No abstract

Green security — protection of forests, fish and wildlife — is a critical problem in environmental sustainability. We focus on the problem  of  optimizing the defense of forests againstillegal logging, where often we are faced with the challenge of teaming up many different groups,  from national police to forest guards to NGOs, each with differing capabilities and costs. This paper introduces a new, yet fundamental problem: SimultaneousOptimization of Resource Teams and Tactics (SORT).  SORT contrasts with most previous game-theoretic research for green security — in particular based onsecurity games — that has solely focused on optimizing patrolling tactics, without consideration of team formation or coordination.  We develop new models and scalable algorithms to apply SORT towards illegal logging in large forest areas. We evaluate our methods on a variety of synthetic examples, as well as a real-world case study using data from our on-going collaboration in Madagascar.

A common step in developing an understanding of a vertical domain, e.g. shopping, dining, movies, medicine, etc., is curating a taxonomy of categories specific to the domain. These human created artifacts have been the subject of research in embeddings that attempt to encode aspects of the partial ordering property of taxonomies. We compare Box Embeddings, a natural containment-based representation of taxonomies, to partial-order embeddings and a baseline Bayes Net, in the context of representing the Medical Subject Headings (MeSH) taxonomy given a set of 300K PubMed articles with subject labels from MeSH. We deeply explore the experimental properties of training box embeddings, including preparation of the training data, sampling ratios and class balance, initialization strategies, and propose a fix to the original box objective. We then present first results in using these techniques for representing a bipartite learning problem (i.e. collaborative filtering) in the presence of taxonomic relations within each partition, inferring disease (anatomical) locations from their use as subject labels in journal articles. Our box model substantially outperforms all baselines for taxonomic reconstruction and bipartite relationship experiments. This performance improvement is observed both in overall accuracy and the weighted spread by true taxonomic depth.