7,226 research outputs found
Free vacuum for loop quantum gravity
We linearize extended ADM-gravity around the flat torus, and use the
associated Fock vacuum to construct a state that could play the role of a free
vacuum in loop quantum gravity. The state we obtain is an element of the
gauge-invariant kinematic Hilbert space and restricted to a cutoff graph, as a
natural consequence of the momentum cutoff of the original Fock state. It has
the form of a Gaussian superposition of spin networks. We show that the peak of
the Gaussian lies at weave-like states and derive a relation between the
coloring of the weaves and the cutoff scale. Our analysis indicates that the
peak weaves become independent of the cutoff length when the latter is much
smaller than the Planck length. By the same method, we also construct
multiple-graviton states. We discuss the possible use of these states for
deriving a perturbation series in loop quantum gravity.Comment: 30 pages, 3 diagrams, treatment of phase factor adde
Complex Network Tools to Understand the Behavior of Criminality in Urban Areas
Complex networks are nowadays employed in several applications. Modeling
urban street networks is one of them, and in particular to analyze criminal
aspects of a city. Several research groups have focused on such application,
but until now, there is a lack of a well-defined methodology for employing
complex networks in a whole crime analysis process, i.e. from data preparation
to a deep analysis of criminal communities. Furthermore, the "toolset"
available for those works is not complete enough, also lacking techniques to
maintain up-to-date, complete crime datasets and proper assessment measures. In
this sense, we propose a threefold methodology for employing complex networks
in the detection of highly criminal areas within a city. Our methodology
comprises three tasks: (i) Mapping of Urban Crimes; (ii) Criminal Community
Identification; and (iii) Crime Analysis. Moreover, it provides a proper set of
assessment measures for analyzing intrinsic criminality of communities,
especially when considering different crime types. We show our methodology by
applying it to a real crime dataset from the city of San Francisco - CA, USA.
The results confirm its effectiveness to identify and analyze high criminality
areas within a city. Hence, our contributions provide a basis for further
developments on complex networks applied to crime analysis.Comment: 7 pages, 2 figures, 14th International Conference on Information
Technology : New Generation
POISED: Spotting Twitter Spam Off the Beaten Paths
Cybercriminals have found in online social networks a propitious medium to
spread spam and malicious content. Existing techniques for detecting spam
include predicting the trustworthiness of accounts and analyzing the content of
these messages. However, advanced attackers can still successfully evade these
defenses.
Online social networks bring people who have personal connections or share
common interests to form communities. In this paper, we first show that users
within a networked community share some topics of interest. Moreover, content
shared on these social network tend to propagate according to the interests of
people. Dissemination paths may emerge where some communities post similar
messages, based on the interests of those communities. Spam and other malicious
content, on the other hand, follow different spreading patterns.
In this paper, we follow this insight and present POISED, a system that
leverages the differences in propagation between benign and malicious messages
on social networks to identify spam and other unwanted content. We test our
system on a dataset of 1.3M tweets collected from 64K users, and we show that
our approach is effective in detecting malicious messages, reaching 91%
precision and 93% recall. We also show that POISED's detection is more
comprehensive than previous systems, by comparing it to three state-of-the-art
spam detection systems that have been proposed by the research community in the
past. POISED significantly outperforms each of these systems. Moreover, through
simulations, we show how POISED is effective in the early detection of spam
messages and how it is resilient against two well-known adversarial machine
learning attacks
Dynamics of continuous-time quantum walks in restricted geometries
We study quantum transport on finite discrete structures and we model the
process by means of continuous-time quantum walks. A direct and effective
comparison between quantum and classical walks can be attained based on the
average displacement of the walker as a function of time. Indeed, a fast growth
of the average displacement can be advantageously exploited to build up
efficient search algorithms. By means of analytical and numerical
investigations, we show that the finiteness and the inhomogeneity of the
substrate jointly weaken the quantum walk performance. We further highlight the
interplay between the quantum-walk dynamics and the underlying topology by
studying the temporal evolution of the transfer probability distribution and
the lower bound of long time averages.Comment: 25 pages, 13 figure
Recommended from our members
Multi-objective community detection applied to social and COVID-19 constructed networks
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonCommunity Detection plays an integral part in network analysis, as it facilitates understanding the structures and functional characteristics of the network. Communities organize real-world networks into densely connected groups of nodes. This thesis provides a critical analysis of the Community Detection and highlights the main areas including algorithms, evaluation metrics, applications, and datasets in social networks.
After defining the research gap, this thesis proposes two Attribute-Based Label Propagation algorithms that maximizes both Modularity and homogeneity. Homogeneity is considered as an objective function one time, and as a constraint another time. To better capture the homogeneity of real-world networks, a new Penalized Homogeneity degree (PHd) is proposed, that can be easily personalized based on the network characteristics.
For the first time, COVID-19 tracing data are utilized to form two dataset networks: one is based on the virus transition between the world countries. While the second dataset is an attributed network based on the virus transition among the contact-tracing in the Kingdom of Bahrain. This type of networks that is concerned in tracking a disease was not formed based on COVID-19 virus and has never been studied as a community detection problem. The proposed datasets are validated and tested in several experiments. The proposed Penalized Homogeneity measure is personalized and used to evaluate the proposed attributed network.
Extensive experiments and analysis are carried out to evaluate the proposed methods and benchmark the results with other well-known algorithms. The results are compared in terms of Modularity, proposed PHd, and accuracy measures. The proposed methods have achieved maximum performance among other methods, with 26.6% better performance in Modularity, and 33.96% in PHd on the proposed dataset, as well as noteworthy results on benchmarking datasets with improvement in Modularity measures of 7.24%, and 4.96% respectively, and proposed PHd values 27% and 81.9%
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