20,326 research outputs found
Early Warning Analysis for Social Diffusion Events
There is considerable interest in developing predictive capabilities for
social diffusion processes, for instance to permit early identification of
emerging contentious situations, rapid detection of disease outbreaks, or
accurate forecasting of the ultimate reach of potentially viral ideas or
behaviors. This paper proposes a new approach to this predictive analytics
problem, in which analysis of meso-scale network dynamics is leveraged to
generate useful predictions for complex social phenomena. We begin by deriving
a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes
taking place over social networks with realistic topologies; this modeling
approach is inspired by recent work in biology demonstrating that S-HDS offer a
useful mathematical formalism with which to represent complex, multi-scale
biological network dynamics. We then perform formal stochastic reachability
analysis with this S-HDS model and conclude that the outcomes of social
diffusion processes may depend crucially upon the way the early dynamics of the
process interacts with the underlying network's community structure and
core-periphery structure. This theoretical finding provides the foundations for
developing a machine learning algorithm that enables accurate early warning
analysis for social diffusion events. The utility of the warning algorithm, and
the power of network-based predictive metrics, are demonstrated through an
empirical investigation of the propagation of political memes over social media
networks. Additionally, we illustrate the potential of the approach for
security informatics applications through case studies involving early warning
analysis of large-scale protests events and politically-motivated cyber
attacks
Multiple Loop Self-Triggered Model Predictive Control for Network Scheduling and Control
We present an algorithm for controlling and scheduling multiple linear
time-invariant processes on a shared bandwidth limited communication network
using adaptive sampling intervals. The controller is centralized and computes
at every sampling instant not only the new control command for a process, but
also decides the time interval to wait until taking the next sample. The
approach relies on model predictive control ideas, where the cost function
penalizes the state and control effort as well as the time interval until the
next sample is taken. The latter is introduced in order to generate an adaptive
sampling scheme for the overall system such that the sampling time increases as
the norm of the system state goes to zero. The paper presents a method for
synthesizing such a predictive controller and gives explicit sufficient
conditions for when it is stabilizing. Further explicit conditions are given
which guarantee conflict free transmissions on the network. It is shown that
the optimization problem may be solved off-line and that the controller can be
implemented as a lookup table of state feedback gains. Simulation studies which
compare the proposed algorithm to periodic sampling illustrate potential
performance gains.Comment: Accepted for publication in IEEE Transactions on Control Systems
Technolog
A tug-of-war between driver and passenger mutations in cancer and other adaptive processes
Cancer progression is an example of a rapid adaptive process where evolving
new traits is essential for survival and requires a high mutation rate.
Precancerous cells acquire a few key mutations that drive rapid population
growth and carcinogenesis. Cancer genomics demonstrates that these few 'driver'
mutations occur alongside thousands of random 'passenger' mutations-a natural
consequence of cancer's elevated mutation rate. Some passengers can be
deleterious to cancer cells, yet have been largely ignored in cancer research.
In population genetics, however, the accumulation of mildly deleterious
mutations has been shown to cause population meltdown. Here we develop a
stochastic population model where beneficial drivers engage in a tug-of-war
with frequent mildly deleterious passengers. These passengers present a barrier
to cancer progression that is described by a critical population size, below
which most lesions fail to progress, and a critical mutation rate, above which
cancers meltdown. We find support for the model in cancer age-incidence and
cancer genomics data that also allow us to estimate the fitness advantage of
drivers and fitness costs of passengers. We identify two regimes of adaptive
evolutionary dynamics and use these regimes to rationalize successes and
failures of different treatment strategies. We find that a tumor's load of
deleterious passengers can explain previously paradoxical treatment outcomes
and suggest that it could potentially serve as a biomarker of response to
mutagenic therapies. Collective deleterious effect of passengers is currently
an unexploited therapeutic target. We discuss how their effects might be
exacerbated by both current and future therapies
A nonparametric Bayesian approach to the rare type match problem
The "rare type match problem" is the situation in which the suspect's DNA
profile, matching the DNA profile of the crime stain, is not in the database of
reference. The evaluation of this match in the light of the two competing
hypotheses (the crime stain has been left by the suspect or by another person)
is based on the calculation of the likelihood ratio and depends on the
population proportions of the DNA profiles, that are unknown. We propose a
Bayesian nonparametric method that uses a two-parameter Poisson Dirichlet
distribution as a prior over the ranked population proportions, and discards
the information about the names of the different DNA profiles. This fits very
well the data coming from European Y-STR DNA profiles, and the calculation of
the likelihood ratio becomes quite simple thanks to a justified Empirical Bayes
approach.Comment: arXiv admin note: text overlap with arXiv:1506.0844
A Cluster Based Model to Enhance Acceptance of New Energy Driven Technologies
Resistance against new innovative technologies by customers has been studied in many publications to improve prediction of behavior. Econometrics models, the Technology Acceptance Model by Fred D. Davis (1989), and market research models are the most widely used modeling techniques to predict and understand customer behaviors. The proposed methodology in this paper advances current models by relaxing many of their assumptions and increasing prediction accuracy. A case study in predicting hybrid car buyer behaviors is performed to illustrate and validate the suggested modeling method named as the Energy Efficiency Technology Acceptance Model
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