Laggards or performers? CEE vs. PIIGS countries’ catch-up with the Euro area in the last ten years

This research paper develops a comparative analysis between the new members states of the European Union (EU) – from Central and Eastern Europe (CEE) – and PIIGS countries (Portugal, Italy, Ireland, Greece and Spain) in terms of economic convergence with the Euro area, in the last decade. In addition, the paper emphasizes the changes in the economic convergence levels determined by the recent international crisis. In order to assess these evolutions, we compute an aggregated index of economic convergence, made up of real and structural convergence indexes. Then, by using cluster methodology, we highlight the similarities between the states in the two groups, CEE and PIIGS, from the economic convergence perspective. The comparative analysis reveals that in 2010 only Estonia, Hungary and Slovenia report resembling characteristics to PIIGS group. We also report an important progress of the countries analyzed, as regards real and structural convergence with the Euro area. However, after a decade of catching-up, Romania remains by far the most distanced country from the Euro area.real convergence, structural convergence, Central and Eastern Europe, PIIGS, clusterization

A fully discrete framework for the adaptive solution of inverse problems

We investigate and contrast the differences between the discretize-then-differentiate and differentiate-then-discretize approaches to the numerical solution of parameter estimation problems. The former approach is attractive in practice due to the use of automatic differentiation for the generation of the dual and optimality equations in the first-order KKT system. The latter strategy is more versatile, in that it allows one to formulate efficient mesh-independent algorithms over suitably chosen function spaces. However, it is significantly more difficult to implement, since automatic code generation is no longer an option. The starting point is a classical elliptic inverse problem. An a priori error analysis for the discrete optimality equation shows consistency and stability are not inherited automatically from the primal discretization. Similar to the concept of dual consistency, We introduce the concept of optimality consistency. However, the convergence properties can be restored through suitable consistent modifications of the target functional. Numerical tests confirm the theoretical convergence order for the optimal solution. We then derive a posteriori error estimates for the infinite dimensional optimal solution error, through a suitably chosen error functional. This estimates are constructed using second order derivative information for the target functional. For computational efficiency, the Hessian is replaced by a low order BFGS approximation. The efficiency of the error estimator is confirmed by a numerical experiment with multigrid optimization

Space-time adaptive solution of inverse problems with the discrete adjoint method

Adaptivity in both space and time has become the norm for solving problems modeled by partial differential equations. The size of the discretized problem makes uniformly refined grids computationally prohibitive. Adaptive refinement of meshes and time steps allows to capture the phenomena of interest while keeping the cost of a simulation tractable on the current hardware. Many fields in science and engineering require the solution of inverse problems where parameters for a given model are estimated based on available measurement information. In contrast to forward (regular) simulations, inverse problems have not extensively benefited from the adaptive solver technology. Previous research in inverse problems has focused mainly on the continuous approach to calculate sensitivities, and has typically employed fixed time and space meshes in the solution process. Inverse problem solvers that make exclusive use of uniform or static meshes avoid complications such as the differentiation of mesh motion equations, or inconsistencies in the sensitivity equations between subdomains with different refinement levels. However, this comes at the cost of low computational efficiency. More efficient computations are possible through judicious use of adaptive mesh refinement, adaptive time steps, and the discrete adjoint method. This paper develops a framework for the construction and analysis of discrete adjoint sensitivities in the context of time dependent, adaptive grid, adaptive step models. Discrete adjoints are attractive in practice since they can be generated with low effort using automatic differentiation. However, this approach brings several important challenges. The adjoint of the forward numerical scheme may be inconsistent with the continuous adjoint equations. A reduction in accuracy of the discrete adjoint sensitivities may appear due to the intergrid transfer operators. Moreover, the optimization algorithm may need to accommodate state and gradient vectors whose dimensions change between iterations. This work shows that several of these potential issues can be avoided for the discontinuous Galerkin (DG) method. The adjoint model development is considerably simplified by decoupling the adaptive mesh refinement mechanism from the forward model solver, and by selectively applying automatic differentiation on individual algorithms. In forward models discontinuous Galerkin discretizations can efficiently handle high orders of accuracy, $h/p$-refinement, and parallel computation. The analysis reveals that this approach, paired with Runge Kutta time stepping, is well suited for the adaptive solutions of inverse problems. The usefulness of discrete discontinuous Galerkin adjoints is illustrated on a two-dimensional adaptive data assimilation problem

Unmasking the abnormal events in video

We propose a novel framework for abnormal event detection in video that requires no training sequences. Our framework is based on unmasking, a technique previously used for authorship verification in text documents, which we adapt to our task. We iteratively train a binary classifier to distinguish between two consecutive video sequences while removing at each step the most discriminant features. Higher training accuracy rates of the intermediately obtained classifiers represent abnormal events. To the best of our knowledge, this is the first work to apply unmasking for a computer vision task. We compare our method with several state-of-the-art supervised and unsupervised methods on four benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve state-of-the-art results, while running in real-time at 20 frames per second.Comment: Accepted at the 2017 International Conference on Computer Vision (ICCV 2017

Elements of Bio-Geographical Regional Determination in the Subcarpathians between the Prahova and the Dâmboviţa River

Generally, the Subcarpathian area between the Dâmboviţa and the Prahova is characterized by very diverse landscapes and an intense human impact. Secondary or derived vegetal associations are also present to a large extent, in relative balance with a certain type and degree of human pressure. Some of these vegetal associations are extremely rich in flora and fauna. The most frequent are the lawn associations used as hayfields and pastures. Orchards are often associated with grassy vegetation used as grazing land or more often as hayfields. There are relatively frequent shrub clusters and they represent isolated vegetation on rocky crests or, quite often, an intermediary stage towards the initial forest vegetation on formerly deforested terrains where there is more or less intense degradation. All these remain stable as long as man uses them rationally. When human impact goes beyond nature’s capacity to withstand it, most of the terrains used as grazing land or covered by shrub clusters show significant degradation, which leads to much fewer species with diminished productive potential.The landscape types of this Subcarpathian area are: the high hill landscape, the low and medium altitude hill landscape and the large corridors and depressions landscape

How does economic crisis change the landscape of real convergence for Central and Eastern Europe?

The paper aims at analyzing the impact of the recent economic crisis on the real convergence with the Euro area for ten countries from Central and Eastern Europe that joined the European Union in 2004 and 2007. We use 2000, 2008 and 2010 as benchmark years for our study and GDP per capita at PPP, as the most relevant indicator in terms of real convergence. The study is based on Euclidian distance analysis. The results reveal that most of the countries recorded higher distances from the Euro area average, while Poland and Slovakia improved their convergence

Impact of misfit strain on the properties of tetragonal Pb(Zr,Ti)O3 thin film heterostructures

Heterostructures consisting of PbZr0.2Ti0.8O3 and PbZr0.4Ti0.6O3 films grown on a SrTiO3 (100) substrate with a SrRuO3 bottom electrode were prepared by pulsed laser deposition. Using the additional interface provided by the ferroelectric bilayer structure and changing the sequence of the layers, the dislocation content and domain patterns were varied. The resulting microstructure was investigated by transmission electron microscopy. Macroscopic ferroelectric measurements have shown a large impact of the formation of dislocations and 90° domains on the ferroelectric polarization and dielectric constant. A thermodynamic analysis using the LANDAU-GINZBURGDEVONSHIRE approach that takes into account the ratio of the thicknesses of the two ferroelectric layers and electrostatic coupling is used to describe the experimental data