8,624 research outputs found
Approximate Majority with Catalytic Inputs
Population protocols are a class of algorithms for modeling distributed
computation in networks of finite-state agents communicating through pairwise
interactions. Their suitability for analyzing numerous chemical processes has
motivated the adaptation of the original population protocol framework to
better model these chemical systems. In this paper, we further the study of two
such adaptations in the context of solving approximate majority:
persistent-state agents (or catalysts) and spontaneous state changes (or
leaks).
Based on models considered in recent protocols for populations with
persistent-state agents, we assume a population with catalytic input agents
and worker agents, and the goal of the worker agents is to compute some
predicate over the states of the catalytic inputs. We call this model the
Catalytic Input (CI) model. For , we show that computing the
parity of the input population with high probability requires at least
total interactions, demonstrating a strong separation between the
CI model and the standard population protocol model. On the other hand, we show
that the simple third-state dynamics of Angluin et al. for approximate majority
in the standard model can be naturally adapted to the CI model: we present such
a constant-state protocol for the CI model that solves approximate majority in
total steps with high probability when the input margin is
.
We then show the robustness of third-state dynamics protocols to the
transient leaks events introduced by Alistarh et al. In both the original and
CI models, these protocols successfully compute approximate majority with high
probability in the presence of leaks occurring at each step with probability
, exhibiting a resilience to leaks
similar to that of Byzantine agents in previous works
Model reproduces individual, group and collective dynamics of human contact networks
Empirical data on the dynamics of human face-to-face interactions across a variety of social venues have recently revealed a number of context-independent structural and temporal properties of human contact networks. This universality suggests that some basic mechanisms may be responsible for the unfolding of human interactions in the physical space. Here we discuss a simple model that reproduces the empirical distributions for the individual, group and collective dynamics of face-to-face contact networks. The model describes agents that move randomly in a two-dimensional space and tend to stop when meeting âattractiveâ peers, and reproduces accurately the empirical distributions.Postprint (author's final draft
PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms
Mobile phones provide a powerful sensing platform that researchers may adopt
to understand proximity interactions among people and the diffusion, through
these interactions, of diseases, behaviors, and opinions. However, it remains a
challenge to track the proximity-based interactions of a whole community and
then model the social diffusion of diseases and behaviors starting from the
observations of a small fraction of the volunteer population. In this paper, we
propose a novel approach that tries to connect together these sparse
observations using a model of how individuals interact with each other and how
social interactions happen in terms of a sequence of proximity interactions. We
apply our approach to track the spreading of flu in the spatial-proximity
network of a 3000-people university campus by mobilizing 300 volunteers from
this population to monitor nearby mobile phones through Bluetooth scanning and
to daily report flu symptoms about and around them. Our aim is to predict the
likelihood for an individual to get flu based on how often her/his daily
routine intersects with those of the volunteers. Thus, we use the daily
routines of the volunteers to build a model of the volunteers as well as of the
non-volunteers. Our results show that we can predict flu infection two weeks
ahead of time with an average precision from 0.24 to 0.35 depending on the
amount of information. This precision is six to nine times higher than with a
random guess model. At the population level, we can predict infectious
population in a two-week window with an r-squared value of 0.95 (a random-guess
model obtains an r-squared value of 0.2). These results point to an innovative
approach for tracking individuals who have interacted with people showing
symptoms, allowing us to warn those in danger of infection and to inform health
researchers about the progression of contact-induced diseases
SciTech News Volume 71, No. 2 (2017)
Columns and Reports From the Editor 3
Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division 9 Aerospace Section of the Engineering Division 12 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 14
Reviews Sci-Tech Book News Reviews 16
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Initiating e-learning by stealth, participation and consultation in a late majority institution
The extent to which opportunities afforded by e-learning are embraced by an institution can depend in large measure on whether it is perceived as enabling and transformative or as a major and disruptive distraction. Most case studies focus on the former. This paper describes how e-learning was introduced into the latter environment. The sensitivity of competing pressures in a research intensive university substantially influenced the manner in which e-learning was promoted. This paper tells that story, from initial stealth to eventual university acknowledgement of the relevance of e-learning specifically to its own context
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