Towards a Testbed for Dynamic Vehicle Routing Algorithms

Since modern transport services are becoming more flexible, demand-responsive, and energy/cost efficient, there is a growing demand for large-scale microscopic simulation platforms in order to test sophisticated routing algorithms. Such platforms have to simulate in detail, not only the dynamically changing demand and supply of the relevant service, but also traffic flow and other relevant transport services. This paper presents the DVRP extension to the open-source MATSim simulator. The extension is designed to be highly general and customizable to simulate a wide range of dynamic rich vehicle routing problems. The extension allows plugging in of various algorithms that are responsible for continuous re-optimisation of routes in response to changes in the system. The DVRP extension has been used in many research and commercial projects dealing with simulation of electric and autonomous taxis, demand-responsive transport, personal rapid transport, free-floating car sharing and parking search

Empirical likelihood estimators for the error distribution in nonparametric regression models

The aim of this paper is to show that existing estimators for the error distribution in nonparametric regression models can be improved when additional information about the distribution is included by the empirical likelihood method. The weak convergence of the resulting new estimator to a Gaussian process is shown and the performance is investigated by comparison of asymptotic mean squared errors and by means of a simulation study. As a by-product of our proofs we obtain stochastic expansions for smooth linear estimators based on residuals from the nonparametric regression model. --empirical distribution function,empirical likelihood,error distribution,estimating function,nonparametric regression,Owen estimator

Mobility traces and spreading of COVID-19

We use human mobility models, for which we are experts, and attach a virus infection dynamics to it, for which we are not experts but have taken it from the literature, including recent publications. This results in a virus spreading dynamics model. The results should be verified, but because of the current time pressure, we publish them in their current state. Recommendations for improvement are welcome. We come to the following conclusions: 1. Complete lockdown works. About 10 days after lockdown, the infection dynamics dies down. This assumes that lockdown is complete, which can be guaranteed in the simulation, but not in reality. Still, it gives strong support to the argument that it is never too late for complete lockdown. 2. As a rule of thumb, we would suggest complete lockdown no later than once 10% of hospital capacities available for COVID-19 are in use, and possibly much earlier. This is based on the following insights: a. Even after lockdown, the infection dynamics continues at home, leading to another tripling of the cases before the dynamics is slowed. b. There will be many critical cases coming from people who were infected before lockdown. Because of the exponential growth dynamics, their number will be large. c. Researchers with more detailed disease progression models should improve upon these statements. 3. Our simulations say that complete removal of infections at child care, primary schools, workplaces and during leisure activities will not be enough to sufficiently slow down the infection dynamics. It would have been better, but still not sufficient, if initiated earlier. 4. Infections in public transport play an important role. In the simulations shown later, removing infections in the public transport system reduces the infection speed and the height of the peak by approximately 20%. Evidently, this depends on the infection parameters, which are not well known. – This does not point to reducing public transport capacities as a reaction to the reduced demand, but rather use it for lower densities of passengers and thus reduced infection rates. 5. In our simulations, removal of infections at child care, primary schools, workplaces, leisure activities, and in public transport may barely have been sufficient to control the infection dynamics if implemented early on. Now according to our simulations it is too late for this, and (even) harsher measures will have to be initiated until possibly a return to such a restrictive, but still somewhat functional regime will again be possible. Evidently, all of these results have to be taken with care. They are based on preliminary infection parameters taken from the literature, used inside a model that has more transport/movement details than all others that we are aware of but still not enough to describe all aspects of reality, and suffer from having to write computer code under time pressure. Optimally, they should be confirmed independently. Short of that, given current knowledge we believe that they provide justification for “complete lockdown” at the latest when about 10% of available hospital capacities for COVID-19 are in use (and possibly earlier; we are no experts of hospital capabilities). What was not investigated in detail in our simulations was contact tracing, i.e. tracking down the infection chains and moving all people along infection chains into quarantine. The case of Singapore has so far shown that this may be successful. Preliminary simulation of that tactic shows that it is difficult to implement for COVID-19, since the incubation time is rather long, people are contagious before they feel sick, or maybe never feel sufficiently sick at all. We will investigate in future work if and how contact tracing can be used together with a restrictive, but not totally locked down regime. When opening up after lockdown, it would be important to know the true fraction of people who are already immune, since that would slow down the infection dynamics by itself. For Wuhan, the currently available numbers report that only about 0.1% of the population was infected, which would be very far away from “herd immunity”. However, there have been and still may be many unknown infections (Frankfurter Allgemeine Zeitung GmbH 2020)

