Automatic Detection of Slow Slip Events Using the PICCA: Application to Chilean GNSS Data

The detection of transient events related to slow earthquakes in GNSS positional time series is key to understanding seismogenic processes in subduction zones. Here, we present a novel Principal and Independent Components Correlation Analysis (PICCA) method that allows for the temporal and spatial detection of transient signals. The PICCA is based on an optimal combination of the principal (PCA) and independent component analysis (ICA) of positional time series of a GNSS network. We assume that the transient signal is mostly contained in one of the principal or independent components. To detect the transient, we applied a method where correlations between sliding windows of each PCA/ICA component and each time series are calculated, obtaining the stations affected by the slow slip event and the onset time from the resulting correlation peaks. We first tested and calibrated the method using synthetic signals from slow earthquakes of different magnitudes and durations and modelled their effect in the network of GNSS stations in Chile. Then, we analyzed three transient events related to slow earthquakes recorded in Chile, in the areas of Iquique, Copiapó, and Valparaíso. For synthetic data, a 150 days event was detected using the PCA-based method, while a 3 days event was detected using the ICA-based method. For the real data, a long-term transient was detected by PCA, while a 16 days transient was detected by ICA. It is concluded that simultaneous use of both signal separation methods (PICCA) is more effective when searching for transient events. The PCA method is more useful for long-term events, while the ICA method is better suited to recognize events of short duration. PICCA is a promising tool to detect transients of different characteristics in GNSS time series, which will be used in a next stage to generate a catalog of SSEs in Chile.

robustica: customizable robust independent component analysis

Motivation: Independent Component Analysis (ICA) allows the dissection of omic datasets into modules that help to interpret global molecular signatures. The inherent randomness of this algorithm can be overcome by clustering many iterations of ICA together to obtain robust components. Existing algorithms for robust ICA are dependent on the choice of clustering method and on computing a potentially biased and large Pearson distance matrix. Results: We present robustica, a Python-based package to compute robust independent components with a fully customizable clustering algorithm and distance metric. Here, we exploited its customizability to revisit and optimize robust ICA systematically. From the 6 popular clustering algorithms considered, DBSCAN performed the best at clustering independent components across ICA iterations. After confirming the bias introduced with Pearson distances, we created a subroutine that infers and corrects the components′ signs across ICA iterations to enable using Euclidean distance. Our subroutine effectively corrected the bias while simultaneously increasing the precision, robustness, and memory efficiency of the algorithm. Finally, we show the applicability of robustica by dissecting over 500 tumor samples from low-grade glioma (LGG) patients, where we define a new gene expression module with the key modulators of tumor aggressiveness downregulated upon IDH1 mutation. Availability and implementation: robustica is written in Python under the open-source BSD 3-Clause license. The source code and documentation are freely available at <A HREF="https://github.com/CRG-CNAG/robustica">https://github.com/CRG-CNAG/robustica</A>. Additionally, all scripts to reproduce the work presented are available at <A HREF="https://github.com/MiqG/publication_robustica">https://github.com/MiqG/publication_robustica</A>.

Structure of the plane waves for a spin 3/2 particle, massive and massless cases, gauge symmetry

The structure of the plane waves solutions for a relativistic spin 3/2 particle described by 16-component vector-bispinor is studied. In massless case, two representations are used: Rarita – Schwinger basis, and a special second basis in which the wave equation contains the Levi-Civita tensor. In the second representation it becomes evident the existence of gauge solutions in the form of 4-gradient of an arbitrary bispinor. General solution of the massless equation consists of six independent components, it is proved in an explicit form that four of them may be identified with the gauge solutions, and therefore may be removed. This procedure is performed in the Rarita – Schwinger basis as well. For the massive case, in Rarita – Schwinger basis four independent solutions are constructed explicitly.

Towards Continuous Deployment for Blockchain

Ensuring a production-ready state of the application under development is the immanent feature of the continuous delivery approach. In a blockchain network, nodes communicate, storing data in a decentralized manner. Each node executes the same business application but operates in a distinct execution environment. The literature lacks research, focusing on continuous practices for blockchain and distributed ledger technology. In particular, such works with support for both software development disciplines of design and deployment. Artifacts from considered disciplines have been placed in the 1 + 5 architectural views model. The approach aims to ensure the continuous deployment of containerized blockchain distributed applications. The solution has been divided into two independent components: Delivery and deployment. They interact through Git distributed version control. Dedicated GitHub repositories should store the business application and deployment configurations for nodes. The delivery component has to ensure the deployment package in the actual version of the business application with the node-specific up-to-date version of deployment configuration files. The deployment component is responsible for providing running distributed applications in containers for all blockchain nodes. The approach uses Jenkins and Kubernetes frameworks. For the sake of verification, preliminary tests have been conducted for the Electricity Consumption and Supply Management blockchain-based system for prosumers of renewable energy.

