Learning Matchable Image Transformations for Long-term Metric Visual Localization

Long-term metric self-localization is an essential capability of autonomous mobile robots, but remains challenging for vision-based systems due to appearance changes caused by lighting, weather, or seasonal variations. While experience-based mapping has proven to be an effective technique for bridging the `appearance gap,' the number of experiences required for reliable metric localization over days or months can be very large, and methods for reducing the necessary number of experiences are needed for this approach to scale. Taking inspiration from color constancy theory, we learn a nonlinear RGB-to-grayscale mapping that explicitly maximizes the number of inlier feature matches for images captured under different lighting and weather conditions, and use it as a pre-processing step in a conventional single-experience localization pipeline to improve its robustness to appearance change. We train this mapping by approximating the target non-differentiable localization pipeline with a deep neural network, and find that incorporating a learned low-dimensional context feature can further improve cross-appearance feature matching. Using synthetic and real-world datasets, we demonstrate substantial improvements in localization performance across day-night cycles, enabling continuous metric localization over a 30-hour period using a single mapping experience, and allowing experience-based localization to scale to long deployments with dramatically reduced data requirements.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the IEEE International Conference on Robotics and Automation (ICRA'20), Paris, France, May 31-June 4, 202

The Impact of Choice on Students with Disabilities

A comprehensive research of the impact of choice on students with disabilities

Winter Elk Distribution and the Risk Of Brucellosis Transmission from Elk to Livestock in the Northern Greater Yellowstone Ecosystem

Predicting spatio-temporal variations in elk (Cervus elaphus) distributions is necessary to forecast the risk of brucellosis transmission from elk to livestock within the Greater Yellowstone Ecosystem (GYE). Using Global Positioning System (GPS) data collected from 49 telemetry-collared female elk during 2005-2006, we developed predictive resource selection function models for the brucellosis transmission risk period. To determine applicability of predictive models across the larger GYE landscape, we validated predictive models internally, as well as externally at two additional elk ranges within the GYE using 63 telemetry-collared cow elk during 2002-2009. Finally, we integrated extrapolated resource selection function maps and domestic livestock distributions to forecast elk to domestic livestock brucellosis transmission risk. We found elk distributions varied spatially and temporally during the risk period and predictive accuracy was highest in the study area where the model was developed. Predictive accuracy of extrapolated resource selection function maps was lower in other study areas indicating that risk models developed in one portion of the GYE are not as accurate in other portions of the GYE. Relative to the other areas included in this study, the Madison Valley and northern Paradise Valley areas were predicted to have the highest risk of elk to livestock transmission risk. Predictions regarding spatio-temporal variations in transmission risk may be used to prioritize management actions aimed at reducing the potential for brucellosis transmission risk, for example hazing to reduce elk-livestock commingling or producer management of livestock distribution

Atrial fibrillation and survival in colorectal cancer

BACKGROUND: Survival in colorectal cancer may correlate with the degree of systemic inflammatory response to the tumour. Atrial fibrillation may be regarded as an inflammatory complication. We aimed to determine if atrial fibrillation is a prognostic factor in colorectal cancer. PATIENTS AND METHODS: A prospective colorectal cancer patient database was cross-referenced with the hospital clinical-coding database to identify patients who had underwent colorectal cancer surgery and were in atrial fibrillation pre- or postoperatively. RESULTS: A total of 175 patients underwent surgery for colorectal cancer over a two-year period. Of these, 13 patients had atrial fibrillation pre- or postoperatively. Atrial fibrillation correlated with worse two-year survival (p = 0.04; log-rank test). However, in a Cox regression analysis, atrial fibrillation was not significantly associated with survival. CONCLUSION: The presence or development of atrial fibrillation in patients undergoing surgery for colorectal cancer is associated with worse overall survival, however it was not found to be an independent factor in multivariate analysis

Astronaut Risk Levels During Crew Module (CM) Land Landing

The NASA Engineering Safety Center (NESC) is investigating the merits of water and land landings for the crew exploration vehicle (CEV). The merits of these two options are being studied in terms of cost and risk to the astronauts, vehicle, support personnel, and general public. The objective of the present work is to determine the astronaut dynamic response index (DRI), which measures injury risks. Risks are determined for a range of vertical and horizontal landing velocities. A structural model of the crew module (CM) is developed and computational simulations are performed using a transient dynamic simulation analysis code (LS-DYNA) to determine acceleration profiles. Landing acceleration profiles are input in a human factors model that determines astronaut risk levels. Details of the modeling approach, the resulting accelerations, and astronaut risk levels are provided

Comparing and Combining Lexicase Selection and Novelty Search

Lexicase selection and novelty search, two parent selection methods used in evolutionary computation, emphasize exploring widely in the search space more than traditional methods such as tournament selection. However, lexicase selection is not explicitly driven to select for novelty in the population, and novelty search suffers from lack of direction toward a goal, especially in unconstrained, highly-dimensional spaces. We combine the strengths of lexicase selection and novelty search by creating a novelty score for each test case, and adding those novelty scores to the normal error values used in lexicase selection. We use this new novelty-lexicase selection to solve automatic program synthesis problems, and find it significantly outperforms both novelty search and lexicase selection. Additionally, we find that novelty search has very little success in the problem domain of program synthesis. We explore the effects of each of these methods on population diversity and long-term problem solving performance, and give evidence to support the hypothesis that novelty-lexicase selection resists converging to local optima better than lexicase selection