Communities in complex networks

The study of complex networks has attracted the attention of the scientific community for many obvious reasons. A vast number of systems, from the brain to ecosystems, power grid, and the Internet, can be represented as large complex networks, i.e, assemblies of many interacting components with nontrivial topological properties. The link between these components can describe a global behaviour such as the Internet traffic, electricity supply service, market trend, etc. One of the most relevant topological feature of graphs representing these complex systems is community structure which aims to identify the modules and, possibly, their hierarchical organization, by only using the information encoded in the graph topology. Deciphering network community structure is not only important in order to characterize the graph topologically, but gives some information both on the formation of the network and on its functionality.

Surface Expression and Subunit Specific Control of Steady Protein Levels by the Kv7.2 Helix A-B Linker

12 p.

Systematic Studies on 3d- and 4f-Metal Containing Polyoxometalates Suitable for Organic Derivatization

232 p.

Contributions to the efficient use of general purpose coprocessors: kernel density estimation as case study

142 p.

Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition

Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements.