Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis
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Text summarization is considered as a challenging task in the NLP community. The availability of datasets for the task of multilingual text summarization is rare, and such datasets are difficult to construct. In this work, we
build an abstract text summari ...
Association for Computational Linguistics (ACL)2019
Here we demonstrate the suitability of a local mutual information measure for estimating the temporal dynamics of cross-frequency coupling (CFC) in brain electrophysiological signals. In CFC, concurrent activity streams in different frequency ranges intera ...
This paper presents an early-stage application of the design science research (DSR) method to obtain a new idea selection approach, which uses clustering to filter ideas while taking into account the seeker’s goals and the learning dynamics. Most of previo ...
In materials with nearly commensurate band filling the electron liquid may spontaneously separate into components with distinct properties, yielding complex intra- and interunit cell ordering patterns and a reduced dimensionality. Polarization-dependent an ...
Purpose of ReviewData science is an exploding trans-disciplinary field that aims to harness the power of data to gain information or insights on researcher-defined topics of interest. In this paper, we review how data science can help advance environmental ...
A Multichromatic Calibration (MC) method of at least a spectral sensor which is one of a list comprising at least a spectrometer, a multispectral sensor, a hyperspectral sensor, a spectral camera, a color camera. The method comprises a. generating a plural ...
Covariance operators play a fundamental role in functional data analysis, providing the canonical means to analyse functional variation via the celebrated Karhunen-Loève expansion. These operators may themselves be subject to variation, for instance in con ...
The 2.5 quintillion bytes of data created each day brings new opportunities, but also new stimulating challenges for the discrete choice community. Opportunities because more and more new and larger data sets will undoubtedly become available in the future ...
We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The virtues of neural networks as unbiased function approximants make them particu ...
In the Internet of Things (IoT), the large volume of data generated by sensors poses significant computational challenges in resource-constrained environments. Most existing machine learning algorithms are unable to train a proper model using a significant ...
