9 research outputs found
Complexity of the COVID-19 pandemic in Maringa
While extensive literature exists on the COVID-19 pandemic at regional and
national levels, understanding its dynamics and consequences at the city level
remains limited. This study investigates the pandemic in Maring\'a, a
medium-sized city in Brazil's South Region, using data obtained by actively
monitoring the disease from March 2020 to June 2022. Despite prompt and robust
interventions, COVID-19 cases increased exponentially during the early spread
of COVID-19, with a reproduction number lower than that observed during the
initial outbreak in Wuhan. Our research demonstrates the remarkable impact of
non-pharmaceutical interventions on both mobility and pandemic indicators,
particularly during the onset and the most severe phases of the emergency.
However, our results suggest that the city's measures were primarily reactive
rather than proactive. Maring\'a faced six waves of cases, with the third and
fourth waves being the deadliest, responsible for over two-thirds of all deaths
and overwhelming the local healthcare system. Excess mortality during this
period exceeded deaths attributed to COVID-19, indicating that the burdened
healthcare system may have contributed to increased mortality from other
causes. By the end of the fourth wave, nearly three-quarters of the city's
population had received two vaccine doses, significantly decreasing deaths
despite the surge caused by the Omicron variant. Finally, we compare these
findings with the national context and other similarly sized cities,
highlighting substantial heterogeneities in the spread and impact of the
disease.Comment: 20 pages, 5 figures, supplementary information; accepted for
publication in Scientific Report
Deep Learning Criminal Networks
Recent advances in deep learning methods have enabled researchers to develop
and apply algorithms for the analysis and modeling of complex networks. These
advances have sparked a surge of interest at the interface between network
science and machine learning. Despite this, the use of machine learning methods
to investigate criminal networks remains surprisingly scarce. Here, we explore
the potential of graph convolutional networks to learn patterns among networked
criminals and to predict various properties of criminal networks. Using
empirical data from political corruption, criminal police intelligence, and
criminal financial networks, we develop a series of deep learning models based
on the GraphSAGE framework that are capable to recover missing criminal
partnerships, distinguish among types of associations, predict the amount of
money exchanged among criminal agents, and even anticipate partnerships and
recidivism of criminals during the growth dynamics of corruption networks, all
with impressive accuracy. Our deep learning models significantly outperform
previous shallow learning approaches and produce high-quality embeddings for
node and edge properties. Moreover, these models inherit all the advantages of
the GraphSAGE framework, including the generalization to unseen nodes and
scaling up to large graph structures.Comment: 14 two-column pages, 5 figure
Age and market capitalization drive large price variations of cryptocurrencies
Abstract Cryptocurrencies are considered the latest innovation in finance with considerable impact across social, technological, and economic dimensions. This new class of financial assets has also motivated a myriad of scientific investigations focused on understanding their statistical properties, such as the distribution of price returns. However, research so far has only considered Bitcoin or at most a few cryptocurrencies, whilst ignoring that price returns might depend on cryptocurrency age or be influenced by market capitalization. Here, we therefore present a comprehensive investigation of large price variations for more than seven thousand digital currencies and explore whether price returns change with the coming-of-age and growth of the cryptocurrency market. We find that tail distributions of price returns follow power-law functions over the entire history of the considered cryptocurrency portfolio, with typical exponents implying the absence of characteristic scales for price variations in about half of them. Moreover, these tail distributions are asymmetric as positive returns more often display smaller exponents, indicating that large positive price variations are more likely than negative ones. Our results further reveal that changes in the tail exponents are very often simultaneously related to cryptocurrency age and market capitalization or only to age, with only a minority of cryptoassets being affected just by market capitalization or neither of the two quantities. Lastly, we find that the trends in power-law exponents usually point to mixed directions, and that large price variations are likely to become less frequent only in about 28% of the cryptocurrencies as they age and grow in market capitalization
Clustering free-falling paper motion with complexity and entropy
Many simple natural phenomena are characterized by complex motion that appears random at first glance, but that often displays underlying patterns and behavior that can be clustered in groups. The movement of small pieces of paper falling through the air is one of these systems whose complete mathematical description seems unworkable. Understanding these types of motion thus demands automated experimentation capable of producing large datasets covering different behaviors —a task that has become feasible only recently with advances in computer vision and machine learning methods. Here we use one of these datasets related to the motion of free-falling paper with different shapes to propose an information-theoretical approach that automatically clusters different types of behavior. We evaluate the permutation entropy and statistical complexity from time series related to the observable area of free-falling paper pieces captured by a video camera. We find that chaotic and tumbling motions have a distinct average degree of entropy and complexity, allowing us to accurately discriminate between these two types of behavior with a simple unsupervised machine learning algorithm. Our method has a performance comparable to other approaches based on physical quantities but does not depend on reconstructing the three-dimensional falling trajectory
Unequal P distribution in nanowires and the planar layer during GaAsP growth on GaAs {111}B by metal-organic chemical vapor deposition
In this study, the behavior of P incorporation GaAsP during ternary nanowires epitaxial growth is investigated. Detailed electron microscopy investigations indicate that (1) the P concentration in the nanowires is higher than that in the simultaneously grown planar layer and (2) the higher growth temperature leads to a higher P concentration in ternary nanowires. We anticipate that the minimization of misfit strain between the GaAsP layer and its underlying GaAs substrate and the complexity of precursor decomposition are responsible for the observed varied P concentrations. These findings implicate that the compositional control in ternary GaAsP nanowires is much more complicated than anticipated