The Political Economy of Corporate Tax Harmonization: Why Do European Politicians (Dis)like Minimum Tax Rates?

Setting minimum tax rates is a well discussed way of mitigating pressure from tax competition. This paper investigates which motives shape the support for a minimum corporate tax among politicians. We make use of a unique data base: a survey among members of the European parliament. Our results confirm that the politicians' ideology as well as individual characteristics such as educational background exert a major in uence. Moreover, several predictions regarding national interests are derived from various standard tax competition models. These hypotheses are partly supported by the data; in particular, different national preferences for social equality shape the support. A comparison with survey results from the German Bundestag reveals that German politicians on the national level do not show different preferences towards tax rate harmonization. --company taxation,tax harmonization,political economy,European Parliament

Optimal decision making for sperm chemotaxis in the presence of noise

For navigation, microscopic agents such as biological cells rely on noisy sensory input. In cells performing chemotaxis, such noise arises from the stochastic binding of signaling molecules at low concentrations. Using chemotaxis of sperm cells as application example, we address the classic problem of chemotaxis towards a single target. We reveal a fundamental relationship between the speed of chemotactic steering and the strength of directional fluctuations that result from the amplification of noise in the chemical input signal. This relation implies a trade-off between slow, but reliable, and fast, but less reliable, steering. By formulating the problem of optimal navigation in the presence of noise as a Markov decision process, we show that dynamic switching between reliable and fast steering substantially increases the probability to find a target, such as the egg. Intriguingly, this decision making would provide no benefit in the absence of noise. Instead, decision making is most beneficial, if chemical signals are above detection threshold, yet signal-to-noise ratios of gradient measurements are low. This situation generically arises at intermediate distances from a target, where signaling molecules emitted by the target are diluted, thus defining a `noise zone' that cells have to cross. Our work addresses the intermediate case between well-studied perfect chemotaxis at high signal-to-noise ratios close to a target, and random search strategies in the absence of navigation cues, e.g. far away from a target. Our specific results provide a rational for the surprising observation of decision making in recent experiments on sea urchin sperm chemotaxis. The general theory demonstrates how decision making enables chemotactic agents to cope with high levels of noise in gradient measurements by dynamically adjusting the persistence length of a biased persistent random walk.Comment: 9 pages, 5 figure

Who's afraid of an EU tax and why? Revenue system preferences in the European Parliament

The EU's revenue system is still typical for an organisation based on international cooperation and stands in contrast to the Union's far advanced legislative and political role. This contrast feeds the debate on granting the EU an autonomous tax source. Our contribution explores the factors which shape the acceptance of the EU tax option among European policy makers. We make use of a unique database : A survey among Members of the European Parliament (MEP) which resulted in a response of some 150 of the representatives. Our results confirm an important role for party ideology and individual characteristics but they also demonstrate that country-specific factors are important to understand the support for an EU tax. In the light of our findings the status quo bias in the EU's revenue system can be attributed to the persistent importance of national interests with respect to fiscal burden sharing and tax policy. --European Parliament,EU tax,revenue system

Novel Methods to Incorporate Physiological Prior Knowledge into the Inverse Problem of Electrocardiography - Application to Localization of Ventricular Excitation Origins

17 Millionen Todesfälle jedes Jahr werden auf kardiovaskuläre Erkankungen zurückgeführt. Plötzlicher Herztod tritt bei ca. 25% der Patienten mit kardiovaskulären Erkrankungen auf und kann mit ventrikulärer Tachykardie in Verbindung gebracht werden. Ein wichtiger Schritt für die Behandlung von ventrikulärer Tachykardie ist die Detektion sogenannter Exit-Points, d.h. des räumlichen Ursprungs der Erregung. Da dieser Prozess sehr zeitaufwändig ist und nur von fähigen Kardiologen durchgeführt werden kann, gibt es eine Notwendigkeit für assistierende Lokalisationsmöglichkeiten, idealerweise automatisch und nichtinvasiv. Elektrokardiographische Bildgebung versucht, diesen klinischen Anforderungen zu genügen, indem die elektrische Aktivität des Herzens aus Messungen der Potentiale auf der Körperoberfläche rekonstruiert wird. Die resultierenden Informationen können verwendet werden, um den Erregungsursprung zu detektieren. Aktuelle Methoden um das inverse Problem zu lösen weisen jedoch entweder eine geringe Genauigkeit oder Robustheit auf, was ihren klinischen Nutzen einschränkt. Diese Arbeit analysiert zunächst das Vorwärtsproblem im Zusammenhang mit zwei Quellmodellen: Transmembranspannungen und extrazelluläre Potentiale. Die mathematischen Eigenschaften der Relation zwischen den Quellen des Herzens und der Körperoberflächenpotentiale werden systematisch analysiert und der Einfluss auf das inverse Problem verdeutlicht. Dieses Wissen wird anschließend zur Lösung des inversen Problems genutzt. Hierzu werden drei neue Methoden eingeführt: eine verzögerungsbasierte Regularisierung, eine Methode basierend auf einer Regression von Körperoberflächenpotentialen und eine Deep-Learning-basierte Lokalisierungsmethode. Diese drei Methoden werden in einem simulierten und zwei klinischen Setups vier etablierten Methoden gegenübergestellt und bewertet. Auf dem simulierten Datensatz und auf einem der beiden klinischen Datensätze erzielte eine der neuen Methoden bessere Ergebnisse als die konventionellen Ansätze, während Tikhonov-Regularisierung auf dem verbleibenden klinischen Datensatz die besten Ergebnisse erzielte. Potentielle Ursachen für diese Ergebnisse werden diskutiert und mit Eigenschaften des Vorwärtsproblems in Verbindung gebracht

The political cconomy of corporate tax harmonization : why do European politicians (dis)like minimum tax rates?

