Helsinki University of Technology

About: Helsinki University of Technology is a based out in . It is known for research contribution in the topics: Thin film & Vortex. The organization has 8962 authors who have published 20136 publications receiving 723787 citations. The organization is also known as: TKK & Teknillinen korkeakoulu.

Clustering and information in correlation based financial networks

Abstract: Networks of companies can be constructed by using return correlations. A crucial issue in this approach is to select the relevant correlations from the correlation matrix. In order to study this problem, we start from an empty graph with no edges where the vertices correspond to stocks. Then, one by one, we insert edges between the vertices according to the rank of their correlation strength, resulting in a network called asset graph. We study its properties, such as topologically different growth types, number and size of clusters and clustering coefficient. These properties, calculated from empirical data, are compared against those of a random graph. The growth of the graph can be classified according to the topological role of the newly inserted edge. We find that the type of growth which is responsible for creating cycles in the graph sets in much earlier for the empirical asset graph than for the random graph, and thus reflects the high degree of networking present in the market. We also find the number of clusters in the random graph to be one order of magnitude higher than for the asset graph. At a critical threshold, the random graph undergoes a radical change in topology related to percolation transition and forms a single giant cluster, a phenomenon which is not observed for the asset graph. Differences in mean clustering coefficient lead us to conclude that most information is contained roughly within 10% of the edges.

Removal of magnetoencephalographic artifacts with temporal signal-space separation: Demonstration with single- trial auditory-evoked responses

Abstract: Magnetic interference signals often hamper analysis of magnetoencephalographic (MEG) measurements. Artifact sources in the proximity of the sensors cause strong and spatially complex signals that are particularly challenging for the existing interference-suppression methods. Here we demonstrate the performance of the temporally extended signal space separation method (tSSS) in removing strong interference caused by external and nearby sources on auditory-evoked magnetic fields-the sources of which are well established. The MEG signals were contaminated by normal environmental interference, by artificially produced additional external interference, and by nearby artifacts produced by a piece of magnetized wire in the subject's lip. After tSSS processing, even the single-trial auditory responses had a good-enough signal-to-noise ratio for detailed waveform and source analysis. Waveforms and source locations of the tSSS-reconstructed data were in good agreement with the responses from the control condition without extra interference. Our results demonstrate that tSSS is a robust and efficient method for removing a wide range of different types of interference signals in neuromagnetic multichannel measurements.