7,594 research outputs found
Community detection in networks: Structural communities versus ground truth
Algorithms to find communities in networks rely just on structural
information and search for cohesive subsets of nodes. On the other hand, most
scholars implicitly or explicitly assume that structural communities represent
groups of nodes with similar (non-topological) properties or functions. This
hypothesis could not be verified, so far, because of the lack of network
datasets with information on the classification of the nodes. We show that
traditional community detection methods fail to find the metadata groups in
many large networks. Our results show that there is a marked separation between
structural communities and metadata groups, in line with recent findings. That
means that either our current modeling of community structure has to be
substantially modified, or that metadata groups may not be recoverable from
topology alone.Comment: 21 pages, 19 figure
A simple model for the kinetics of packaging of DNA in to a capsid against an external force
We propose a simple model for the kinetics of packaging of viral DNA in to a
capsid against an external force trying to prevent it. The model leads to a
Butler-Volmer type dependence of the rate of packaging on the pulling force F
Synchronization of chaotic modulated time delay networks in presence of noise
We study the constructive role of noises in a Lorenz system with functional
delay. The effect of delay can change the dynamics of the system to a chaotic
one from its steady state. Induced synchronization with white and colored (red
and green) noises are observed between two identical uncoupled systems and
enhancement of synchrony is also observed with unidirectional coupling. We
investigate both the phenomena in a globally coupled network in the presence of
white and color noises.Comment: 10 pages, 7 figure
Commentary: The case for caution in predicting scientists’ future impact
We stress-test the career predictability model proposed by Acuna et al.
[Nature 489, 201-202 2012] by applying their model to a longitudinal career
data set of 100 Assistant professors in physics, two from each of the top 50
physics departments in the US. The Acuna model claims to predict h(t+\Delta t),
a scientist's h-index \Delta t years into the future, using a linear
combination of 5 cumulative career measures taken at career age t. Here we
investigate how the "predictability" depends on the aggregation of career data
across multiple age cohorts. We confirm that the Acuna model does a respectable
job of predicting h(t+\Delta t) up to roughly 6 years into the future when
aggregating all age cohorts together. However, when calculated using subsets of
specific age cohorts (e.g. using data for only t=3), we find that the model's
predictive power significantly decreases, especially when applied to early
career years. For young careers, the model does a much worse job of predicting
future impact, and hence, exposes a serious limitation. The limitation is
particularly concerning as early career decisions make up a significant
portion, if not the majority, of cases where quantitative approaches are likely
to be applied.Comment: 2 pages, 1 figur
Benchmark model to assess community structure in evolving networks
Detecting the time evolution of the community structure of networks is
crucial to identify major changes in the internal organization of many complex
systems, which may undergo important endogenous or exogenous events. This
analysis can be done in two ways: considering each snapshot as an independent
community detection problem or taking into account the whole evolution of the
network. In the first case, one can apply static methods on the temporal
snapshots, which correspond to configurations of the system in short time
windows, and match afterwards the communities across layers. Alternatively, one
can develop dedicated dynamic procedures, so that multiple snapshots are
simultaneously taken into account while detecting communities, which allows us
to keep memory of the flow. To check how well a method of any kind could
capture the evolution of communities, suitable benchmarks are needed. Here we
propose a model for generating simple dynamic benchmark graphs, based on
stochastic block models. In them, the time evolution consists of a periodic
oscillation of the system's structure between configurations with built-in
community structure. We also propose the extension of quality comparison
indices to the dynamic scenario.Comment: 11 pages, 7 figures, 3 table
The dynamics of loop formation in a semiflexible polymer
The dynamics of loop formation by linear polymer chains has been a topic of
several theoretical/experimental studies. Formation of loops and their opening
are key processes in many important biological processes. Loop formation in
flexible chains has been extensively studied by many groups. However, in the
more realistic case of semiflexible polymers, not much results are available.
In a recent study (K. P. Santo and K. L. Sebastian, Phys. Rev. E, \textbf{73},
031293 (2006)), we investigated opening dynamics of semiflexible loops in the
short chain limit and presented results for opening rates as a function of the
length of the chain. We presented an approximate model for a semiflexible
polymer in the rod limit, based on a semiclassical expansion of the bending
energy of the chain. The model provided an easy way to describe the dynamics.
In this paper, using this model, we investigate the reverse process, i.e., the
loop formation dynamics of a semiflexible polymer chain by describing the
process as a diffusion-controlled reaction. We perform a detailed
multidimensional analysis of the problem and calculate closing times for a
semiflexible chain which leads to results that are physically expected. Such a
multidimensional analysis leading to these results does not seem to exist in
the literature so far.Comment: 37 pages 4 figure
Phase synchronization of instrumental music signals
Signal analysis is one of the finest scientific techniques in communication
theory. Some quantitative and qualitative measures describe the pattern of a
music signal, vary from one to another. Same musical recital, when played by
different instrumentalists, generates different types of music patterns. The
reason behind various patterns is the psychoacoustic measures - Dynamics,
Timber, Tonality and Rhythm, varies in each time. However, the psycho-acoustic
study of the music signals does not reveal any idea about the similarity
between the signals. For such cases, study of synchronization of long-term
nonlinear dynamics may provide effective results. In this context, phase
synchronization (PS) is one of the measures to show synchronization between two
non-identical signals. In fact, it is very critical to investigate any other
kind of synchronization for experimental condition, because those are
completely non identical signals. Also, there exists equivalence between the
phases and the distances of the diagonal line in Recurrence plot (RP) of the
signals, which is quantifiable by the recurrence quantification measure
tau-recurrence rate. This paper considers two nonlinear music signals based on
same raga played by two eminent sitar instrumentalists as two non-identical
sources. The psycho-acoustic study shows how the Dynamics, Timber, Tonality and
Rhythm vary for the two music signals. Then, long term analysis in the form of
phase space reconstruction is performed, which reveals the chaotic phase spaces
for both the signals. From the RP of both the phase spaces, tau-recurrence rate
is calculated. Finally by the correlation of normalized tau-recurrence rate of
their 3D phase spaces and the PS of the two music signals has been established.
The numerical results well support the analysis
Design of LTCC-based Ceramic Structure for Chemical Microreactor
The design of ceramic chemical microreactor for the production of hydrogen needed in portable polymer-electrolyte membrane (PEM) fuel cells is presented. The microreactor was developed for the steam reforming of liquid fuels with water into hydrogen. The complex three-dimensional ceramic structure of the microreactor includes evaporator(s), mixer(s), reformer and combustor. Low-temperature co-fired ceramic (LTCC) technology was used to fabricate the ceramic structures with buried cavities and channels, and thick-film technology was used to make electrical heaters, temperature sensors and pressure sensors. The final 3D ceramic structure consists of 45 LTCC tapes. The dimensions of the structure are 75 × 41 × 9 mm3 and the weight is about 73 g
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