Provably Safe Robot Navigation with Obstacle Uncertainty

As drones and autonomous cars become more widespread it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy is safe with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information. We present efficient algorithms that can prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our methods ability to evaluate if a trajectory or policy is safe. We then use these safety checking methods to design a safe variant of the RRT planning algorithm.Comment: RSS 201

COVID-19 Information on YouTube in the Early Pandemic

As people sheltered globally during the COVID-19 pandemic, many YouTube videos and channels pivoted to providing COVID-19 information. But were these videos helpful and constructive or did they undermine official public health messaging? This paper addresses these questions through a mixed methods study of COVID-19 videos on YouTube produced from January to May 2020. We find that a preponderance of YouTube COVID-19 videos either came from major news studio outlets or offered official public health communication. While YouTube moved quickly against obvious false messages, other more subtle ones still managed to leak through. Medical information channels presented conflicting information at times, reflecting factors such as medical uncertainties, political currents, and audience pressures associated with uncertain information around a novel pandemic

YouTube Children’s Videos: Development of a Genre under Algorithm

YouTube children’s video has been claimed to have a preponderance of violent, disturbing or otherwise in-appropriate content. To assess this claim, we conduct a content analysis of a sample of children’s videos published between January 2016 and December 2018. Our analysis reveals an evolving ecosystem involving a variety of production modes and messages which nonetheless bears the heavy imprint of the algorithm-centered commercial incentives of marketing to children and attracting YouTube advertising. Hence, while content formerly causing public concern appears to be effectively policed at this juncture, algorithmic incentives do appear to distort children’s content in potentially unhealthy ways

Scaffolding for Optimal Challenge in K–12 Problem-Based Learning

Establishing optimal challenge enhances intrinsic motivation, interest, and the probability of success in the learning activity. In K–12 problem-based learning (PBL), students may struggle to address associated tasks that are beyond their current ability levels. This paper suggested learner-centered scaffolding systems (LSS) to improve K–12 students’ perception of optimal challenge by addressing their learning issues in PBL. LSS enhances students’ experience in autonomy and competence by providing multiple types of scaffolding in accordance with students’ different needs and difficulties in PBL. Students can control the nature and frequency of scaffolding by themselves according to their needs and ability, and it plays a role in improving their self-directed learning skills. Last, peer scaffolding between students with similar abilities satisfies students’ needs for relatedness

Cascading Globalization and Local Response: Indian Fishers’ Response to Export Market Liberalization

Scholars have long debated whether trade liberalization has positive or negative effects on resource use and ecosystems. This study examines the conditions under which resource use increases or decreases in response to reduced trade barriers, specifically after the 2008 World Trade Organization decision that led the United States to reduce anti-dumping duties on Indian shrimp. At the district level in South India, fishing fleet expansion was correlated with access to global market information via mobile phones. Model simulations indicate that increased mobile phone saturation could expand fish- ing effort sufficiently to deplete multiple marine species groups, while other species benefit from the loss of predators. However, scenario analysis suggests that regulatory interventions could mitigate these ecosystem pressures while still permitting fishers to benefit from increased access to global market information

Random Expert Sampling for Deep Learning Segmentation of Acute Ischemic Stroke on Non-contrast CT

Purpose: Multi-expert deep learning training methods to automatically quantify ischemic brain tissue on Non-Contrast CT Materials and Methods: The data set consisted of 260 Non-Contrast CTs from 233 patients of acute ischemic stroke patients recruited in the DEFUSE 3 trial. A benchmark U-Net was trained on the reference annotations of three experienced neuroradiologists to segment ischemic brain tissue using majority vote and random expert sampling training schemes. We used a one-sided Wilcoxon signed-rank test on a set of segmentation metrics to compare bootstrapped point estimates of the training schemes with the inter-expert agreement and ratio of variance for consistency analysis. We further compare volumes with the 24h-follow-up DWI (final infarct core) in the patient subgroup with full reperfusion and we test volumes for correlation to the clinical outcome (mRS after 30 and 90 days) with the Spearman method. Results: Random expert sampling leads to a model that shows better agreement with experts than experts agree among themselves and better agreement than the agreement between experts and a majority-vote model performance (Surface Dice at Tolerance 5mm improvement of 61% to 0.70 +- 0.03 and Dice improvement of 25% to 0.50 +- 0.04). The model-based predicted volume similarly estimated the final infarct volume and correlated better to the clinical outcome than CT perfusion. Conclusion: A model trained on random expert sampling can identify the presence and location of acute ischemic brain tissue on Non-Contrast CT similar to CT perfusion and with better consistency than experts. This may further secure the selection of patients eligible for endovascular treatment in less specialized hospitals

Non-inferiority of Deep Learning Model to Segment Acute Stroke on Non-contrast CT Compared to Neuroradiologists

Purpose: To develop a deep learning model to segment the acute ischemic infarct on non-contrast Computed Tomography (NCCT). Materials and Methods In this retrospective study, 227 Head NCCT examinations from 200 patients enrolled in the multicenter DEFUSE 3 trial were included. Three experienced neuroradiologists (experts A, B and C) independently segmented the acute infarct on each study. The dataset was randomly split into 5 folds with training and validation cases. A 3D deep Convolutional Neural Network (CNN) architecture was optimized for the data set properties and task needs. The input to the model was the NCCT and the output was a segmentation mask. The model was trained and optimized on expert A. The outcome was assessed by a set of volume, overlap and distance metrics. The predicted segmentations of the best model and expert A were compared to experts B and C. Then we used a paired Wilcoxon signed-rank test in a one-sided test procedure for all metrics to test for non-inferiority in terms of bias and precision. Results: The best performing model reached a Surface Dice at Tolerance (SDT)5mm of 0.68 \pm 0.04. The predictions were non-inferior when compared to independent experts in terms of bias and precision (paired one-sided test procedure for differences in medians and bootstrapped standard deviations with non-inferior boundaries of -0.05, 2ml, and 2mm, p < 0.05, n=200). Conclusion: For the segmentation of acute ischemic stroke on NCCT, our 3D CNN trained with the annotations of one neuroradiologist is non-inferior when compared to two independent neuroradiologists