192 research outputs found
A Markov model for inferring flows in directed contact networks
Directed contact networks (DCNs) are a particularly flexible and convenient
class of temporal networks, useful for modeling and analyzing the transfer of
discrete quantities in communications, transportation, epidemiology, etc.
Transfers modeled by contacts typically underlie flows that associate multiple
contacts based on their spatiotemporal relationships. To infer these flows, we
introduce a simple inhomogeneous Markov model associated to a DCN and show how
it can be effectively used for data reduction and anomaly detection through an
example of kernel-level information transfers within a computer.Comment: 12 page
Active contractility in actomyosin networks
Contractile forces are essential for many developmental processes involving
cell shape change and tissue deformation. Recent experiments on reconstituted
actomyosin networks, the major component of the contractile machinery, have
shown that active contractility occurs above a threshold motor concentration
and within a window of crosslink concentration. We present a microscopic
dynamic model that incorporates two essential aspects of actomyosin
self-organization: the asymmetric load response of individual actin filaments
and the correlated motor-driven events mimicking myosin-induced filament
sliding. Using computer simulations we examine how the concentration and
susceptibility of motors contribute to their collective behavior and interplay
with the network connectivity to regulate macroscopic contractility. Our model
is shown to capture the formation and dynamics of contractile structures and
agree with the observed dependence of active contractility on microscopic
parameters including the contractility onset. Cooperative action of
load-resisting motors in a force-percolating structure integrates local
contraction/buckling events into a global contractile state via an active
coarsening process, in contrast to the flow transition driven by uncorrelated
kicks of susceptible motors.Comment: 15 pages, 4 main figures, 4 supplementary figure
Probing empirical contact networks by simulation of spreading dynamics
Disease, opinions, ideas, gossip, etc. all spread on social networks. How
these networks are connected (the network structure) influences the dynamics of
the spreading processes. By investigating these relationships one gains
understanding both of the spreading itself and the structure and function of
the contact network. In this chapter, we will summarize the recent literature
using simulation of spreading processes on top of empirical contact data. We
will mostly focus on disease simulations on temporal proximity networks --
networks recording who is close to whom, at what time -- but also cover other
types of networks and spreading processes. We analyze 29 empirical networks to
illustrate the methods
From sparse to dense and from assortative to disassortative in online social networks
Inspired by the analysis of several empirical online social networks, we
propose a simple reaction-diffusion-like coevolving model, in which individuals
are activated to create links based on their states, influenced by local
dynamics and their own intention. It is shown that the model can reproduce the
remarkable properties observed in empirical online social networks; in
particular, the assortative coefficients are neutral or negative, and the power
law exponents are smaller than 2. Moreover, we demonstrate that, under
appropriate conditions, the model network naturally makes transition(s) from
assortative to disassortative, and from sparse to dense in their
characteristics. The model is useful in understanding the formation and
evolution of online social networks.Comment: 10 pages, 7 figures and 2 table
Diffusion on networked systems is a question of time or structure
Network science investigates the architecture of complex systems to understand their functional and dynamical properties. Structural patterns such as communities shape diffusive processes on networks. However, these results hold under the strong assumption that networks are static entities where temporal aspects can be neglected. Here we propose a generalized formalism for linear dynamics on complex networks, able to incorporate statistical properties of the timings at which events occur. We show that the diffusion dynamics is affected by the network community structure and by the temporal properties of waiting times between events. We identify the main mechanism—network structure, burstiness or fat tails of waiting times—determining the relaxation times of stochastic processes on temporal networks, in the absence of temporal–structure correlations. We identify situations when fine-scale structure can be discarded from the description of the dynamics or, conversely, when a fully detailed model is required due to temporal heterogeneities
Exploring concurrency and reachability in the presence of high temporal resolution
Network properties govern the rate and extent of spreading processes on
networks, from simple contagions to complex cascades. Recent advances have
extended the study of spreading processes from static networks to temporal
networks, where nodes and links appear and disappear. We review previous
studies on the effects of temporal connectivity for understanding the spreading
rate and outbreak size of model infection processes. We focus on the effects of
"accessibility", whether there is a temporally consistent path from one node to
another, and "reachability", the density of the corresponding "accessibility
graph" representation of the temporal network. We study reachability in terms
of the overall level of temporal concurrency between edges, quantifying the
overlap of edges in time. We explore the role of temporal resolution of
contacts by calculating reachability with the full temporal information as well
as with a simplified interval representation approximation that demands less
computation. We demonstrate the extent to which the computed reachability
changes due to this simplified interval representation.Comment: To appear in Holme and Saramaki (Editors). "Temporal Network Theory".
