Cognitive workload monitoring in virtual reality based rescue missions with drones
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Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over ma ...
Application of instream habitat models such as the Mesohabitat Simulation Model (MesoHABSIM) is becoming increasingly popular. Such models can predict alteration to a river physical habitat caused by hydropower operation or river training. They are a tool ...
Purpose The present study was designed to evaluate the long-term results (more than 10 years) of mobile bearing total knee arthroplasty (TKA) and to compare the survival of medial pivot axis (MPA) and central pivot axis (CPA) TKAs. The primary hypothesis w ...
In generalized linear estimation (GLE) problems, we seek to estimate a signal that is observed through a linear transform followed by a component-wise, possibly nonlinear and noisy, channel. In the Bayesian optimal setting, generalized approximate message ...
A versatile method to automatically classify ice particle habit from various airborne optical array probes is presented. The classification is achieved using a multinomial logistic regression model. For each airborne probe, the model determines the particl ...
Suppose we have randomized decision trees for an outer function f and an inner function g. The natural approach for obtaining a randomized decision tree for the composed function (f∘ gⁿ)(x¹,…,xⁿ) = f(g(x¹),…,g(xⁿ)) involves amplifying the success probabili ...
Schloss Dagstuhl - Leibniz-Zentrum für Informatik2020
Although it is known that having accurate Lipschitz estimates is essential for certain models to deliver good predictive performance, refining this constant in practice can be a difficult task especially when the input dimension is high. In this letter, we ...
Discrete Choice Models (DCMs) have a distinct advantage over Machine Learning (ML) classification algorithms, in that they employ a highly interpretable linear structure. However, a key drawback of DCMs compared to ML is the need to specify the utility fun ...
Data from animal-borne inertial sensors are widely used to investigate several aspects of an animal's life, such as energy expenditure, daily activity patterns and behaviour. Accelerometer data used in conjunction with machine learning algorithms have b ...
The nematode Caenorhabditis elegans is increasingly used as a model for human biology. However, in vivo culturing platforms for C. elegans allowing high-content phenotyping during their life cycle in an automated fashion are lacking so far. Here, a multipl ...