EGN-Based Optimization of the APSK Constellations for the Non-Linear Fiber Channel Based on the Symbol-Wise Mutual Information

Abstract Bigenic expression relationships are conventionally defined based on metrics such as Pearson or Spearman correlation that cannot typically detect latent, non-linear dependencies or require the relationship to be monotonic. Further, the combination of intrinsic and extrinsic noise as well as embedded relationships between sample sub-populations reduces the probability of extracting biologically relevant edges during the construction of gene co-expression networks (GCNs). In this report, we address these problems via our NetExtractor algorithm. NetExtractor examines all pairwise gene expression profiles first with Gaussian mixture models (GMMs) to identify sample sub-populations followed by mutual information (MI) analysis that is capable of detecting non-linear differential bigenic expression relationships. We applied NetExtractor to brain tissue RNA profiles from the Genotype-Tissue Expression (GTEx) project to obtain a brain tissue specific gene expression relationship network centered on cerebellar and cerebellar hemisphere enriched edges. We leveraged the PsychENCODE pre-frontal cortex (PFC) gene regulatory network (GRN) to construct a cerebellar cortex (cerebellar) GRN associated with transcriptionally active regions in cerebellar tissue. Thus, we demonstrate the utility of our NetExtractor approach to detect biologically relevant and novel non-linear binary gene relationships.

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A Unifying Framework for Mutual Information Methods for Use in Non-linear Optimisation

Computer Vision – ECCV 2006 - Lecture Notes in Computer Science ◽

10.1007/11744023_29 ◽

2006 ◽

pp. 365-378 ◽

Cited By ~ 10

Author(s):

Nicholas Dowson ◽

Richard Bowden

Keyword(s):

Mutual Information ◽

Non Linear ◽

Linear Optimisation

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Non-linear variable selection for artificial neural networks using partial mutual information

Environmental Modelling & Software ◽

10.1016/j.envsoft.2008.03.007 ◽

2008 ◽

Vol 23 (10-11) ◽

pp. 1312-1326 ◽

Cited By ~ 169

Author(s):

Robert J. May ◽

Holger R. Maier ◽

Graeme C. Dandy ◽

T.M.K. Gayani Fernando

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Mutual Information ◽

Variable Selection ◽

Non Linear ◽

Selection For ◽

Artificial Neural

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ANALYSIS OF THE TIME EVOLUTION OF NON-LINEAR FINANCIAL NETWORKS

Acta Universitatis Lodziensis Folia oeconomica ◽

10.18778/0208-6018.314.09 ◽

2015 ◽

Vol 3 (314) ◽

Author(s):

Paweł Fiedor

Keyword(s):

Mutual Information ◽

Stock Returns ◽

Time Evolution ◽

Correlation Coefficient ◽

Spanning Trees ◽

Stock Exchange ◽

Financial Networks ◽

Information Theoretic ◽

Non Linear ◽

Information Theoretic Measures

We treat financial markets as complex networks. It is commonplace to create a filtered graph (usually a Minimally Spanning Tree) based on an empirical correlation matrix. In our previous studies we have extended this standard methodology by exchanging Pearson’s correlation coefficient with information—theoretic measures of mutual information and mutual information rate, which allow for the inclusion of non-linear relationships. In this study we investigate the time evolution of financial networks, by applying a running window approach. Since information—theoretic measures are slow to converge, we base our analysis on the Hirschfeld-Gebelein-Rényi Maximum Correlation Coefficient, estimated by the Randomized Dependence Coefficient (RDC). It is defined in terms of canonical correlation analysis of random non-linear copula projections. On this basis we create Minimally Spanning Trees for each window moving along the studied time series, and analyse the time evolution of various network characteristics, and their market significance. We apply this procedure to a dataset describing logarithmic stock returns from Warsaw Stock Exchange for the years between 2006 and 2013, and comment on the findings, their applicability and significance.

