This paper aims at introducing the concept of selection graph with an application to a non-trivial problem. A selection graph is made of a set of classes, each class having multiple realizations, and of an uncompatibility graph built on these realizations. Coloring a selection graph can be used to solve constrained scheduling, constrained ressource allocation, and dichotomy-based constrained en...

Stochastic graph neural networks (SGNNs) are information processing architectures that learn representations from data over random graphs. SGNNs trained with respect to the expected performance, which comes no guarantee about deviations of particular output realizations around optimal expectation. To overcome this issue, we propose a variance-constrained optimization problem for SGNNs, balancin...

Knowledge graphs are a key technique for linking and integrating cross-domain data, concepts, tools, knowledge to enable data-driven analytics. As much of the world's data have become massive in size, visualizing graph entities their interrelationships intuitively interactively has crucial task ingesting better utilizing content support semantic reasoning, discovering hidden discovering, scient...

Background Despite decades of evidence gathering and calls for action, few countries have systematically attenuated health inequities (HI) through action on the social determinants of health (SDH). This is at least partly because doing so presents a significant political and policy challenge. This paper explores this challenge through a review of the empirical literature, asking: what factors h...

concerns regarding the trans-pacific partnership (tpp) have raised awareness about the negative public health impacts of trade and investment agreements. in the past decade, we have learned much about the implications of trade agreements for public health: reduced equity in access to health services; increased flows of unhealthy commodities; limits on access to medicines; and constrained policy...