Researchers explored how machine learning and quantum computing can be used to improve early detection of chronic kidney disease, aiming to develop faster, more accurate diagnostic tools for clinicians.

A computer that can calculate hundreds of scientific tasks simultaneously and thus helps provide a solution to key social challenges: this is the new IT heart of Paderborn University. The ‘Otus’ supercomputer was put into operation at the Paderborn Center for Parallel Computing (PC2) on Monday 10 November. From now onwards, researchers all over Germany can use it to run challenging computer simulations and conduct scientific enquiry at the highest level.  

Motor imagery electroencephalography (MI-EEG) is crucial for brain-computer interfaces, serving as a valuable tool for motor function rehabilitation and fundamental neuroscience research. However, decoding MI-EEG signals is extremely challenging, and traditional methods overlook dependencies between spatiotemporal features and spectral-topological features. Now, researchers have developed a new topology-aware method that effectively captures the deep dependencies across different feature domains of EEG signals, ensuring accurate and robust decoding, paving the way for more brain-responsive technology.