In this supplementary material we present more information about the dataset (including a datasheet for the dataset) and extensive results that could not fit in the main paper. In sec. 2 we include a datasheet for our dataset. In sec. 4 we look at the statistics of our two benchmarks CalFire and CaiRoad. The data is publicly available at https://www.cs.cornell.edu/projects/ Our code for accessing Sentinel-2 images, creating change events and baselines can be found at https://github.com/utkarshmall13/ We include a datasheet for our dataset following the methodology from "Datasheets for Datasets" [7]. In this section we include the prompts from [7] in blue and in black are our answers. Was there a specific task in mind? Was there a specific gap that needed to be filled? The dataset was created to foster research on the problem of automatic discovery and semantic understanding of change events in satellite imagery. More specifically, the dataset should aid in developing systems that can automatically detect change events in satellite imagery and assign to each a semantic label that indicates the nature of the event, e.g., forest fires, road construction etc. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. The dataset contains RGB bands from Sentinel-2 satellite imagery. Users should keep in mind that changes smaller than the resolution be undetectable. For example, changes to roofs of houses, movements of traffic will not be detected. The datasets should be used for larger changes such as forest fire, crop changes etc. 2.2 Composition What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)?

Satellite imagery is increasingly available, high resolution, and temporally detailed. Changes in spatio-temporal datasets such as satellite images are particularly interesting as they reveal the many events and forces that shape our world.

Real world applications such as economics and policy making often involve solving multi-agent games with two unique features: (1) The agents are inherently asymmetric and partitioned into leaders and followers; (2) The agents have different reward functions, thus the game is general-sum . The majority of existing results in this field focuses on either symmetric solution concepts (e.g.

Real world applications such as economics and policy making often involve solving multi-agent games with two unique features: (1) The agents are inherently asymmetric and partitioned into leaders and followers; (2) The agents have different reward functions, thus the game is general-sum . The majority of existing results in this field focuses on either symmetric solution concepts (e.g.

Sortition is an age-old democratic paradigm, widely manifested today through the random selection of citizens' assemblies. Recently-deployed algorithms select assemblies maximally fairly, meaning that subject to demographic quotas, they give all potential participants as equal a chance as possible of being chosen. While these fairness gains can bolster the legitimacy of citizens' assemblies and facilitate their uptake, existing algorithms remain limited by their lack of transparency. To overcome this hurdle, in this work we focus on panel selection by uniform lottery, which is easy to realize in an observable way. By this approach, the final assembly is selected by uniformly sampling some pre-selected set of m possible assemblies. We provide theoretical guarantees on the fairness attainable via this type of uniform lottery, as compared to the existing maximally fair but opaque algorithms, for two different fairness objectives. We complement these results with experiments on real-world instances that demonstrate the viability of the uniform lottery approach as a method of selecting assemblies both fairly and transparently.

Forecasting, ML Safety, AI X-Risk

To overcome these limitations, we propose a ZSAR framework which does not rely on nearest neighbor classification, but rather consists of a pairwise scoring function.