Non-Tariff Barriers and Trade Liberalization

This paper shows that governments have no incentive to introduce non-tariff barriers when they are free to set tariffs but they do when tariffs are determined cooperatively. We then show three results. First, with trade liberalization, there is a progression from u sing tariffs only to quotas, and to antidumping constraints (when quotas are jointly eliminated). Second, there is a narrowing of the range of industries in which each instrument is used. Third, the degree of tariff liberalization and of replacement of ta riffs by NTBs depend on industry characteristics. These results are roughly in line with the empirical evidence.Tariffs, trade policy, reciprocal dumping, quotas, antidumping

Non-Tariff Barriers and Trade Liberalization

This paper shows that governments have no incentive to introduce non-tariff barriers when they are free to set tariffs but they do when tariffs are determined cooperatively. We then show three results. First, with trade liberalization, there is a progression from using tariffs only to quotas, and to antidumping constraints (when quotas are jointly eliminated). Second, there is a narrowing of the range of industries in which each instrument is used. Third,the degree of tariff liberalization and of replacement of tariffs by NTBs depend on industry characteristics.These results are roughly in line with the empirical evidence.Tariffs, Trade Policy, Reciprocal Dumping, Quotas, Antidumping

X-ray emission from the remarkable A-type star HR 8799

We present a Chandra observation of the exceptional planet bearing A5V star HR 8799, more precisely classified as a kA5hF0mA5 star and search for intrinsic X-ray emission. We clearly detect HR 8799 at soft X-ray energies with the ACIS-S detector in a 10 ks exposure; minor X-ray brightness variability is present during the observation. The coronal plasma is described well by a model with a temperature of around 3 MK and an X-ray luminosity of about Lx = 1.3 x 10^28 erg/s in the 0.2-2.0 keV band, corresponding to an activity level of log Lx/Lbol ~ -6.2. Altogether, these findings point to a rather weakly active and given a RASS detection, long-term stable X-ray emitting star. The X-ray emission from HR 8799 resembles those of a late A/early F-type stars, in agreement with its classification from hydrogen lines and effective temperature determination and thus resolving the apparent discrepancy with the standard picture of magnetic activity that predicts mid A-type stars to be virtually X-ray dark.Comment: 4 pages, 3 figures, accepted by A&

Brain morphological and functional correlates of genetic, psychological, prenatal and prodromal risk for major mental disorders and their behavioural links

Cross-sectional mri-studies comparing psychiatric patients with healthy individuals have shown that patients show brain morphometric as well as functional changes. However, it is unclear whether these are pathological factors or whether these neurobiological changes are simply a risk factor for mental disorders, a consequence of therapy, only occur in certain subgroups. Therefore, the influence of a broad spectrum of different risk factors for mental disorders on brain morphometry as well as function was investigated in the present study: polygenic risk scores for psychiatric disorders, temporal perspective, shortened prenatal development as well as an extremely high risk for the development of psychosis. It can be shown that these risk factors significantly influence brain structural parameters as well as brain function. Some of these changes also correlated with behavioural changes such as poorer cognitive performance. These behavioural correlates could be valuable diagnostic or prognostic markers and could also be important research targets for the development of new therapeutic approaches

Carbon Oxidation at the Atomic Level: A Computational Study on Oxidative Graphene Etching and Pitting of Graphitic Carbon Surfaces

In order to understand the oxidation of solid carbon materials by oxygen-containing gases, carbon oxidation has to be studied on the atomic level where the surface reactions occur. Graphene and graphite are etched by oxygen to form characteristic pits that are scattered across the material surface, and pitting in turn leads to microstructural changes that determine the macroscopic oxidation behavior. While this is a well-documented phenomenon, it is heretofore poorly understood due to the notorious difficulty of experiments and a lack of comprehensive computational studies. The main objective of the present work is the development of a computational framework from first principles to study carbon oxidation at the atomic level. First, the large body of literature on carbon oxidation is examined with regards to experimental observations of the pitting phenomenon as well as relevant theoretical studies on different aspects of the mechanistic details of carbon oxidation. Next, a comprehensive, atomic-scale kinetic mechanism for carbon oxidation is developed, which comprises only elementary surface reactions with reaction rates derived from first principles. The mechanism is then implemented using the Kinetic Monte Carlo (KMC) method. This framework for the first time allows the simulation of oxidative graphene etching at the atomic scale to relevant time- and lengthscales (up to seconds and hundreds of nanometers), and in a wide range of conditions (temperatures up to 2000 Kelvin, pressures ranging from vacuum to atmospheric pressure). The numerical results reveal information about the pitting process in heretofore unattained detail: Pit growth rates (and therefore intrinsic oxidation rates) are calculated and validated against a set of different experimental data at a wide range of conditions. Such information is crucial for modelling of material behavior on meso- and macroscales. The dependence of the pit geometry (hexagonal vs. circular) on temperature and gas pressure is assessed. This is important for utilizing oxidative etching as a manufacturing technique for graphene-based nanodevices. More subtle phenomena like pit inhibition at low pressures and temperatures are also discussed. Moreover, all these findings are examined with respect to the underlying reaction mechanism. This unveils the fundamental reasons for the observed reaction behavior, in particular different activation energies and reaction orders at low and high temperatures, as well as the transition of the pit geometry. The present work is a first step in an ongoing effort to develop predictive models for carbon oxidation in Thermal Protection Systems (TPS), with the ultimate goal of improved safety for hypersonic flight vehicles

