The Machine Learning Landscape of Top Taggers

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.Comment: Yet another tagger included

Modelling e-participation implementation:A network-based approach for online and offline participation

E-participation consists of several phases such as planning, implementation and evaluation. However, when representing this process, the implementation phase tends to be considered as a single block (the so-called "black-box"). This becomes a problem when the implementation combines offline and online methods, as it requires a detailed characterization and representation of all elements involved. In this paper we tackle this issue by proposing a network-based model to describe these methods. This choice is motivated by the fact that network models allow to better describe the distributed nature of these activities. To build this model we make use of the theory in Social Networks Analysis (SNA) to represent the main interactions between all actors involved. To asses the reliability and added value of the presented model, this approach is applied to four different use cases that showcase various combinations of online and offline participation methods. The results of these use cases show the great potential of the network-based model as a tool for designing, comparing and evaluating different types of implementations. Namely, the visualization of the model allows to asses the level of participation, the role of the different actors and how different instruments are combined

Modelling e-participation implementation:A network-based approach for online and offline participation

E-participation consists of several phases such as planning, implementation and evaluation. However, when representing this process, the implementation phase tends to be considered as a single block (the so-called "black-box"). This becomes a problem when the implementation combines offline and online methods, as it requires a detailed characterization and representation of all elements involved. In this paper we tackle this issue by proposing a network-based model to describe these methods. This choice is motivated by the fact that network models allow to better describe the distributed nature of these activities. To build this model we make use of the theory in Social Networks Analysis (SNA) to represent the main interactions between all actors involved. To asses the reliability and added value of the presented model, this approach is applied to four different use cases that showcase various combinations of online and offline participation methods. The results of these use cases show the great potential of the network-based model as a tool for designing, comparing and evaluating different types of implementations. Namely, the visualization of the model allows to asses the level of participation, the role of the different actors and how different instruments are combined

Network diversity and maritime flows

Coupled and interdependent networks constitute a relatively recent research field that has been so far little invested by port and maritime specialists. The extent to which certain ports benefit from being connected to multiple commodity flows in the maritime network has in fact been poorly addressed. A global database of merchant vessel inter-port movements that occurred in October and November 2004 allows building the respective weighted graphs of solid bulk, liquid bulk, container, general cargo, and passenger/vehicles. Main results underline a very strong influence of commodity diversity on the distribution of maritime traffics among ports and links between them. The research also underlines the role of different regional settings in the specialization of traffic flows

Path Patterns Visualization in Semantic Graphs

Graphs with a large number of nodes and edges are difficult to visualize. Semantic graphs add to the challenge since their nodes and edges have types and this information must be mirrored in the visualization. A common approach to cope with this difficulty is to omit certain nodes and edges, displaying sub-graphs of smaller size. However, other transformations can be used to abstract semantic graphs and this research explores a particular one, both to reduce the graph\u27s size and to focus on its path patterns. Antigraphs are a novel kind of graph designed to highlight path patterns using this kind of abstraction. They are composed of antinodes connected by antiedges, and these reflect respectively edges and nodes of the semantic graph. The prefix "anti" refers to this inversion of the nature of the main graph constituents. Antigraphs trade the visualization of nodes and edges by the visualization of graph path patterns involving typed edges. Thus, they are targeted to users that require a deep understanding of the semantic graph it represents, in particular of its path patterns, rather than to users wanting to browse the semantic graph\u27s content. Antigraphs help programmers querying the semantic graph or designers of semantic measures interested in using it as a semantic proxy. Hence, antigraphs are not expected to compete with other forms of semantic graph visualization but rather to be used a complementary tool. This paper provides a precise definition both of antigraphs and of the mapping of semantic graphs into antigraphs. Their visualization is obtained with antigraphs diagrams. A web application to visualize and interact with these diagrams was implemented to validate the proposed approach. Diagrams of well-known semantic graphs are also presented and discussed