Tank construction for space vehicles Patent

Liquid propellant tank design with semitoroidal bulkhea

Model Choice and Diagnostics for Linear Mixed-Effects Models Using Statistics on Street Corners

The complexity of linear mixed-effects (LME) models means that traditional diagnostics are rendered less effective. This is due to a breakdown of asymptotic results, boundary issues, and visible patterns in residual plots that are introduced by the model fitting process. Some of these issues are well known and adjustments have been proposed. Working with LME models typically requires that the analyst keeps track of all the special circumstances that may arise. In this paper we illustrate a simpler but generally applicable approach to diagnosing LME models. We explain how to use new visual inference methods for these purposes. The approach provides a unified framework for diagnosing LME fits and for model selection. We illustrate the use of this approach on several commonly available data sets. A large-scale Amazon Turk study was used to validate the methods. R code is provided for the analyses.Comment: 52 pages, 15 figures, 3 table

Market power of German food and beverage industries on international markets

In this paper the existence and magnitude of market power for German beer, cocoa powder, chocolate, and sugar confectionary exporters are tested. Two theoretical approaches are employed, the 'pricing of market' (PTM) and the 'residual demand elasticity' (RDE) approach. Even though all markets show a significant violation of the 'law of one price' estimations for monthly data from 1991 to 1998 reveal that markets are in most cases perfectly competitive. However, while in some cases significant market power is indicated for the PTM approach, the RDE results do not support these findings. This leads to the conclusion that the underlying theoretical models fail to consistently match the observed price equilibria on the market under study. --

Market Power of the German Beer Industry on Export Markets - An Empirical Study -

In this paper the existence and magnitude of market power for the German beer exporters is tested. Two theoretical approaches to model incomplete competition on international markets are employed, the ?pricing to market? (PTM) model the ?residual demand elasticity? (RDE) approach. Estimations for both models over the period from 1991 to 1998 reveal incompatible results regarding the underlying theoretical models and with respect to the approach that is used. While significant market power is indicated in the PTM model, the RDE approach signalizes perfect competition. This leads to the conclusion that the underlying theoretical models have to be extended to consistently match the observed market solutions in this case. --

Aesthetic-Driven Image Enhancement by Adversarial Learning

We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement. Traditional image enhancement frameworks typically involve training models in a fully-supervised manner, which require expensive annotations in the form of aligned image pairs. In contrast to these approaches, our proposed EnhanceGAN only requires weak supervision (binary labels on image aesthetic quality) and is able to learn enhancement operators for the task of aesthetic-based image enhancement. In particular, we show the effectiveness of a piecewise color enhancement module trained with weak supervision, and extend the proposed EnhanceGAN framework to learning a deep filtering-based aesthetic enhancer. The full differentiability of our image enhancement operators enables the training of EnhanceGAN in an end-to-end manner. We further demonstrate the capability of EnhanceGAN in learning aesthetic-based image cropping without any groundtruth cropping pairs. Our weakly-supervised EnhanceGAN reports competitive quantitative results on aesthetic-based color enhancement as well as automatic image cropping, and a user study confirms that our image enhancement results are on par with or even preferred over professional enhancement

A Flexible Modeling Approach for Robust Multi-Lane Road Estimation

A robust estimation of road course and traffic lanes is an essential part of environment perception for next generations of Advanced Driver Assistance Systems and development of self-driving vehicles. In this paper, a flexible method for modeling multiple lanes in a vehicle in real time is presented. Information about traffic lanes, derived by cameras and other environmental sensors, that is represented as features, serves as input for an iterative expectation-maximization method to estimate a lane model. The generic and modular concept of the approach allows to freely choose the mathematical functions for the geometrical description of lanes. In addition to the current measurement data, the previously estimated result as well as additional constraints to reflect parallelism and continuity of traffic lanes, are considered in the optimization process. As evaluation of the lane estimation method, its performance is showcased using cubic splines for the geometric representation of lanes in simulated scenarios and measurements recorded using a development vehicle. In a comparison to ground truth data, robustness and precision of the lanes estimated up to a distance of 120 m are demonstrated. As a part of the environmental modeling, the presented method can be utilized for longitudinal and lateral control of autonomous vehicles