2,990,047 research outputs found
Collaboration networks from a large CV database: dynamics, topology and bonus impact
Understanding the dynamics of research production and collaboration may
reveal better strategies for scientific careers, academic institutions and
funding agencies. Here we propose the use of a large and multidisciplinar
database of scientific curricula in Brazil, namely, the Lattes Platform, to
study patterns of scientific production and collaboration. In this database,
detailed information about publications and researchers are made available by
themselves so that coauthorship is unambiguous and individuals can be evaluated
by scientific productivity, geographical location and field of expertise. Our
results show that the collaboration network is growing exponentially for the
last three decades, with a distribution of number of collaborators per
researcher that approaches a power-law as the network gets older. Moreover,
both the distributions of number of collaborators and production per researcher
obey power-law behaviors, regardless of the geographical location or field,
suggesting that the same universal mechanism might be responsible for network
growth and productivity.We also show that the collaboration network under
investigation displays a typical assortative mixing behavior, where teeming
researchers (i.e., with high degree) tend to collaborate with others alike.
Finally, our analysis reveals that the distinctive collaboration profile of
researchers awarded with governmental scholarships suggests a strong bonus
impact on their productivity.Comment: 8 pages, 8 figure
Comparison of echo state network output layer classification methods on noisy data
Echo state networks are a recently developed type of recurrent neural network
where the internal layer is fixed with random weights, and only the output
layer is trained on specific data. Echo state networks are increasingly being
used to process spatiotemporal data in real-world settings, including speech
recognition, event detection, and robot control. A strength of echo state
networks is the simple method used to train the output layer - typically a
collection of linear readout weights found using a least squares approach.
Although straightforward to train and having a low computational cost to use,
this method may not yield acceptable accuracy performance on noisy data.
This study compares the performance of three echo state network output layer
methods to perform classification on noisy data: using trained linear weights,
using sparse trained linear weights, and using trained low-rank approximations
of reservoir states. The methods are investigated experimentally on both
synthetic and natural datasets. The experiments suggest that using regularized
least squares to train linear output weights is superior on data with low
noise, but using the low-rank approximations may significantly improve accuracy
on datasets contaminated with higher noise levels.Comment: 8 pages. International Joint Conference on Neural Networks (IJCNN
2017
A morphospace of functional configuration to assess configural breadth based on brain functional networks
The best approach to quantify human brain functional reconfigurations in
response to varying cognitive demands remains an unresolved topic in network
neuroscience. We propose that such functional reconfigurations may be
categorized into three different types: i) Network Configural Breadth, ii)
Task-to-Task transitional reconfiguration, and iii) Within-Task
reconfiguration. In order to quantify these reconfigurations, we propose a
mesoscopic framework focused on functional networks (FNs) or communities. To do
so, we introduce a 2D network morphospace that relies on two novel mesoscopic
metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology
and integration of information within and between a reference set of FNs. In
this study, we use this framework to quantify the Network Configural Breadth
across different tasks. We show that the metrics defining this morphospace can
differentiate FNs, cognitive tasks and subjects. We also show that network
configural breadth significantly predicts behavioral measures, such as episodic
memory, verbal episodic memory, fluid intelligence and general intelligence. In
essence, we put forth a framework to explore the cognitive space in a
comprehensive manner, for each individual separately, and at different levels
of granularity. This tool that can also quantify the FN reconfigurations that
result from the brain switching between mental states.Comment: main article: 24 pages, 8 figures, 2 tables. supporting information:
11 pages, 5 figure
Dynamical complexity in the perception-based network formation model
Many link formation mechanisms for the evolution of social networks have been
successful to reproduce various empirical findings in social networks. However,
they have largely ignored the fact that individuals make decisions on whether
to create links to other individuals based on cost and benefit of linking, and
the fact that individuals may use perception of the network in their decision
making. In this paper, we study the evolution of social networks in terms of
perception-based strategic link formation. Here each individual has her own
perception of the actual network, and uses it to decide whether to create a
link to another individual. An individual with the least perception accuracy
can benefit from updating her perception using that of the most accurate
individual via a new link. This benefit is compared to the cost of linking in
decision making. Once a new link is created, it affects the accuracies of other
individuals' perceptions, leading to a further evolution of the actual network.
As for initial actual networks, we consider homogeneous and heterogeneous
cases. The homogeneous initial actual network is modeled by Erd\H{o}s-R\'enyi
(ER) random networks, while we take a star network for the heterogeneous case.
In any cases, individual perceptions of the actual network are modeled by ER
random networks with controllable linking probability. Then the stable link
density of the actual network is found to show discontinuous transitions or
jumps according to the cost of linking. As the number of jumps is the
consequence of the dynamical complexity, we discuss the effect of initial
conditions on the number of jumps to find that the dynamical complexity
strongly depends on how much individuals initially overestimate or
underestimate the link density of the actual network. For the heterogeneous
case, the role of the highly connected individual as an information spreader is
discussed.Comment: 8 pages, 7 figure
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