A semigroup characterization of well-posed linear control systems

We study the well-posedness of a linear control system $\Sigma(A,B,C,D)$ with unbounded control and observation operators. To this end we associate to our system an operator matrix $\mathcal{A}$ on a product space $\mathcal{X}^p$ and call it $p$-well-posed if $\mathcal{A}$ generates a strongly continuous semigroup on $\mathcal{X}^p$. Our approach is based on the Laplace transform and Fourier multipliers

External Costs of Road, Rail and Air Transport - a Bottom-Up Approach

This paper aims to describe the calculation of environmental and health externalities caused by air pollutants, accidents and noise from different transport modes (road, rail, air) on the route Frankfurt-Milan. The investigation is part of the QUITS project (QUITS = Quality Indicators for Transport Systems), commissioned by the European Commission DG VII. The evaluation of the external costs is based on a bottom-up approach. The calculation involves four stages: emissions, dispersion, impacts, and costs, following the impact pathway approach. An integrated model for the valuation of environmental and health costs due to air pollutants will be presented consisting of three computer programmes which are linked together. For passenger road traffic, total external costs amount to about 44 ECU/1000 pkm on the route Frankfurt -Milan, including the impact categories air pollutants (15.6), global warming (5.2), noise (3.8), and accidents (19.6 ECU/1000 pkm). Concerning a comparison of the transport modes, external costs of passenger road traffic are about 9 times as high as those of rail traffic and about twice as high as those of air traffic. For goods transport by road, the total external costs (30.6 ECU/1000 tkm) are about 11 times as high as those of rail traffic. --external costs,transport systems,environmental impacts,bottom-up approach

The dark side of price cap regulation: a laboratory experiment

In a nutshell, price cap regulation is meant to establish a quid pro quo: regulators are obliged by law to intervene only at rare, previously defined points in time, and only by imposing an upper bound on prices; firms are meant to justify regulatory restraint by adopting socially beneficial innovations. In the policy debate, a potential downside of the arrangement has featured less prominently: the economic environment is unlikely to be stable while the cap is in place. If regulators take this into account, they have to decide under uncertainty and also anticipate how regulated firms will react. In a lab experiment, we manipulate the degree of regulatory uncertainty. We compare a baseline when regulators have the same information as firms about demand with treatments wherein they receive only a noisy signal and another when they know only the distribution from which demand realizations are taken. In the face of uncertainty, regulators impose overly generous price caps, which firms exploit. In the experiment, the social damage is severe, and does not disappear with experience

Point Transformer

In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets. We design Point Transformer to extract local and global features and relate both representations by introducing the local-global attention mechanism, which aims to capture spatial point relations and shape information. For that purpose, we propose SortNet, as part of the Point Transformer, which induces input permutation invariance by selecting points based on a learned score. The output of Point Transformer is a sorted and permutation invariant feature list that can directly be incorporated into common computer vision applications. We evaluate our approach on standard classification and part segmentation benchmarks to demonstrate competitive results compared to the prior work. Code is publicly available at: https://github.com/engelnico/point-transforme