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This paper proposes a dimension reduction technique for estimation in linear mixed models. Specifically, we show that in a linear mixed model, the maximum-likelihood (ML) problem can be rewritten as a substantially simpler optimization problem which presen ...
Kernelized Support Vector Machines (SVM) have gained the status of o-the-shelf classiers, able to deliver state of the art performance on almost any problem. Still, their practical use is constrained by their computational and memory complexity, which grow ...
Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of image denoising, inpainting, deconvolution or reconstruction substantially, be ...
Summary: NoRSE was developed to analyze high-frequency datasets collected from multistate, dynamic expts., such as mol. adsorption and desorption onto carbon nanotubes. As technol. improves sampling frequency, these stochastic datasets become increasingly ...
Rainfall is poorly modeled by general circulation models (GCMs) and requires appropriate downscaling for local-scale hydrological impact studies. Such downscaling methods should be robust and accurate (to handle, e.g., extreme events and uncertainties), bu ...
The Three-Georges Dam holds many records in the history of engineering. While the dam has produced benefits in terms of flood control, hydropower generation and increased navigation capacity of the Yangtze River, serious questions have been raised concerni ...
Cell analysis requires increasingly more complex equipment to investigate cellular and molecular mechanisms. The goal of the present research is to develop a platform of integrated amperometric biosensors to better understand biological processes by real-t ...
The increased accessibility of data that are geographically referenced and correlated increases the demand for techniques of spatial data analysis. The subset of such data comprised of discrete counts exhibit particular difficulties and the challenges furt ...
Since the seminal work of Watts & Strogatz and others in the late 90s [1], graph-theoretic analyses have been performed on many complex dynamic networks, including brain structures. Most studies have focused on functional connectivity defined between whole ...
Mathematical models can provide key insights into the course of an ongoing epidemic, potentially aiding real-time emergency management in allocating health care resources and by anticipating the impact of alternative interventions. We study the ex post rel ...