Human Rights Treaty Commitment and Compliance: A Machine Learning-based Causal Inference Approach

Why do states ratify international human rights treaties? How much do human rights treaties influence state behaviors directly and indirectly? Why are some human rights treaty monitoring procedures more effective than others? What are the most predictively and causally important factors that can reduce and prevent state repression and human rights violations? This dissertation provide answers to these keys causal questions in political science research, using a novel approach that combines machine learning and the structural causal model framework. The four research questions are arranged in a chronological order that refects the causal process relating to international human rights treaties, going from (a) the causal determinants of treaty ratification to (b) the causal mechanisms of human rights treaties to (c) the causal effects of human rights treaty monitoring procedures to (d) other factors that causally influence human rights violations. Chapter 1 identifies the research traditions within which this dissertation is located, offers an overview of the methodological advances that enable this research, specifies the research questions, and previews the findings. Chapters 2, 3, 4, and 5 present in chronological order four empirical studies that answer these four research questions. Finally, Chapter 6 summarizes the substantive findings, suggests some other research questions that could be similarly investigated, and recaps the methodological approach and the contributions of the dissertation

Chemical enrichment of the pre-solar cloud by supernova dust grains

The presence of short-lived radioisotopes (SLRs) in solar system meteorites has been interpreted as evidence that the solar system was exposed to a supernova shortly before or during its formation. Yet results from hydrodynamical models of SLR injection into the proto-solar cloud or disc suggest that gas-phase mixing may not be efficient enough to reproduce the observed abundances. As an alternative, we explore the injection of SLRs via dust grains as a way to overcome the mixing barrier. We numerically model the interaction of a supernova remnant containing SLR-rich dust grains with a nearby molecular cloud. The dust grains are subject to drag forces and both thermal and non-thermal sputtering. We confirm that the expanding gas shell stalls upon impact with the dense cloud and that gas-phase SLR injection occurs slowly due to hydrodynamical instabilities at the cloud surface. In contrast, dust grains of sufficient size (> 1 micron) decouple from the gas and penetrate into the cloud within 0.1 Myr. Once inside the cloud, the dust grains are destroyed by sputtering, releasing SLRs and rapidly enriching the dense (potentially star-forming) regions. Our results suggest that SLR transport on dust grains is a viable mechanism to explain SLR enrichment.Comment: 15 pages, 10 figures, Accepted for publication in MNRAS. Movies can be found here: http://user.physics.unc.edu/~mdgood86/research.htm

Introducing User Feedback-based Counterfactual Explanations (UFCE)

Machine learning models are widely used in real-world applications. However, their complexity makes it often challenging to interpret the rationale behind their decisions. Counterfactual explanations (CEs) have emerged as a viable solution for generating comprehensible explanations in eXplainable Artificial Intelligence (XAI). CE provides actionable information to users on how to achieve the desired outcome with minimal modifications to the input. However, current CE algorithms usually operate within the entire feature space when optimizing changes to turn over an undesired outcome, overlooking the identification of key contributors to the outcome and disregarding the practicality of the suggested changes. In this study, we introduce a novel methodology, that is named as user feedback-based counterfactual explanation (UFCE), which addresses these limitations and aims to bolster confidence in the provided explanations. UFCE allows for the inclusion of user constraints to determine the smallest modifications in the subset of actionable features while considering feature dependence, and evaluates the practicality of suggested changes using benchmark evaluation metrics. We conducted three experiments with five datasets, demonstrating that UFCE outperforms two well-known CE methods in terms of \textit{proximity}, \textit{sparsity}, and \textit{feasibility}. Reported results indicate that user constraints influence the generation of feasible CEs.Comment: preprint of paper submitted to IJCIS Springe

