Archaeologists often face major challenges when trying to connect new discoveries with information from old books: How can the findings of 200 years of archaeological research be combined with new ...

To tackle this problem, we propose a Multi-channel Disentangled Graph Neural Network (MD-GraphNet), which effectively classifies self-supervised constraints by learning disentangled representations.

We propose to apply GNNs to the problem of modeling transformations of functions defined on continuous spaces, using a structure we call graph element networks (GENs). Inspired by finite element ...

We employ a graph neural network (GNN)-driven transformer algorithm, called GNN-Transformer, to classify every active event pixel in the raw stream into real log-intensity variation or noise. Within ...

BNPool can learn a different number of clusters for each input graph. ./ ├── config/ # Hydra configuration files ├── source/ # Directory with the scripts │ ├── data/ # Dataset handling scripts │ ├── ...

Accelerate your tech game Paid Content How the New Space Race Will Drive Innovation How the metaverse will change the future of work and society Managing the Multicloud The Future of the Internet ...

3.2 Parametric bifurcation graphs and lyapunov exponents In the dynamic analysis, the chaotic characteristics of the model can be evaluated by using tools such as the Lyapunov exponent and the ...