Wikipedia and Digital Currencies: Interplay Between Collective Attention and Market Performance

The production and consumption of information about Bitcoin and other digital-, or 'crypto'-, currencies have grown together with their market capitalisation. However, a systematic investigation of the relationship between online attention and market dynamics, across multiple digital currencies, is still lacking. Here, we quantify the interplay between the attention towards digital currencies in Wikipedia and their market performance. We consider the entire edit history of currency-related pages, and their view history from July 2015. First, we quantify the evolution of the cryptocurrency presence in Wikipedia by analysing the editorial activity and the network of co-edited pages. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Then, we show that a simple trading strategy informed by Wikipedia views performs better, in terms of returns on investment, than classic baseline strategies for most of the covered period. Our results contribute to the recent literature on the interplay between online information and investment markets, and we anticipate it will be of interest for researchers as well as investors

Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyer

Empirical analysis is often the first step towards the birth of a conjecture. This is the case of the Birch-Swinnerton-Dyer (BSD) Conjecture describing the rational points on an elliptic curve, one of the most celebrated unsolved problems in mathematics. Here we extend the original empirical approach, to the analysis of the Cremona database of quantities relevant to BSD, inspecting more than 2.5 million elliptic curves by means of the latest techniques in data science, machine-learning and topological data analysis. Key quantities such as rank, Weierstrass coefficients, period, conductor, Tamagawa number, regulator and order of the Tate-Shafarevich group give rise to a high-dimensional point-cloud whose statistical properties we investigate. We reveal patterns and distributions in the rank versus Weierstrass coefficients, as well as the Beta distribution of the BSD ratio of the quantities. Via gradient boosted trees, machine learning is applied in finding inter-correlation amongst the various quantities. We anticipate that our approach will spark further research on the statistical properties of large datasets in Number Theory and more in general in pure Mathematics

Collective Dynamics of Dark Web Marketplaces

Dark markets are commercial websites that use Bitcoin to sell or broker transactions involving drugs, weapons, and other illicit goods. Being illegal, they do not offer any user protection, and several police raids and scams have caused large losses to both customers and vendors over the past years. However, this uncertainty has not prevented a steady growth of the dark market phenomenon and a proliferation of new markets. The origin of this resilience have remained unclear so far, also due to the difficulty of identifying relevant Bitcoin transaction data. Here, we investigate how the dark market ecosystem re-organises following the disappearance of a market, due to factors including raids and scams. To do so, we analyse 24 episodes of unexpected market closure through a novel datasets of 133 million Bitcoin transactions involving 31 dark markets and their users, totalling 4 billion USD. We show that coordinated user migration from the closed market to coexisting markets guarantees overall systemic resilience beyond the intrinsic fragility of individual markets. The migration is swift, efficient and common to all market closures. We find that migrants are on average more active users in comparison to non-migrants and move preferentially towards the coexisting market with the highest trading volume. Our findings shed light on the resilience of the dark market ecosystem and we anticipate that they may inform future research on the self-organisation of emerging online markets

Individual mobility and social behaviour: Two sides of the same coin

According to personality psychology, personality traits determine many aspects of human behaviour. However, validating this insight in large groups has been challenging so far, due to the scarcity of multi-channel data. Here, we focus on the relationship between mobility and social behaviour by analysing two high-resolution longitudinal datasets collecting trajectories and mobile phone interactions of $\sim 1000$ individuals. We show that there is a connection between the way in which individuals explore new resources and exploit known assets in the social and spatial spheres. We point out that different individuals balance the exploration-exploitation trade-off in different ways and we explain part of the variability in the data by the big five personality traits. We find that, in both realms, extraversion correlates with an individual's attitude towards exploration and routine diversity, while neuroticism and openness account for the tendency to evolve routine over long time-scales. We find no evidence for the existence of classes of individuals across the spatio-social domains. Our results bridge the fields of human geography, sociology and personality psychology and can help improve current models of mobility and tie formation

Temporal and cultural limits of privacy in smartphone app usage

Large-scale collection of human behavioral data by companies raises serious privacy concerns. We show that behavior captured in the form of application usage data collected from smartphones is highly unique even in very large datasets encompassing millions of individuals. This makes behavior-based re-identification of users across datasets possible. We study 12 months of data from 3.5 million users and show that four apps are enough to uniquely re-identify 91.2% of users using a simple strategy based on public information. Furthermore, we show that there is seasonal variability in uniqueness and that application usage fingerprints drift over time at an average constant rate

Machine Learning the Cryptocurrency Market

Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for $1,681$ cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that non-trivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market

Relação entre a matriz de liderança e a capacitação de enfermeiros: estudo de caso em um hospital privado do estado de Santa Catarina

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Sócio-Econômico. Programa de Pós-Graduação em Administração.O presente estudo teve como objetivo caracterizar a matriz de liderança e a capacitação apresentada pelos enfermeiros chefes de setor de um hospital privado, de grande porte do estado de Santa Catarina. Trata-se de um estudo de casos, que apresenta uma abordagem quantitativa e qualitativa. A população envolvida integra enfermeiros em posição de liderança com uma amostra composta de 28 enfermeiros selecionados através de sorteio. Os dados para a realização do estudo foram coletados através de fontes primárias (entrevista semi-estruturada e questionário aplicados junto aos enfermeiros envolvidos no processo de chefiar as unidades do hospital). O estudo demonstrou que o estilo de liderança adotado é na maioria das vezes autocrático, seguido de predominância democrática, e de uma pequena parcela da amostra com características de co-participação e integratividade. De acordo com os resultados, a capacitação dos enfermeiros é privilegiada na sua maioria na área técnica e há deficiências na capacitação dos enfermeiros na área administrativa, bem como, visualiza-se um desajuste entre a área de capacitação e a área de atuação. As conclusões do estudo sugerem haver necessidade de remodelar os métodos de liderar a equipe, de modo a visualizar novas atitudes e relações menos diretivas, mais participativas e integrativas. O objeto final deste estudo se concretiza na reflexão sobre a necessidade de mudança de paradigmas e de relações inovadoras no agir do enfermeiro, enquanto coordenador da assistência de enfermagem, sempre em busca da especialização, no intuito desenvolver sua liderança

Individual mobility in context: from high resolution trajectories to social behaviour

Understanding human mobility can help creating solutions to society-wide issues, from urban planning and traffic forecasting, to the modelling of epidemics. Existing studies have shown that knowledge on how single individuals take spatial decisions is fundamental for modelling collective mobility patterns. However, individual mobility remains poorly understood, also due to the lack of suitable data. In this thesis, we use novel datasets to characterize and model mobility in relation to other individual aspects: social behaviour, personality, and demographic attributes. Our study focuses on mobility across unprecedented spatial ranges, from ~ 10 m to ~ 10000 Km, and temporal scales, from seconds to years