Solar flares are caused by magnetic eruptions in active regions (ARs) on the surface of the sun. These events can have significant impacts on human activity, many of which can be mitigated with enough advance warning from good forecasts. To date, machine learning-based flare-prediction methods have employed physics-based attributes of the AR images as features; more recently, there has been some work that uses features deduced automatically by deep learning methods (such as convolutional neural networks). We describe a suite of novel shape-based features extracted from magnetogram images of the Sun using the tools of computational topology and computational geometry. We evaluate these features in the context of a multi-layer perceptron (MLP) neural network and compare their performance against the traditional physics-based attributes. We show that these abstract shape-based features outperform the features chosen by the human experts, and that a combination of the two feature sets improves the forecasting capability even further.

This paper describes the development and implementation of the "Next Generation" Cells to Society Pre-Clerkship Curriculum at the University of Virginia School of Medicine through the lens of Design-Based Implementation Research. Since the launch of this curriculum reform in 2008, more than 100 members of the faculty and staff have collaborated to design and deliver novel instructional activities for first and second year medical students. To date, five iterative processes of analysis, development, evaluation, and revision have been completed with large classes of medical students, with each class enrolling over 150 students. The 18-month Pre-Clerkship curriculum is supported by newly created system and technological infrastructure, and is continuously evaluated to assess the impact on students' learning. Lessons learned from each iteration inform revisions to curricular practices and infrastructure for subsequent iterations of the curriculum. As implementation of this innovative curriculum has progressed over several years, emergent issues have also stimulated medical education research projects that explore the implementation of Flipped Classroom Model and Team-Based Learning. In summary, the curriculum reform described in this paper provides valuable lessons for educators interested in implementing large-scale curricular innovations.

This paper introduces a new technique for intelligently exploring the reachability set of a spacecraft: the set of trajectories from a given initial condition that are possible under a specified range of control actions. The high dimension of this problem and the nonlinear nature of gravitational interactions make the geometry of these sets complicated, hard to compute, and all but impossible to visualize. Currently, exploration of a problem’s state space is done heuristically, based on previously identified solutions. This potentially misses out on improved mission design solutions that are not close to previous approaches. The goal of the work described here is to map out reachability sets automatically. This would not only aid human mission planners, but also allow a spacecraft to determine its own course without input from Earth-based controllers. Brute-force approaches to this are computationally prohibitive, so one must focus the effort on regions that are of interest: where neighboring trajectories diverge quickly, for instance, or come close to a body that the spacecraft is orbiting. In this paper, we focus on the first of those two criteria; the goal is to identify regions in the system’s state space where small changes have large effects— or vice versa—and concentrate the computational mesh accordingly.

Plants can have positive effects on each other in numerous ways, including protection from harsh environmental conditions. This phenomenon, known as facilitation, occurs in water-stressed environments when shade from larger shrubs protects smaller annuals from harsh sun, enabling them to exist on scarce water. The topic of this paper is a model of this phenomenon that allows search algorithms to find residential landscape designs that incorporate facilitation to conserve water. This model is based in botany; it captures the growth requirements of real plant species in a fitness function, but also includes a penalty term in that function that encourages facilitative interactions with other plants on the landscape. To evaluate the effectiveness of this approach, two search strategies--simulated annealing and agent-based search--were applied to models of different collections of simulated plant types and landscapes with different light distributions. These two search strategies produced landscape designs with different spatial distributions of the larger plants. All designs exhibited facilitation and lower water use than designs where facilitation was not included.

Human experts in scientific fields routinely work with evidence that is noisy and untrustworthy, heuristics that are unproven, and possible conclusions that are contradictory. We present a fully implemented AI system, Calvin, for cosmogenic isotope dating, a domain that is fraught with these difficult issues. Calvin solves these problems using an argumentation framework and a system of confidence that uses two-dimensional vectors to express the quality of heuristics and the applicability of evidence. The arguments it produces are strikingly similar to published expert arguments. Calvin is in daily use by isotope dating experts.

The computer program PRET automatically constructs mathematical models of physical systems. A critical part of this task is automating the processing of sensor data. PRET's intelligent data analyzer uses geometric reasoning to infer qualitative information from quantitative data; if critical variables are either unknown or cannot be measured, it uses delay-coordinate embedding to reconstruct the internal dynamics from the external sensor measurements. Successful modeling results for two sensor-equipped systems, a driven pendulum and a radio-controlled car, demonstrate the effectiveness of these techniques.

In this paper, we present and discuss some circuit experiments that demonstrate how chaos can be used to broaden the capture range of the common phase-locked loop circuit.