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Current solutions to global challenges place tension between global benefits and local impacts. The result is increasing opposition to implementation of beneficial climate policies. Prioritizing investment in projects with tangible local benefits that also contribute to global climate change can resolve this tension and make local communities’ partners instead of antagonists to change; the approach advocated is a new take on “thinking globally, acting locally”. This approach is a departure from the usual strategy of focusing resources on solutions perceived to have the largest potential global impact, without regards to local concerns. Reclamation of polluted mine sites by using fast growing bamboo to remove heavy metals provides a case study to show what is possible. Effective implementation of thinking globally while acting locally will require increased coordination between different types of researchers, new educational models, and greater stakeholder participation in problem identification and solution development. 

Abstract In 2003, Bohman, Frieze, and Martin initiated the study of randomly perturbed graphs and digraphs. For digraphs, they showed that for every$$\alpha \gt 0$$, there exists a constant$$C$$such that for every$$n$$-vertex digraph of minimum semi-degree at least$$\alpha n$$, if one adds$$Cn$$random edges then asymptotically almost surely the resulting digraph contains a consistently oriented Hamilton cycle. We generalize their result, showing that the hypothesis of this theorem actually asymptotically almost surely ensures the existence of every orientation of a cycle of every possible length, simultaneously. Moreover, we prove that we can relax the minimum semi-degree condition to a minimum total degree condition when considering orientations of a cycle that do not contain a large number of vertices of indegree$$1$$. Our proofs make use of a variant of an absorbing method of Montgomery. 

Table2Text systems generate textual output based on structured data utilizing machine learning. These systems are essential for fluent natural language interfaces in tools such as virtual assistants; however, left to generate freely these ML systems often produce misleading or unexpected outputs. GenNI (Generation Negotiation Interface) is an interactive visual system for high-level human-AI collaboration in producing descriptive text. The tool utilizes a deep learning model designed with explicit control states. These controls allow users to globally constrain model generations, without sacrificing the representation power of the deep learning models. The visual interface makes it possible for users to interact with AI systems following a Refine-Forecast paradigm to ensure that the generation system acts in a manner human users find suitable. We report multiple use cases on two experiments that improve over uncontrolled generation approaches, while at the same time providing fine-grained control. A demo and source code are available at https://genni.vizhub.ai.