7,890 research outputs found
The fragility of decentralised trustless socio-technical systems
The blockchain technology promises to transform finance, money and even governments. However, analyses of blockchain applicability and robustness typically focus on isolated systems whose actors contribute mainly by running the consensus algorithm. Here, we highlight the importance of considering trustless platforms within the broader ecosystem that includes social and communication networks. As an example, we analyse the flash-crash observed on 21st June 2017 in the Ethereum platform and show that a major phenomenon of social coordination led to a catastrophic cascade of events across several interconnected systems. We propose the concept of “emergent centralisation” to describe situations where a single system becomes critically important for the functioning of the whole ecosystem, and argue that such situations are likely to become more and more frequent in interconnected socio-technical systems. We anticipate that the systemic approach we propose will have implications for future assessments of trustless systems and call for the attention of policy-makers on the fragility of our interconnected and rapidly changing world
Fast detection of nonlinearity and nonstationarity in short and noisy time series
We introduce a statistical method to detect nonlinearity and nonstationarity
in time series, that works even for short sequences and in presence of noise.
The method has a discrimination power similar to that of the most advanced
estimators on the market, yet it depends only on one parameter, is easier to
implement and faster. Applications to real data sets reject the null hypothesis
of an underlying stationary linear stochastic process with a higher confidence
interval than the best known nonlinear discriminators up to date.Comment: 5 pages, 6 figure
Modeling Structure and Resilience of the Dark Network
While the statistical and resilience properties of the Internet are no more
changing significantly across time, the Darknet, a network devoted to keep
anonymous its traffic, still experiences rapid changes to improve the security
of its users. Here, we study the structure of the Darknet and we find that its
topology is rather peculiar, being characterized by non-homogenous distribution
of connections -- typical of scale-free networks --, very short path lengths
and high clustering -- typical of small-world networks -- and lack of a core of
highly connected nodes.
We propose a model to reproduce such features, demonstrating that the
mechanisms used to improve cyber-security are responsible for the observed
topology. Unexpectedly, we reveal that its peculiar structure makes the Darknet
much more resilient than the Internet -- used as a benchmark for comparison at
a descriptive level -- to random failures, targeted attacks and cascade
failures, as a result of adaptive changes in response to the attempts of
dismantling the network across time.Comment: 8 pages, 5 figure
Multiscale autocorrelation function: a new approach to anisotropy studies
We present a novel catalog-independent method, based on a scale dependent
approach, to detect anisotropy signatures in the arrival direction distribution
of the ultra highest energy cosmic rays (UHECR). The method provides a good
discrimination power for both large and small data sets, even in presence of
strong contaminating isotropic background. We present some applications to
simulated data sets of events corresponding to plausible scenarios for charged
particles detected by world-wide surface detector-based observatories, in the
last decades.Comment: 18 pages, 9 figure
The Anatomy of a Scientific Rumor
The announcement of the discovery of a Higgs boson-like particle at CERN will
be remembered as one of the milestones of the scientific endeavor of the 21st
century. In this paper we present a study of information spreading processes on
Twitter before, during and after the announcement of the discovery of a new
particle with the features of the elusive Higgs boson on 4th July 2012. We
report evidence for non-trivial spatio-temporal patterns in user activities at
individual and global level, such as tweeting, re-tweeting and replying to
existing tweets. We provide a possible explanation for the observed
time-varying dynamics of user activities during the spreading of this
scientific "rumor". We model the information spreading in the corresponding
network of individuals who posted a tweet related to the Higgs boson discovery.
Finally, we show that we are able to reproduce the global behavior of about
500,000 individuals with remarkable accuracy.Comment: 11 pages, 8 figure
Multilayer Network Modeling of Integrated Biological Systems
Biological systems, from a cell to the human brain, are inherently complex. A
powerful representation of such systems, described by an intricate web of
relationships across multiple scales, is provided by complex networks.
Recently, several studies are highlighting how simple networks -- obtained by
aggregating or neglecting temporal or categorical description of biological
data -- are not able to account for the richness of information characterizing
biological systems. More complex models, namely multilayer networks, are needed
to account for interdependencies, often varying across time, of biological
interacting units within a cell, a tissue or parts of an organism.Comment: 8 pages, Accepted. Comment on "Network Science of Biological Systems
at Different Scales: A Review" by Gosak et al.
(https://doi.org/10.1016/j.plrev.2017.11.003), Physics of Life Reviews (2018
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