Who Is Willing to Migrate in the CEECS? Evidence From the Czech Republic

This paper explores the willingness to migrate in the Czech Republic. We find that variables measuring regional labour market conditions and amenities contribute little to explaining the willingness to migrate, but that personal and household characteristics are more important. Persons owning family houses are substantially less willing to migrate and the relationship between the willingness to migrate and income is U shaped, persons experiencing longer unemployment spells are not less willing to migrate and commuting may at least partially compensate for low willingness to migrate. Finally, with the exception of the less educated, the willingness to migrate of all groups analysed reacts only weakly to regional labour market conditions and amenities.

A Stable Multi-Scale Kernel for Topological Machine Learning

Topological data analysis offers a rich source of valuable information to study vision problems. Yet, so far we lack a theoretically sound connection to popular kernel-based learning techniques, such as kernel SVMs or kernel PCA. In this work, we establish such a connection by designing a multi-scale kernel for persistence diagrams, a stable summary representation of topological features in data. We show that this kernel is positive definite and prove its stability with respect to the 1-Wasserstein distance. Experiments on two benchmark datasets for 3D shape classification/retrieval and texture recognition show considerable performance gains of the proposed method compared to an alternative approach that is based on the recently introduced persistence landscapes

The Willingness to Migrate in the CEECs. Evidence from the Czech Republic

Given the low levels of migration in the CEECs found in the literature, this paper raises the issue of who is willing to migrate in these countries. Using data on the willingness to migrate in the Czech Republic we show that variables measuring regional labour market conditions and amenities contribute little to explaining willingness to migrate, but that personal and household characteristics are more important. The least willing to migrate are the family-house owners, the less educated and the elderly as well as persons residing in regions with above-average unemployment rates. Improving the efficiency of the housing market and focusing on the problems of peripheral regions should thus be primary foci of a policy aimed at improving labour-market adjustment through migration. These policies are, however, unlikely to yield rapid returns, since the willingness to migrate of all subgroups analysed (except for the less educated) reacts only weakly to regional labour market incentives and amenities

Who Is Willing to Migrate in the CEECS? Evidence From the Czech Republic

This paper explores the willingness to migrate in the Czech Republic. We find that variables measuring regional labour market conditions and amenities contribute little to explaining the willingness to migrate, but that personal and household characteristics are more important. Persons owning family houses are substantially less willing to migrate and the relationship between the willingness to migrate and income is U shaped, persons experiencing longer unemployment spells are not less willing to migrate and commuting may at least partially compensate for low willingness to migrate. Finally, with the exception of the less educated, the willingness to migrate of all groups analysed reacts only weakly to regional labour market conditions and amenities

Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions

Modeling and predicting extreme movements in GDP is notoriously difficult and the selection of appropriate covariates and/or possible forms of nonlinearities are key in obtaining precise forecasts. In this paper, our focus is on using large datasets in quantile regression models to forecast the conditional distribution of US GDP growth. To capture possible non-linearities, we include several nonlinear specifications. The resulting models will be huge dimensional and we thus rely on a set of shrinkage priors. Since Markov Chain Monte Carlo estimation becomes slow in these dimensions, we rely on fast variational Bayes approximations to the posterior distribution of the coefficients and the latent states. We find that our proposed set of models produces precise forecasts. These gains are especially pronounced in the tails. Using Gaussian processes to approximate the nonlinear component of the model further improves the good performance, in particular in the right tail

LODE: Linking Digital Humanities Content to the Web of Data

Numerous digital humanities projects maintain their data collections in the form of text, images, and metadata. While data may be stored in many formats, from plain text to XML to relational databases, the use of the resource description framework (RDF) as a standardized representation has gained considerable traction during the last five years. Almost every digital humanities meeting has at least one session concerned with the topic of digital humanities, RDF, and linked data. While most existing work in linked data has focused on improving algorithms for entity matching, the aim of the LinkedHumanities project is to build digital humanities tools that work "out of the box," enabling their use by humanities scholars, computer scientists, librarians, and information scientists alike. With this paper, we report on the Linked Open Data Enhancer (LODE) framework developed as part of the LinkedHumanities project. With LODE we support non-technical users to enrich a local RDF repository with high-quality data from the Linked Open Data cloud. LODE links and enhances the local RDF repository without compromising the quality of the data. In particular, LODE supports the user in the enhancement and linking process by providing intuitive user-interfaces and by suggesting high-quality linking candidates using tailored matching algorithms. We hope that the LODE framework will be useful to digital humanities scholars complementing other digital humanities tools

On sensitivity calculations for neutrino oscillation experiments

Calculations of sensitivities of future experiments are a necessary ingredient in experimental high energy physics. Especially in the context of measurements of the neutrino oscillation parameters extensive studies are performed to arrive at the optimal configuration. In this note we clarify the definition of sensitivity as often applied in these studies. In addition we examine two of the most common methods to calculate sensitivity from a statistical perspective using a toy model. The importance of inclusion of uncertainties in nuisance parameters for the interpretation of sensitivity calculations is pointed out.Comment: 12 pages, 5 figure

Molecular effects in the ionization of N2_2, O2_2 and F2_2 by intense laser fields

In this paper we study the response in time of N$_2$, O$_2$ and F$_2$ to laser pulses having a wavelength of 390nm. We find single ionization suppression in O$_2$ and its absence in F$_2$, in accordance with experimental results at $\lambda = 800$nm. Within our framework of time-dependent density functional theory we are able to explain deviations from the predictions of Intense-Field Many-Body $S$-Matrix Theory (IMST). We confirm the connection of ionization suppression with destructive interference of outgoing electron waves from the ionized electron orbital. However, the prediction of ionization suppression, justified within the IMST approach through the symmetry of the highest occupied molecular orbital (HOMO), is not reliable since it turns out that, e.g. in the case of F$_2$, the electronic response to the laser pulse is rather complicated and does not lead to dominant depletion of the HOMO. Therefore, the symmetry of the HOMO is not sufficient to predict ionization suppression. However, at least for F$_2$, the symmetry of the dominantly ionized orbital is consistent with the non-suppression of ionization.Comment: 19 pages, 5 figure