Gossip protocols are programs that can be used by a group of agents to synchronize what information they have. Namely, assuming each agent holds a secret, the goal of a protocol is to reach a situation in which all agents know all secrets. Distributed epistemic gossip protocols use epistemic formulas in the component programs for the agents. In this paper, we study the simplest classes of such gossip protocols: propositional gossip protocols, in which whether an agent wants to initiate a call depends only on the set of secrets that the agent currently knows. It was recently shown that such a protocol can be correct, i.e., always terminates in a state where all agents know all secrets, only when its communication graph is complete. We show here that this characterization dramatically changes when the usual fairness constraints are imposed on the call scheduler used. Finally, we establish that checking the correctness of a given propositional protocol under a fair scheduler is a coNP-complete problem.

In this paper we build a neural network model to predict prevalence of malaria for a given geographic location and year. We report on our experience of building the most suitable neural network architecture for this problem. We show that both utilizing dropout and Adam optimizer in the network training process is very effective and can lead to a precise model without overfitting issues. Incorporating rainfall data leads to a significant improvement in the precision of the model, highlighting the fact that this is an important factor in the spread of malaria. We then utilize the selected best neural network to predict the outcome of eradicating malaria at given locations. This can help to decide where to use limited resources, like vaccines or insecticides, for the largest possible impact in malaria control.