Sequential visibility-graph motifs

Visibility algorithms transform time series into graphs and encode dynamical information in their topology, paving the way for graph-theoretical time series analysis as well as building a bridge between nonlinear dynamics and network science. In this work we introduce and study the concept of sequential visibility graph motifs, smaller substructures of n consecutive nodes that appear with characteristic frequencies. We develop a theory to compute in an exact way the motif profiles associated to general classes of deterministic and stochastic dynamics. We find that this simple property is indeed a highly informative and computationally efficient feature capable to distinguish among different dynamics and robust against noise contamination. We finally confirm that it can be used in practice to perform unsupervised learning, by extracting motif profiles from experimental heart-rate series and being able, accordingly, to disentangle meditative from other relaxation states. Applications of this general theory include the automatic classification and description of physical, biological, and financial time series

Functional Multiplex PageRank

(7 pages, 5 figures)(7 pages, 5 figures)(7 pages, 5 figures

Triadic closure as a basic generating mechanism of communities in complex networks

R.K.D. and S.F. gratefully acknowledge MULTIPLEX, Grant No. 317532 of the European Commission

IonFlow: a galaxy tool for the analysis of ionomics data sets.

INTRODUCTION: Inductively coupled plasma mass spectrometry (ICP-MS) experiments generate complex multi-dimensional data sets that require specialist data analysis tools. OBJECTIVE: Here we describe tools to facilitate analysis of the ionome composed of high-throughput elemental profiling data. METHODS: IonFlow is a Galaxy tool written in R for ionomics data analysis and is freely accessible at https://github.com/wanchanglin/ionflow . It is designed as a pipeline that can process raw data to enable exploration and interpretation using multivariate statistical techniques and network-based algorithms, including principal components analysis, hierarchical clustering, relevance network extraction and analysis, and gene set enrichment analysis. RESULTS AND CONCLUSION: The pipeline is described and tested on two benchmark data sets of the haploid S. Cerevisiae ionome and of the human HeLa cell ionome

Visibility graphs for image processing.

The family of image visibility graphs (IVG/IHVG) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such operator in the scenario of image processing and image classication. We demonstrate that the link architecture of the image visibility graphs encapsulates relevant information on the structure of the images and we explore their potential as image filters. We introduce several graph features, including the novel concept of Visibility Patches, and show through several examples that these features are highly informative, computationally efficient and universally applicable for general pattern recognition and image classication tasks

Visibility graphs for image processing.

The family of image visibility graphs (IVG/IHVG) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such operator in the scenario of image processing and image classication. We demonstrate that the link architecture of the image visibility graphs encapsulates relevant information on the structure of the images and we explore their potential as image filters. We introduce several graph features, including the novel concept of Visibility Patches, and show through several examples that these features are highly informative, computationally efficient and universally applicable for general pattern recognition and image classication tasks

Designing and interpreting 'multi-omic' experiments that may change our understanding of biology.

Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment

Sequential motif profile of natural visibility graphs

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Extracting information from multiplex networks

11 pages; 5 figure

Test of candidate light distributors for the muon (g−-2) laser calibration system

The new muon (g-2) experiment E989 at Fermilab will be equipped with a laser calibration system for all the 1296 channels of the calorimeters. An integrating sphere and an alternative system based on an engineered diffuser have been considered as possible light distributors for the experiment. We present here a detailed comparison of the two based on temporal response, spatial uniformity, transmittance and time stability.Comment: accepted to Nucl.Instrum.Meth.