PROTA: a useful tool for forest and tree plantations management in Tropical Africa

A good management of a natural forest depends on a good knowledge of the uses of all the trees species growing in that forest. These uses are generally not well known and may be cause of conflicts between stakeholders. Only a few number of tree species are planted at a large scale because of a lack of knowledge on other promising species: biological properties, nursery and planting techniques. However, the information exists somewhere: research has been done for over a century; but this knowledge is scattered, often through grey literature, and difficult to find and consult. The main objective of Prota (Plant Resources of Tropical Africa) encyclopaedia is to collect and synthesize scattered information on 8,000 useful plants of Tropical Africa and among them 1,100 are timber trees. Prota aims to provide a wide access to that information through Webdatabases, Books, CD-Roms and Special Products. ?Timbers? is founded by ITTO and translated by CTA. So, PROTA is a fully bilingual (English/French) information system. Actually 700 timber species are freely accessible on the Prota Webdatabase. A short presentation on how to use that database is given. (Résumé d'auteur

Adversarial Variational Optimization of Non-Differentiable Simulators

Complex computer simulators are increasingly used across fields of science as generative models tying parameters of an underlying theory to experimental observations. Inference in this setup is often difficult, as simulators rarely admit a tractable density or likelihood function. We introduce Adversarial Variational Optimization (AVO), a likelihood-free inference algorithm for fitting a non-differentiable generative model incorporating ideas from generative adversarial networks, variational optimization and empirical Bayes. We adapt the training procedure of generative adversarial networks by replacing the differentiable generative network with a domain-specific simulator. We solve the resulting non-differentiable minimax problem by minimizing variational upper bounds of the two adversarial objectives. Effectively, the procedure results in learning a proposal distribution over simulator parameters, such that the JS divergence between the marginal distribution of the synthetic data and the empirical distribution of observed data is minimized. We evaluate and compare the method with simulators producing both discrete and continuous data.Comment: v4: Final version published at AISTATS 2019; v5: Fixed typo in Eqn 1

Visualization of Publication Impact

Measuring scholarly impact has been a topic of much interest in recent years. While many use the citation count as a primary indicator of a publications impact, the quality and impact of those citations will vary. Additionally, it is often difficult to see where a paper sits among other papers in the same research area. Questions we wished to answer through this visualization were: is a publication cited less than publications in the field?; is a publication cited by high or low impact publications?; and can we visually compare the impact of publications across a result set? In this work we address the above questions through a new visualization of publication impact. Our technique has been applied to the visualization of citation information in INSPIREHEP (http://www.inspirehep.net), the largest high energy physics publication repository

Division de recherche en zones de savanes. Rapport d'activités 1993

En 1993, le projet Recherche d'accompagnement au projet de reboisement et d'aménagement forestier dans la zone de savane a commencé et le projet "Appui au développement agricole de la région centre" s'est terminé. (Résumé d'auteur