Adrian Egli

Efficient automated scheduling of trains remains a major challenge for modern railway systems. The underlying vehicle rescheduling problem (VRSP) has been a major focus of Operations Research (OR) since decades. Traditional approaches use complex simulators to study VRSP, where experimenting with a broad range of novel ideas is time consuming and has a huge computational overhead. In this paper, we introduce a two-dimensional simplified grid environment called "Flatland" that allows for faster experimentation. Flatland does not only reduce the complexity of the full physical simulation, but also provides an easy-to-use interface to test novel approaches for the VRSP, such as Reinforcement Learning (RL) and Imitation Learning (IL). In order to probe the potential of Machine Learning (ML) research on Flatland, we (1) ran a first series of RL and IL experiments and (2) design and executed a public Benchmark at NeurIPS 2020 to engage a large community of researchers to work on...

ABSTRACT A method for determining a distance between a first point and a second point on an object under examination inside the body of a person under examination by way of x-ray imaging by an x-ray device. The method includes the recording of x-ray images at different relative positionings of the x-ray device, which contain the first and the second point respectively. The relative positionings are substantially shifted in relation to one another in parallel to a central beam. Stereo reconstruction is carried out to define a 3D position of the first point and of the second point. The distance between the first point and the second point is determined from the 3D position of the first point and the 3D position of the second point.