Quantitative Assessment of Robotic Swarm Coverage

This paper studies a generally applicable, sensitive, and intuitive error metric for the assessment of robotic swarm density controller performance. Inspired by vortex blob numerical methods, it overcomes the shortcomings of a common strategy based on discretization, and unifies other continuous notions of coverage. We present two benchmarks against which to compare the error metric value of a given swarm configuration: non-trivial bounds on the error metric, and the probability density function of the error metric when robot positions are sampled at random from the target swarm distribution. We give rigorous results that this probability density function of the error metric obeys a central limit theorem, allowing for more efficient numerical approximation. For both of these benchmarks, we present supporting theory, computation methodology, examples, and MATLAB implementation code.Comment: Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Porto, Portugal, 29--31 July 2018. 11 pages, 4 figure

Semi-Supervised First-Person Activity Recognition in Body-Worn Video

Body-worn cameras are now commonly used for logging daily life, sports, and law enforcement activities, creating a large volume of archived footage. This paper studies the problem of classifying frames of footage according to the activity of the camera-wearer with an emphasis on application to real-world police body-worn video. Real-world datasets pose a different set of challenges from existing egocentric vision datasets: the amount of footage of different activities is unbalanced, the data contains personally identifiable information, and in practice it is difficult to provide substantial training footage for a supervised approach. We address these challenges by extracting features based exclusively on motion information then segmenting the video footage using a semi-supervised classification algorithm. On publicly available datasets, our method achieves results comparable to, if not better than, supervised and/or deep learning methods using a fraction of the training data. It also shows promising results on real-world police body-worn video

High-Throughput Omics Technologies: Potential Tools for the Investigation of Influences of EMF on Biological Systems

The mode of action of a huge amount of agents on biological systems is still unknown. One example where more questions than answers exist is covered by the term electromagnetic fields (EMF). Use of wireless communication, e.g. mobile phones, has been escalated in the last few years. Due to this fact, a lot of discussions dealt with health consequences of EMF emitted by these devices and led to an increased investigation of their effects to biological systems, mainly by using traditional methods. Omics technologies have the advantage to contain methods for investigations on DNA-, RNA- and protein level as well as changes in the metabolism

A Simple Theory of Condensation

A simple assumption of an emergence in gas of small atomic clusters consisting of $c$ particles each, leads to a phase separation (first order transition). It reveals itself by an emergence of ``forbidden'' density range starting at a certain temperature. Defining this latter value as the critical temperature predicts existence of an interval with anomalous heat capacity behaviour $c_p\propto\Delta T^{-1/c}$. The value $c=13$ suggested in literature yields the heat capacity exponent $\alpha=0.077$.Comment: 9 pages, 1 figur

Uncertainty quantification for semi-supervised multi-class classification in image processing and ego-motion analysis of body-worn videos

Semi-supervised learning uses underlying relationships in data with a scarcity of ground-truth labels. In this paper, we introduce an uncertainty quantification (UQ) method for graph-based semi-supervised multi-class classification problems. We not only predict the class label for each data point, but also provide a confidence score for the prediction. We adopt a Bayesian approach and propose a graphical multi-class probit model together with an effective Gibbs sampling procedure. Furthermore, we propose a confidence measure for each data point that correlates with the classification performance. We use the empirical properties of the proposed confidence measure to guide the design of a human-in-the-loop system. The uncertainty quantification algorithm and the human-in-the-loop system are successfully applied to classification problems in image processing and ego-motion analysis of body-worn videos

Girls\u27 Education Roadmap: 2021 Report

While insufficient to meet the vast needs, billions of dollars are being invested in girls’ education advocacy, program, and policy solutions around the world. At the same time, hundreds of millions are invested in research about what works in education. And yet, the policies and approaches that are pursued often don’t line up with what researchers find is effective. The result is that governments, international organizations, and NGOs are too often investing scarce resources in policies or interventions without knowing whether they work. In partnership with Echidna Giving, the GIRL Center at the Population Council developed the Evidence for Gender and Education Resource (EGER), an interactive online database that practitioners, researchers, donors, and decisionmakers can use to drive better results for girls, boys, and communities around the world. EGER addresses how to ensure that limited resources are invested in the most effective solutions to achieve gender equality in education. The resulting data provide valuable insights into gaps and opportunities for the global girls’ education field, and this report shares those insights