The Role of LinkedIn Profile on Hiring Preference: Evidence from Bangladeshi Employers

This empirical paper aims to identify the role of LinkedIn, a profession based social networking site (SNS) on overall hiring preference (HP) in Bangladesh. As the independent components, the author considered LinkedIn profile richness (LPR), LinkedIn skill endorsement (LSE) and self-presentation on LinkedIn (SL). The author collected primary data based on 391 survey responses. For descriptive statistics, the author utilized SPSS (version 24) and for examining the hypotheses, he utilized structural equation modeling technique through AMOS 24. After a careful and thorough analysis, it was found that all the independent components have significant positive roles over HP. This empirical paper is expected to be a founding guideline for the jobseekers having active LinkedIn profiles. Further, it can also guide the hiring managers to formulate and implement an efficient social media policy (SMP) for hiring.

The structure of studentsʼ subjective well-being

Introduction. Subjective well-being is one of the indicators of success and a basis of person`s socio-psychological adjustment to uncertain situations and unstable social relations. The complexity of this phenomenon requires clarifying its structure. Aim. To determine the structure of studentsʼ subjective well-being. Methods. Cognitive Features of Subjective Well-Being (KOSB-4) (O. Kaliuk, O. Savchenko), Subjective Well-Being Scale (A. Perrudet-Badoux, G. Mendelsohn, J. Chiche, adapted by M. Sokolova), Life Satisfaction Index A, LSIA (B.L. Neugarten, adapted by N. Panina), Arousability and Optimism Scale, AOS (I.S. Schuller, A.L. Comunian, adapted by N. Vodopyanova). The methodological basis is a structural-functional approach. Factor and correlation analyses were done using «STATISTICA 10.0». Results. Empirical verification of the author's model of subjective well-being revealed the existence of three independent components in its structure (cognitive-behavioral, emotional, and contrasting). Conclusions. Students’ cognitive and behavioral aspects of well-being are not separated, they form a single factor. There is a polarity in well-being in the form of positive and negative factors.

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography

Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye movements and muscle artifacts from electroencephalography (EEG). A rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts’ involvement. As also revealed by our study, experts’ opinions about the nature of a component often disagree, highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm to remove artifacts and find specific patterns in EEG signals using ICA decomposition based on accumulated experts’ knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on the estimation of IC time-series, IC amplitude topography, and spectral power distribution. The platform allows supervised machine learning (ML) model training and re-training on available data subsamples for better performance in specific tasks (i.e., movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model could detect noisy IC and components related to the functional brain oscillations such as alpha and mu rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will improve ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community.

Independent components of spatial-temporal structure of chlorophyll a patterns in the upper layer of the north-western shelf of the Black Sea

In the present study, the results of independent component decomposition of satellite-derived chlorophyll a (Chla) patterns for the north-western part of the Black Sea are presented. The study has been carried out on the basis of the DINEOF-reconstructed dataset of 8-day average log-transformed Chla (alChla) patterns for 1997-2016. The alChla patterns were decomposed into six independent components of its spatio-temporal variability in the north-western shelf of the Black Sea. The independent components reflect the spatial distribution of alChla anomalies which are likely to be formed under the influence of sea circulation factors driven by wind. The paper presents the results of the analysis of the intra-annual variability of independent components. The interpretation of the patterns of intra-annual independent components variability is given, taking into account the seasonal variability of the wind factor, the flow of the Danube, the Dnieper and Southern Bug rivers and the fact of modulation of independent components dynamics by seasonal phytoplankton succession.

Optimal dimensionality selection for independent component analysis of transcriptomic data

Background Independent component analysis is an unsupervised machine learning algorithm that separates a set of mixed signals into a set of statistically independent source signals. Applied to high-quality gene expression datasets, independent component analysis effectively reveals both the source signals of the transcriptome as co-regulated gene sets, and the activity levels of the underlying regulators across diverse experimental conditions. Two major variables that affect the final gene sets are the diversity of the expression profiles contained in the underlying data, and the user-defined number of independent components, or dimensionality, to compute. Availability of high-quality transcriptomic datasets has grown exponentially as high-throughput technologies have advanced; however, optimal dimensionality selection remains an open question. Methods We computed independent components across a range of dimensionalities for four gene expression datasets with varying dimensions (both in terms of number of genes and number of samples). We computed the correlation between independent components across different dimensionalities to understand how the overall structure evolves as the number of user-defined components increases. We then measured how well the resulting gene clusters reflected known regulatory mechanisms, and developed a set of metrics to assess the accuracy of the decomposition at a given dimension. Results We found that over-decomposition results in many independent components dominated by a single gene, whereas under-decomposition results in independent components that poorly capture the known regulatory structure. From these results, we developed a new method, called OptICA, for finding the optimal dimensionality that controls for both over- and under-decomposition. Specifically, OptICA selects the highest dimension that produces a low number of components that are dominated by a single gene. We show that OptICA outperforms two previously proposed methods for selecting the number of independent components across four transcriptomic databases of varying sizes. Conclusions OptICA avoids both over-decomposition and under-decomposition of transcriptomic datasets resulting in the best representation of the organism’s underlying transcriptional regulatory network.