Did Going North Give Us Migraine? An Evolutionary Approach on Understanding Latitudinal Differences in Migraine Epidemiology

This commentary discusses a recent publication by evolutionary biologists with strong implications for migraine experts. The Authors showed that a gene polymorphism associated with migraine gave our ancestors an evolutionary advantage when colonizing northern, and thus colder, territories. They then highlight that the prevalence of migraine may differ among countries because of climatic adaptation. These results may prove useful in planning both epidemiological and physiological studies in the field of migraine

Estimating Dynamic Traffic Matrices by using Viable Routing Changes

Abstract: In this paper we propose a new approach for dealing with the ill-posed nature of traffic matrix estimation. We present three solution enhancers: an algorithm for deliberately changing link weights to obtain additional information that can make the underlying linear system full rank; a cyclo-stationary model to capture both long-term and short-term traffic variability, and a method for estimating the variance of origin-destination (OD) flows. We show how these three elements can be combined into a comprehensive traffic matrix estimation procedure that dramatically reduces the errors compared to existing methods. We demonstrate that our variance estimates can be used to identify the elephant OD flows, and we thus propose a variant of our algorithm that addresses the problem of estimating only the heavy flows in a traffic matrix. One of our key findings is that by focusing only on heavy flows, we can simplify the measurement and estimation procedure so as to render it more practical. Although there is a tradeoff between practicality and accuracy, we find that increasing the rank is so helpful that we can nevertheless keep the average errors consistently below the 10% carrier target error rate. We validate the effectiveness of our methodology and the intuition behind it using commercial traffic matrix data from Sprint's Tier-1 backbon

Coupled DEM-LBM method for the free-surface simulation of heterogeneous suspensions

The complexity of the interactions between the constituent granular and liquid phases of a suspension requires an adequate treatment of the constituents themselves. A promising way for numerical simulations of such systems is given by hybrid computational frameworks. This is naturally done, when the Lagrangian description of particle dynamics of the granular phase finds a correspondence in the fluid description. In this work we employ extensions of the Lattice-Boltzmann Method for non-Newtonian rheology, free surfaces, and moving boundaries. The models allows for a full coupling of the phases, but in a simplified way. An experimental validation is given by an example of gravity driven flow of a particle suspension

Diagnostic Compression of Biomedical Volumes

In this work we deal with lossy compression of biomedical volumes. By force of circumstances, diagnostic compression is bound to a subjective judgment. However, with respect to the algorithms, there is a need to shape the coding methodology so as to highlight beyond compression three important factors: the medical data, the specic usage and the particular end-user. Biomedical volumes may have very dierent characteristics which derive from imaging modality, resolution and voxel aspect ratio. Moreover, volumes are usually viewed slice by slice on a lightbox, according to dierent cutting direction (typically one of the three voxel axes). We will see why and how these aspects impact on the choice of the coding algorithm and on a possible extension of 2D well known algorithms to more ecient 3D versions. Cross-correlation between reconstruction error and signal is a key aspect to keep into account; we suggest to apply a non uniform quantization to wavelet coefficients in order to reduce slice PSNR variation. Once a good neutral coding for a certain volume is obtained, non uniform quantization can also be made space variant in order to reach more objective quality on Volumes of Diagnostic Interest (VoDI), which in turns can determine the diagnostic quality of the entire data set