activity
20242026
collaborators

6 papers

eess.SY2026

Toward a Decision Support System for Energy-Efficient Ferry Operation on Lake Constance based on Optimal Control

Hannes Homburger, Bastian Jäckl, Stefan Wirtensohn +5

The maritime sector is undergoing a disruptive technological change driven by three main factors: autonomy, decarbonization, and digital transformation. Addressing these factors ne…

cs.LG2025

Leveraging LLMs for Semi-Automatic Corpus Filtration in Systematic Literature Reviews

Lucas Joos, Daniel A. Keim, Maximilian T. Fischer

The creation of systematic literature reviews (SLR) is critical for analyzing the landscape of a research field and guiding future research directions. However, retrieving and filt…

cs.HC2025

Visual Network Analysis in Immersive Environments: A Survey

Lucas Joos, Maximilian T. Fischer, Julius Rauscher +4

The increasing complexity and volume of network data demand effective analysis approaches, with visual exploration proving particularly beneficial. Immersive technologies, such as…

cs.HC2025

Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings

Lucas Joos, Gavin J. Mooney, Maximilian T. Fischer +4

The visual analysis of graphs in 3D has become increasingly popular, accelerated by the rise of immersive technology, such as augmented and virtual reality. Unlike 2D drawings, 3D…

cs.LG2025

Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews

Lucas Joos, Daniel A. Keim, Maximilian T. Fischer

Systematic literature reviews (SLRs) are essential but labor-intensive due to high publication volumes and inefficient keyword-based filtering. To streamline this process, we evalu…

cs.CV2024

Leveraging Color Channel Independence for Improved Unsupervised Object Detection

Bastian Jäckl, Yannick Metz, Udo Schlegel +2

Object-centric architectures can learn to extract distinct object representations from visual scenes, enabling downstream applications on the object level. Similarly to autoencoder…