65,747 research outputs found
Enhancing the significance of gravitational wave bursts through signal classification
The quest to observe gravitational waves challenges our ability to
discriminate signals from detector noise. This issue is especially relevant for
transient gravitational waves searches with a robust eyes wide open approach,
the so called all- sky burst searches. Here we show how signal classification
methods inspired by broad astrophysical characteristics can be implemented in
all-sky burst searches preserving their generality. In our case study, we apply
a multivariate analyses based on artificial neural networks to classify waves
emitted in compact binary coalescences. We enhance by orders of magnitude the
significance of signals belonging to this broad astrophysical class against the
noise background. Alternatively, at a given level of mis-classification of
noise events, we can detect about 1/4 more of the total signal population. We
also show that a more general strategy of signal classification can actually be
performed, by testing the ability of artificial neural networks in
discriminating different signal classes. The possible impact on future
observations by the LIGO-Virgo network of detectors is discussed by analysing
recoloured noise from previous LIGO-Virgo data with coherent WaveBurst, one of
the flagship pipelines dedicated to all-sky searches for transient
gravitational waves
Deep transfer learning for improving single-EEG arousal detection
Datasets in sleep science present challenges for machine learning algorithms
due to differences in recording setups across clinics. We investigate two deep
transfer learning strategies for overcoming the channel mismatch problem for
cases where two datasets do not contain exactly the same setup leading to
degraded performance in single-EEG models. Specifically, we train a baseline
model on multivariate polysomnography data and subsequently replace the first
two layers to prepare the architecture for single-channel
electroencephalography data. Using a fine-tuning strategy, our model yields
similar performance to the baseline model (F1=0.682 and F1=0.694,
respectively), and was significantly better than a comparable single-channel
model. Our results are promising for researchers working with small databases
who wish to use deep learning models pre-trained on larger databases.Comment: Accepted for presentation at EMBC202
Fault detection in operating helicopter drive train components based on support vector data description
The objective of the paper is to develop a vibration-based automated procedure dealing with early detection of
mechanical degradation of helicopter drive train components using Health and Usage Monitoring Systems (HUMS) data. An anomaly-detection method devoted to the quantification of the degree of deviation of the mechanical state of a component from its nominal condition is developed. This method is based on an Anomaly Score (AS) formed by a combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CI), thus the operational variability is implicitly included in the model through the CI correlation. The problem of fault detection is then recast as a one-class classification problem in the space spanned by a set of CI, with the aim of a global differentiation between normal and anomalous observations, respectively related to healthy and supposedly faulty components. In this paper, a procedure based on an efficient one-class classification method that does not require any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows an efficient data description without the need of a significant amount of statistical data. Several analyses have been carried out in order to validate the proposed procedure, using flight vibration data collected from a H135, formerly known as EC135, servicing helicopter, for which micro-pitting damage on a gear was detected by HUMS and assessed through visual inspection. The capability of the proposed approach of providing better trade-off between false alarm rates and missed detection rates with respect to individual CI and to the AS obtained assuming jointly-Gaussian-distributed CI has been also analysed
Non-Parametric Causality Detection: An Application to Social Media and Financial Data
According to behavioral finance, stock market returns are influenced by
emotional, social and psychological factors. Several recent works support this
theory by providing evidence of correlation between stock market prices and
collective sentiment indexes measured using social media data. However, a pure
correlation analysis is not sufficient to prove that stock market returns are
influenced by such emotional factors since both stock market prices and
collective sentiment may be driven by a third unmeasured factor. Controlling
for factors that could influence the study by applying multivariate regression
models is challenging given the complexity of stock market data. False
assumptions about the linearity or non-linearity of the model and inaccuracies
on model specification may result in misleading conclusions.
In this work, we propose a novel framework for causal inference that does not
require any assumption about the statistical relationships among the variables
of the study and can effectively control a large number of factors. We apply
our method in order to estimate the causal impact that information posted in
social media may have on stock market returns of four big companies. Our
results indicate that social media data not only correlate with stock market
returns but also influence them.Comment: Physica A: Statistical Mechanics and its Applications 201
Evidence against the Detectability of a Hippocampal Place Code Using Functional Magnetic Resonance Imaging
Individual hippocampal neurons selectively increase their firing rates in specific spatial locations. As a population, these neurons provide a decodable representation of space that is robust against changes to sensory- and path-related cues. This neural code is sparse and distributed, theoretically rendering it undetectable with population recording methods such as functional magnetic resonance imaging (fMRI). Existing studies nonetheless report decoding spatial codes in the human hippocampus using such techniques. Here we present results from a virtual navigation experiment in humans in which we eliminated visual- and path-related confounds and statistical limitations present in existing studies, ensuring that any positive decoding results would represent a voxel-place code. Consistent with theoretical arguments derived from electrophysiological data and contrary to existing fMRI studies, our results show that although participants were fully oriented during the navigation task, there was no statistical evidence for a place code
Study of microRNAs-21/221 as potential breast cancer biomarkers in Egyptian women
microRNAs (miRNAs) play an important role in cancer prognosis. They are small molecules, approximately 17–25 nucleotides in length, and their high stability in human serum supports their use as novel diagnostic biomarkers of cancer and other pathological conditions. In this study, we analyzed the expression patterns of miR-21 and miR-221 in the serum from a total of 100 Egyptian female subjects with breast cancer, fibroadenoma, and healthy control subjects. Using microarray-based expression profiling followed by real-time polymerase chain reaction validation, we compared the levels of the two circulating miRNAs in the serum of patients with breast cancer (n = 50), fibroadenoma (n = 25), and healthy controls (n = 25). The miRNA SNORD68 was chosen as the housekeeping endogenous control. We found that the serum levels of miR-21 and miR-221 were significantly overexpressed in breast cancer patients compared to normal controls and fibroadenoma patients. Receiver Operating Characteristic (ROC) curve analysis revealed that miR-21 has greater potential in discriminating between breast cancer patients and the control group, while miR-221 has greater potential in discriminating between breast cancer and fibroadenoma patients. Classification models using k-Nearest Neighbor (kNN), Naïve Bayes (NB), and Random Forests (RF) were developed using expression levels of both miR-21 and miR-221. Best classification performance was achieved by NB Classification models, reaching 91% of correct classification. Furthermore, relative miR-221 expression was associated with histological tumor grades. Therefore, it may be concluded that both miR-21 and miR-221 can be used to differentiate between breast cancer patients and healthy controls, but that the diagnostic accuracy of serum miR-21 is superior to miR-221 for breast cancer prediction. miR-221 has more diagnostic power in discriminating between breast cancer and fibroadenoma patients. The overexpression of miR-221 has been associated with the breast cancer grade. We also demonstrated that the combined expression of miR-21 and miR-221can be successfully applied as breast cancer biomarkers
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