Investigating the mobility habits of electric bike owners through GPS data

This paper investigates the mobility habits of electric bike owners as well as their preferred routes. Through a GPS tracking campaign conducted in the city of Ghent (Belgium) we analyze the mobility habits (travel distance, time spent, speed) during the week of some e-bike users. Moreover, we propose the results of our map matching, based on the Hausdorff criterion, and preliminary results on the route choice of our sample. We strongly believe that investigating the behavior of electric bikes’ owners can help us in better understanding how to incentivize the use of this mode of transport. First results show that the trips with a higher travel distance are performed during the working days. It could be easily correlated with the daily commuting trips (home-work). Moreover, the results of our map-matching highlight how 61% of the trips are performed using the shortest path

Side-View Face Recognition

Side-view face recognition is a challenging problem with many applications. Especially in real-life scenarios where the environment is uncontrolled, coping with pose variations up to side-view positions is an important task for face recognition. In this paper we discuss the use of side view face recognition techniques to be used in house safety applications. Our aim is to recognize people as they pass through a door, and estimate their location in the house. Here, we compare available databases appropriate for this task, and review current methods for profile face recognition

Comparing Graphs via Persistence Distortion

Metric graphs are ubiquitous in science and engineering. For example, many data are drawn from hidden spaces that are graph-like, such as the cosmic web. A metric graph offers one of the simplest yet still meaningful ways to represent the non-linear structure hidden behind the data. In this paper, we propose a new distance between two finite metric graphs, called the persistence-distortion distance, which draws upon a topological idea. This topological perspective along with the metric space viewpoint provide a new angle to the graph matching problem. Our persistence-distortion distance has two properties not shared by previous methods: First, it is stable against the perturbations of the input graph metrics. Second, it is a continuous distance measure, in the sense that it is defined on an alignment of the underlying spaces of input graphs, instead of merely their nodes. This makes our persistence-distortion distance robust against, for example, different discretizations of the same underlying graph. Despite considering the input graphs as continuous spaces, that is, taking all points into account, we show that we can compute the persistence-distortion distance in polynomial time. The time complexity for the discrete case where only graph nodes are considered is much faster