184,100 research outputs found
A State of the Art of Governance Literature on adaptation to climate change. Towards a research agenda
This report provides a state-of-the-art overview of governance literature on adaptation strategies. What has recent research taught us on adaptation from the perspective of governance and to what research agenda does this lead? This report is structured as followed. Firstly, it will be argued why adaptation is a matter of governance. Secondly, the research methods for the literature study will be outlined. Thirdly, the results of the literature study will portray the findings in terms of the themes and foci with, respectively, environmental studies, spatial planning and development studies, and public administration studies. Finally, a comparative analysis of these findings will lead to a research agenda for future research on governance of adaptatio
Optimal Transport for Domain Adaptation
Domain adaptation from one data space (or domain) to another is one of the
most challenging tasks of modern data analytics. If the adaptation is done
correctly, models built on a specific data space become more robust when
confronted to data depicting the same semantic concepts (the classes), but
observed by another observation system with its own specificities. Among the
many strategies proposed to adapt a domain to another, finding a common
representation has shown excellent properties: by finding a common
representation for both domains, a single classifier can be effective in both
and use labelled samples from the source domain to predict the unlabelled
samples of the target domain. In this paper, we propose a regularized
unsupervised optimal transportation model to perform the alignment of the
representations in the source and target domains. We learn a transportation
plan matching both PDFs, which constrains labelled samples in the source domain
to remain close during transport. This way, we exploit at the same time the few
labeled information in the source and the unlabelled distributions observed in
both domains. Experiments in toy and challenging real visual adaptation
examples show the interest of the method, that consistently outperforms state
of the art approaches
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