Scalable Privacy-Compliant Virality Prediction on Twitter

The digital town hall of Twitter becomes a preferred medium of communication for individuals and organizations across the globe. Some of them reach audiences of millions, while others struggle to get noticed. Given the impact of social media, the question remains more relevant than ever: how to model the dynamics of attention in Twitter. Researchers around the world turn to machine learning to predict the most influential tweets and authors, navigating the volume, velocity, and variety of social big data, with many compromises. In this paper, we revisit content popularity prediction on Twitter. We argue that strict alignment of data acquisition, storage and analysis algorithms is necessary to avoid the common trade-offs between scalability, accuracy and privacy compliance. We propose a new framework for the rapid acquisition of large-scale datasets, high accuracy supervisory signal and multilanguage sentiment prediction while respecting every privacy request applicable. We then apply a novel gradient boosting framework to achieve state-of-the-art results in virality ranking, already before including tweet's visual or propagation features. Our Gradient Boosted Regression Tree is the first to offer explainable, strong ranking performance on benchmark datasets. Since the analysis focused on features available early, the model is immediately applicable to incoming tweets in 18 languages.Comment: AffCon@AAAI-19 Best Paper Award; Presented at AAAI-19 W1: Affective Content Analysi

Characteristics of a future aeronautical satellite communications system

A possible operational system scenario for providing satellite communications services to the future aviation community was analyzed. The system concept relies on a Ka-band (20/30 GHz) satellite that utilizes multibeam antenna (MBA) technology. The aircraft terminal uses an extremely small aperture antenna as a result of using this higher spectrum at Ka-band. The satellite functions as a relay between the aircraft and the ground stations. The ground stations function as interfaces to the existing terrestrial networks such as the Public Service Telephone Network (PSTN). Various system tradeoffs are first examined to ensure optimized system parameters. High level performance specifications and design approaches are generated for the space, ground, and aeronautical elements in the system. Both technical and economical issues affecting the feasibility of the studied concept are addressed with the 1995 timeframe in mind

Reflexive self-organization and path dependency in institutionalization processes

The purpose of this paper is to work toward developing evolutionary reasoning in the social sciences. Along with that, we argue to overcome the artificial divide of natural and social science for the sake of understanding behaviour. We make the case for an evolutionary and culturally sensitive view on longsurviving institutions and its base - individual behaviour. By taking into consideration the unsatisfying answers in the debate on structure and agency, we emphasize the importance of resonance for evolution and stability. We use case studies to make the point for an evolutionary understanding of institutions and to reflect on institutional path dependency.Institutionalizion, behavioural and institutional path dependancy, reflexive self-organization, historic institutionalism, methodological individualism

Recommendations for Future Efforts in RANS Modeling and Simulation

The roadmap laid out in the CFD Vision 2030 document suggests that a decision to move away from RANS research needs to be made in the current timeframe (around 2020). This paper outlines industry requirements for improved predictions of turbulent flows and the cost-barrier that is often associated with reliance on scale resolving methods. Capabilities of RANS model accuracy for simple and complex flow flow fields are assessed, and modeling practices that degrade predictive accuracy are identified. Suggested research topics are identified that have the potential to improve the applicability and accuracy of RANS models. We conclude that it is important that some part of a balanced turbulence modeling research portfolio should include RANS efforts

REASONING ON EVOLUTION OF CULTURE AND STRUCTURE

The purpose of this paper is to work toward developing evolutionary reasoning in the social sciences. Along with that, we argue to overcome the artificial divide of natural and social science for the sake of understanding behaviour. We make the case for an evolutionary and culturally sensitive view on long-surviving institutions and its base - individual behaviour. By taking into consideration the unsatisfying answers in the debate on structure and agency, we emphasize the importance of resonance for evolution and stability. We use case studies to make the point for an evolutionary understanding of institutions and to reflect on institutional path dependency.economic growth, sustainable growth, development, sustainability