Computing the Oja Median in R : The Package OjaNP

Acknowledgements The work of Klaus Nordhausen was supported by the Academy of Finland (grant 268703). Oleksii Pokotylo is supported by the Cologne Graduate School of Management, Economics and Social Sciences. The work of Daniel Vogel was supported by the DFG collaborate research grant SFB 823

Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes

ACKNOWLEDGMENTS MW and RVD have been supported by the German Federal Ministry for Education and Research (BMBF) via the Young Investigators Group CoSy-CC2 (grant no. 01LN1306A). JFD thanks the Stordalen Foundation and BMBF (project GLUES) for financial support. JK acknowledges the IRTG 1740 funded by DFG and FAPESP. MT Gastner is acknowledged for providing his data on the airline, interstate, and Internet network. P Menck thankfully provided his data on the Scandinavian power grid. We thank S Willner on behalf of the entire zeean team for providing the data on the world trade network. All computations have been performed using the Python package pyunicorn [41] that is available at https://github.com/pik-copan/pyunicorn.

Statistical modelling of variability in sediment-water nutrient and oxygen fluxes

Peer reviewed

Exploring Explanations of Subglacial Bedform Sizes Using Statistical Models

Peer reviewed

Bone morphology of the femur and tibia captured by statistical shape modelling predicts rapid bone loss in acute spinal cord injury patients

We would like to thank the study participants and the clinical and research staff at the Queen Elizabeth National Spinal Injury Unit, as without them this study would not have been possible. We are grateful for the funding received from Glasgow Research Partnership in Engineering for the employment of SC during data collection for this study. We would like to thank the Royal Society of Edinburgh's Scottish Crucible scheme for providing the opportunity for this collaboration to occur. We are also indebted to Maria Dumitrascuta for her time and effort in producing inter-repeatability results for the shape models.

Cascade failure analysis of power grid using new load distribution law and node removal rule

The work is supported in part by NSFC (Grant no. 61172070), IRT of Shaanxi Province (2013KCT-04), EPSRC (Grant no.Ep/1032606/1).

Bone morphology of the femur and tibia captured by statistical shape modelling predicts rapid bone loss in acute spinal cord injury patients

We would like to thank the study participants and the clinical and research staff at the Queen Elizabeth National Spinal Injury Unit, as without them this study would not have been possible. We are grateful for the funding received from Glasgow Research Partnership in Engineering for the employment of SC during data collection for this study. We would like to thank the Royal Society of Edinburgh's Scottish Crucible scheme for providing the opportunity for this collaboration to occur. We are also indebted to Maria Dumitrascuta for her time and effort in producing inter-repeatability results for the shape models.

Cascade failure analysis of power grid using new load distribution law and node removal rule

The work is supported in part by NSFC (Grant no. 61172070), IRT of Shaanxi Province (2013KCT-04), EPSRC (Grant no.Ep/1032606/1).

Heat transport in low dimensions : introduction and phenomenology

Review paper, to appear in the Springer Lecture Notes in Physics volume "Thermal transport in low dimensions: from statistical physics to nanoscale heat transfer" (S. Lepri ed.)