Model International Mobility Convention

While people are as mobile as they ever were in our globalized world, the movement of people across borders lacks global regulation. This leaves many refugees in protracted displacement and many migrants unprotected in irregular and dire situations. Meanwhile, some states have become concerned that their borders have become irrelevant. International mobility—the movement of individuals across borders for any length of time as visitors, students, tourists, labor migrants, entrepreneurs, long-term residents, asylum seekers, or refugees—has no common definition or legal framework. To address this key gap in international law, and the growing gaps in protection and responsibility that are leaving people vulnerable, the "Model International Mobility Convention" proposes a framework for mobility with the goals of reaffirming the existing rights afforded to mobile people (and the corresponding rights and responsibilities of states) as well as expanding those basic rights where warranted. In 213 articles divided over eight chapters, the Convention establishes both the minimum rights afforded to all people who cross state borders as visitors, and the special rights afforded to tourists, students, migrant workers, investors and residents, forced migrants, refugees, migrant victims of trafficking and migrants caught in countries in crisis. Some of these categories are covered by existing international legal regimes. However, in this Convention these groups are for the first time brought together under a single framework. An essential feature of the Convention is that it is cumulative. This means, for the most part, that the chapters build on and add rights to the set of rights afforded to categories of migrants covered by earlier chapters. The Convention contains not only provisions that afford rights to migrants and, to a lesser extent, States (such as the right to decide who can enter and remain in their territory). It also articulates the responsibilities of migrants vis-à-vis States and the rights and responsibilities of different institutions that do not directly respond to a right held by migrants

Deep learning for visualization and novelty detection in large X-ray diffraction datasets

We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn't know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both "on-the-fly" and during $\textit {post hoc}$ analysis