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The history of populations or species is of fundamental importance in a variety of areas. Gaining details about demographic, cultural, climatic or political aspects of the past may provide insights that improve the understanding of how populations have evolved over time and how they may evolve in future. Different types of resources can be informative about different periods of time. One especially important resource is genetic data, either from a single individual or a group of organisms. Environmental conditions and circumstances can directly affect the existence and success of a group of individuals. Since genetic material gets passed on from generation to generation, traces of past events can still be detected in today\''s genetic data. For many decades scientists have tried to understand the principles of how external influences can directly affect the appearance and features of populations, leading to theoretical models that can interpret modern day genetic variation in the light of past events. Among other influencing factors like migration, natural selection etc., population size changes can have a great impact on the genetic diversity of a group of organisms. For example, in the field of conservation biology, gaining insights into how the size of a population evolves may assist in detecting past or ongoing temporal reductions of population size. This seems crucial since the reduction in size also correlates with a reduction in genetic diversity which in turn might negatively affect the evolutionary potential of a population. Using computational and population genetics methods, sequences from whole genomes can be scanned for traces of such events and therefore assist in new interpretations of historical details of populations or groups of interest. This thesis focuses on the detection and interpretation of past population size changes. Two approaches to infer particular parameters from underlying demographic models are described. The first part of this thesis introduces two summary statistics which were designed to detect fluctuations in size from genome-wide Single Nucleotide Polymorphism (SNP) data. Demographic inferences from such data are inherently complicated due to recombination and ascertainment bias. Hence, two new statistics are introduced: allele frequency-identity by descent (AF-IBD) and allele frequency-identity by state (AF-IBS). Both make use of linkage disequilibrium information and exhibit defined relationships to the time of the underlying mathematical process. A fast and efficient Approximate Bayesian Computation framework based on AF-IBD and AF-IBS is constructed that can accurately estimate demographic parameters. These two statistics were tested for the biasing effects of hidden recombination events, ascertainment bias and phasing errors. The statistics were found to be robust to a variety of these tested biases. The inference approach was then applied to genome-wide SNP data to infer the demographic histories of two human populations: (i) Yoruba from Africa and (ii) French from Europe. Results suggest, that AF-IBD and AF-IBS are able to capture sufficient amounts of information from underlying data sets in order to accurately infer parameters of interest, such as the beginning, end and strength of periods of varying size. Additionally the results from empirical data suggest a rather stable ancestral population size with a mild recent expansion for Yoruba, whereas the French apparently experienced a rather long-lasting strong bottleneck followed by a drastic population growth. The second part of this thesis introduces a new way of summarizing information from the site frequency spectrum. Commonly applied site frequency spectrum based inference methods make use of allele frequency information from individual segregating sites. Our newly developed method, the 2 point spectrum, summarizes allele frequency information from all possible pairs of segregating sites, thereby increasing the number of potentially informative values from the same underlying data set. These additional information are then incorporated into a Markov Chain Monte Carlo framework. This allows for a high degree of flexibility and implements an efficient method to infer population size trajectories over time. We tested the method on a variety of different simulated data sets from underlying demographic models. Furthermore, we compared the performance and accuracy of our method to already established methods like PSMC and diCal. Results indicate that this non-parametric 2 point spectrum method can accurately infer the extent and times of past population size changes and therefore correctly estimates the history of temporal size fluctuations. Furthermore, the initial results suggest that the amount of required data and the accuracy of the final results are comparable with other publicly available non-parametric methods. An easy to use command line program was implemented and will be made publicly available. In summary, we introduced three highly sensitive summary statistics and proposed different approaches to infer parameters from demographic models of interest. Both methods provide powerful frameworks for accurate parameter inference from genome-wide genetic data. They were tested for a variety of demographic models and provide highly accurate results. They may be used in the settings as described above or incorporated into already existing inference frameworks. Nevertheless, the statistics should prove useful for new insights into populations, especially those with complex demographic histories.

