"The European Community and Japan: Bi(tri)lateral Trade in World Context"

This paper first examines the institutional context of EC trade policy and assesses the real level of protection that policy has afforded. It then examines the question of how "common" the policy has in fact been and how it has related to competition policy, devoting a special section to the Common Agricultural Policy (CAP). The next two sections discuss crucial issues in the trilateral relationship between the EC, Japan, and the US by focusing on the manufacturing sectors of electronics and cars. In shifting the perspective towards the future this paper focuses first on the concept of "strategic trade policy" and then at the special issues raised by the reform process that "1992 has brought, if it has, in Eastern Europe. The paper ends by posing two fundamental and interrelated questions. Has "1992" brought the European Community closer to the rest of the world? And what is the future position of Europe in the international division of labor

Landau levels in wrinkled and rippled graphene sheets

We study the discrete energy spectrum of curved graphene sheets in the presence of a magnetic field. The shifting of the Landau levels is determined for complex and realistic geometries of curved graphene sheets. The energy levels follow a similar square root dependence on the energy quantum number as for rippled and flat graphene sheets. The Landau levels are shifted towards lower energies proportionally to the average deformation and the effect is larger compared to a simple uni-axially rippled geometry. Furthermore, the resistivity of wrinkled graphene sheets is calculated for different average space curvatures and shown to obey a linear relation. The study is carried out with a quantum lattice Boltzmann method, solving the Dirac equation on curved manifolds.Comment: 6 pages, 4 figures, 27th International Conference on Discrete Simulation of Fluid Dynamic

Quantum spin-Hall effect on the M\"obius graphene ribbon

Topological phases of matter have revolutionized quantum engineering. Implementing a curved space Dirac equation solver based on the quantum Lattice Boltzmann method, we study the topological and geometrical transport properties of a M\"obius graphene ribbon. In the absence of a magnetic field, we measure a quantum spin-Hall current on the graphene strip, originating from topology and curvature, whereas a quantum Hall current is not observed. In the torus geometry a Hall current is measured. Additionally, a specific illustration of the equivalence between the Berry and Ricci curvature is presented through a travelling wave-packet around the M\"obius band.Comment: arXiv admin note: substantial text overlap with arXiv:1810.0210

Integrated Airline Organizational Frameworks and Crew Resource Management Effectiveness

It is only through well-designed and implemented Crew Resource Management being deeply rooted in an airline\u27s organizational culture that an airline can achieve its highest possible standard of safety, by having the highest degree of operational efficiency. It is not Crew Resource Management training itself that contributes to well-trained crew members who implement Crew Resource Management principles in flight operations. Rather, it is a strong company organizational culture that contributes, ultimately, to the effectiveness of Crew Resource Management

Large-scale Parallel Stratified Defeasible Reasoning

We are recently experiencing an unprecedented explosion of available data from the Web, sensors readings, scientific databases, government authorities and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling huge amounts of data for these applications. In this paper, we consider inconsistency-tolerant reasoning in the form of defeasible logic, and analyze how parallelization, using the MapReduce framework, can be used to reason with defeasible rules over huge datasets. We extend previous work by dealing with predicates of arbitrary arity, under the assumption of stratification. Moving from unary to multi-arity predicates is a decisive step towards practical applications, e.g. reasoning with linked open (RDF) data. Our experimental results demonstrate that defeasible reasoning with millions of data is performant, and has the potential to scale to billions of facts

Global dimensions in the educational legislation, social studies curriculum and textbooks of Greek compulsory education (grades 1-9)

This study involves a content analysis of Greek educational legislation as well as of the social studies curriculum and textbooks in Greece. The purpose of the study is to determine if global themes and supranational elements are contained in these materials and to what degree they translate into teachable knowledge. The analysis revealed that the above dimensions are, to some degree, evident, but they have not been adequately adapted to correspond to the pronouncements of the Greek educational establishment and to the new realities of the European and international space. The global dimensions found in these materials mainly address the geophysical aspects of the globe and to a lesser degree the human, political and socio-cultural issues and problems. It is recommended that Greece, as well as other nation states, undertake an in-depth examination of their curricula and textbooks, especially in the area of social studies, so that a balanced and globalised curriculum is developed.peer-reviewe

Confining massless Dirac particles in two-dimensional curved space

Dirac particles have been notoriously difficult to confine. Implementing a curved space Dirac equation solver based on the quantum Lattice Boltzmann method, we show that curvature in a 2-D space can confine a portion of a charged, mass-less Dirac fermion wave-packet. This is equivalent to a finite probability of confining the Dirac fermion within a curved space region. We propose a general power law expression for the probability of confinement with respect to average spatial curvature for the studied geometry.Comment: 10 pages 8 figure

Canonical normalizing flows for manifold learning

Manifold learning flows are a class of generative modelling techniques that assume a low-dimensional manifold description of the data. The embedding of such a manifold into the high-dimensional space of the data is achieved via learnable invertible transformations. Therefore, once the manifold is properly aligned via a reconstruction loss, the probability density is tractable on the manifold and maximum likelihood can be used to optimize the network parameters. Naturally, the lower-dimensional representation of the data requires an injective-mapping. Recent approaches were able to enforce that the density aligns with the modelled manifold, while efficiently calculating the density volume-change term when embedding to the higher-dimensional space. However, unless the injective-mapping is analytically predefined, the learned manifold is not necessarily an efficient representation of the data. Namely, the latent dimensions of such models frequently learn an entangled intrinsic basis, with degenerate information being stored in each dimension. Alternatively, if a locally orthogonal and/or sparse basis is to be learned, here coined canonical intrinsic basis, it can serve in learning a more compact latent space representation. Toward this end, we propose a canonical manifold learning flow method, where a novel optimization objective enforces the transformation matrix to have few prominent and non-degenerate basis functions. We demonstrate that by minimizing the off-diagonal manifold metric elements $\ell_1$-norm, we can achieve such a basis, which is simultaneously sparse and/or orthogonal. Canonical manifold flow yields a more efficient use of the latent space, automatically generating fewer prominent and distinct dimensions to represent data, and a better approximation of target distributions than other manifold flow methods in most experiments we conducted, resulting in lower FID scores.Comment: NeurIPS 202

On Measuring Bias in Online Information

Bias in online information has recently become a pressing issue, with search engines, social networks and recommendation services being accused of exhibiting some form of bias. In this vision paper, we make the case for a systematic approach towards measuring bias. To this end, we discuss formal measures for quantifying the various types of bias, we outline the system components necessary for realizing them, and we highlight the related research challenges and open problems.Comment: 6 pages, 1 figur