Springer- Nature, New York. 201
Quantum Secure Direct Communication with Mutual Authentication using a Single Basis
In this paper, we propose a new theoretical scheme for quantum secure direct
communication (QSDC) with user authentication. Different from the previous QSDC
protocols, the present protocol uses only one orthogonal basis of single-qubit
states to encode the secret message. Moreover, this is a one-time and one-way
communication protocol, which uses qubits prepared in a randomly chosen
arbitrary basis, to transmit the secret message. We discuss the security of the
proposed protocol against some common attacks and show that no eaves-dropper
can get any information from the quantum and classical channels. We have also
studied the performance of this protocol under realistic device noise. We have
executed the protocol in IBMQ Armonk device and proposed a repetition code
based protection scheme that requires minimal overhead
Discovering universal statistical laws of complex networks
Different network models have been suggested for the topology underlying
complex interactions in natural systems. These models are aimed at replicating
specific statistical features encountered in real-world networks. However, it
is rarely considered to which degree the results obtained for one particular
network class can be extrapolated to real-world networks. We address this issue
by comparing different classical and more recently developed network models
with respect to their generalisation power, which we identify with large
structural variability and absence of constraints imposed by the construction
scheme. After having identified the most variable networks, we address the
issue of which constraints are common to all network classes and are thus
suitable candidates for being generic statistical laws of complex networks. In
fact, we find that generic, not model-related dependencies between different
network characteristics do exist. This allows, for instance, to infer global
features from local ones using regression models trained on networks with high
generalisation power. Our results confirm and extend previous findings
regarding the synchronisation properties of neural networks. Our method seems
especially relevant for large networks, which are difficult to map completely,
like the neural networks in the brain. The structure of such large networks
cannot be fully sampled with the present technology. Our approach provides a
method to estimate global properties of under-sampled networks with good
approximation. Finally, we demonstrate on three different data sets (C.
elegans' neuronal network, R. prowazekii's metabolic network, and a network of
synonyms extracted from Roget's Thesaurus) that real-world networks have
statistical relations compatible with those obtained using regression models
Portable anthrax detection system (PADS)
Poster presented at Biomedical Technology Showcase 2006, Philadelphia, PA. Retrieved 18 Aug 2006 from http://www.biomed.drexel.edu/new04/Content/Biomed_Tech_Showcase/Poster_Presentations/Lec_6.pdf.Biosensors such as the Quartz Crystal Microbalance (QCM) and micro cantilever (MC) are becoming increasingly popular in homeland security applications due to their high sensitivity. Furthermore, they can be functionalized for specific pathogens such as bacillus anthracis (anthrax) to give high selectivity. These sensing platforms are currently available, but expensive laboratory equipment is required for sample preparation, measurement and analysis. The ideal device will give accurate and repeatable results on a real-time basis using a single system that completes the entire process for use in the laboratory or field. The Portable Anthrax Detection System (PADS) has been developed to meet these requirements. It utilizes a cartridge that contains either a QCM or MC sensor. The system introduces a sample to the sensor, then measures and analyzes it for bacillus anthracis. Sensor results are quantified based on a specific algorithm for the pathogen. The PADS is user friendly, inexpensive, compact, flexible, and is currently undergoing reliability testing
Evidence for a nuclear compartment of transcription and splicing located at chromosome domain boundaries
The nuclear topography of splicing snRNPs, mRNA transcripts and chromosome domains in various mammalian cell types are described. The visualization of splicing snRNPs, defined by the Sm antigen, and coiled bodies, revealed distinctly different distribution patterns in these cell types. Heat shock experiments confirmed that the distribution patterns also depend on physiological parameters. Using a combination of fluorescencein situ hybridization and immunodetection protocols, individual chromosome domains were visualized simultaneously with the Sm antigen or the transcript of an integrated human papilloma virus genome. Three-dimensional analysis of fluorescence-stained target regions was performed by confocal laser scanning microscopy. RNA transcripts and components of the splicing machinery were found to be generally excluded from the interior of the territories occupied by the individual chromosomes. Based on these findings we present a model for the functional compartmentalization of the cell nucleus. According to this model the space between chromosome domains, including the surface areas of these domains, defines a three-dimensional network-like compartment, termed the interchromosome domain (ICD) compartment, in which transcription and splicing of mRNA occurs
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