Multi-task Deep Reinforcement Learning with PopArt

The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance. This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on. In this work, we study the problem of learning to master not one but multiple sequential-decision tasks at once. A general issue in multi-task learning is that a balance must be found between the needs of multiple tasks competing for the limited resources of a single learning system. Many learning algorithms can get distracted by certain tasks in the set of tasks to solve. Such tasks appear more salient to the learning process, for instance because of the density or magnitude of the in-task rewards. This causes the algorithm to focus on those salient tasks at the expense of generality. We propose to automatically adapt the contribution of each task to the agent's updates, so that all tasks have a similar impact on the learning dynamics. This resulted in state of the art performance on learning to play all games in a set of 57 diverse Atari games. Excitingly, our method learned a single trained policy - with a single set of weights - that exceeds median human performance. To our knowledge, this was the first time a single agent surpassed human-level performance on this multi-task domain. The same approach also demonstrated state of the art performance on a set of 30 tasks in the 3D reinforcement learning platform DeepMind Lab

A Polarizing Dynamic by Center Cabinets? The Mechanism of Limited Contestation

What effect does the presence of a coalition of the ideological center have on polarization in party systems? Studies of party positioning demonstrate the impact of a party’s affiliation to the cabinet for its electoral campaigning. In addition, comparative studies of party systems analyzed the effects of the competitive situation between the coalition and the opposition on party competition dynamics. Nevertheless, the linkage between findings of both branches of literature is still missing. On the one hand, studies of party competition models generally focus on explaining party behavior and do not aggregate these insights. On the other hand, party system studies usually lack an analytical micro-foundation. Thus, we do not know the mechanism that drives a polity to the extreme. To find this missing link, we derive two potential explanations based on the spatial theory of party competition and Satori’s study of party systems: incumbent punishment and limited contestation. We elaborate these mechanisms with the help of an agent-based model. Then, we trace the effect of cabinet type back to the limited contestation between coalition parties. If the incumbent parties avoid contestation with each other, a center cabinet induces polarizing dynamics since the opposition then has no incentive for responsible office-seeking. Specific circumstances such as a polarized electorate and voters’ negative evaluation of the cabinet parties support this mechanism. Methodologically, our simulation study reveals three advantages of the agent-based modeling approach: (1) the uncovering of thus far implicit assumptions; (2) the possibility of analyzing causal dependencies within a complex and dynamic model; and (3) the precision of our theoretical expectations based on the micro-foundation

Agile Science: Co-Creating Research on Digital Transformation

The dynamics of the digital transformation generate important and complex research questions: disruptive technological upheavals are entangled with serious social consequences and their effects and mechanisms need to be researched to be better understood. But the complex societal changes brought along by digital innovations also challenge science and research. So far, research on digital transformation often does not adequately meet the challenges created by the intersection of social and technological aspects. Borrowing from participatory and co-creative innovation approaches, we suggest the concept of "Agile Science", i.e., a balanced structure for disciplined work and interdisciplinary collaboration, which allows for adaptability and participation. With this, we want to shape the future of innovative and responsive research on digital transformation. We aim to support a shift toward an understanding of, and accountability for, increasing complexities while staying in touch with affected citizens and generating relevant findings and solutions for them. The present paper introduces the main ideas of this concept and illustrates this exemplarily by describing the Research Innovation Hub (RIH) at the Center for Advanced Internet Studies (CAIS).Die komplexen gesellschaftlichen Veränderungen, die mit der digitalen Transformation einhergehen, fordern auch Wissenschaft und Forschung heraus. Die bisherige Forschung zu digitaler Transformation wird den Herausforderungen, die sich aus der Überschneidung von sozialen und technologischen Aspekten ergeben, jedoch oft nicht gerecht. In Anlehnung an partizipative und co-kreative Innovationsansätze schlagen wir vor diesem Hintergrund das Konzept der "Agilen Wissenschaft" vor. Darunter verstehen wir eine balancierte Struktur von interdisziplinärer Kollaboration und disziplinärem Arbeiten. Diese soll Partizipation und Anpassungsfähigkeit gewährleisten. Wir wollen die Zukunft einer innovativen und reaktionsfähigen Forschung zur digitalen Transformation so gestalten, dass die zunehmende Komplexität verstanden und verantwortet wird, während gleichzeitig der Kontakt zu den betroffenen Bürgerinnen und Bürgern aufrechterhalten wird und relevante Erkenntnisse und Lösungen für sie geschaffen werden. Der vorliegende Text stellt die Grundgedanken dieses Konzepts vor und veranschaulicht es exemplarisch anhand des Forschungsinkubators des Center for Advanced Internet Studies (CAIS)