Main Characteristics of the Hungarian Income Inequality as Shown by the Data of the Income Surveys Carried out by the CSO in the Last Half Century

The study shortly surveys the main characteristics of the income surveys carried out by the Hungarian CSO in the last half century, then examines how the incomes of the households and especially the income inequalities developed in this period. The changes in the income inequality are shown in several inequality measures in the study. The emphasis is on the Theil inequality measure, because it can be unequivocally additively decomposed into parts representing the differences in the mean income between the various social groups and their weights on the one hand and the average within group inequalities on the other. The decomposition enlightens how and to what extent the various personal, household and regional characteristics contribute to the income inequality within the population and how the extent of this contribution changes in time and because of what causes. Based on the data of the last two income surveys the study examines the contribution to the inequality not only on the basis of the per capita income, but also on that of the equivalent income. Finally, on the basis of the huge amount of empirical data the study makes a few summary statements.income statistics, income inequality, index numbers

Testing weighted splitting schemes on a one-column transport-chemistry model

In many transport-chemistry models, a huge system of ODE’s of the advection-diffusion-reaction type has to be integrated in time. Typically, this is done with the help of operator splitting. Rosenbrock schemes combined with approximate matrix factorization (ROS-AMF) are an alternative to operator splitting which does not suffer from splitting errors. However, implementation of ROS-AMF schemes often requires serious changes in the code. In this paper we test another classical second order splitting introduced by Strang in 1963, which, unlike the popular Strang splitting, seemed to be forgotten and rediscovered recently (partially due to its intrinsic parallellism). This splitting, called symmetrically weighted sequential (SWS) splitting, is simple and straightforward to apply, independent of the order of the operators and has an operator-level parallelism. In the experiments, the SWS scheme compares favorably to the Strang splitting, but is less accurate than ROS-AMF

Operátor-szeletelés és variációs adatasszimiláció szennyezőanyag-terjedési és dinamikai légkörmodellekben = Operator splitting and variational data assimilation in air pollution and dynamic models

A részoperátorok páronkénti kommutálása a magasabb rendű szeletelési módszereknél nem szükséges a szeletelési hiba nullává válásához. A hagyományos szeletelési módszereknek a konstans mátrixoperátorú feladatokra ismeretes konzisztenciarendje megőrződik C0-félcsoportok generátoraira, és nem túl erős megkötések mellett időfüggő mátrixú feladatokra is. A szeleteléssel kapott numerikus megoldás javítható a Richardson-extrapoláció módszerével, és a megoldás gépideje hatékonyan növelhető párhuzamosított számítógépeken. A szeletelés alkalmazása befolyásolhatja a megoldás kvalitatív tulajdonságait, pl. a hullámmegoldások terjedési sebességét a sekélyvízi egyenletrendszerben. A szeletelési módszerek hatékonyan alkalmazhatók valós feladatokban, pl. szennyezőanyag-terjedési és dinamikai modellekben. A szeletelési módszer rendjével összhangban kell megválasztani a részfeladatok megoldására alkalmazott numerikus módszerek rendjét. A variációs adatasszimiláció hatékony alkalmazásához megfelelő rendű numerikus módszert és szeletelést kell együtt alkalmazni. | In the case of the traditional second-order splitting methods, the pairwise commutativity of the sub-operators is not a necessary condition for zero local splitting error. The consistency orders of the traditional splitting methods, derived for constant sub-matrices, is preserved for generators of C0-semigroups, and – under some rather general conditions – for time-dependent matrix operators as well. The numerical solution obtained by splitting can be improved by using the method of Richardson extrapolation, and the computational time of the solution can be efficiently reduced by the use of parallel computers. The application of operator splitting may influence the qualitative properties of the solution, e.g., the phase velocities of the wave solutions of the shallow water equations. Splitting methods can be successfully applied in real-life problems, e.g., in air pollution transport models and dynamical models. The numerical methods for the solution of the splitting sub-systems must be chosen in accordance with the order of the splitting method. For the efficient application of variational data assimilation both the order of the numerical method and the order of the splitting method should be chosen appropriately

