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We propose a semi-supervised image segmentation method that relies on a non-local continuous version of the min-cut algorithm and labels or seeds provided by a user. The segmentation process is performed via energy minimization. The proposed energy is comp ...
Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of ...
Institute of Electrical and Electronics Engineers2008
The aim of this thesis is to develop a new methodology to determine dynamic Origin-Destination (OD) matrices for urban networks characterized by a high number of traffic hubs, complex route choice possibilities and a high level of traffic controls. By revi ...
The mitogen-activated protein kinase (MAPK) cascades are ubiquitous in eukaryotic signal transduction, and these pathways are conserved in cells from yeast to mammals. They relay extracellular stimuli from the plasma membrane to targets in the cytoplasm an ...
The resource constrained elementary shortest path problem (RCESPP) arises as a pricing subproblem in branch-and-price algorithms for vehicle-routing problems with additional constraints. We address the optimization of the RCESPP and we present and compare ...
In a distributed stream processing system, streaming data are continuously disseminated from the sources to the distributed processing servers. To enhance the dissemination efficiency, these servers are typically organized into one or more dissemination tr ...
Dynamic Programming OPtimization (DPOP) is an algorithm proposed for solving distributed constraint optimization problems. In this algorithm, Hypercubes are used as the format of the messages exchanged between the agents. Then, Hybrid-DPOP (H-DPOP), a hybr ...
We study bounding approximations for a multistage stochastic program with expected value constraints. Two simpler approximate stochastic programs, which provide upper and lower bounds on the original problem, are obtained by replacing the original stochast ...
Multistage stochastic programs have applications in many areas and support policy makers in finding rational decisions that hedge against unforeseen negative events. In order to ensure computational tractability, continuous-state stochastic programs are us ...
A powerful approach for dynamic optimization in the presence of uncertainty is to incorporate measurements into the optimization framework so as to track the necessary conditions of optimality (NCO), the so-called NCO- tracking approach. For nonsingular co ...