Bayesian Optimisation for Safe Navigation under Localisation Uncertainty

In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform. Ideally, the robot should be able to identify areas that are safe for navigation based on its own percepts about the environment while avoiding damage to itself. Bayesian optimisation (BO) has been successfully applied to the task of learning a model of terrain traversability while guiding the robot through more traversable areas. An issue, however, is that localisation uncertainty can end up guiding the robot to unsafe areas and distort the model being learnt. In this paper, we address this problem and present a novel method that allows BO to consider localisation uncertainty by applying a Gaussian process model for uncertain inputs as a prior. We evaluate the proposed method in simulation and in experiments with a real robot navigating over rough terrain and compare it against standard BO methods.Comment: To appear in the proceedings of the 18th International Symposium on Robotics Research (ISRR 2017

Strengths and Silences: The Experiences of Lesbian, Gay, Bisexual and Transgender Students in Rural and Small Town Schools

For more than 20 years, GLSEN has worked to make schools safer for all students; it has sought specifically to reduce the bullying and harassment targeted at students' sexual orientation, gender identity, and gender expression. For lesbian, gay, bisexual, and transgender (LGBT) students across the country, violence and harassment experienced in school affect their ability to learn. Although schools in urban areas are typically regarded as more violent or dangerous than schools in other areas, findings from our National School Climate Surveys consistently show that it is most often rural schools that may pose the greatest threats for LGBT students. It may be that community characteristics, such as religious and cultural traditions, income, and educational levels, influence individual beliefs and attitudes toward LGBT people in these areas. It may also be that a lack of positive LGBT-related school resources negatively affects LGBT students' school engagement and academic performance, particularly if they also experience bullying and harassment. Although research on the educational experiences of LGBT youth has grown considerably over the past 25 years, less is known about rural students specifically. This research report examines the experiences of LGBT students in small town and rural areas on matters related to biased language in schools, school safety, harassment and victimization, educational outcomes, school engagement, and LGBT-related resources and support. It also examines the prevalence and utility of LGBT-related resources in rural schools. Finally, this report concludes by advocating for more intentional policies, measures, and programs that protect LGBT students

On the optimality of the spherical Mexican hat wavelet estimator for the primordial non-Gaussianity

We study the spherical Mexican hat wavelet (SMHW) as a detector of primordial non-Gaussianity of the local type on the Cosmic Microwave Background (CMB) anisotropies. For this purpose we define third order statistics based on the wavelet coefficient maps and the original map. We find the dependence of these statistics in terms of the non-linear coupling parameter fnl and the bispectrum of this type of non-Gaussianity. We compare the analytical values for these statistics with the results obtained with non-Gaussian simulations for an ideal full-sky CMB experiment without noise. We study the power of this method to detect fnl, i. e. the variance of this parameter, and compare it with the variance obtained from the primary bispectrum for the same experiment. Finally we apply our wavelet based estimator on WMAP-like maps with incomplete sky and inhomogeneous noise and compare with the optimal bispectrum estimator. The results show that the wavelet cubic statistics are as efficient as the bispectrum as optimal detectors of this type of primordial non-Gaussianity.Comment: 10 pages, 9 figures, 1 table. Minor revision, references added, accepted for publication in MNRA