Sub-cycle time resolution of multi-photon momentum transfer in strong-field ionization

During multi-photon ionization of an atom it is well understood how the involved photons transfer their energy to the ion and the photoelectron. However, the transfer of the photon linear momentum is still not fully understood. Here, we present a time-resolved measurement of linear momentum transfer along the laser pulse propagation direction. Beyond the limit of the electric dipole approximation we observe a time-dependent momentum transfer. We can show that the time-averaged photon radiation pressure picture is not generally applicable and the linear momentum transfer to the photoelectron depends on the ionization time within the electromagnetic wave cycle using the attoclock technique. We can mostly explain the measured linear momentum transfer within a classical model for a free electron in a laser field. However, corrections are required due to the interaction of the outgoing photoelectron with the parent ion and due to the initial momentum when the electron appears in the continuum. The parent ion interaction induces a measurable negative attosecond time delay between the appearance in the continuum of the electron with minimal linear momentum transfer and the point in time with maximum ionization rate

Propensity score adjustment of a treatment effect with missing data in psychiatric health services research

Background: Missing values are a common problem for data analyses in observational studies, which are frequently applied in health services research. This paper examines the usefulness of different approaches to tackle the problem of incomplete observational data, focusing whether the Multiple Imputation (MI) strategy yields adequate estimates when applied to a complex analysis framework. Methods: Based on observational study data originally comparing three forms of psychotherapy, a simulation study with different missing data scenarios was conducted. The considered analysis model comprised a propensity score-adjusted treatment effect estimation. Missing values were handled by complete case analysis, different MI approaches, as well as mean and regression imputation. Results: All point estimators of the applied methods lay within the 95% confidence interval of the treatment effect derived from the complete simulation data set. Highest deviation was observed for complete case analysis. A distinct superiority of MI methods could not be demonstrated. Conclusion: Since there was no clear benefit of one method to deal with missing values over another, health services researchers faced with incomplete observational data are well-advised to apply different imputation methods and compare the results in order to get an impression of their sensitivity

DeMoN: Depth and Motion Network for Learning Monocular Stereo

In this paper we formulate structure from motion as a learning problem. We train a convolutional network end-to-end to compute depth and camera motion from successive, unconstrained image pairs. The architecture is composed of multiple stacked encoder-decoder networks, the core part being an iterative network that is able to improve its own predictions. The network estimates not only depth and motion, but additionally surface normals, optical flow between the images and confidence of the matching. A crucial component of the approach is a training loss based on spatial relative differences. Compared to traditional two-frame structure from motion methods, results are more accurate and more robust. In contrast to the popular depth-from-single-image networks, DeMoN learns the concept of matching and, thus, better generalizes to structures not seen during training.Comment: Camera ready version for CVPR 2017. Supplementary material included. Project page: http://lmb.informatik.uni-freiburg.de/people/ummenhof/depthmotionnet

The location of domestic and foreign production affiliates by French multinational firms

In this paper we combine two traditions in the analysis of firms’ location patterns. One led by trade economists who try to understand why do firms invest abroad, and another one led by urban/regional economists, who frequently use patterns of inter-regional or inter-city choices to estimate agglomeration economies. We contribute to the trade-motivated set of papers on location choices by adding the domestic country in the choice set, while accounting for firm’s heterogeneity in the choices. Our econometric results using French firm-level data show an important ‘‘home bias” in manufacturing investment decisions. A crucial finding, which bridges with our contribution to the agglomeration literature, is that the spatial clustering of affiliates belonging to the same industrial group accounts for the lion’s share of this home bias

On the Risk of Stepping into a Cowpat when Crossing a Pasture

Stepping into a cowpat is a common nuisance for millions of farmers around the globe. Due to a rising demand for meat as a consequence of population growth and the desire for species-appropriate husbandry, huge amounts of cow pastures are accessed to meet these requirements. As a result, the frequency of unwelcomed missteps increases. To investigate the risk of an unpleasant encounter with a cow's legacy, a simulation study has been conducted on the basis of two-dimensional random walks, incorporating various scenarios of different shoe sizes, step lengths, number of steps and number of cowpats. The length of a random walk did not affect the mean number of steps into a cowpat (p=0.964). On average, people with smallest investigated shoe size had 8.9 (SD 5.8) missteps less than those with largest shoe size. The number of missteps decreases if the length of a crossing walk increases, moreover misstep frequency shows some kind of an asymptotic behaviour. Crossing cow pastures without explicitly watching each step does not require to keep the walk preferably short in order to minimize the risk of stepping into a cowpat. The more cowpats on a pasture are, the more beneficial is it to have small feet

Developing the greatest Blue Economy: Water productivity, fresh water depletion, and virtual water trade in the Great Lakes basin

The Great Lakes basin hosts the world\u27s most abundant surface fresh water reserve. Historically an industrial and natural resource powerhouse, the region has suffered economic stagnation in recent decades. Meanwhile, growing water resource scarcity around the world is creating pressure on water-intensive human activities. This situation creates the potential for the Great Lakes region to sustainably utilize its relative water wealth for economic benefit. We combine economic production and trade datasets with water consumption data and models of surface water depletion in the region. We find that, on average, the current economy does not create significant impacts on surface waters, but there is some risk that unregulated large water uses can create environmental flow impacts if they are developed in the wrong locations. Water uses drawing on deep groundwater or the Great Lakes themselves are unlikely to create a significant depletion, and discharge of groundwater withdrawals to surface waters offsets most surface water depletion. This relative abundance of surface water means that science-based management of large water uses to avoid accidentally creating “hotspots” is likely to be successful in avoiding future impacts, even if water use is significantly increased. Commercial water uses are the most productive, with thermoelectric, mining, and agricultural water uses in the lowest tier of water productivity. Surprisingly for such a water-abundant economy, the region is a net importer of water-derived goods and services. This, combined with the abundance of surface water, suggests that the region\u27s water-based economy has room to grow in the 21st century