56,205 research outputs found
Flow on sweeping networks
International audienceWe introduce a cellular automaton model coupled with a transport equation for flows on graphs. The direction of the flow is described by a switching process where the switching probability dynamically changes according to the value of the transported quantity in the neighboring cells. A motivation is pedestrian dynamics in a small corridor where the propagation of people in a part of the corridor can be either left or rightgoing. Under the assumptions of propagation of chaos and mean-field limit, we derive a master equation and the corresponding meanfield kinetic and macroscopic models. Steady--states are computed and analyzed analytically and exhibit the possibility of multiple meta-stable states and hysteresis
Encoding dynamics for multiscale community detection: Markov time sweeping for the Map equation
The detection of community structure in networks is intimately related to
finding a concise description of the network in terms of its modules. This
notion has been recently exploited by the Map equation formalism (M. Rosvall
and C.T. Bergstrom, PNAS, 105(4), pp.1118--1123, 2008) through an
information-theoretic description of the process of coding inter- and
intra-community transitions of a random walker in the network at stationarity.
However, a thorough study of the relationship between the full Markov dynamics
and the coding mechanism is still lacking. We show here that the original Map
coding scheme, which is both block-averaged and one-step, neglects the internal
structure of the communities and introduces an upper scale, the `field-of-view'
limit, in the communities it can detect. As a consequence, Map is well tuned to
detect clique-like communities but can lead to undesirable overpartitioning
when communities are far from clique-like. We show that a signature of this
behavior is a large compression gap: the Map description length is far from its
ideal limit. To address this issue, we propose a simple dynamic approach that
introduces time explicitly into the Map coding through the analysis of the
weighted adjacency matrix of the time-dependent multistep transition matrix of
the Markov process. The resulting Markov time sweeping induces a dynamical
zooming across scales that can reveal (potentially multiscale) community
structure above the field-of-view limit, with the relevant partitions indicated
by a small compression gap.Comment: 10 pages, 6 figure
Size distribution and diffuse pollution impacts of PAHs in street dust in urban streams in the Yangtze River Delta
Particles of dust washed off streets by stormwater are an important pathway of polyaromatic hydrocarbons (PAHs) into urban streams. This article presented a comprehensive assessment of the size distribution of PAHs in street dust particles, the potential risks of the particles in urban streams, and the sources and sinks of PAHs in the stream network. This assessment was based on measurements of 16 PAHs from the USEPA priority list in street dust particles and river sediments in Xincheng, China. The content of total PAHs ranged from 1629 to 8986 μg/kg in street dust particles, where smaller particles have a higher concentrations. Approximately 55% of the total PAHs were associated with particles less than 250 μm which accounted for 40% of the total mass of street dust. The PAH quantities increased from 2.41 to 46.86 μg/m2 in the sequence of new residential, rising through main roads, old town residential, commercial and industrial areas. The sediments in stream reaches in town were found to be sinks for street dust particle PAHs. The research findings suggested that particle size, land use and the hydrological conditions in the stream network were the factors which most influenced the total loads of PAH in the receiving water bodies.<br/
Spatiotemporal patterns and predictability of cyberattacks
A relatively unexplored issue in cybersecurity science and engineering is
whether there exist intrinsic patterns of cyberattacks. Conventional wisdom
favors absence of such patterns due to the overwhelming complexity of the
modern cyberspace. Surprisingly, through a detailed analysis of an extensive
data set that records the time-dependent frequencies of attacks over a
relatively wide range of consecutive IP addresses, we successfully uncover
intrinsic spatiotemporal patterns underlying cyberattacks, where the term
"spatio" refers to the IP address space. In particular, we focus on analyzing
{\em macroscopic} properties of the attack traffic flows and identify two main
patterns with distinct spatiotemporal characteristics: deterministic and
stochastic. Strikingly, there are very few sets of major attackers committing
almost all the attacks, since their attack "fingerprints" and target selection
scheme can be unequivocally identified according to the very limited number of
unique spatiotemporal characteristics, each of which only exists on a
consecutive IP region and differs significantly from the others. We utilize a
number of quantitative measures, including the flux-fluctuation law, the Markov
state transition probability matrix, and predictability measures, to
characterize the attack patterns in a comprehensive manner. A general finding
is that the attack patterns possess high degrees of predictability, potentially
paving the way to anticipating and, consequently, mitigating or even preventing
large-scale cyberattacks using macroscopic approaches
Spatiotemporal Patterns and Predictability of Cyberattacks
Y.C.L. was supported by Air Force Office of Scientific Research (AFOSR) under grant no. FA9550-10-1-0083 and Army Research Office (ARO) under grant no. W911NF-14-1-0504. S.X. was supported by Army Research Office (ARO) under grant no. W911NF-13-1-0141. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD
Limited resolution and multiresolution methods in complex network community detection
Detecting community structure in real-world networks is a challenging
problem. Recently, it has been shown that the resolution of methods based on
optimizing a modularity measure or a corresponding energy is limited;
communities with sizes below some threshold remain unresolved. One possibility
to go around this problem is to vary the threshold by using a tuning parameter,
and investigate the community structure at variable resolutions. Here, we
analyze the resolution limit and multiresolution behavior for two different
methods: a q-state Potts method proposed by Reichard and Bornholdt, and a
recent multiresolution method by Arenas, Fernandez, and Gomez. These methods
are studied analytically, and applied to three test networks using simulated
annealing.Comment: 6 pages, 2 figures.Minor changes from previous version, shortened a
couple of page
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