AbstractTo the best of my knowledge, the minimum wage's influence on poverty in Russia has never been investigated. Russian data provide a unique opportunity for studying the effects of the minimum wage on poverty, given significant increases in the minimum wage in recent years, almost complete coverage, and a high representation of full‐time workers in poor households. This article examines the effect of the minimum wage on the incidence of poverty and the transitions into and out of poverty in Russia using data from the Russia Longitudinal Monitoring Survey of Higher School of Economics (RLMS‐HSE) from 2006 to 2011. The results indicate that the minimum wage in Russia had moderate poverty‐reducing effects.

Although sample selection bias is a frequent problem of applied research, there has been no generalization of sample selection models with binary dependent variables of interest to data with temporal error correlations. We suggest a generalized estimating equation approach to panel data selection models, considering binary responses in both equations. We demonstrate the utility of this model by a simulation study and by analyzing highly unbalanced annual panel data taken from the German Socio-Economic Panel Study covering two decades of Green party support.

In this rapidly growing world, communication involves transfer of confidential information across the globe within a short amount of time. This kind of technical communication involves network of communicating devices. These devices will be carrying the confidential data across the devices which may be far apart. In military environment, communication disruption can occur because of intermittent network connectivity. Solution found for these kinds of disruption is the Disruption Tolerant Network (DTN) technology. This technology allows the soldiers to reliably access the data by using the capacity of the external storage nodes. Most challenging issue found in this DTN technology is enforcement of authorization policies and update of policies for the retrieval of confidential data. CP-ABE is one such cryptographic method which provides the solution to the access control issues. But there exists several problems with regard to key escrow, attribute revocation and coordination of attributes which are issued by different key authorities when applying CP-ABE in decentralized DTNs. In this paper, more secured method for the retrieval of confidential data using CP-ABE for decentralized DTNs is proposed where sets of attributes will be generated and managed by multiple authorities independently and addresses several existing problem

The Black Box Society was one of first scholarly accounts to propose a social theory of the use of data in constructing personal reputations, new media audiences, and financial power, by illuminating recurrent patterns of power and exploitation in the digital economy. While many corporations have a direct window into our lives through constant, ubiquitous data collection, our knowledge of their inner workings is often partial and incomplete. Closely guarded by private companies and inaccessible to most researchers or the broader public, too much algorithmic decision-making remains a black box to this day. Much has happened since 2015 that vindicates and challenges the book's main themes. To answer many of the concerns raised in the volume in light of the most recent developments, we have brought together leading thinkers who have explored the interplay of politics, economics, and culture in domains ordered algorithmically by managers, bureaucrats, and technology workers. While the contributions are diverse, a unifying theme animates them. Each offers a sophisticated critique of the interplay between state and market forces in building or eroding the many layers of our common lives, as well as the privatization of spheres of reputation, search, and finance. Unsatisfied with narrow methodologies of economics or political science, they advance politico-economic analysis. They therefore succeed in unveiling the foundational role that the turn to big data has in organizing economic and social relations. ; https://doi.org/10.1177/2053951720935146

Emission of greenhouse gases (GHGs) and removals from land, including both anthropogenic and natural fluxes, require reliable quantification, including estimates of uncertainties, to support credible mitigation action under the Paris Agreement. This study provides a state-of-the-art scientific overview of bottom-up anthropogenic emissions data from agriculture, forestry and other land use (AFOLU) in the European Union (EU281). The data integrate recent AFOLU emission inventories with ecosystem data and land carbon models and summarize GHG emissions and removals over the period 1990–2016. This compilation of bottom-up estimates of the AFOLU GHG emissions of European national greenhouse gas inventories (NGHGIs), with those of land carbon models and observation-based estimates of large-scale GHG fluxes, aims at improving the overall estimates of the GHG balance in Europe with respect to land GHG emissions and removals. Whenever available, we present uncertainties, its propagation and role in the comparison of different estimates. While NGHGI data for the EU28 provide consistent quantification of uncertainty following the established IPCC Guidelines, uncertainty in the estimates produced with other methods needs to account for both within model uncertainty and the spread from different model results. The largest inconsistencies between EU28 estimates are mainly due to different sources of data related to human activity, referred to here as activity data (AD) and methodologies (tiers) used for calculating emissions and removals from AFOLU sectors. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.3662371 (Petrescu et al., 2020). ; publishedVersion

