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What is augmented in Augmented Reality (AR)? In this paper, we review existing opinions and show how little consensus exists on this matter. Subsequently, we approach the question from a theoretical and technology-independent perspective. We identify spatial and content-based relationships between the virtual and the real as being decisive for AR and come to the conclusion that virtual content augments that to which it relates. Subsequently, we categorize different forms of AR based on what is augmented. We distinguish between augmented environments, augmented objects, augmented humans and augmented content and consider the possibility of augmented perception. The categories are illustrated with AR (art) works and conceptual differences between them are pointed out. Moreover, we discuss what the real contributes to AR and how it can shape (future) AR experiences. A summary of our findings and suggestions for future research and practice, such as research into multimodal and crossmodal AR, conclude the paper.

We are interested in sonifying the molecular structures of amino acids. This paper describes the context and the first design choices for our approach. So far, we believe an amino acid molecule is too complex to be perceived at once. Therefore, we have designed an interactive form of sonification in which the listener navigates through the molecule over the network of carbon atoms. We describe our different approaches and discuss the topic of immediacy: the time it takes to recognize the structure surrounding the listener’s position while navigating. Furthermore, we touch upon the question how many atoms we can sonify simultaneously and the role auditory masking plays in this context. To overcome auditory masking, we propose to use irregular but easy to recognize sounds. We conclude with an interest in a three-dimensional navigation environment using general molecular structures for further research and development.

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VAN DER HEIDE. (2020). Predicting survival in dairy cattle using machine learning. PhD thesis, Wageningen University, the Netherlands A lthough cows can live to be twenty years old, the average lifespan for a dairy cow is only five to six years. Improving the lifespan of dairy cows would have several benefits such as increasing farm profitability and reducing the environmental impact of milk production. However, the complexity of survival makes it difficult to improve this trait in practice. In this thesis, I proposed using phenotypic prediction of survival to select young cows for the dairy herd, improving survival through increased lifespan of selected cows and better heifer management. The aim of this thesis was to investigate if it was possible to predict survival phenotype accurately enough to be of use in selection. I investigated three different methods to predict survival: multiple logistic regression, random forest and naive Bayes. In chapters two to four of this thesis I predicted the survival trait "survival to second lactation" using all three aforementioned methods. In chapter five, I predicted the survival trait "number of parities reached" using only the random forest method. Random forest and naive Bayes proved the best methods for predicting survival to second lactation, although predictive performance overall was low. The correlations between predictions for individual cows were much lower than expected, which indicated that the models predicted individual cows differently. Therefore, in chapter four I investigated if combining the results into an ensemble could improve predictive performance. An ensemble using multiple logistic regression resulted in the largest increase in performance, although none of the explored ensemble methods improved performance consistently across datasets. I further investigated if there was a benefit in including genomic information or a farm-specific effect. In chapter two, I investigated the benefit of combining genomic and phenotypic information. Genomic breeding values especially improved the prediction of survival early in life, with breeding values for fertility and longevity remained informative even after first calving. In chapter five I tested several different methods to include a farm effect and described the advantages and disadvantages of the various approaches. The results of this thesis provide valuable insights in the challenges of predicting survival traits and the suitability of various (machine learning) methods for the prediction of survival in dairy cattle.