85 research outputs found
Anomalous segregation dynamics of self-propelled particles
A number of novel experimental and theoretical results have recently been
obtained on active soft matter, demonstrating the various interesting universal
and anomalous features of this kind of driven systems. Here we consider a
fundamental but still unexplored aspect of the patterns arising in the system
of actively moving units, i.e., their segregation taking place when two kinds
of them with different adhesive properties are present. The process of
segregation is studied by a model made of self-propelled particles such that
the particles have a tendency to adhere only to those which are of the same
kind. The calculations corresponding to the related differential equations can
be made in parallel, thus a powerful GPU card allows large scale simulations.
We find that the segregation kinetics is very different from the non-driven
counterparts and is described by the new scaling exponents and
for the 1:1 and the non-equal ratio of the two constituents,
respectively. Our results are in agreement with a recent observation of
segregating tissue cells \emph{in vitro}
The Role of Gender in Social Network Organization
The digital traces we leave behind when engaging with the modern world offer
an interesting lens through which we study behavioral patterns as expression of
gender. Although gender differentiation has been observed in a number of
settings, the majority of studies focus on a single data stream in isolation.
Here we use a dataset of high resolution data collected using mobile phones, as
well as detailed questionnaires, to study gender differences in a large cohort.
We consider mobility behavior and individual personality traits among a group
of more than university students. We also investigate interactions among
them expressed via person-to-person contacts, interactions on online social
networks, and telecommunication. Thus, we are able to study the differences
between male and female behavior captured through a multitude of channels for a
single cohort. We find that while the two genders are similar in a number of
aspects, there are robust deviations that include multiple facets of social
interactions, suggesting the existence of inherent behavioral differences.
Finally, we quantify how aspects of an individual's characteristics and social
behavior reveals their gender by posing it as a classification problem. We ask:
How well can we distinguish between male and female study participants based on
behavior alone? Which behavioral features are most predictive
Shock waves on complex networks
Power grids, road maps, and river streams are examples of infrastructural
networks which are highly vulnerable to external perturbations. An abrupt local
change of load (voltage, traffic density, or water level) might propagate in a
cascading way and affect a significant fraction of the network. Almost
discontinuous perturbations can be modeled by shock waves which can eventually
interfere constructively and endanger the normal functionality of the
infrastructure. We study their dynamics by solving the Burgers equation under
random perturbations on several real and artificial directed graphs. Even for
graphs with a narrow distribution of node properties (e.g., degree or
betweenness), a steady state is reached exhibiting a heterogeneous load
distribution, having a difference of one order of magnitude between the highest
and average loads. Unexpectedly we find for the European power grid and for
finite Watts-Strogatz networks a broad pronounced bimodal distribution for the
loads. To identify the most vulnerable nodes, we introduce the concept of
node-basin size, a purely topological property which we show to be strongly
correlated to the average load of a node
The effectiveness of backward contact tracing in networks
Discovering and isolating infected individuals is a cornerstone of epidemic
control. Because many infectious diseases spread through close contacts,
contact tracing is a key tool for case discovery and control. However, although
contact tracing has been performed widely, the mathematical understanding of
contact tracing has not been fully established and it has not been clearly
understood what determines the efficacy of contact tracing. Here, we reveal
that, compared with "forward" tracing---tracing to whom disease spreads,
"backward" tracing---tracing from whom disease spreads---is profoundly more
effective. The effectiveness of backward tracing is due to simple but
overlooked biases arising from the heterogeneity in contacts. Using simulations
on both synthetic and high-resolution empirical contact datasets, we show that
even at a small probability of detecting infected individuals, strategically
executed contact tracing can prevent a significant fraction of further
transmissions. We also show that---in terms of the number of prevented
transmissions per isolation---case isolation combined with a small amount of
contact tracing is more efficient than case isolation alone. By demonstrating
that backward contact tracing is highly effective at discovering
super-spreading events, we argue that the potential effectiveness of contact
tracing has been underestimated. Therefore, there is a critical need for
revisiting current contact tracing strategies so that they leverage all forms
of biases. Our results also have important consequences for digital contact
tracing because it will be crucial to incorporate the capability for backward
and deep tracing while adhering to the privacy-preserving requirements of these
new platforms.Comment: 15 pages, 4 figure
Class attendance, peer similarity, and academic performance in a large field study
Identifying the factors that determine academic performance is an essential
part of educational research. Existing research indicates that class attendance
is a useful predictor of subsequent course achievements. The majority of the
literature is, however, based on surveys and self-reports, methods which have
well-known systematic biases that lead to limitations on conclusions and
generalizability as well as being costly to implement. Here we propose a novel
method for measuring class attendance that overcomes these limitations by using
location and bluetooth data collected from smartphone sensors. Based on
measured attendance data of nearly 1,000 undergraduate students, we demonstrate
that early and consistent class attendance strongly correlates with academic
performance. In addition, our novel dataset allows us to determine that
attendance among social peers was substantially correlated (0.5), suggesting
either an important peer effect or homophily with respect to attendance
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
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