Poster

Seismic and environmental noise, particularly Newtonian noise, constitute a fundamentallimitation for the Einstein Telescope, motivating the development ofadvanced, data-driven approaches for noise mitigation to improve the sensitivityand operational stability of its interferometric systems. Real-time compensationof Newtonian noise can improve sensitivity of the sensor and allow rapidtriggering of multi-messenger observations when a gravitational wave signal isdetected.A neural network is trained to reconstruct the waveform of a target sensorbased on measurements from neighboring nodes. [...]

Neurodevelopment lays the foundation for lifelong brain health.Deviations therein underpin various neurological and psychiatricdisorders. Identifying and characterizing these deviations early is acentral goal of clinical neuroscience.• It is well-established that intra-individual changes are a stronger predictorof behavioral and clinical outcomes, compared to inter-individualdifferences1. [...]

Spiking neural networks are nature’s solution for robust, energy-efficient information processing and can be effectively trained [1, 2]. While the theoretical understanding of artificial neural networks has advanced substantially, comparable frameworks for trained spiking neural networks remain underdeveloped [...]

The regional division of the human brain based on cytoarchitectural, functional, or structural signatures, also known as brain atlas or cortical parcellation, is a widely used approach in current neuroimaging research. The numerous existing parcellation schemes1 usually reflect group-level averages to provide robust and generalizable definitions of brain regions. [...]