60,955 research outputs found
Information dynamics algorithm for detecting communities in networks
The problem of community detection is relevant in many scientific
disciplines, from social science to statistical physics. Given the impact of
community detection in many areas, such as psychology and social sciences, we
have addressed the issue of modifying existing well performing algorithms by
incorporating elements of the domain application fields, i.e. domain-inspired.
We have focused on a psychology and social network - inspired approach which
may be useful for further strengthening the link between social network studies
and mathematics of community detection. Here we introduce a community-detection
algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method
by considering networks' nodes as agents capable to take decisions. In this
framework we have introduced a memory factor to mimic a typical human behavior
such as the oblivion effect. The method is based on information diffusion and
it includes a non-linear processing phase. We test our method on two classical
community benchmark and on computer generated networks with known community
structure. Our approach has three important features: the capacity of detecting
overlapping communities, the capability of identifying communities from an
individual point of view and the fine tuning the community detectability with
respect to prior knowledge of the data. Finally we discuss how to use a Shannon
entropy measure for parameter estimation in complex networks.Comment: Submitted to "Communication in Nonlinear Science and Numerical
Simulation
Patterns of trading profiles at the Nordic Stock Exchange. A correlation-based approach
We investigate the trading behavior of Finnish individual investors trading
the stocks selected to compute the OMXH25 index in 2003 by tracking the
individual daily investment decisions. We verify that the set of investors is a
highly heterogeneous system under many aspects. We introduce a correlation
based method that is able to detect a hierarchical structure of the trading
profiles of heterogeneous individual investors. We verify that the detected
hierarchical structure is highly overlapping with the cluster structure
obtained with the approach of statistically validated networks when an
appropriate threshold of the hierarchical trees is used. We also show that the
combination of the correlation based method and of the statistically validated
method provides a way to expand the information about the clusters of investors
with similar trading profiles in a robust and reliable way.Comment: 25 pages, 8 figure
Latent Class Model with Application to Speaker Diarization
In this paper, we apply a latent class model (LCM) to the task of speaker
diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in
that it uses soft information and avoids premature hard decisions in its
iterations. In contrast to the VB method, which is based on a generative model,
LCM provides a framework allowing both generative and discriminative models.
The discriminative property is realized through the use of i-vector (Ivec),
probabilistic linear discriminative analysis (PLDA), and a support vector
machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and
LCM-Ivec-Hybrid are introduced. In addition, three further improvements are
applied to enhance its performance. 1) Adding neighbor windows to extract more
speaker information for each short segment. 2) Using a hidden Markov model to
avoid frequent speaker change points. 3) Using an agglomerative hierarchical
cluster to do initialization and present hard and soft priors, in order to
overcome the problem of initial sensitivity. Experiments on the National
Institute of Standards and Technology Rich Transcription 2009 speaker
diarization database, under the condition of a single distant microphone, show
that the diarization error rate (DER) of the proposed methods has substantial
relative improvements compared with mainstream systems. Compared to the VB
method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and
LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments
on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial
conditions also show that the proposed LCM-Ivec-Hybrid system has the best
overall performance
Enhanced free space detection in multiple lanes based on single CNN with scene identification
Many systems for autonomous vehicles' navigation rely on lane detection.
Traditional algorithms usually estimate only the position of the lanes on the
road, but an autonomous control system may also need to know if a lane marking
can be crossed or not, and what portion of space inside the lane is free from
obstacles, to make safer control decisions. On the other hand, free space
detection algorithms only detect navigable areas, without information about
lanes. State-of-the-art algorithms use CNNs for both tasks, with significant
consumption of computing resources. We propose a novel approach that estimates
the free space inside each lane, with a single CNN. Additionally, adding only a
small requirement concerning GPU RAM, we infer the road type, that will be
useful for path planning. To achieve this result, we train a multi-task CNN.
Then, we further elaborate the output of the network, to extract polygons that
can be effectively used in navigation control. Finally, we provide a
computationally efficient implementation, based on ROS, that can be executed in
real time. Our code and trained models are available online.Comment: Will appear in the 2019 IEEE Intelligent Vehicles Symposium (IV 2019
Élőlények kollektív viselkedésének statisztikus fizikája = Statistical physics of the collective behaviour of organisms
Experiments: We have carried out quantitative experiments on the collective motion of cells as a function of their density. A sharp transition could be observed from the random motility in sparse cultures to the flocking of dense islands of cells. Using ultra light GPS devices developed by us, we have determined the existing hierarchical relations within a flock of 10 homing pigeons. Modelling: From the simulations of our new model of flocking we concluded that the information exchange between particles was maximal at the critical point, in which the interplay of such factors as the level of noise, the tendency to follow the direction and the acceleration of others results in large fluctuations. Analysis: We have proposed a novel link-density based approach to finding overlapping communities in large networks. The algorithm used for the implementation of this technique is very efficient for most real networks, and provides full statistics quickly. Correspondingly, we have developed a by now popular, user-friendly, freely downloadable software for finding overlapping communities. Extending our method to the time-dependent regime, we found that large groups in evolving networks persist for longer if they are capable of dynamically altering their membership, thus, an ability to change the group composition results in better adaptability. We also showed that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime. Experiments: We have carried out quantitative experiments on the collective motion of cells as a function of their density. A sharp transition could be observed from the random motility in sparse cultures to the flocking of dense islands of cells. Using ultra light GPS devices developed by us, we have determined the existing hierarchical relations within a flock of 10 homing pigeons. Modelling: From the simulations of our new model of flocking we concluded that the information exchange between particles was maximal at the critical point, in which the interplay of such factors as the level of noise, the tendency to follow the direction and the acceleration of others results in large fluctuations. Analysis: We have proposed a novel link-density based approach to finding overlapping communities in large networks. The algorithm used for the implementation of this technique is very efficient for most real networks, and provides full statistics quickly. Correspondingly, we have developed a by now popular, user-friendly, freely downloadable software for finding overlapping communities. Extending our method to the time-dependent regime, we found that large groups in evolving networks persist for longer if they are capable of dynamically altering their membership, thus, an ability to change the group composition results in better adaptability. We also showed that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime
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