When is it democratic to postpone an election? Elections during natural disasters, COVID-19 and emergency situations

Holding regular elections is an essential feature of democratic practices. The case for postponing elections is often made during emergency situations, however. Despite the critical nature of the issue for democracy, peace, and security, there has been sparse academic literature on election postponement. This article provides a new typology of reasons why elections might be delayed to disentangle the causal factors and normative rationale. It distinguishes the humanitarian case for temporary postponements during natural disasters. It then argues that substantive concepts of democracy and electoral integrity, rather than existing international/national laws and standards, should be used to inform decisions about postponement by relevant stakeholders, be it an electoral management body (EMB), government, parliament, or the judiciary. The possible effects of natural disasters on electoral integrity are traced through a comparative analysis of past cases. The article holds that variations in context and the ability of actors to strategically adapt to situations will make the effects contingent. Nonetheless, holding elections during natural disasters will often lead to severely compromised opportunities for deliberation, contestation, participation, and election management quality. There is therefore a strong, democratic case for time-limited postponement. However, the postponement will break institutional certainty, which could pose threats of democratic breakdown-especially in presidential systems. The best available safeguards for electoral integrity during natural disasters include the introduction or expansion of low-tech solutions such as early voting, strengthened risk management, but also transparency and inclusivity in decision making. Overall, there are important lessons for the broader scholarship and practice of democracy during emergency situations

Stable population structure in Europe since the Iron Age, despite high mobility

Ancient DNA research in the past decade has revealed that European population structure changed dramatically in the prehistoric period (14,000–3000 years before present, YBP), reflecting the widespread introduction of Neolithic farmer and Bronze Age Steppe ancestries. However, little is known about how population structure changed from the historical period onward (3000 YBP - present). To address this, we collected whole genomes from 204 individuals from Europe and the Mediterranean, many of which are the first historical period genomes from their region (e.g. Armenia and France). We found that most regions show remarkable inter-individual heterogeneity. At least 7% of historical individuals carry ancestry uncommon in the region where they were sampled, some indicating cross-Mediterranean contacts. Despite this high level of mobility, overall population structure across western Eurasia is relatively stable through the historical period up to the present, mirroring geography. We show that, under standard population genetics models with local panmixia, the observed level of dispersal would lead to a collapse of population structure. Persistent population structure thus suggests a lower effective migration rate than indicated by the observed dispersal. We hypothesize that this phenomenon can be explained by extensive transient dispersal arising from drastically improved transportation networks and the Roman Empire’s mobilization of people for trade, labor, and military. This work highlights the utility of ancient DNA in elucidating finer scale human population dynamics in recent history

Bat Algorithm for Kernel Computation in Fractal Image Reconstruction

Publisher Copyright: © 2019, Springer Nature Switzerland AG.Computer reconstruction of digital images is an important problem in many areas such as image processing, computer vision, medical imaging, sensor systems, robotics, and many others. A very popular approach in that regard is the use of different kernels for various morphological image processing operations such as dilation, erosion, blurring, sharpening, and so on. In this paper, we extend this idea to the reconstruction of digital fractal images. Our proposal is based on a new affine kernel particularly tailored for fractal images. The kernel computes the difference between the source and the reconstructed fractal images, leading to a difficult nonlinear constrained continuous optimization problem, solved by using a powerful nature-inspired metaheuristics for global optimization called the bat algorithm. An illustrative example is used to analyze the performance of this approach. Our experiments show that the method performs quite well but there is also room for further improvement. We conclude that this approach is promising and that it could be a very useful technique for efficient fractal image reconstruction.Akemi Gálvez and Andrés Iglesias acknowledge the financial support from the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 778035, and from the Spanish Ministry of Science, Innovation and Universities (Computer Science National Program) under grant #TIN2017-89275-R of the Agencia Estatal de Investigación and European Funds FEDER (AEI/FEDER, UE). Eneko Osaba and Javier Del Ser would like to thank the Basque Government for its funding support through the EMAITEK program. Acknowledgments. Akemi Gálvez and Andrés Iglesias acknowledge the financial support from the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 778035, and from the Spanish Ministry of Science, Innovation and Universities (Computer Science National Program) under grant #TIN2017-89275-R of the Agencia Estatal de Investigación and European Funds FEDER (AEI/FEDER, UE). Eneko Osaba and Javier Del Ser would like to thank the Basque Government for its funding support through the EMAITEK program.Peer reviewe