13,356 research outputs found
Phase Changes in the Evolution of the IPv4 and IPv6 AS-Level Internet Topologies
In this paper we investigate the evolution of the IPv4 and IPv6 Internet
topologies at the autonomous system (AS) level over a long period of time.We
provide abundant empirical evidence that there is a phase transition in the
growth trend of the two networks. For the IPv4 network, the phase change
occurred in 2001. Before then the network's size grew exponentially, and
thereafter it followed a linear growth. Changes are also observed around the
same time for the maximum node degree, the average node degree and the average
shortest path length. For the IPv6 network, the phase change occurred in late
2006. It is notable that the observed phase transitions in the two networks are
different, for example the size of IPv6 network initially grew linearly and
then shifted to an exponential growth. Our results show that following decades
of rapid expansion up to the beginning of this century, the IPv4 network has
now evolved into a mature, steady stage characterised by a relatively slow
growth with a stable network structure; whereas the IPv6 network, after a slow
startup process, has just taken off to a full speed growth. We also provide
insight into the possible impact of IPv6-over-IPv4 tunneling deployment scheme
on the evolution of the IPv6 network. The Internet topology generators so far
are based on an inexplicit assumption that the evolution of Internet follows
non-changing dynamic mechanisms. This assumption, however, is invalidated by
our results.Our work reveals insights into the Internet evolution and provides
inputs to future AS-Level Internet models.Comment: 12 pages, 21 figures; G. Zhang et al.,Phase changes in the evolution
of the IPv4 and IPv6 AS-Level Internet topologies, Comput. Commun. (2010
Filterscape: energy recycling in a creative ecosystem
This paper extends previous work in evolutionary ecosystemic approaches to generative art. Filterscape, adopts the implicit fitness specification that is fundamental to this approach and explores the use of resource recycling as a means of generating coherent sonic diversity in a generative sound work. Filterscape agents consume and deposit energy that is manifest in the simulation as sound. Resource recycling is shown to support cooperative as well as competitive survival strategies. In the context of our simulation, these strategies are recognised by their characteristic audible signatures. The model provides a novel means to generate sonic diversity through de-centralised agent interactions
Inferring clonal evolution of tumors from single nucleotide somatic mutations
High-throughput sequencing allows the detection and quantification of
frequencies of somatic single nucleotide variants (SNV) in heterogeneous tumor
cell populations. In some cases, the evolutionary history and population
frequency of the subclonal lineages of tumor cells present in the sample can be
reconstructed from these SNV frequency measurements. However, automated methods
to do this reconstruction are not available and the conditions under which
reconstruction is possible have not been described.
We describe the conditions under which the evolutionary history can be
uniquely reconstructed from SNV frequencies from single or multiple samples
from the tumor population and we introduce a new statistical model, PhyloSub,
that infers the phylogeny and genotype of the major subclonal lineages
represented in the population of cancer cells. It uses a Bayesian nonparametric
prior over trees that groups SNVs into major subclonal lineages and
automatically estimates the number of lineages and their ancestry. We sample
from the joint posterior distribution over trees to identify evolutionary
histories and cell population frequencies that have the highest probability of
generating the observed SNV frequency data. When multiple phylogenies are
consistent with a given set of SNV frequencies, PhyloSub represents the
uncertainty in the tumor phylogeny using a partial order plot. Experiments on a
simulated dataset and two real datasets comprising tumor samples from acute
myeloid leukemia and chronic lymphocytic leukemia patients demonstrate that
PhyloSub can infer both linear (or chain) and branching lineages and its
inferences are in good agreement with ground truth, where it is available
Signed Network Modeling Based on Structural Balance Theory
The modeling of networks, specifically generative models, have been shown to
provide a plethora of information about the underlying network structures, as
well as many other benefits behind their construction. Recently there has been
a considerable increase in interest for the better understanding and modeling
of networks, but the vast majority of this work has been for unsigned networks.
However, many networks can have positive and negative links(or signed
networks), especially in online social media, and they inherently have
properties not found in unsigned networks due to the added complexity.
Specifically, the positive to negative link ratio and the distribution of
signed triangles in the networks are properties that are unique to signed
networks and would need to be explicitly modeled. This is because their
underlying dynamics are not random, but controlled by social theories, such as
Structural Balance Theory, which loosely states that users in social networks
will prefer triadic relations that involve less tension. Therefore, we propose
a model based on Structural Balance Theory and the unsigned Transitive Chung-Lu
model for the modeling of signed networks. Our model introduces two parameters
that are able to help maintain the positive link ratio and proportion of
balanced triangles. Empirical experiments on three real-world signed networks
demonstrate the importance of designing models specific to signed networks
based on social theories to obtain better performance in maintaining signed
network properties while generating synthetic networks.Comment: CIKM 2018: https://dl.acm.org/citation.cfm?id=327174
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Functional Brain Imaging with Multi-Objective Multi-Modal Evolutionary Optimization
Functional brain imaging is a source of spatio-temporal data mining problems.
A new framework hybridizing multi-objective and multi-modal optimization is
proposed to formalize these data mining problems, and addressed through
Evolutionary Computation (EC). The merits of EC for spatio-temporal data mining
are demonstrated as the approach facilitates the modelling of the experts'
requirements, and flexibly accommodates their changing goals
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