Hysteretic behavior of spatially coupled phase-oscillators

Motivated by phenomena related to biological systems such as the synchronously flashing swarms of fireflies, we investigate a network of phase oscillators evolving under the generalized Kuramoto model with inertia. A distance-dependent, spatial coupling between the oscillators is considered. Zeroth and first order kernel functions with finite kernel radii were chosen to investigate the effect of local interactions. The hysteretic dynamics of the synchronization depending on the coupling parameter was analyzed for different kernel radii. Numerical investigations demonstrate that (1) locally locked clusters develop for small coupling strength values, (2) the hysteretic behavior vanishes for small kernel radii, (3) the ratio of the kernel radius and the maximal distance between the oscillators characterizes the behavior of the network

Írásbeliség-képiség az internet korában

„Ha az archeológia és a művészettörténet történeti képtudományként újították meg hagyományaikat, ha a filmtudomány az elbeszélés helyett immár a filmek képszerűségét állítja előtérbe, ha a filozófia a kép reflexióját részesíti előnyben, ha az irodalomtudomány írás és kép kölcsönviszonyát elemzi, ha a történettudomány felmenti a képi forrásokat az illusztráció ódiuma alól, ha a tudománytörténet a tudományok vizuális feltételeit hangsúlyozza, ha a jogtudomány a jog ikonológiáján dolgozik, ha a matematikában a Bourbaki-csoport ikonoklazmusával szemben álló kijelentés, a „Seeing is believing” érezteti hatását, ha a biológia Darwin óta most először tárgyalja a szépséget mint a kiválasztás kritériumát, s ha a természettudományok minden területén fellépő számítógépes vizualizáció elemzés tárgyává válik, mindez azt jelzi, hogy a kutatás területén is végbemegy az a mélyreható s a kultúra egészében érzékelhető fordulat, amelyet a modern képi technikák és a vizuális részvétel vágya hívtak elő.” (Bredekamp, 2006, 13.

Advances in Compression using Probabilistic Models

The increasing demand for data transmission and storage necessitate the use of efficient compression methods. Compression algorithms work by mapping data to a more compact representation from which the original data can be recovered. To operate efficiently, they need to capture the characteristics of the data distribution, which can be difficult, especially for high-dimensional data. One emerging solution lies in applying probabilistic machine learning to capture the data distribution in an unsupervised manner. Once a probabilistic model for the data is defined, variational inference can be used to infer its parameters from data. Variational inference is closely related to the optimal compression size, as stated by Hinton's bits-back argument: the evidence lower bound, the objective optimized by variational inference, corresponds to a lower bound on the optimal compression size of the average datapoint. However, current compression methods rely on variational inference merely as a heuristic, and they do not approach its postulated efficiency. In this thesis, we present principled and practical algorithms that get closer to this limit. After discussing our approach, we demonstrate its efficacy in image compression and model compression. First, we focus on image compression, where we use a variational autoencoder to learn a mapping between the images and their unobserved, latent representations. We propose a stochastic coding scheme to encode the latent representation, from which the original image can be approximately reconstructed. Next, we look at the compression of deep learning models. We use variational inference to approximate the posterior distribution of the weights in a neural network, and apply our stochastic coding scheme to encode a weight configuration. Finally, we investigate a connection between variational inference and our compression algorithm. We show that a technique we used for compression can improve variational inference by generating samples from a highly flexible posterior approximation, without significantly increasing the computational costs

Characterisation and practical importance of exercise-induced cardiovascular response in a 6 - to 18-year-old population

- Haemodynamic effect also has individual (fitness and health specifics) and population-level (public health impact) relevance to exercise. - Confirmed evidence about the pupils: differences exist between recovery HR and recovery BP trends: recovery HR remained at a high level, in contrast, the recovery BP decreased to starting level or below. - Established a pilot, exercise-related screening test, called “Fit-test”. It provides an opportunity to gain new insight into the relationship between later manifestations of illness and juvenile burden response. Fit-test is a low-budget, whole-population screening test, which easily fits into an existing school and school health system. We have found that is also suitable for screening for MHT, preHT, and sustained HT students, and for monitoring the effects of treatment. - We established a database, the first large dataset of haemodynamic changes of normal-weight pupils during a field exercise test. We defined the population-specific dynamics and experienced individual dissimilarities. It provided an opportunity to evaluate the physical and cardiovascular fitness together. Established a possibility for subsequent monitoring of the health status of the affected generation, and risk group. This dataset is useful for physical education teachers, coaches, physicians and exercise physiologists to evaluate actual cardiovascular fitness and haemodynamic responses to exercise in children or adolescents and follow its change