DEVELOPMENT AND IMPLEMENTATION OF ROBOT OPERATING SYSTEMS FOR UNDERGRADUATES

The purpose of this project was to create an undergraduate junior lab to teach students about Robotic Operating System (ROS). The labs were designed to highlight the usefulness of ROS and the process used. Designing algorithms, how to send/receive messages, and the hierarchy of how nodes work with each other are emphasized. Taking packages that are open-source then modifying them is also emphasized. This is done so that students can transfer their knowledge from this course to other robot operating systems

A powerful test based on tapering for use in functional data analysis

A test based on tapering is proposed for use in testing a global linear hypothesis under a functional linear model. The test statistic is constructed as a weighted sum of squared linear combinations of Fourier coefficients, a tapered quadratic form, in which higher Fourier frequencies are down-weighted so as to emphasize the smooth attributes of the model. A formula is $Q_n^{OPT}=n\sum_{j=1}^{p_n}j^{-1/2}\|\boldsymbol{Y}_{n,j}\|^2$. Down-weighting by $j^{-1/2}$ is selected to achieve adaptive optimality among tests based on tapering with respect to its ``rates of testing,'' an asymptotic framework for measuring a test's retention of power in high dimensions under smoothness constraints. Existing tests based on truncation or thresholding are known to have superior asymptotic power in comparison with any test based on tapering; however, it is shown here that high-order effects can be substantial, and that a test based on $Q_n^{OPT}$ exhibits better (non-asymptotic) power against the sort of alternatives that would typically be of concern in functional data analysis applications. The proposed test is developed for use in practice, and demonstrated in an example application.Comment: Published in at http://dx.doi.org/10.1214/08-EJS172 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Energiesuffizienz - Transformation von Energiebedarf, Versorgungsökonomie, Geschlechterverhältnissen und Suffizienz : Bericht zum emanzipativen Suffizienz-Ansatz, zur neuen genderreflektierten Methodik und Auswertung einer Fokusgruppendiskussion

Effizienzpolitiken allein werden nicht mehr ausreichen, um Klimaschutzziele zu erreichen. Diese Erkenntnis setzt sich in der aktuellen Nachhaltigkeitsdebatte immer mehr durch, partiell selbst innerhalb der Green Economy-Diskurse. Wir werden um Politiken der Eindämmung struktureller Energiebedarfs-Erzeugung nicht herumkommen. Allerdings besteht die Gefahr, dass die Forderungen nach Suffizienz und "Maß-Halten" nicht die Erwerbsökonomie und Wachstumspolitiken adressieren, sondern die privaten Haushalte: die genderbedingt erwerbsökonomisch und politisch externalisierte Versorgungsökonomie (Haushaltswirtschaft) und persönliches Handeln. Deshalb ist ein emanzipativer Energiesuffizienz-Politikansatz umso wichtiger. Wie aber lässt sich ein Energie-bezogener Suffizienz-Ansatz des "Genug - es reicht!" anwendungsorientiert und methodisch konkret fassen? Auf welches Sichtbarmachen von den in der Energieforschung und -politik fast immer ausgeblendeten Fragen nach dem gutem Leben, Versorgen und Versorgt werden kommt es an? Wie lassen sich dabei implizite Genderverzerrungen, die aus traditionell an Maskulinität als Norm orientiertem Denken stammen, gemeinsam überwinden? Welche Strategien, welche Potenziale, welche Eingriffspunkte für Energiesuffizienz-Politiken und welcher Art Instrumente resultieren daraus? Die im ersten größeren, vom BMBF geförderten Forschungsprojekt zu diesen Fragen erarbeiteten Analysen, Ansätze und Methoden wurden durch genderkompetente ExpertInnen aus den beteiligten Disziplinen in einer Fokusgruppen-Diskussion reflektiert, kritisch gewürdigt, mit Anregungen, disziplinären Wissensbeständen und praktischen Beispielen bereichert. Der Wuppertal Report 8 präsentiert die Auswertung und die Zusammenfassung des emanzipativen Ansatzes und neuen Methode. Er gibt damit einen Einblick in die vielfältigen Ergebnisse des Gesamtprojekts "Strategien und Instrumente für eine technische, systemische und kulturelle Transformation zur nachhaltigen Begrenzung des Energiebedarfs im Konsumfeld Bauen/Wohnen"

Scaling laws for random walks in long-range correlated disordered media

We study the scaling laws of diffusion in two-dimensional media with long-range correlated disorder through exact enumeration of random walks. The disordered medium is modelled by percolation clusters with correlations decaying with the distance as a power law, $r^{-a}$, generated with the improved Fourier filtering method. To characterize this type of disorder, we determine the percolation threshold $p_{\text c}$ by investigating cluster-wrapping probabilities. At $p_{\text c}$, we estimate the (sub-diffusive) walk dimension $d_{\text w}$ for different correlation exponents $a$. Above $p_{\text c}$, our results suggest a normal random walk behavior for weak correlations, whereas anomalous diffusion cannot be ruled out in the strongly correlated case, i.e., for small $a$.Comment: 11 pages, 6 figure

A spatio-temporal entropy-based approach for the analysis of cyber attacks (demo paper)

Computer networks are ubiquitous systems growing exponentially with a predicted 50 billion devices connected by 2050. This dramatically increases the potential attack surface of Internet networks. A key issue in cyber defense is to detect, categorize and identify these attacks, the way they are propagated and their potential impacts on the systems affected. The research presented in this paper models cyber attacks at large by considering the Internet as a complex system in which attacks are propagated over a network. We model an attack as a path from a source to a target, and where each attack is categorized according to its intention. We setup an experimental testbed with the concept of honeypot that evaluates the spatiotemporal distribution of these Internet attacks. The preliminary results show a series of patterns in space and time that illustrate the potential of the approach, and how cyber attacks can be categorized according to the concept and measure of entropy

Percolation thresholds and fractal dimensions for square and cubic lattices with long-range correlated defects

We study long-range power-law correlated disorder on square and cubic lattices. In particular, we present high-precision results for the percolation thresholds and the fractal dimension of the largest clusters as function of the correlation strength. The correlations are generated using a discrete version of the Fourier filtering method. We consider two different metrics to set the length scales over which the correlations decay, showing that the percolation thresholds are highly sensitive to such system details. By contrast, we verify that the fractal dimension $d_{\rm f}$ is a universal quantity and unaffected by the choice of metric. We also show that for weak correlations, its value coincides with that for the uncorrelated system. In two dimensions we observe a clear increase of the fractal dimension with increasing correlation strength, approaching $d_{\rm f}\rightarrow 2$. The onset of this change does not seem to be determined by the extended Harris criterion.Comment: 12 pages, 8 figure

Virum magnificum et summe venerabilem Fr. Volkm. Reinhardum quum regio iussu Academiam Vitebergensem viseret, rite salutatum esse voluit Seminarium academicum : insunt observationes criticae in Apollonii Rhodii Argonautica

https://repository.brynmawr.edu/digitizedbooks/1138/thumbnail.jp

Inferring change points in the COVID-19 spreading reveals the effectiveness of interventions

As COVID-19 is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A main challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change when first interventions show an effect. By combining an established epidemiological model with Bayesian inference, we analyze the time dependence of the effective growth rate of new infections. Focusing on the COVID-19 spread in Germany, we detect change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we can quantify the effect of interventions, and we can incorporate the corresponding change points into forecasts of future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.Comment: 23 pages, 11 figures. Our code is freely available and can be readily adapted to any country or region ( https://github.com/Priesemann-Group/covid19_inference_forecast/