Adaptable Demonstrator Platform for the Simulation of Distributed Agent-Based Automotive Systems

Future autonomous vehicles will no longer have a driver as a fallback solution in case of critical failure scenarios. However, it is costly to add hardware redundancy to achieve a fail-operational behaviour. Here, graceful degradation can be used by repurposing the allocated resources of non-critical applications for safety-critical applications. The degradation problem can be solved as a part of an application mapping problem. As future automotive software will be highly customizable to meet customers\u27 demands, the mapping problem has to be solved for each individual configuration and the architecture has to be adaptable to frequent software changes. Thus, the mapping problem has to be solved at run-time as part of the software platform. In this paper we present an adaptable demonstrator platform consisting of a distributed simulation environment to evaluate such approaches. The platform can be easily configured to evaluate different hardware architectures. We discuss the advantages and limitations of this platform and present an exemplary demonstrator configuration running an agent-based graceful degradation approach

Towards a home-use BCI: fast asynchronous control and robust non-control state detection

Eine Hirn-Computer Schnittstelle (engl. Brain-Computer Interface, BCI) erlaubt einem Nutzer einen Computer nur mittels Gehirn-Aktivität zu steuern. Der Hauptanwendungszweck ist die Wiederherstellung verschiedener Funktionen von motorisch eingeschränkten Menschen, zum Beispiel, die Wiederherstellung der Kommunikationsfähigkeit. Bisherige BCIs die auf visuell evozierten Potentialen (VEPs) basieren, erlauben bereits hohe Kommunikationsgeschwindigkeiten. VEPs sind Reaktionen, die im Gehirn durch visuelle Stimulation hervorgerufen werden. Allerdings werden bisherige BCIs hauptsächlich in der Forschung verwendet und sind nicht für reale Anwendungszwecke geeignet. Grund dafür ist, dass sie auf dem synchronen Steuerungsprinzip beruhen, dies bedeutet, dass Aktionen nur in vorgegebenen Zeitslots ausgeführt werden können. Dies bedeutet wiederum, dass der Nutzer keine Aktionen nach seinem Belieben ausführen kann, was für reale Anwendungszwecke ein Problem darstellt. Um dieses Problem zu lösen, müssen BCIs die Intention des Nutzers, das System zu steuern oder nicht, erkennen. Solche BCIs werden asynchron oder selbstbestimmt genannt. Bisherige asynchrone BCIs zeigen allerdings keine ausreichende Genauigkeit bei der Erkennung der Intention und haben zudem eine deutlich reduzierte Kommunikationsgeschwindigkeit im Vergleich zu synchronen BCIs. In dieser Doktorarbeit wird das erste asynchrone BCI vorgestellt, welches sowohl eine annäherungsweise perfekte Erkennung der Intention des Nutzers als auch eine ähnliche Kommunikationsgeschwindigkeit wie synchrone BCIs erzielt. Dies wurde durch die Entwicklung eines allgemeinen Modells für die Vorhersage von sensorischen Reizen erzielt. Dadurch können beliebige visuelle Stimulationsmuster basierend auf den gemessenen VEPs vorhergesagt werden. Das Modell wurde sowohl mit einem "traditionellen" maschinellen Lernverfahren als auch mit einer deep-learning Methode implementiert und evaluiert. Das resultierende asynchrone BCI übertrifft bisherige Methoden in mehreren Disziplinen um ein Vielfaches und ist ein wesentlicher Schritt, um BCI-Anwendungen aus dem Labor in die Praxis zu bringen. Durch weitere Optimierungen, die in dieser Arbeit diskutiert werden, könnte es sich zum allerersten geeigneten BCI für Endanwender entwickeln, da es effektiv (hohe Genauigkeit), effizient (schnelle Klassifizierungen), und einfach zu bedienen ist. Ein weiteres Alleinstellungsmerkmal ist, dass das entwickelte BCI für beliebige Szenarien verwendet werden kann, da es annähernd unendlich viele gleichzeitige Aktionsfelder erlaubt.Brain-Computer Interfaces (BCIs) enable users to control a computer by using pure brain activity. Their main purpose is to restore several functionalities of motor disabled people, for example, to restore the communication ability. Recent BCIs based on visual evoked potentials (VEPs), which are brain responses to visual stimuli, have shown to achieve high-speed communication. However, BCIs have not really found their way out of the lab yet. This is mainly because all recent high-speed BCIs are based on synchronous control, which means commands can only be executed in time slots controlled by the BCI. Therefore, the user is not able to select a command at his own convenience, which poses a problem in real-world applications. Furthermore, all those BCIs are based on stimulation paradigms which restrict the number of possible commands. To be suitable for real-world applications, a BCI should be asynchronous, or also called self-paced, and must be able to identify the user’s intent to control the system or not. Although there some asynchronous BCI approaches, none of them achieved suitable real-world performances. In this thesis, the first asynchronous high-speed BCI is proposed, which allows using a virtually unlimited number of commands. Furthermore, it achieved a nearly perfect distinction between intentional control (IC) and non-control (NC), which means commands are only executed if the user intends to. This was achieved by a completely different approach, compared to recent methods. Instead of using a classifier trained on specific stimulation patterns, the presented approach is based on a general model that predicts arbitrary stimulation patterns. The approach was evaluated with a "traditional" as well as a deep machine learning method. The resultant asynchronous BCI outperforms recent methods by a multi-fold in multiple disciplines and is an essential step for moving BCI applications out of the lab and into real life. With further optimization, discussed in this thesis, it could evolve to the very first end-user suitable BCI, as it is effective (high accuracy), efficient (fast classifications), ease of use, and allows to perform as many different tasks as desired

