en

Subjects:

Article

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Copyright (C) 2007, Oxford University Press

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Title:

Variational Correlation and Decomposition Methods for Particle Image Velocimetry

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Particle Image Velocimetry (PIV) is a non-intrusive optical measurement technique for industrial fluid flow questions. Small particles are introduced into liquids or gases and act as indicators for the movement of the investigated substance around obstacles or in regions where fluids mix. For the two-dimensional variant of the PIV method, a thin...

Particle Image Velocimetry (PIV) is a non-intrusive optical measurement technique for industrial fluid flow questions. Small particles are introduced into liquids or gases and act as indicators for the movement of the investigated substance around obstacles or in regions where fluids mix. For the two-dimensional variant of the PIV method, a thin plane is illuminated by laser light rendering the particles therein visible. A high speed camera system records an image sequence of the highlighted area. The analysis of this data allows to determine the movement of the particles, and in this way to measure the speed, turbulence or other derived physical properties of the fluid. In state-of-the-art implementations, correspondences between regions of two subsequent image frames are determined using cross-correlation as similarity measurement. In practice it has proven to be robust against disturbances typically found in PIV data. Usually, an exhaustive search over a discrete set of velocity vectors is performed to find the one which describes the data best. In our work we consider a variational formulation of this problem, motivated by the extensive work on variational optical flow methods which allows to incorporate physical priors on the fluid. Furthermore, we replace the usually square shaped correlation window, which defines the image regions whose correspondence is investigated, by a Gaussian function. This design drastically increases the flexibility of the process to adjust to features in the experimental data. A sound criterion is proposed to adapt the size and shape of the correlation window, which directly formulates the aim to improve the measurement accuracy. The velocity measurement and window adaption are formulated as an interdependent variational problem. We apply continuous optimisation methods to determine a solution to this non-linear and non-convex problem. In the experimental section, we demonstrate that our approach can handle both synthetic and real data with high accuracy and compare its performance to state-of-the-art methods. Furthermore, we show that the proposed window adaption scheme increases the measurement accuracy. In particular, high gradients in motion fields are resolved well. In the second part of our work, we investigate an approach for solving very large convex optimisation problems. This is motivated by the fact that a variational formulation on the one hand allows to easily incorporate prior knowledge on data and variables to improve the quality of the solution. Furthermore, convex problems often occur as subprograms of solvers for non-convex optimisation tasks, as it is the case in the first part of this work. However, the extension of two-dimensional approaches to 3D, or to the time axis, as well as the ever increasing resolution of sensors, let the number of variables virtually explode. For many interesting applications, e.g. in medical imaging or fluid mechanics, the problem description easily exceeds the memory limits of available, single computational nodes. Thus, we investigate a decomposition method for the class of unconstrained, convex and quadratic optimisation problems. Our approach is based on the idea of Dual Decomposition, or Lagrangian Relaxation, and splits up the problem into a couple of smaller tasks, which can be distributed to parallel hardware. Each subproblem is again quadratic and convex and thus can be solved efficiently using standard methods. Their interconnection is respected to ensure that we find a solution to the original, non-decomposed problem. Furthermore we propose a framework to modify the numerical properties of the subproblems, which enables us to improve their convergence rates. The theoretical part is completed by the analysis of convergence conditions and rate. Finally, we demonstrate our approach by means of three relevant variational problems from image processing. Error measurements in comparison to single-domain solutions are presented to assess the accuracy of the decomposition. Minimize

Year of Publication:

2009

Document Type:

Dissertation ; info:eu-repo/semantics/doctoralThesis ; NonPeerReviewed

Language:

eng

Subjects:

004 ; 004 Data processing Computer science

004 ; 004 Data processing Computer science Minimize

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info:eu-repo/semantics/openAccess

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http://archiv.ub.uni-heidelberg.de/volltextserver/9766/1/2009_08_13_Veroeffentlichung_print.pdf ; http://archiv.ub.uni-heidelberg.de/volltextserver/9766/2/2009_08_13_Veroeffentlichung_web.pdf ; urn:nbn:de:bsz:16-heidok-97660 ; Becker, Florian (2009) Variational Correlation and Decomposition Methods for Particle Image Velocimetry. [Dissertation]

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Title:

Market Regulation and the 'Right to Property' in the European Economic Constitution

Publisher:

Oxford University Press

Year of Publication:

2007-01-01 00:00:00.0

Document Type:

TEXT

Language:

en

Subjects:

Articles

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Copyright (C) 2007, No

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Title:

THE CASE OF EXPORT CARTEL EXEMPTIONS: BETWEEN COMPETITION AND PROTECTIONISM

Description:

Most competition laws do not prohibit anticompetitive conduct that affects foreign target markets as long as there is no spill over effect on the home market. The U.S. in particular justifies this leniency towards export cartels by the aim of increasing efficiency in target markets that are suffering from high entrance barriers for importers. At...

Most competition laws do not prohibit anticompetitive conduct that affects foreign target markets as long as there is no spill over effect on the home market. The U.S. in particular justifies this leniency towards export cartels by the aim of increasing efficiency in target markets that are suffering from high entrance barriers for importers. Attempts to use the legal regime of the WTO to overcome private restrictions of competition are likely to fail, because of the fundamental differences between trade policy and competition policy. Although a multilateral competition policy would be best suited to challenge export cartels, the current state of the political debate makes it more likely that second-best solutions such as capacity building in lesser developed target states will have to be established. Minimize

Publisher:

Oxford University Press

Year of Publication:

2007-03-23

Document Type:

TEXT

Language:

en

Subjects:

ARTICLES

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Copyright (C) 2007, Oxford University Press

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