Migration and animal husbandry: Competing or complementary livelihood strategies. Evidence from Kyrgyzstan

Animal husbandry and labour migration are important livelihood strategies for a large proportion of the rural population in developing countries. Up to now, the two strategies have usually been studied by looking at either one or the other; their interlinkages have rarely been examined. Based on a case study in rural Kyrgyzstan, the aim of this paper is to explore the links between animal husbandry and labour migration. Results show that for most rural households, livestock is crucial yet not sufficient to make a living. Therefore, many people diversify their income sources by migrating to work elsewhere. This generates cash for daily expenses and the acquisition of new livestock, but also leads to an absence of workforce in households. Yet since remittances usually exceed the expenses for hiring additional workforce, most people consider migration profitable. From a socio-economic point of view, migration and animal husbandry can thus be considered important complementary livelihood strategies for the rural Kyrgyz population, at least for the time being. In the long term, however, the failure of young migrants to return to rural places and their settlement in urban areas might also cause remittance dependency and lead to an increasing lack of qualified labour. From an environmental point of view, the investment of remittances into animal husbandry poses challenges to sustainable pasture management. Increasing livestock numbers in rural areas raise pressure on pasture resources. Since most people consider animal husbandry their main future prospect while continuing to use pastures in a fairly unsustainable way, this may further exacerbate the over-utilization of pastures in future

Demonstration of angular anisotropy in the output of Thematic Mapper

There is a dependence of TM output (proportional to scene radiance in a manner which will be discussed) upon season, upon cover type and upon view angle. The existence of a significant systematic variation across uniform scenes in p-type (radiometrically and geometrically pre-processed) data is demonstrated. Present pre-processing does remove the effects and the problem must be addressed because the effects are large. While this is in no way attributable to any shortcomings in the thematic mapper, it is an effect which is sufficiently important to warrant more study, with a view to developing suitable pre-processing correction algorithms

A method to polarise antiprotons in storage rings and create polarised antineutrons

An intense circularely polarised photon beam interacts with a cooled antiproton beam in a storage ring. Due to spin dependent absorption cross sections for the reaction gamma+antiproton > pi- + antineutron a built-up of polarisation of the stored antiprotons takes place. Figures-of-merit around 0.1 can be reached in principle over a wide range of antiproton energies. In this process antineutrons with Polarisation > 70% emerge. The method is presented for the case of 300 MeV/c cooled antiproton beam

Secure Vehicular Communication Systems: Implementation, Performance, and Research Challenges

Vehicular Communication (VC) systems are on the verge of practical deployment. Nonetheless, their security and privacy protection is one of the problems that have been addressed only recently. In order to show the feasibility of secure VC, certain implementations are required. In [1] we discuss the design of a VC security system that has emerged as a result of the European SeVeCom project. In this second paper, we discuss various issues related to the implementation and deployment aspects of secure VC systems. Moreover, we provide an outlook on open security research issues that will arise as VC systems develop from today's simple prototypes to full-fledged systems

The Gerasimov-Drell-Hearn Sum Rule and the Spin Structure of the Nucleon

The Gerasimov-Drell-Hearn sum rule is one of several dispersive sum rules that connect the Compton scattering amplitudes to the inclusive photoproduction cross sections of the target under investigation. Being based on such universal principles as causality, unitarity, and gauge invariance, these sum rules provide a unique testing ground to study the internal degrees of freedom that hold the system together. The present article reviews these sum rules for the spin-dependent cross sections of the nucleon by presenting an overview of recent experiments and theoretical approaches. The generalization from real to virtual photons provides a microscope of variable resolution: At small virtuality of the photon, the data sample information about the long range phenomena, which are described by effective degrees of freedom (Goldstone bosons and collective resonances), whereas the primary degrees of freedom (quarks and gluons) become visible at the larger virtualities. Through a rich body of new data and several theoretical developments, a unified picture of virtual Compton scattering emerges, which ranges from coherent to incoherent processes, and from the generalized spin polarizabilities on the low-energy side to higher twist effects in deep inelastic lepton scattering.Comment: 32 pages, 19 figures, review articl

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure