Phase Synchronization in Railway Timetables

Timetable construction belongs to the most important optimization problems in public transport. Finding optimal or near-optimal timetables under the subsidiary conditions of minimizing travel times and other criteria is a targeted contribution to the functioning of public transport. In addition to efficiency (given, e.g., by minimal average travel times), a significant feature of a timetable is its robustness against delay propagation. Here we study the balance of efficiency and robustness in long-distance railway timetables (in particular the current long-distance railway timetable in Germany) from the perspective of synchronization, exploiting the fact that a major part of the trains run nearly periodically. We find that synchronization is highest at intermediate-sized stations. We argue that this synchronization perspective opens a new avenue towards an understanding of railway timetables by representing them as spatio-temporal phase patterns. Robustness and efficiency can then be viewed as properties of this phase pattern

ResearchFlow: Understanding the Knowledge Flow between Academia and Industry

Understanding, monitoring, and predicting the flow of knowledge between academia and industry is of critical importance for a variety of stakeholders, including governments, funding bodies, researchers, investors, and companies. To this purpose, we introduce ResearchFlow, an approach that integrates semantic technologies and machine learning to quantifying the diachronic behaviour of research topics across academia and industry. ResearchFlow exploits the novel Academia/Industry DynAmics (AIDA) Knowledge Graph in order to characterize each topic according to the frequency in time of the related i) publications from academia, ii) publications from industry, iii) patents from academia, and iv) patents from industry. This representation is then used to produce several analytics regarding the academia/industry knowledge flow and to forecast the impact of research topics on industry. We applied ResearchFlow to a dataset of 3.5M papers and 2M patents in Computer Science and highlighted several interesting patterns. We found that 89.8% of the topics first emerge in academic publications, which typically precede industrial publications by about 5.6 years and industrial patents by about 6.6 years. However this does not mean that academia always dictates the research agenda. In fact, our analysis also shows that industrial trends tend to influence academia more than academic trends affect industry. We evaluated ResearchFlow on the task of forecasting the impact of research topics on the industrial sector and found that its granular characterization of topics improves significantly the performance with respect to alternative solutions

Television pictures of Phobos: first results

In February-March 1989, 37 television images of the Martian satellite Phobos were obtained by the Phobos 2 spacecraft from distances of 200-1100 km. These images provide an important supplement to the TV data from the American Mariner 9 and Viking spacecraft in coverage of t4e surface of Phobos and in resolution in certain regions, in spectral range, and in range of phase angles. They make it possible to refine the figure and topographic and geological maps of the surface of Phobos, its spectral and angular reflective characteristics, the surface composition and texture, and characteristics of the orbital and librational motion

Motifs in co-authorship networks and their relation to the impact of scientific publications

Co-authorship networks, where the nodes are authors and a link indicates joint publications, are very helpful representations for studying the processes that shape the scientific community. At the same time, they are social networks with a large amount of data available and can thus serve as vehicles for analyzing social phenomena in general. Previous work on co-authorship networks concentrates on statistical properties on the scale of individual authors and individual publications within the network (e.g., citation distribution, degree distribution), on properties of the network as a whole (e.g., modularity, connectedness), or on the topological function of single authors (e.g., distance, betweenness). Here we show that the success of individual authors or publications depends unexpectedly strongly on an intermediate scale in co-authorship networks. For two large-scale data sets, CiteSeerX and DBLP, we analyze the correlation of (three- and four-node) network motifs with citation frequencies. We find that the average citation frequency of a group of authors depends on the motifs these authors form. In particular, a box motif (four authors forming a closed chain) has the highest average citation frequency per link. This result is robust across the two databases, across different ways of mapping the citation frequencies of publications onto the (uni-partite) co-authorship graph, and over time. We also relate this topological observation to the underlying social and socio-scientific processes that have been shaping the networks. We argue that the box motif may be an interesting category in a broad range of social and technical networks

Can We Find Better Process Models? Process Model Improvement using Motif-based Graph Adaptation

In today’s organizations efficient and reliable business processes have a high influence on success. Organizations spend high effort in analyzing processes to stay in front of the competition. However, in practice it is a huge challenge to find better processes based on process mining results due to the high complexity of the underlying model. This paper presents a novel approach which provides suggestions for redesigning business processes by using discovered as-is process models from event logs and apply motif-based graph adaptation. Motifs are graph patterns of small size, building the core blocks of graphs. Our approach uses the LoMbA algorithm, which takes a desired motif frequency distribution and adjusts the model to fit that distribution under the consideration of side constraints. The paper presents the underlying concepts, discusses how the motif distribution can be selected and shows the applicability using real-life event logs. Our results show that motif-based graph adaptation adjusts process graphs towards defined improvement goals