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Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This ...
We present a novel probabilistic finite element method (FEM) for the solution and uncertainty quantification of elliptic partial differential equations based on random meshes, which we call random mesh FEM (RM-FEM). Our methodology allows to introduce a pr ...
Over the past 60-80 years, the design methodology of steel structures comprising conventional steel profiles, such as HEA, HEB or HEM, has remained unchanged. However, during the 21st century, production methods have improved considerably. The aim of this ...
This paper analyzes the relative distribution of annual saturation and contrast effects of discomfort glare in a deep open-plan office. A previous comparative study has shown that while hybrid glare metrics were found to predict glare well in most scenario ...
Visual crowding is the inability to perceive properly peripheral stimuli within clutter. Previous work has shown that crowding is affected by perceptual grouping: when the flankers do not group with the target, crowding decreases, leading to uncrowding. Ty ...
Robust and distributionally robust optimization are modeling paradigms for decision-making under uncertainty where the uncertain parameters are only known to reside in an uncertainty set or are governed by any probability distribution from within an ambigu ...
Our aim is to develop a 3D model unit of cardiac muscle: an in-vitro analog of the trabeculae carneae found in vivo. As a base hydrogel matrix for cardiomyocyte culture in 3D, we develop a blend of decellularized extracellular matrix (dECM) and fibrin. Thi ...
Adaptability and ease of programming are key features necessary for a wider spread of robotics in factories and everyday assistance. Learning from demonstration (LfD) is an approach to address this problem. It aims to develop algorithms and interfaces such ...
We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be exp ...
Background Coercion in psychiatry is a controversial issue. Identifying its predictors and their interaction using traditional statistical methods is difficult, given the large number of variables involved. The purpose of this study was to use machine-lear ...