A scoring rule is a device for eliciting and assessing probabilistic forecasts from an agent. When dealing with continuous outcome spaces, and absent any prior insights into the structure of the agent's beliefs, the rule should allow for a flexible reporting interface that can accurately represent complicated, multi-modal distributions. In this paper, we provide such a scoring rule based on a nonparametric approach of eliciting a set of samples from the agent and efficiently evaluating the score using kernel methods. We prove that sampled reports of increasing size converge rapidly to the true score, and that sampled reports are approximately optimal. We also demonstrate a connection between the scoring rule and the maximum mean discrepancy divergence. Experimental results are provided that confirm rapid convergence and that the expected score correlates well with standard notions of divergence, both important considerations for ensuring that agents are incentivized to report accurate information.

Formal verification of industrial systems is very challenging, due to reasons ranging from scalability issues to communication difficulties with engineering-focused teams. More importantly, industrial systems are rarely designed for verification, but rather for operational needs. In this paper we present an overview of our experience using hybrid systems theorem proving to formally verify ACAS X, an airborne collision avoidance system for airliners scheduled to be operational around 2020. The methods and proof techniques presented here are an overview of the work already presented in [8], while the evaluation of ACAS X has been significantly expanded and updated to the most recent version of the system, run 13. The effort presented in this paper is an integral part of the ACAS X development and was performed in tight collaboration with the ACAS X development team.

The rapid growth of fast analytics systems, that require data processing in memory, makes memory capacity an increasingly-precious resource. This paper introduces a new compressed data structure called a Compressed Buffer Tree (CBT). Using a combination of techniques including buffering, compression, and serialization, CBTs improve the memory efficiency and performance of the GroupBy-Aggregate abstraction that forms the basis of not only batch-processing models like MapReduce, but recent fast analytics systems too. For streaming workloads, aggregation using the CBT uses 21--42% less memory than using Google SparseHash with up to 16% better throughput. The CBT is also compared to batch-mode aggregators in MapReduce runtimes such as Phoenix++ and Metis and consumes 4x and 5x less memory with 1.5--2x and 3--4x more performance respectively.