12,693 research outputs found
Predicting Scientific Success Based on Coauthorship Networks
We address the question to what extent the success of scientific articles is
due to social influence. Analyzing a data set of over 100000 publications from
the field of Computer Science, we study how centrality in the coauthorship
network differs between authors who have highly cited papers and those who do
not. We further show that a machine learning classifier, based only on
coauthorship network centrality measures at time of publication, is able to
predict with high precision whether an article will be highly cited five years
after publication. By this we provide quantitative insight into the social
dimension of scientific publishing - challenging the perception of citations as
an objective, socially unbiased measure of scientific success.Comment: 21 pages, 2 figures, incl. Supplementary Materia
Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning
Accurate localization of brain regions responsible for language and cognitive
functions in Epilepsy patients should be carefully determined prior to surgery.
Electrocorticography (ECoG)-based Real Time Functional Mapping (RTFM) has been
shown to be a safer alternative to the electrical cortical stimulation mapping
(ESM), which is currently the clinical/gold standard. Conventional methods for
analyzing RTFM signals are based on statistical comparison of signal power at
certain frequency bands. Compared to gold standard (ESM), they have limited
accuracies when assessing channel responses.
In this study, we address the accuracy limitation of the current RTFM signal
estimation methods by analyzing the full frequency spectrum of the signal and
replacing signal power estimation methods with machine learning algorithms,
specifically random forest (RF), as a proof of concept. We train RF with power
spectral density of the time-series RTFM signal in supervised learning
framework where ground truth labels are obtained from the ESM. Results obtained
from RTFM of six adult patients in a strictly controlled experimental setup
reveal the state of the art detection accuracy of for the
language comprehension task, an improvement of over the conventional
RTFM estimation method. To the best of our knowledge, this is the first study
exploring the use of machine learning approaches for determining RTFM signal
characteristics, and using the whole-frequency band for better region
localization. Our results demonstrate the feasibility of machine learning based
RTFM signal analysis method over the full spectrum to be a clinical routine in
the near future.Comment: This paper will appear in the Proceedings of IEEE International
Conference on Systems, Man and Cybernetics (SMC) 201
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