17,325 research outputs found
Trends in condensed matter physics: is research going faster and faster?
In this paper we study research trends in condensed matter physics. Trends
are analyzed by means of the the number of publications in the different
sub-fields as function of the years. We found that many research topics have a
similar behavior with an initial fast growth and a next slower exponential
decay. We derived a simple model to describe this behavior and built up some
predictions for future trends
Living on the edge of chaos: minimally nonlinear models of genetic regulatory dynamics
Linearized catalytic reaction equations modeling e.g. the dynamics of genetic
regulatory networks under the constraint that expression levels, i.e. molecular
concentrations of nucleic material are positive, exhibit nontrivial dynamical
properties, which depend on the average connectivity of the reaction network.
In these systems the inflation of the edge of chaos and multi-stability have
been demonstrated to exist. The positivity constraint introduces a nonlinearity
which makes chaotic dynamics possible. Despite the simplicity of such minimally
nonlinear systems, their basic properties allow to understand fundamental
dynamical properties of complex biological reaction networks. We analyze the
Lyapunov spectrum, determine the probability to find stationary oscillating
solutions, demonstrate the effect of the nonlinearity on the effective in- and
out-degree of the active interaction network and study how the frequency
distributions of oscillatory modes of such system depend on the average
connectivity.Comment: 11 pages, 5 figure
Modeling the Internet's Large-Scale Topology
Network generators that capture the Internet's large-scale topology are
crucial for the development of efficient routing protocols and modeling
Internet traffic. Our ability to design realistic generators is limited by the
incomplete understanding of the fundamental driving forces that affect the
Internet's evolution. By combining the most extensive data on the time
evolution, topology and physical layout of the Internet, we identify the
universal mechanisms that shape the Internet's router and autonomous system
level topology. We find that the physical layout of nodes form a fractal set,
determined by population density patterns around the globe. The placement of
links is driven by competition between preferential attachment and linear
distance dependence, a marked departure from the currently employed exponential
laws. The universal parameters that we extract significantly restrict the class
of potentially correct Internet models, and indicate that the networks created
by all available topology generators are significantly different from the
Internet
Thresholds for epidemic spreading in networks
We study the threshold of epidemic models in quenched networks with degree
distribution given by a power-law. For the susceptible-infected-susceptible
(SIS) model the activity threshold lambda_c vanishes in the large size limit on
any network whose maximum degree k_max diverges with the system size, at odds
with heterogeneous mean-field (HMF) theory. The vanishing of the threshold has
not to do with the scale-free nature of the connectivity pattern and is instead
originated by the largest hub in the system being active for any spreading rate
lambda>1/sqrt{k_max} and playing the role of a self-sustained source that
spreads the infection to the rest of the system. The
susceptible-infected-removed (SIR) model displays instead agreement with HMF
theory and a finite threshold for scale-rich networks. We conjecture that on
quenched scale-rich networks the threshold of generic epidemic models is
vanishing or finite depending on the presence or absence of a steady state.Comment: 5 pages, 4 figure
Self-similarity of complex networks
Complex networks have been studied extensively due to their relevance to many
real systems as diverse as the World-Wide-Web (WWW), the Internet, energy
landscapes, biological and social networks
\cite{ab-review,mendes,vespignani,newman,amaral}. A large number of real
networks are called ``scale-free'' because they show a power-law distribution
of the number of links per node \cite{ab-review,barabasi1999,faloutsos}.
However, it is widely believed that complex networks are not {\it length-scale}
invariant or self-similar. This conclusion originates from the ``small-world''
property of these networks, which implies that the number of nodes increases
exponentially with the ``diameter'' of the network
\cite{erdos,bollobas,milgram,watts}, rather than the power-law relation
expected for a self-similar structure. Nevertheless, here we present a novel
approach to the analysis of such networks, revealing that their structure is
indeed self-similar. This result is achieved by the application of a
renormalization procedure which coarse-grains the system into boxes containing
nodes within a given "size". Concurrently, we identify a power-law relation
between the number of boxes needed to cover the network and the size of the box
defining a finite self-similar exponent. These fundamental properties, which
are shown for the WWW, social, cellular and protein-protein interaction
networks, help to understand the emergence of the scale-free property in
complex networks. They suggest a common self-organization dynamics of diverse
networks at different scales into a critical state and in turn bring together
previously unrelated fields: the statistical physics of complex networks with
renormalization group, fractals and critical phenomena.Comment: 28 pages, 12 figures, more informations at http://www.jamlab.or
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
Existing FNNs are mostly developed under a shallow network configuration
having lower generalization power than those of deep structures. This paper
proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be
automatically extracted from data streams or removed if they play limited role
during their lifespan. The structure of the network can be deepened on demand
by stacking additional layers using a drift detection method which not only
detects the covariate drift, variations of input space, but also accurately
identifies the real drift, dynamic changes of both feature space and target
space. DEVFNN is developed under the stacked generalization principle via the
feature augmentation concept where a recently developed algorithm, namely
gClass, drives the hidden layer. It is equipped by an automatic feature
selection method which controls activation and deactivation of input attributes
to induce varying subsets of input features. A deep network simplification
procedure is put forward using the concept of hidden layer merging to prevent
uncontrollable growth of dimensionality of input space due to the nature of
feature augmentation approach in building a deep network structure. DEVFNN
works in the sample-wise fashion and is compatible for data stream
applications. The efficacy of DEVFNN has been thoroughly evaluated using seven
datasets with non-stationary properties under the prequential test-then-train
protocol. It has been compared with four popular continual learning algorithms
and its shallow counterpart where DEVFNN demonstrates improvement of
classification accuracy. Moreover, it is also shown that the concept drift
detection method is an effective tool to control the depth of network structure
while the hidden layer merging scenario is capable of simplifying the network
complexity of a deep network with negligible compromise of generalization
performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System
The Small World of Osteocytes: Connectomics of the Lacuno-Canalicular Network in Bone
Osteocytes and their cell processes reside in a large, interconnected network
of voids pervading the mineralized bone matrix of most vertebrates. This
osteocyte lacuno-canalicular network (OLCN) is believed to play important roles
in mechanosensing, mineral homeostasis, and for the mechanical properties of
bone. While the extracellular matrix structure of bone is extensively studied
on ultrastructural and macroscopic scales, there is a lack of quantitative
knowledge on how the cellular network is organized. Using a recently introduced
imaging and quantification approach, we analyze the OLCN in different bone
types from mouse and sheep that exhibit different degrees of structural
organization not only of the cell network but also of the fibrous matrix
deposited by the cells. We define a number of robust, quantitative measures
that are derived from the theory of complex networks. These measures enable us
to gain insights into how efficient the network is organized with regard to
intercellular transport and communication. Our analysis shows that the cell
network in regularly organized, slow-growing bone tissue from sheep is less
connected, but more efficiently organized compared to irregular and
fast-growing bone tissue from mice. On the level of statistical topological
properties (edges per node, edge length and degree distribution), both network
types are indistinguishable, highlighting that despite pronounced differences
at the tissue level, the topological architecture of the osteocyte canalicular
network at the subcellular level may be independent of species and bone type.
Our results suggest a universal mechanism underlying the self-organization of
individual cells into a large, interconnected network during bone formation and
mineralization
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