There is growing interest in quantitatively analyzing in vivo image data, as this facilitates objective comparisons and measurement of effect. In this regard, people increasingly turn to pharmacokinetic models and estimation of parameters of such models. In this work several parameter estimation methodologies were compared within the context of the most common pharmacokinetic model used in posi...

Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge is to determine the model parameters that best match a given image by searching for the global optimum of the involved objective function. Unfortunately, this function is usually designed manually, based on implicit and domain-dependent ...

We consider the asymptotic behavior of the Newton/log barrier method for inequality constrained optimization. We show that, when the objective function is linear, an eeective step can be taken along the Newton direction after each reduction in the barrier parameter, leading to eecient performance during the nal stages of the algorithm. This behavior contrasts with the case of a nonlinear object...

The paper deals with the open-shop problems with unit-time operations and nonde-creasing symmetric objective functions depending on job completion times. We construct two schedules, one of them is optimal for any symmetric convex function, the other is optimal for any symmetric concave function. Each of these two schedules is given by an analytically deened function which assigns to each operat...

What I would like to accomplish in these meeting... 1. Representing nonlinear functions in a higher dimensional linear space. (Generalized) total least squares. 2. Nonlinear least squares estimation. The Levenberg-Marquardt method. 3. Mean shift in the Euclidean domain for segmentation and for tracking. 4. Applying nonlinear mean shift to different types of Riemannian manifold. 5. Mean shift cl...

MT evaluation metrics are tested for correlation with human judgments either at the sentenceor the corpus-level. Trained metrics ignore corpus-level judgments and are trained for high sentence-level correlation only. We show that training only for one objective (sentence or corpus level), can not only harm the performance on the other objective, but it can also be suboptimal for the objective b...

Decomposition has become an increasingly popular technique for evolutionary multiobjective optimization (EMO). A decomposition-based EMO algorithm is usually designed to approximate a whole Pareto-optimal front (PF). However, in practice, the decision maker (DM) might only be interested in her/his region of interest (ROI), i.e., a part of the PF. Solutions outside that might be useless or even ...

Selection of a suitable objective function is an integral part of the inverse problem, and poor selection can have a strong influence on the inverse result. Objective functions are here derived for many practical occasions such as for single frequency and broadband, with and without knowledge of source strength, and with and without the received signal phase. These objective functions are all d...

CP-Nets are a framework for dealing with qualitative preferences, both conditional and unconditional. They have received a lot of attention recently, and many extensions have been provided. In particular, the framework of constrained CP-Nets aims to choose, amongst the solutions that satisfy a set of constraints, the preferred one. While the semantics of CP-Nets allows for cycles (and, indeed, ...

Detecting communities of complex networks has been an effective way to identify substructures that could correspond to important functions. Conventional approaches usually consider community detection as a singleobjective optimization problem, which may confine the solution to a particular community structure property. Recently, a new community detection paradigm is emerging: multiobjective opt...