11,661 research outputs found
Epidemic processes in complex networks
In recent years the research community has accumulated overwhelming evidence
for the emergence of complex and heterogeneous connectivity patterns in a wide
range of biological and sociotechnical systems. The complex properties of
real-world networks have a profound impact on the behavior of equilibrium and
nonequilibrium phenomena occurring in various systems, and the study of
epidemic spreading is central to our understanding of the unfolding of
dynamical processes in complex networks. The theoretical analysis of epidemic
spreading in heterogeneous networks requires the development of novel
analytical frameworks, and it has produced results of conceptual and practical
relevance. A coherent and comprehensive review of the vast research activity
concerning epidemic processes is presented, detailing the successful
theoretical approaches as well as making their limits and assumptions clear.
Physicists, mathematicians, epidemiologists, computer, and social scientists
share a common interest in studying epidemic spreading and rely on similar
models for the description of the diffusion of pathogens, knowledge, and
innovation. For this reason, while focusing on the main results and the
paradigmatic models in infectious disease modeling, the major results
concerning generalized social contagion processes are also presented. Finally,
the research activity at the forefront in the study of epidemic spreading in
coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio
Emergence of influential spreaders in modified rumor models
The burst in the use of online social networks over the last decade has
provided evidence that current rumor spreading models miss some fundamental
ingredients in order to reproduce how information is disseminated. In
particular, recent literature has revealed that these models fail to reproduce
the fact that some nodes in a network have an influential role when it comes to
spread a piece of information. In this work, we introduce two mechanisms with
the aim of filling the gap between theoretical and experimental results. The
first model introduces the assumption that spreaders are not always active
whereas the second model considers the possibility that an ignorant is not
interested in spreading the rumor. In both cases, results from numerical
simulations show a higher adhesion to real data than classical rumor spreading
models. Our results shed some light on the mechanisms underlying the spreading
of information and ideas in large social systems and pave the way for more
realistic diffusion models.Comment: 14 Pages, 6 figures, accepted for publication in Journal of
Statistical Physic
Cellular signaling networks function as generalized Wiener-Kolmogorov filters to suppress noise
Cellular signaling involves the transmission of environmental information
through cascades of stochastic biochemical reactions, inevitably introducing
noise that compromises signal fidelity. Each stage of the cascade often takes
the form of a kinase-phosphatase push-pull network, a basic unit of signaling
pathways whose malfunction is linked with a host of cancers. We show this
ubiquitous enzymatic network motif effectively behaves as a Wiener-Kolmogorov
(WK) optimal noise filter. Using concepts from umbral calculus, we generalize
the linear WK theory, originally introduced in the context of communication and
control engineering, to take nonlinear signal transduction and discrete
molecule populations into account. This allows us to derive rigorous
constraints for efficient noise reduction in this biochemical system. Our
mathematical formalism yields bounds on filter performance in cases important
to cellular function---like ultrasensitive response to stimuli. We highlight
features of the system relevant for optimizing filter efficiency, encoded in a
single, measurable, dimensionless parameter. Our theory, which describes noise
control in a large class of signal transduction networks, is also useful both
for the design of synthetic biochemical signaling pathways, and the
manipulation of pathways through experimental probes like oscillatory input.Comment: 15 pages, 5 figures; to appear in Phys. Rev.
Physical Representation-based Predicate Optimization for a Visual Analytics Database
Querying the content of images, video, and other non-textual data sources
requires expensive content extraction methods. Modern extraction techniques are
based on deep convolutional neural networks (CNNs) and can classify objects
within images with astounding accuracy. Unfortunately, these methods are slow:
processing a single image can take about 10 milliseconds on modern GPU-based
hardware. As massive video libraries become ubiquitous, running a content-based
query over millions of video frames is prohibitive.
One promising approach to reduce the runtime cost of queries of visual
content is to use a hierarchical model, such as a cascade, where simple cases
are handled by an inexpensive classifier. Prior work has sought to design
cascades that optimize the computational cost of inference by, for example,
using smaller CNNs. However, we observe that there are critical factors besides
the inference time that dramatically impact the overall query time. Notably, by
treating the physical representation of the input image as part of our query
optimization---that is, by including image transforms, such as resolution
scaling or color-depth reduction, within the cascade---we can optimize data
handling costs and enable drastically more efficient classifier cascades.
In this paper, we propose Tahoma, which generates and evaluates many
potential classifier cascades that jointly optimize the CNN architecture and
input data representation. Our experiments on a subset of ImageNet show that
Tahoma's input transformations speed up cascades by up to 35 times. We also
find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy,
and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In
Proceedings of the 35th IEEE International Conference on Data Engineering
(ICDE 2019
Synaptic Noise Facilitates the Emergence of Self-Organized Criticality in the Caenorhabditis elegans Neuronal Network
Avalanches with power-law distributed size parameters have been observed in
neuronal networks. This observation might be a manifestation of the
self-organized criticality (SOC). Yet, the physiological mechanicsm of this
behavior is currently unknown. Describing synaptic noise as transmission
failures mainly originating from the probabilistic nature of neurotransmitter
release, this study investigates the potential of this noise as a mechanism for
driving the functional architecture of the neuronal networks towards SOC. To
this end, a simple finite state neuron model, with activity dependent and
synapse specific failure probabilities, was built based on the known anatomical
connectivity data of the nematode Ceanorhabditis elegans. Beginning from random
values, it was observed that synaptic noise levels picked out a set of synapses
and consequently an active subnetwork which generates power-law distributed
neuronal avalanches. The findings of this study brings up the possibility that
synaptic failures might be a component of physiological processes underlying
SOC in neuronal networks
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