Gasoline and Diesel Demand in Europe: New Insights

This study utilizes a panel data set from 14 European countries over the period 1990-2004 to estimate a dynamic model specification for gasoline and diesel demand. Previous studies estimating gasoline consumption per total passenger cars ignore the recent increase in the number of diesel cars in most European countries leading to biased elasticity estimates. We apply several common dynamic panel estimators to our small sample. Results show that specifications neglecting the share of diesel cars overestimate short-run income, price and car ownership elasticities. It appears that the results of standard pooled estimators are more reliable than common IV/GMM estimators applied to our small data set.Dynamic panel data, Gasoline demand, Error components, Omitted variable

Electrophilic attack at allylsilanes. A quantitative determination of the β-silyl effect

The relative reactivities of allylsilanes and alkenes towards diarylmethyl cations have been determined by competition experiments. Introduction of a β-trimethylsilyl group increases the reactivity of propene towards the diphenylmethyl cation by a factor of 30700

Acceleration of the PDHGM on strongly convex subspaces

We propose several variants of the primal-dual method due to Chambolle and Pock. Without requiring full strong convexity of the objective functions, our methods are accelerated on subspaces with strong convexity. This yields mixed rates, $O(1/N^2)$ with respect to initialisation and $O(1/N)$ with respect to the dual sequence, and the residual part of the primal sequence. We demonstrate the efficacy of the proposed methods on image processing problems lacking strong convexity, such as total generalised variation denoising and total variation deblurring

Total variation on a tree

We consider the problem of minimizing the continuous valued total variation subject to different unary terms on trees and propose fast direct algorithms based on dynamic programming to solve these problems. We treat both the convex and the non-convex case and derive worst case complexities that are equal or better than existing methods. We show applications to total variation based 2D image processing and computer vision problems based on a Lagrangian decomposition approach. The resulting algorithms are very efficient, offer a high degree of parallelism and come along with memory requirements which are only in the order of the number of image pixels.Comment: accepted to SIAM Journal on Imaging Sciences (SIIMS