Core-collapse supernovae (CC-SNe) represent the final evolutionary explosive phase of sufficiently massive stars. Their characterization consists in understanding the progenitor …
Recent progress in brain-guided image generation has improved the quality of fMRI-based reconstructions; however, fundamental challenges remain in preserving object-level structure …
Reconstructing functional brain networks from functional Magnetic Resonance Imaging (fMRI) data typically relies on statistical pruning, where pairwise correlations between brain …
Abstract The discovery of novel small molecules is challenging because of the vastness of chemical space and the complexity of protein-ligand interactions, leading to low success …
Discrete diffusion models are a prominent family for graph generation, but standard class-conditional mechanisms embed the class signal in the denoiser during training, tying the …
This paper investigates the preservation of human visual attention in brain activity patterns captured through fMRI during visual decoding tasks. We explore how well attention …
Recent advancements in diffusion-based image generation and large vision/language models have revolutionized visual brain decoding (VBD), driving progress in neuroscience and …
In this work, we present a Deep Learning framework to predict the progenitor star’s characteristics of Supernovae (SNe) from their observed light curves. This task is crucial for …
Due to their intrinsic capabilities in capturing, modeling, and representing relationships between pieces of information, graphs have been used in a wide variety of application …
Graph generation is a crucial task in many fields, including network science and bioinformatics, as it enables the creation of synthetic graphs that mimic the properties of …