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Mutual information based dimensionality reduction with application to non-linear regression

2010 IEEE International Workshop on Machine Learning for Signal Processing ◽

10.1109/mlsp.2010.5589176 ◽

2010 ◽

Cited By ~ 2

Author(s):

Lev Faivishevsky ◽

Jacob Goldberger

Keyword(s):

Linear Regression ◽

Mutual Information ◽

Dimensionality Reduction ◽

Non Linear

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Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-12699-2018 ◽

2018 ◽

Vol 18 (17) ◽

pp. 12699-12714 ◽

Cited By ~ 9

Author(s):

Martha A. Zaidan ◽

Ville Haapasilta ◽

Rishi Relan ◽

Pauli Paasonen ◽

Veli-Matti Kerminen ◽

...

Keyword(s):

Mutual Information ◽

Atmospheric Chemistry ◽

Data Science ◽

Sulfuric Acid Concentration ◽

Particle Formation ◽

Linear Process ◽

Theoretical Research ◽

New Particle Formation ◽

Non Linear ◽

Relevant Variables

Abstract. Atmospheric new-particle formation (NPF) is a very non-linear process that includes atmospheric chemistry of precursors and clustering physics as well as subsequent growth before NPF can be observed. Thanks to ongoing efforts, now there exists a tremendous amount of atmospheric data, obtained through continuous measurements directly from the atmosphere. This fact makes the analysis by human brains difficult but, on the other hand, enables the usage of modern data science techniques. Here, we calculate and explore the mutual information (MI) between observed NPF events (measured at Hyytiälä, Finland) and a wide variety of simultaneously monitored ambient variables: trace gas and aerosol particle concentrations, meteorology, radiation and a few derived quantities. The purpose of the investigations is to identify key factors contributing to the NPF. The applied mutual information method finds that the formation events are strongly linked to sulfuric acid concentration and water content, ultraviolet radiation, condensation sink (CS) and temperature. Previously, these quantities have been well-established to be important players in the phenomenon via dedicated field, laboratory and theoretical research. The novelty of this work is to demonstrate that the same results are now obtained by a data analysis method which operates without supervision and without the need of understanding the physics deeply. This suggests that the method is suitable to be implemented widely in the atmospheric field to discover other interesting phenomena and their relevant variables.

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Exaggerated Cortical Representation of Speech in Older Listeners: Mutual Information Analysis

10.1101/2019.12.18.881334 ◽

2019 ◽

Author(s):

Peng Zan ◽

Alessandro Presacco ◽

Samira Anderson ◽

Jonathan Z. Simon

Keyword(s):

Information Theory ◽

Mutual Information ◽

Speech Intelligibility ◽

Coarse Grained ◽

Speech Comprehension ◽

Late Component ◽

Age Related ◽

Cortical Responses ◽

Non Linear ◽

Speech Envelope

AbstractAging is associated with an exaggerated representation of the speech envelope in auditory cortex. The relationship between this age-related exaggerated response and a listener’s ability to understand speech in noise remains an open question. Here, information-theory-based analysis methods are applied to magnetoencephalography (MEG) recordings of human listeners, investigating their cortical responses to continuous speech, using the novel non-linear measure of phase-locked mutual information between the speech stimuli and cortical responses. The cortex of older listeners shows an exaggerated level of mutual information, compared to younger listeners, for both attended and unattended speakers. The mutual information peaks for several distinct latencies: early (∼50 ms), middle (∼100 ms) and late (∼200 ms). For the late component, the neural enhancement of attended over unattended speech is affected by stimulus SNR, but the direction of this dependency is reversed by aging. Critically, in older listeners and for the same late component, greater cortical exaggeration is correlated with decreased behavioral inhibitory control. This negative correlation also carries over to speech intelligibility in noise, where greater cortical exaggeration in older listeners is correlated with worse speech intelligibility scores. Finally, an age-related lateralization difference is also seen for the ∼100 ms latency peaks, where older listeners show a bilateral response compared to younger listeners’ right-lateralization. Thus, this information-theory-based analysis provides new, and less coarse-grained, results regarding age-related change in auditory cortical speech processing, and its correlation with cognitive measures, compared to related linear measures.New & NoteworthyCortical representations of natural speech are investigated using a novel non-linear approach based on mutual information. Cortical responses, phase-locked to the speech envelope, show an exaggerated level of mutual information associated with aging, appearing at several distinct latencies (∼50, ∼100 and ∼200 ms). Critically, for older listeners only, the ∼200 ms latency response components are correlated with specific behavioral measures, including behavioral inhibition and speech comprehension.

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