The COVID-19 pandemic has bringing the "infodemic" in the social media worlds. Various social platforms play a significant role in instantly acquiring the latest updates of the pandemic. Social media such as Twitter and Facebook produce vast amounts of posts related to the virus, vaccines, economics, and politics. In order to figure out how public opinion and sentiments are expressed during the pandemic, this work analyzes the long-term social posts from social media and conducts sentiment analysis on tweets within 12 months. Our findings show the trend topics of long-term social communities during the pandemic and express people's attitudes towards progress of major actions during the pandemic. We explore the main topics during the prolonged pandemic, including information surrounding economics, vaccines, and politics. Besides, we show the differences in gender-based attitudes and propose future research questions refer to the "infodemic". We believe that our work contributes to attracting public attention to the "infodemic" of the social crisis.

The extent to which word learning is delayed by maturation as opposed to accumulating data is a longstanding question in language acquisition. Further, the precise way in which data influence learning on a large scale is unknown—experimental results reveal that children can rapidly learn words from single instances as well as by aggregating ambiguous information across multiple situations. We analyze Wordbank, a large cross-linguistic dataset of word acquisition norms, using a statistical waiting time model to quantify the role of data in early language learning, building off Hidaka ( 2013 ). We find that the model both fits and accurately predicts the shape of children's growth curves. Further analyses of model parameters suggest a primarily data-driven account of early word learning. The parameters of the model directly characterize both the amount of data required and the rate at which informative data occurs. With high statistical certainty, words require on the order of ∼ 10 learning instances, which occur on average once every two months. Our method is extremely simple, statistically principled, and broadly applicable to modeling data-driven learning effects in development.

Any nation on the planet that wishes to achieve meaningful success must embark on developmental endeavors. They are responsible for maintaining a peaceful sociopolitical environment for their people. It does, however, differ significantly by region and place. Utilizing democratic jargon in news bulletins helps to criticize the stance and provide the public with literary satire. The aim of this research is to show the impact of 'Open Data' on the development of news bulletins. The OD analysis helps news marketers to ascertain the degree to which their bulletin captures the interest of audiences. Pakistan's economy is insufficient to provide the standard of security needed by its citizens. Numerous strategies have been emphasized to achieve this aim, enabling the government to prioritize its people' health. The open data scheme, which allows widespread access to databases, is one of these crucial advancements within Pakistan's architecture. There are online repositories that motivate their users to achieve greater success in existence. It improves their ability to read and comprehend and teaches them to think creatively. Additionally, it allows them to hone their ability to learn and make the requisite compromises to maximize the potential of their talents. Although there is some pressure to use private data sources, it is critical to remember that not all data is safe. As a consequence, individuals must capture and use this type of clear data. The thesis evaluates the benefits and drawbacks of using current data systems inside the newsroom in order to determine their effect on Pakistan's political communication. The use of open data in news analysis allows journalists to work more efficiently; it is important to have immediate access to each item of news. In other words, these structures allow national institutions to operate with a high degree of accountability. Thus, the possibility of encountering an element of inequality in a world capable of wreaking havoc is extremely remote. Open data models are important for innovation and creativity to accelerate. It has been shown that it was instrumental in establishing a trend of revolutionary development that was fuelled by the provision of these facilities. Five privately owned news organizations with the highest scores were selected to gather data. A critical conversation was held to ascertain the words they used to attract interest.