Felix Schmitt

Learn More
We present a particle-in-cell simulation of the relativistic Kelvin-Helmholtz Instability (KHI) that for the first time delivers angularly resolved radiation spectra of the particle dynamics during the formation of the KHI. This enables studying the formation of the KHI with unprecedented spatial, angular and spectral resolution. Our results are of great(More)
In this paper we present a concept for integrating Multimedia-Enriched Learning Objects (MELOs) within a unifying technological framework for schools. First, we discuss the general role of objects for child development and learning. We then outline the framework architecture of the proposed Networked Environment for Multimedia Objects (NEMO). After-wards,(More)
This paper outlines our current research program in the fields of ambient intelligence and context-aware computing and the tools we are building to accomplish this research program. From a discussion of our conception of mental models in the domain of ambient context-aware computer systems we derive hypotheses which we intend to test empirically. A modular(More)
In this paper, we describe our current research concerning users' mental models of what can be called " disappearing computer systems ". This notion comprises computer systems, applications, and appliances related to ubiquitous, pervasive, or ambient computing which blend more or less seamlessly into the users' natural environment. Mental models enable(More)
In this paper, we present CASi (Context Awareness Simulator), a software system for the simulation of context-aware computer systems and environments. CASi provides an abstract framework of components for simulating smart world applications like a smart office or house with ambient sensors and actuators. Agents moving through these application worlds are(More)
INTRODUCTION The role of reactive carbonyl species, such as methylglyoxal (MG), has been overlooked within the context of the sepsis syndrome. The aims of this study were to assess the impact of MG formation in different inflammatory settings and to evaluate its use for early diagnosis as well as prognosis of the sepsis syndrome. METHODS In total, 120(More)
Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent. Since the agent's behavior originates in its policy and MDP policies depend on both the stochastic system dynamics as well as the reward function, the solution of the inverse problem is(More)
— Driver distraction strongly contributes to crash-risk. Therefore, assistance systems that warn the driver if her distraction poses a hazard to road safety, promise a great safety benefit. Current approaches either seek to detect critical situations using environmental sensors or estimate a driver's attention state solely from her behavior. However, this(More)
— Runway incursions are among the most serious safety concerns in air traffic control. Traditional A-SMGCS level 2 safety systems detect runway incursions with the help of surveillance information only. In the context of SESAR, complementary safety systems are emerging that also use other information in addition to surveillance, and that aim at warning(More)
— Maximum Causal Entropy (MCE) Inverse Optimal Control (IOC) has become an effective tool for modeling human behavior in many control tasks. Its advantage over classic techniques for estimating human policies is the trans-ferability of the inferred objectives: Behavior can be predicted in variations of the control task by policy computation using a relaxed(More)