A Survey of Social Network Forensics

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks

Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

Black Hole Formation in the First Stellar Clusters

The early Universe was composed almost entirely of hydrogen and helium, with only trace amounts of heavy elements. It was only after the first generation of star formation that the Universe became sufficiently polluted to produce a second generation (Population II) of stars which are similar to those in our local Universe. Evidence of massive star cluster formation is nearly ubiquitous among the observed galaxy population and if this mode of star formation occurred at early enough epochs, the higher densities in the early Universe may have caused many of the stars in the cluster to strongly interact. In this scenario, it may be possible to form a very massive star by repeated stellar collisions that may directly collapse into a black hole and form a supermassive black hole seed. In this chapter, we will explore this scenario in detail to understand the dynamics which allow for this process to ensue and measure the probability for this type of seed to represent the supermassive black hole population observed at z > 6.Comment: Preprint of the chapter "Black Hole Formation in the First Stellar Clusters", to be published in the review volume "Formation of the First Black Holes", Latif, M. and Schleicher, D. R. G., eds., World Scientific Publishing Company, 2018, pp 125-143 [see https://www.worldscientific.com/worldscibooks/10.1142/10652

A Time Series Analysis: Exploring the Link between Human Activity and Blood Glucose Fluctuation

In this thesis, time series models are developed to explore the correlates of blood glucose (BG) fluctuation of diabetic patients. In particular, it is investigated whether certain human activities and lifestyle events (e.g. food and medication consumption, physical activity, travel and social interaction) influence BG, and if so, how. A unique dataset is utilized consisting of 40 diabetic patients who participated in a 3-day study involving continuous monitoring of blood glucose (BG) at five minute intervals, combined with measures for sugar; carbohydrate; calorie and insulin intake; physical activity; distance from home; time spent traveling via public transit and private automobile; and time spent with other people, dining and shopping. Using a dynamic regression model fitted with autoregressive integrated moving average (ARIMA) components, the influence of independent predictive variables on BG levels is quantified, while at the same time the impact of unknown factors is defined by an error term. Models were developed for individuals with overall findings demonstrating the potential for continuous monitoring of diabetic (DM) patients who are trying to control their BG. Model results produced significant BG predicting variables that include food consumption, exogenous insulin administration and physical activity

Discovering Causal Relations and Equations from Data

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.Comment: 137 page

Organizāciju mācīšanās un cilvēkresursu vadīšanas ietekme uz organizāciju sasniegumiem: Austrijas uzņēmumu piemērs

Promocijas darba mērķis ir izstrādāt saikņu modeli starp teorētiskajiem konstruktiem ar organizāciju mācīšanās objektu un organizāciju mācīšanās priekšmetu un cilvēkresursu vadību, ko arvien vairāk uzskata par galvenajiem ilgstošas konkurences priekšrocību atbalsta elementiem organizācijās, kuras veic uzņēmējdarbību. Pētniecības plānā ir iekļauta metožu triangulācija, tostarp literatūras metaanalīze, pirmsizpēte, empīriskie dati un aprakstošā analīze, kā arī pētījumu novērtējums. Pētniecības ieteikumu praktiskā īstenošana tiek apspriesta, izmantojot starptautiska rūpniecības uzņēmuma labākās prakses piemēru. Galvenie secinājumi ir, ka ekonomisko sniegumu nevar uzskatīt par organizāciju mācīšanās prognozētāju, un cilvēkresursu vadība pozitīvi neietekmē ekonomiskos rādītājus tiešā veidā. Pierādīta galvenā hipotēze, ka organizāciju mācīšanās pozitīvi ietekmē ekonomisko sniegumu un konkurētspēju.The purpose of the thesis is to develop a model of the linkages between theoretical constructs with the object of organizational learning and the subject of organizational learning and human resource management which are increasingly perceived as key elements in supporting lasting competitive advantage in business organizations. The research roadmap incorporates method triangulation including literature meta-analysis, pre-study, empirical data and descriptive analysis, and research evaluation. Practical implementation of research suggestions is discussed via a best-practice-example of an international industrial business enterprise. Main conclusions include that economic performance cannot be seen as predictor for organizational learning and human resource management does not directly positively influence economic performance. The main hypothesis that organizational learning positively influences economic performance and competitive capacity can be substantiated