30,862 research outputs found
voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data
Relationships in scientific data, such as the numerical and spatial
distribution relations of features in univariate data, the scalar-value
combinations' relations in multivariate data, and the association of volumes in
time-varying and ensemble data, are intricate and complex. This paper presents
voxel2vec, a novel unsupervised representation learning model, which is used to
learn distributed representations of scalar values/scalar-value combinations in
a low-dimensional vector space. Its basic assumption is that if two scalar
values/scalar-value combinations have similar contexts, they usually have high
similarity in terms of features. By representing scalar values/scalar-value
combinations as symbols, voxel2vec learns the similarity between them in the
context of spatial distribution and then allows us to explore the overall
association between volumes by transfer prediction. We demonstrate the
usefulness and effectiveness of voxel2vec by comparing it with the isosurface
similarity map of univariate data and applying the learned distributed
representations to feature classification for multivariate data and to
association analysis for time-varying and ensemble data.Comment: Accepted by IEEE Transaction on Visualization and Computer Graphics
(TVCG
RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection
The brain electrical activity presents several short events during sleep that
can be observed as distinctive micro-structures in the electroencephalogram
(EEG), such as sleep spindles and K-complexes. These events have been
associated with biological processes and neurological disorders, making them a
research topic in sleep medicine. However, manual detection limits their study
because it is time-consuming and affected by significant inter-expert
variability, motivating automatic approaches. We propose a deep learning
approach based on convolutional and recurrent neural networks for sleep EEG
event detection called Recurrent Event Detector (RED). RED uses one of two
input representations: a) the time-domain EEG signal, or b) a complex
spectrogram of the signal obtained with the Continuous Wavelet Transform (CWT).
Unlike previous approaches, a fixed time window is avoided and temporal context
is integrated to better emulate the visual criteria of experts. When evaluated
on the MASS dataset, our detectors outperform the state of the art in both
sleep spindle and K-complex detection with a mean F1-score of at least 80.9%
and 82.6%, respectively. Although the CWT-domain model obtained a similar
performance than its time-domain counterpart, the former allows in principle a
more interpretable input representation due to the use of a spectrogram. The
proposed approach is event-agnostic and can be used directly to detect other
types of sleep events.Comment: 8 pages, 5 figures. In proceedings of the 2020 International Joint
Conference on Neural Networks (IJCNN 2020
Drift Correction Methods for gas Chemical Sensors in Artificial Olfaction Systems: Techniques and Challenges
In this chapter the authors introduce the main challenges faced when developing drift correction techniques and will propose a deep overview of state-of-the-art methodologies that have been proposed in the scientific literature trying to underlying pros and cons of these techniques and focusing on challenges still open and waiting for solution
Design Issues and Challenges of File Systems for Flash Memories
This chapter discusses how to properly address the issues of using NAND flash memories as mass-memory devices from the native file system standpoint. We hope that the ideas and the solutions proposed in this chapter will be a valuable starting point for designers of NAND flash-based mass-memory devices
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