Backbone reconstruction in temporal networks from epidemic data

Causal networks as the backbone for temporal data-to-text

Causal networks have been successfully used to explain the process of narrative comprehension in humans (Trabasso et al., 1989). This motivated their use in the automatic creation of fairy tales (Swartjes and Theune, 2006; Theune et al., 2007). Some have suggested that causal relations also play an important role in improving narrative generation from real-life temporal data (Hunter et al., 201...

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

Predicting Epidemic Risk from Past Temporal Contact Data

Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system's functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission c...

image reconstruction from incomplete data

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