This project will provide fresh perspectives on access to justice in the occupied Palestinian territory (oPt), where human rights litigation must often take place across the border in Israeli courts. The project asks how this cross-border dynamic - with intersecting legal regimes defined by space and personal status - affects rule of law outcomes. The project will focus on cases of a cross-border nature, such as family reunification petitions for Palestinian families on both sides of the border, or confiscation of property in occupied east Jerusalem belonging to Palestinians living elsewhere in the West Bank. Methods will include analysis of case law, statutes, and regulations in various Israeli civilian and military courts and interviews with legal practitioners. The project will develop a tool for new evidence-based insights in the form of a research blog that will excerpt, translate, and analyze important legal materials - many of which have never been translated from Hebrew. The blog will also serve to catalyze discussions among stakeholders by inviting contributions from the community of human rights practitioners working on the oPt. Finally, a set of workshops for researchers and practitioners will act to crystallize and extend the online conversation started by the blog.

In recent years, the academic and policy circles around the world are very interested in how weather index insurance, an innovative financial product much less costly than other agricultural insurance products, can be used to mitigate negative impacts of large rainfall shocks among rural households. So when considering an expansion of existing safety net programs, should weather index insurance become a new part? How can it be cost-effectively integrated? Or should the government just give more cash to the poor? Which of these two alternatives will bring larger positive effects on consumption, investments and agricultural production? Which will be more effective in alleviating poverty? To obtain unbiased causal estimates that can properly answer these questions, this project will use a randomized controlled trial (RCT) on a sample of 30 rural villages and 900 rural households in Ethiopia. We will randomly assign the sample into one of the three groups: 1) Pure PSNP: Poor households receiving benefits from the existing Productive Safety Net Program (PSNP); 2) PSNP+WII: Poor PSNP households receiving also Weather Index Insurance (WII) as in-kinds; and 3) PSNP+CCT: Poor PSNP households receiving additional conditional cash transfers (CCT). The RCT design and data collected will allow us to conduct econometric analysis to answer our questions. Overall, this study aims to provide scientific and policy recommendations on the use of agricultural weather insurance as an additional component in safety net program. These insights will be useful for future design on social protection programs and for economic and agricultural development.

The brain is the most intelligent information processor we know, but it does not come into the world this way. Most of the brain’s intelligent functions have to be learned from experience with the world. The key to understanding the brain, therefore, is to understand how the brain learns. I will target this important question, by combining knowledge about the brain and the environment from which it learns, with insights from self-learning computer algorithms. Thanks to recent, exponential developments in these algorithms, we are now in a position to apply similar techniques to model learning in the brain. Using visual perception as a test bed, I will adapt existing supervised learning methods into a new computational model of unsupervised learning in the brain’s visual cortex. From this model, I will distil concrete, testable predictions that I will validate against data from human participants performing perceptual tasks. By thus dovetailing computational and empirical methods, this research aims to understand how neurons wire together into complex information-processing networks. This not only addresses a fundamental and outstanding question in our understanding of the brain, but may also aid the development of more advanced self learning computer algorithms based on the same principles.

Based on noisy sensory evidence, the brain ?decides? what we see. Current models implicitly assume that this decision process is bottom-up and passively driven by input from the sensory areas. This project tests if this view of ?pure vision? is adequate, or whether perception is an active process. First, we use non-invasive brain imaging methods to clarify the neural mechanisms underlying perceptual decision making. In doing so, we aim to resolve several controversies and contradictions in the literature. Having identified these mechanisms, we then investigate how expectations modulate the decision process, and whether such top-down effects may change sensory representations.

This research includes laboratory and field analog studies. We used experimental setups to study the stability of organic molecules in different surface environments on Mars and the importance of those environments for prebiotic chemistry on Mars. We also conducted field studies in a Mars analog environment (Icelandic lava tubes) to gain insight into potential geochemical features of life in the subsurface of Mars. We conclude that bringing laboratory and field analogs together, with both the total analog environment in the field and specific parameters that can be determined in the laboratory, is a necessary step in biosignature research on Mars.