4,558 research outputs found
Identification of influential nodes in network of networks
The network of networks(NON) research is focused on studying the properties
of n interdependent networks which is ubiquitous in the real world. Identifying
the influential nodes in the network of networks is theoretical and practical
significance. However, it is hard to describe the structure property of the NON
based on traditional methods. In this paper, a new method is proposed to
identify the influential nodes in the network of networks base on the evidence
theory. The proposed method can fuse different kinds of relationship between
the network components to constructed a comprehensive similarity network. The
nodes which have a big value of similarity are the influential nodes in the
NON. The experiment results illustrate that the proposed method is reasonable
and significantComment: 3 figure
Multiscale probability transformation of basic probability assignment
Decision making is still an open issue in the application of Dempster-Shafer
evidence theory. A lot of works have been presented for it. In the transferable
belief model (TBM), pignistic probabilities based on the basic probability as-
signments are used for decision making. In this paper, multiscale probability
transformation of basic probability assignment based on the belief function and
the plausibility function is proposed, which is a generalization of the
pignistic probability transformation. In the multiscale probability function, a
factor q based on the Tsallis entropy is used to make the multiscale prob-
abilities diversified. An example is shown that the multiscale probability
transformation is more reasonable in the decision making.Comment: 22 pages, 1 figur
A betweenness structure entropy of complex networks
The structure entropy is an important index to illuminate the structure
property of the complex network. Most of the existing structure entropies are
based on the degree distribution of the complex network. But the structure
entropy based on the degree can not illustrate the structure property of the
weighted networks. In order to study the structure property of the weighted
networks, a new structure entropy of the complex networks based on the
betweenness is proposed in this paper. Comparing with the existing structure
entropy, the proposed method is more reasonable to describe the structure
property of the complex weighted networks.Comment: 18 pages, 10 figure
Cooling a charged mechanical resonator with time-dependent bias gate voltages
We show a purely electronic cooling scheme to cool a charged mechanical
resonator (MR) down to nearly the vibrational ground state by elaborately
tuning bias gate voltages on the electrodes, which couple the MR by Coulomb
interaction. The key step is the modification of time-dependent effective
eigen-frequency of the MR based on the Lewis-Riesenfeld invariant. With respect
to a relevant idea proposed previously [Li et al., Phys. Rev. A 83, 043803
(2011)], our scheme is simpler, more practical and completely within the reach
of current technology.Comment: 9 pages,7 figures, accepted by J.Phys: Cond.Matt (Fast track
communication
Distance function of D numbers
Dempster-Shafer theory is widely applied in uncertainty modelling and
knowledge reasoning due to its ability of expressing uncertain information. A
distance between two basic probability assignments(BPAs) presents a measure of
performance for identification algorithms based on the evidential theory of
Dempster-Shafer. However, some conditions lead to limitations in practical
application for Dempster-Shafer theory, such as exclusiveness hypothesis and
completeness constraint. To overcome these shortcomings, a novel theory called
D numbers theory is proposed. A distance function of D numbers is proposed to
measure the distance between two D numbers. The distance function of D numbers
is an generalization of distance between two BPAs, which inherits the advantage
of Dempster-Shafer theory and strengthens the capability of uncertainty
modeling. An illustrative case is provided to demonstrate the effectiveness of
the proposed function.Comment: 29 pages, 7 figure
Tsallis entropy of complex networks
How complex of the complex networks has attracted many researchers to explore
it. The entropy is an useful method to describe the degree of the of
the complex networks. In this paper, a new method which is based on the Tsallis
entropy is proposed to describe the of the complex networks. The
results in this paper show that the complex of the complex networks not only
decided by the structure property of the complex networks, but also influenced
by the relationship between each nodes. In other word, which kinds of nodes are
chosen as the main part of the complex networks will influence the value of the
entropy of the complex networks. The value of q in the Tsallis entropy of the
complex networks is used to decided which kinds of nodes will be chosen as the
main part in the complex networks. The proposed Tsallis entropy of the complex
networks is a generalised method to describe the property of the complex
networks.Comment: 12 page
Tsallis information dimension of complex networks
The fractal and self-similarity properties are revealed in many complex
networks. In order to show the influence of different part in the complex
networks to the information dimension, we have proposed a new information
dimension based on Tsallis entropy namely Tsallis information dimension. The
Tsallis information dimension can show the fractal property from different
perspective by set different value of q.Comment: 14 pages, 4 figure
Local structure entropy of complex networks
Identifying influential nodes in the complex networks is of theoretical and
practical significance. There are many methods are proposed to identify the
influential nodes in the complex networks. In this paper, a local structure
entropy which is based on the degree centrality and the statistical mechanics
is proposed to identifying the influential nodes in the complex network.
In the definition of the local structure entropy, each node has a local
network, the local structure entropy of each node is equal to the structure
entropy of the local network. The main idea in the local structure entropy is
try to use the influence of the local network to replace the node's influence
on the whole network.
The influential nodes which are identified by the local structure entropy are
the intermediate nodes in the network. The intermediate nodes which connect
those nodes with a big value of degree.
We use the (SI) model to evaluate the performance of
the influential nodes which are identified by the local structure entropy. In
the SI model the nodes use as the source of infection. According to the SI
model, the bigger the percentage of the infective nodes in the network the
important the node to the whole networks. The simulation on four real networks
show that the proposed method is efficacious and rationality to identify the
influential nodes in the complex networks.Comment: 10 pages, 12 figure
Making Availability as a Service in the Clouds
Cloud computing has achieved great success in modern IT industry as an
excellent computing paradigm due to its flexible management and elastic
resource sharing. To date, cloud computing takes an irrepalceable position in
our socioeconomic system and influences almost every aspect of our daily life.
However, it is still in its infancy, many problems still exist.Besides the
hotly-debated security problem, availability is also an urgent issue.With the
limited power of availability mechanisms provided in present cloud platform, we
can hardly get detailed availability information of current applications such
as the root causes of availability problem,mean time to failure, etc. Thus a
new mechanism based on deep avaliability analysis is neccessary and
benificial.Following the prevalent terminology 'XaaS',this paper proposes a new
win-win concept for cloud users and providers in term of 'Availability as a
Service' (abbreviated as 'AaaS').The aim of 'AaaS' is to provide comprehensive
and aimspecific runtime avaliabilty analysis services for cloud users by
integrating plent of data-driven and modeldriven approaches. To illustrate this
concept, we realize a prototype named 'EagleEye' with all features of 'AaaS'.
By subscribing corresponding services in 'EagleEye', cloud users could get
specific availability information of their applications deployed in cloud
platform. We envision this new kind of service will be merged into the cloud
management mechanism in the near future.Comment:
Multi-view Point Cloud Registration with Adaptive Convergence Threshold and its Application on 3D Model Retrieval
Multi-view point cloud registration is a hot topic in the communities of
multimedia technology and artificial intelligence (AI). In this paper, we
propose a framework to reconstruct the 3D models by the multi-view point cloud
registration algorithm with adaptive convergence threshold, and subsequently
apply it to 3D model retrieval. The iterative closest point (ICP) algorithm is
implemented combining with the motion average algorithm for the registration of
multi-view point clouds. After the registration process, we design applications
for 3D model retrieval. The geometric saliency map is computed based on the
vertex curvature. The test facial triangle is then generated based on the
saliency map, which is applied to compare with the standard facial triangle.
The face and non-face models are then discriminated. The experiments and
comparisons prove the effectiveness of the proposed framework
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