Investigating Local Definitions of Sustainability in the Arctic: Insights from Post-Soviet Sakha Villages

Contemporary survival for post-Soviet Russia’s indigenous communities is complicated both by a Soviet legacy that undermined local ecological knowledge, kinship settlement patterns, land and resource rights, and robust ecosystems, and by the contemporary effects of globalization and modernity. Efforts to achieve sustainability lack a focus on local contexts, although recent research, especially in anthropology, underscores the need to develop sustainability criteria that are both flexible and adaptable to local contexts. Community-based research in post-Soviet Viliui Sakha indigenous communities of northeastern Siberia, Russia, has shown that inhabitants define sustainability as the building of local diversified economies, communities, and health via strong local leadership, a shared vision to work toward common goals, the reinstatement of local knowledge, and rights to land and resources. Realization of these ideas may be achieved by continued collaboration between circumpolar researchers and communities to facilitate the influx of ideas and models of success from other Arctic regions and by potential outcomes of intergovernmental action between the Russian Federation and its circumpolar neighbors through Russia’s chairing of the Arctic Council. Implementation of flexible, locally adaptable sustainability criteria is central to these efforts.La survie contemporaine des collectivités indigènes russes post-soviétiques est rendue complexe par un patrimoine soviétique qui minait le savoir écologique local, les tendances en matière de parenté, les droits à la terre et aux ressources, et les écosystèmes robustes, de même que par les effets contemporains de la mondialisation et la modernisation. Les efforts en matière d’atteinte de la durabilité ne portent pas suffisamment sur les contextes locaux, bien que des recherches récentes, notamment en anthropologie, fassent ressortir la nécessité d’élaborer des critères de durabilité qui sont à la fois souples et adaptables aux contextes locaux. Des recherches communautaires réalisées au sein des collectivités indigènes post-soviétiques de Viliui Sakha dans le nord-est de la Sibérie, en Russie, ont permis de constater que les habitants définissent la durabilité comme l’édification d’économies et de collectivités locales diversifiées et en santé grâce à un bon leadership local, à une vision partagée visant des objectifs communs, au rétablissement du savoir local et aux droits à la terre et aux ressources. La concrétisation de ces idées peut être rendue possible par une collaboration continue entre les chercheurs et les collectivités circumpolaires et ce, dans le but de faciliter l’apport d’idées et de modèles de réussites provenant d’autres régions de l’Arctique ainsi que par les résultats éventuels de mesures intergouvernementales entre la Fédération de Russie et ses voisins circumpolaires au moyen de la présidence du Conseil de l’Arctique par la Russie. Ces efforts reposent principalement sur la mise en oeuvre de critères de durabilité souples et adaptables à l’échelle locale

Inclusive Branding Strategies for Domestic Violence Agencies: Embracing Opportunities to Reach and Better Serve Male-Identified Survivors

Successful strategies for branding that are inclusive to male-identified survivors were identified in this qualitative study through semi-structured interviews with leaders from six domestic violence agencies across the United States: four represented traditional domestic violence agencies and two represented specialized agencies with expertise in providing services to non-traditional survivors. The strategic implementation of 1) inclusive language, 2) visual diversity, 3) community outreach, and 4) communication channels emerged as successful strategies in branding in an inclusive way for male-identified survivors. The implementation of these successful strategies provides the opportunity for domestic violence agencies to create an inclusive environment for male-identified survivors, and would contribute to a paradigm shift in how domestic violence is viewed

Using Learning to Rank Approach to Promoting Diversity for Biomedical Information Retrieval with Wikipedia

In most of the traditional information retrieval (IR) models, the independent relevance assumption is taken, which assumes the relevance of a document is independent of other documents. However, the pitfall of this is the high redundancy and low diversity of retrieval result. This has been seen in many scenarios, especially in biomedical IR, where the information need of one query may refer to different aspects. Promoting diversity in IR takes the relationship between documents into account. Unlike previous studies, we tackle this problem in the learning to rank perspective. The main challenges are how to find salient features for biomedical data and how to integrate dynamic features into the ranking model. To address these challenges, Wikipedia is used to detect topics of documents for generating diversity biased features. A combined model is proposed and studied to learn a diversified ranking result. Experiment results show the proposed method outperforms baseline models

