Report on the Workshop on Refugee and Asylum Policy in Practice in Europe and North America

Western nations have struggled to accomplish the dual goals of refugee and asylum policies: (1) identifying and protecting Convention refugees as well as those fleeing civil conflict; and (2) controlling for abuse. The Workshop on Refugee and Asylum Policy in Practice in Europe and North America was organized to facilitate a transatlantic dialogue to explore just how well these asylum systems are balancing the dual goals. The workshop exa!llined key elements of the U.S. and European asylum systems: decision making on claims, deterrence of abuse, independent review, return of rejected asylum seekers, scope of the refugee concept, social rights and employment, international cooperation, and data and evaluation. The Workshop was convened by the Institute for the Study of International Migration (ISIM) of Georgetown University and the Center for the Study of Immigration, Integration and Citizenship Policies (CEPIC) of the Centre Nationale de Recherche Scientifique, with the support of the German Marshall Fund of the United States. It was held on July 1-3, 1999, at Oxford University. Workshop participants included government officials, scholars, and representatives from non-governmental organizations (NGOs) actively involved in analyzing and implementing refugee and asylum policies. This report outlines the major points of discussion and the areas of consensus at the Workshop, and emphasizes the issues in need of further analysis and agreement. Through this report, the Workshop seeks to encourage further discussion on refugee and asylum policies in practice in order to clarify, develop, and improve the existing mechanisms for protection

Scout: a hardware-accelerated system for quantitatively driven visualization and analysis

Journal ArticleQuantitative techniques for visualization are critical to the successful analysis of both acquired and simulated scientific data. Many visualization techniques rely on indirect mappings, such as transfer functions, to produce the final imagery. In many situations, it is preferable and more powerful to express these mappings as mathematical expressions, or queries, that can then be directly applied to the data. In this paper, we present a hardware-accelerated system that provides such capabilities and exploits current graphics hardware for portions of the computational tasks that would otherwise be executed on the CPU. In our approach, the direct programming of the graphics processor using a concise data parallel language, gives scientists the capability to efficiently explore and visualize data sets

Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data

Informal settlements are home to the most socially and economically vulnerable people on the planet. In order to deliver effective economic and social aid, non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), require detailed maps of the locations of informal settlements. However, data regarding informal and formal settlements is primarily unavailable and if available is often incomplete. This is due, in part, to the cost and complexity of gathering data on a large scale. To address these challenges, we, in this work, provide three contributions. 1) A brand new machine learning data-set, purposely developed for informal settlement detection. 2) We show that it is possible to detect informal settlements using freely available low-resolution (LR) data, in contrast to previous studies that use very-high resolution (VHR) satellite and aerial imagery, something that is cost-prohibitive for NGOs. 3) We demonstrate two effective classification schemes on our curated data set, one that is cost-efficient for NGOs and another that is cost-prohibitive for NGOs, but has additional utility. We integrate these schemes into a semi-automated pipeline that converts either a LR or VHR satellite image into a binary map that encodes the locations of informal settlements.Comment: Published at the AAAI/ACM Conference on AI, ethics and society. Extended results from our previous workshop: arXiv:1812.0081