An Ontology of German Place Names

Ma recherche a conduit à l’élaboration d’une ontologie contenant des entités géographiques et leurs noms en allemand. L’ontologie inclut (1) les caractéristiques des noms de lieux comme leur morphologie flexionnelle et leur comportement syntaxique typique, (2) les noms de lieux associés aux entités géographiques et (3) les relations entre une entité géographique et une autre.My research has given rise to the construction of an ontology containing geographic entities and its German names. The ontology includes (1) the linguistic features of place names, such as their inflectional morphology and characteristic syntactic behaviour, (2) associates place names to geographic entities, and (3) the relationship between one geographic entity and another

Between Efficiency and Resilience: The Classification of Companies According to their Sustainability Performance

In this article, we provide a broad picture of the adaptation of economic classification technologies that were originally used to provide financial information and to classify companies according to their financial performance. The same approach is now available for the benefit of sustainability investors. The adaptation of such financial classification technologies to account for questions of sustainability has been engendered by the growing importance of financial markets and by the recognition of sustainability, as a guiding concept for contemporary societies. Since credit ratings, as well as financial accounting and reporting, are established measures for financial performance, they have inspired the development of similar classification systems for sustainability performance, and can be used to accommodate sustainability investors. We outline the adaptation of financial classification systems to the issue of sustainability and we compare the development and institutionalization, especially as it relates to the current market structure of classification systems in the financial markets, based on both financial and sustainability data. In the second part of this paper we compare the interpretation of social sustainability by three different sustainability accounting and reporting initiatives, in order to illustrate the heterogeneity of the available data applicable to subsequent classification. We point out that the operationalization of the three initiatives differs in respect to the nature and the extent of information requested. While accounting frameworks require relatively few quantitative outcomes, reporting frameworks demand more extensive quantitative and qualitative data. Finally, we discuss the opportunities and difficulties associated with the adaptation of classification systems from the field of finance to the field of sustainability

First Results of the PixelGEM Central Tracking System for COMPASS

For its physics program with a high-intensity hadron beam of up to 2e7 particles/s, the COMPASS experiment at CERN requires tracking of charged particles scattered by very small angles with respect to the incident beam direction. While good resolution in time and space is mandatory, the challenge is imposed by the high beam intensity, requiring radiation-hard detectors which add very little material to the beam path in order to minimize secondary interactions. To this end, a set of triple-GEM detectors with a hybrid readout structure consisting of pixels in the beam region and 2-D strips in the periphery was designed and built. Successful prototype tests proved the performance of this new detector type, showing both extraordinary high rate capability and detection efficiency. The amplitude information allowed to achieve spatial resolutions about a factor of 10 smaller than the pitch and a time resolution close to the theoretical limit imposed by the layout. The PixelGEM central tracking system consisting of five detectors, slightly improved with respect to the prototype, was completely installed in the COMPASS spectrometer in spring 2008