Human-AI complex task planning

The process of complex task planning is ubiquitous and arises in a variety of compelling applications. A few leading examples include designing a personalized course plan or trip plan, designing music playlists/work sessions in web applications, or even planning routes of naval assets to collaboratively discover an unknown destination. For all of these aforementioned applications, creating a plan requires satisfying a basic construct, i.e., composing a sequence of sub-tasks (or items) that optimizes several criteria and satisfies constraints. For instance, in course planning, sub-tasks or items are core and elective courses, and degree requirements capture their complex dependencies as constraints. In trip planning, sub-tasks are points of interest (POIs) and constraints represent time and monetary budget, or user-specified requirements. Needless to say, task plans are to be individualized and designed considering uncertainty. When done manually, the process is human-intensive and tedious, and unlikely to scale. The goal of this dissertation is to present computational frameworks that synthesize the capabilities of human and AI algorithms to enable task planning at scale while satisfying multiple objectives and complex constraints. This dissertation makes significant contributions in four main areas, (i) proposing novel models, (ii) designing principled scalable algorithms, (iii) conducting rigorous experimental analysis, and (iv) deploying designed solutions in the real-world. A suite of constrained and multi-objective optimization problems has been formalized, with a focus on their applicability across diverse domains. From an algorithmic perspective, the dissertation proposes principled algorithms with theoretical guarantees adapted from discrete optimization techniques, as well as Reinforcement Learning based solutions. The memory and computational efficiency of these algorithms have been studied, and optimization opportunities have been proposed. The designed solutions are extensively evaluated on various large-scale real-world and synthetic datasets and compared against multiple baseline solutions after appropriate adaptation. This dissertation also presents user study results involving human subjects to validate the effectiveness of the proposed models. Lastly, a notable outcome of this dissertation is the deployment of one of the developed solutions at the Naval Postgraduate School. This deployment enables simultaneous route planning for multiple assets that are robust to uncertainty under multiple contexts

Scaling Success: Lessons from Adaptation Pilots in the Rainfed Regions of India

"Scaling Success" examines how agricultural communities are adapting to the challenges posed by climate change through the lens of India's rainfed agriculture regions. Rainfed agriculture currently occupies 58 percent of India's cultivated land and accounts for up to 40 percent of its total food production. However, these regions face potential production losses of more than $200 billion USD in rice, wheat, and maize by 2050 due to the effects of climate change. Unless action is taken soon at a large scale, farmers will see sharp decreases in revenue and yields.Rainfed regions across the globe have been an important focus for the first generation of adaptation projects, but to date, few have achieved a scale that can be truly transformational. Drawing on lessons learnt from 21 case studies of rainfed agriculture interventions, the report provides guidance on how to design, fund and support adaptation projects that can achieve scale

Coastal Resource Management in the Wider Caribbean: Resilience, Adaptation, and Community Diversity

The Caribbean Sea is the second largest sea in the world, including more than 30 insular and continental countries with an approximate population of 35 million. In addition to its highly fractionalized territory, it is characterized by a great linguistic and cultural diversity, a phenomenon enhanced by increasing internal migrations and the expansion of tourism. The implementation of coastal management programs, often embedded in top-down approaches, is therefore faced with a series of ecological and social constraints, explaining why they have had only limited success. This book presents an alternative look at existing coastal management initiatives in the North America (Caribbean); focusing on the need to pay more attention to the local community. Emphasizing the great heterogeneity of Caribbean communities, the book shows how the diversity of ecosystems and cultures has generated a significant resilience and capacity to adapt, in which the notion of community itself has to be re-examined. The concluding chapter presents lessons learned and a series of practical recommendations for decision-makers