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Winners of the DSSC XS package

16 March 2023
We are happy to report that the following interdisciplinary teams won the DSSC XS package! Congratulations!

  • Achieving robustness to distribution shifts in instance segmentation of time-lapse microscopy: A brain-inspired inhibition approach (Dr. George Azzopardi - BI; Dr. Andreas Milias Argeitis - GBB)

The aim of this project is to develop instance segmentation algorithms for the delineation of budding yeast cells in brightfield time-lapse microscopy images. This is important for the automatic extraction of cellular readouts in high volumes of imaging data. The main scientific challenge is the high variability in brightfield imaging, especially across labs and imaging platforms (microfluidic chips, agarose pads, glass slides). The proposed methodology will integrate a brain-inspired inhibition mechanism in instance segmentation convolutional neural networks and evaluate them in terms of generalization ability on various real brightfield imaging data sets from international labs. More specifically, we will develop a computational model of the push-pull inhibition phenomenon, which is known to contribute to generalization in the mammalian brain, and integrate with state-of-the-art segmentation networks (e.g. Mask R-CNN).

  • Visual analysis of dis/misinformation and hateful meme (Dr. Jiapan Guo - BI; Dr. Kun He - Faculty of Arts, Media Studies)

The widespread dissemination of mis/disinformation and hate speech through digital media has worsened the already uncertain and turbulent state of the world, especially during the COVID-19 pandemic and the Ukraine-Russia war. Although extensive research has been conducted to detect and analyze text-based mis/disinformation, there is a lack of research on the spread and evolution of visual mis/disinformation and hateful memes. Compared to textual mis/disinformation, visual mis/disinformation is more effective in evoking emotions and influencing people's attitudes, which further fuels social division and polarization. This project aims to address this gap by analyzing the diffusion and evolution of visual mis/disinformation and hateful memes on digital platforms, such as 4chan and Twitter. The project will employ various approaches that combine human expert knowledge with the processing power of computers, utilizing supervised and unsupervised machine learning algorithms.

  • ´╗┐Machine Learning to Optimize Dense Wave Energy Converter Arrays (Prof. Kerstin Bunte - BI, Prof. Bart Besselink - BI, Prof. Antonis Vakis - ENTEG)
Wind has become an important part of renewable energy production. Waves have a higher energy density but technologies have not reached comparable technological maturity yet. Our group has been working on hybrid wind-wave-storage systems that can reduce the cost of wave energy converters (WECs) by combining their infrastructure with that of wind and storage. The optimization of the interplay of the different components to increase gains in energy capture facing a variety of different environmental circumstances is very complex. Our team will investigate model-based and data-driven hybrid machine learning (ML) approaches for the tuning of individual WECs arranged in an array to achieve optimal power absorption dependent on the sea state.  The ability to predict the performance of the WEC array using the combination of model reduction and ML will enable real-time optimization and thus bring us closer to the deployment of our system to practice.

Last modified:16 March 2023 11.31 a.m.

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