Large Language Models for Transforming Categorical Data to Interpretable Feature Vectors.

When analyzing heterogeneous data comprising numerical and categorical attributes, it is common to treat the different data types separately or transform the categorical attributes to numerical ones. The transformation has the advantage of facilitating an integrated multi-variate analysis of all attributes. We propose a novel technique for transforming categorical data into interpretable numerical feature vectors using Large Language Models (LLMs).

2D Embeddings of Multi-dimensional Partitionings.

Partitionings (or segmentations) divide a given domain into disjoint connected regions whose union forms again the entire domain. Multi-dimensional partitionings occur, for example, when analyzing parameter spaces of simulation models, where each segment of the partitioning represents a region of similar model behavior. Having computed a partitioning, one is commonly interested in understanding how large the segments are and which segments lie next to each other.

Interactive Visual Analysis of Spatial Sensitivities.

Sensitivity analyses of simulation ensembles determine how simulation parameters influence the simulation's outcome. Commonly, one global numerical sensitivity value is computed per simulation parameter. However, when considering 3D spatial simulations, the analysis of localized sensitivities in different spatial regions is of importance in many applications.

Art-based observational training for medical students and surgical residents in two Dutch museums.

Background: Art-based education is gaining interest in the medical field, particularly in specialties with a strong visual focus. Visual arts are increasingly used for the development of observational skills and social competencies. While content and objectives of art-based programs widely differ across medical faculties in the Netherlands, the diverse range of options underscore the interest in and the potential of this educational approach.