Setting minimum tax rates is a well discussed way of mitigating pressure from tax competition. This paper investigates which motives shape the support for a minimum corporate tax among politicians. We make use of a unique data base: a survey among members of the European parliament. Our results confirm that the politicians' ideology as well as individual characteristics such as educational background exert a major in uence. Moreover, several predictions regarding national interests are derived from various standard tax competition models. These hypotheses are partly supported by the data; in particular, different national preferences for social equality shape the support. A comparison with survey results from the German Bundestag reveals that German politicians on the national level do not show different preferences towards tax rate harmonization

Shape mode analysis exposes movement patterns in biology: flagella and flatworms as case studies

We illustrate shape mode analysis as a simple, yet powerful technique to concisely describe complex biological shapes and their dynamics. We characterize undulatory bending waves of beating flagella and reconstruct a limit cycle of flagellar oscillations, paying particular attention to the periodicity of angular data. As a second example, we analyze non-convex boundary outlines of gliding flatworms, which allows us to expose stereotypic body postures that can be related to two different locomotion mechanisms. Further, shape mode analysis based on principal component analysis allows to discriminate different flatworm species, despite large motion-associated shape variability. Thus, complex shape dynamics is characterized by a small number of shape scores that change in time. We present this method using descriptive examples, explaining abstract mathematics in a graphic way.Comment: 20 pages, 6 figures, accepted for publication in PLoS On

Surfing along concentration filaments: sperm chemotaxis in physiological shear flows

Many motile biological cells navigate along concentration gradients of signaling molecules: This chemotaxis guides for instance sperm cells from marine invertebrates, which have to find egg cells in the ocean. While chemotaxis has been intensively studied for idealized conditions of rotationally symmetric gradients in still water, natural gradients are usually distorted, e.g., by turbulent flows in the ocean. Recent experiments and direct numerical simulations with sperm cells and bacteria surprisingly suggest the existence of an optimal flow strength at which chemotaxis is more effective than for still water. We use sperm chemotaxis in simple shear flow as a prototypical example to understand the origin of such an optimal flow strength theoretically: We quantify how flow accelerates spreading of signaling molecules released by the egg, but distorts the resulting concentration field into long and thin filaments. The competition between these two effects sets an optimal flow strength that maximizes sperm-egg encounter. We characterize how sperm cells `surf' along concentration filaments, typical for scalar turbulence, revealing a general navigation paradigm in the presence of flow. We compare both simulation and theory with previous experimental results and find good agreement.Comment: manuscript: 6 pages, 4 figures; SI: 11 pages, 8 figure

Who's afraid of an EU tax and why? : revenue system preferences in the European parliament

The EU's revenue system is still typical for an organisation based on international cooperation and stands in contrast to the Union's far advanced legislative and political role. This contrast feeds the debate on granting the EU an autonomous tax source. Our contribution explores the factors which shape the acceptance of the EU tax option among European policy makers. We make use of a unique database: A survey among Members of the European Parliament (MEP) which resulted in a response of some 150 of the representatives. Our results confirm an important role for party ideology and individual characteristics but they also demonstrate that country-specific factors are important to understand the support for an EU tax. In the light of our findings the status quo bias in the EU's revenue system can be attributed to the persistent importance of national interests with respect to fiscal burden sharing and tax policy

Sovereign risk premia: the link between fiscal rules and stability culture

There is a growing empirical literature studying whether fiscal rules reduce borrowing costs. Nevertheless, it remains an open question whether these rules are effective genuinely or just because they mirror fiscal preferences of politicians and voters. In our analysis of European bond spreads, we shed light on this issue by employing several types of stability preference related proxies. These proxies refer to a country’s past stability performance, government characteristics and survey results related to general trust. We find evidence that these preference indicators have an influence on risk premia and dampen the measurable impact of fiscal rules. Yet, the interaction of stability preferences and rules points to a particular potential of fiscal rules in countries with a historically low stability culture

Context-based Normalization of Histological Stains using Deep Convolutional Features

While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available. This work provides a fully automated, end-to-end learning-based setup for normalizing histological stains, which considers the texture context of the tissue. We introduce Feature Aware Normalization, which extends the framework of batch normalization in combination with gating elements from Long Short-Term Memory units for normalization among different spatial regions of interest. By incorporating a pretrained deep neural network as a feature extractor steering a pixelwise processing pipeline, we achieve excellent normalization results and ensure a consistent representation of color and texture. The evaluation comprises a comparison of color histogram deviations, structural similarity and measures the color volume obtained by the different methods.Comment: In: 3rd Workshop on Deep Learning in Medical Image Analysis (DLMIA 2017