Detecting periodicity in experimental data using linear modeling techniques

Fourier spectral estimates and, to a lesser extent, the autocorrelation function are the primary tools to detect periodicities in experimental data in the physical and biological sciences. We propose a new method which is more reliable than traditional techniques, and is able to make clear identification of periodic behavior when traditional techniques do not. This technique is based on an information theoretic reduction of linear (autoregressive) models so that only the essential features of an autoregressive model are retained. These models we call reduced autoregressive models (RARM). The essential features of reduced autoregressive models include any periodicity present in the data. We provide theoretical and numerical evidence from both experimental and artificial data, to demonstrate that this technique will reliably detect periodicities if and only if they are present in the data. There are strong information theoretic arguments to support the statement that RARM detects periodicities if they are present. Surrogate data techniques are used to ensure the converse. Furthermore, our calculations demonstrate that RARM is more robust, more accurate, and more sensitive, than traditional spectral techniques.Comment: 10 pages (revtex) and 6 figures. To appear in Phys Rev E. Modified styl

Surrogate-assisted network analysis of nonlinear time series

The performance of recurrence networks and symbolic networks to detect weak nonlinearities in time series is compared to the nonlinear prediction error. For the synthetic data of the Lorenz system, the network measures show a comparable performance. In the case of relatively short and noisy real-world data from active galactic nuclei, the nonlinear prediction error yields more robust results than the network measures. The tests are based on surrogate data sets. The correlations in the Fourier phases of data sets from some surrogate generating algorithms are also examined. The phase correlations are shown to have an impact on the performance of the tests for nonlinearity.Comment: 9 pages, 5 figures, Chaos (http://scitation.aip.org/content/aip/journal/chaos), corrected typo

Using Search Queries to Understand Health Information Needs in Africa

The lack of comprehensive, high-quality health data in developing nations creates a roadblock for combating the impacts of disease. One key challenge is understanding the health information needs of people in these nations. Without understanding people's everyday needs, concerns, and misconceptions, health organizations and policymakers lack the ability to effectively target education and programming efforts. In this paper, we propose a bottom-up approach that uses search data from individuals to uncover and gain insight into health information needs in Africa. We analyze Bing searches related to HIV/AIDS, malaria, and tuberculosis from all 54 African nations. For each disease, we automatically derive a set of common search themes or topics, revealing a wide-spread interest in various types of information, including disease symptoms, drugs, concerns about breastfeeding, as well as stigma, beliefs in natural cures, and other topics that may be hard to uncover through traditional surveys. We expose the different patterns that emerge in health information needs by demographic groups (age and sex) and country. We also uncover discrepancies in the quality of content returned by search engines to users by topic. Combined, our results suggest that search data can help illuminate health information needs in Africa and inform discussions on health policy and targeted education efforts both on- and offline.Comment: Extended version of an ICWSM 2019 pape

rPICARD: A CASA-based Calibration Pipeline for VLBI Data

Currently, HOPS and AIPS are the primary choices for the time-consuming process of (millimeter) Very Long Baseline Interferometry (VLBI) data calibration. However, for a full end-to-end pipeline, they either lack the ability to perform easily scriptable incremental calibration or do not provide full control over the workflow with the ability to manipulate and edit calibration solutions directly. The Common Astronomy Software Application (CASA) offers all these abilities, together with a secure development future and an intuitive Python interface, which is very attractive for young radio astronomers. Inspired by the recent addition of a global fringe-fitter, the capability to convert FITS-IDI files to measurement sets, and amplitude calibration routines based on ANTAB metadata, we have developed the the CASA-based Radboud PIpeline for the Calibration of high Angular Resolution Data (rPICARD). The pipeline will be able to handle data from multiple arrays: EHT, GMVA, VLBA and the EVN in the first release. Polarization and phase-referencing calibration are supported and a spectral line mode will be added in the future. The large bandwidths of future radio observatories ask for a scalable reduction software. Within CASA, a message passing interface (MPI) implementation is used for parallelization, reducing the total time needed for processing. The most significant gain is obtained for the time-consuming fringe-fitting task where each scan be processed in parallel.Comment: 6 pages, 1 figure, EVN 2018 symposium proceeding

Social Sustainability Strategy Across the Supply Chain: A Conceptual Approach From the Organisational Perspective

Much of the existing literature on the social aspects of sustainability in the supply chain has focused on dyadic buyer-supplier relationships. However, supply chains are much more extensive, featuring multi-tiered systems consisting of many interconnected sequential and parallel dyadic relationships; therefore, a more expansive and holistic approach to exploring the management and integration of social sustainability standards across the extended supply chain is desirable. This research attempts to help fill this void and considers the extent to which a series of sequential upstream and downstream supply chain partners, rather than only a focal organization’s immediate suppliers and buyers, influence the formulation process of the social aspects of a sustainability strategy and the deployment of associated practices across the extended supply chain. Findings in the literature indicate that, inter alia, sustainability efforts in the supply chain are likely to be guided by stakeholders’ sustainability desires/requirements, the geographical location of buyers and suppliers and the associated sustainability enforcement regulations and cultural norms, and the volume of trade between the buyer and supplier. This paper uses the results gleaned from a review of the literature to propose a conceptual framework for selection of sustainability strategy across the multi-tiered supply chain. Finally, we introduce a conceptual approach to the process of implementing and deploying the social aspects of sustainability strategies and practices across the supply chain using an integrated social-sustainability information management system (ISIMS)

Gravitational Waves Probe the Coalescence Rate of Massive Black Hole Binaries

We calculate the expected nHz--$\mu$Hz gravitational wave (GW) spectrum from coalescing Massive Black Hole (MBH) binaries resulting from mergers of their host galaxies. We consider detection of this spectrum by precision pulsar timing and a future Pulsar Timing Array. The spectrum depends on the merger rate of massive galaxies, the demographics of MBHs at low and high redshift, and the dynamics of MBH binaries. We apply recent theoretical and observational work on all of these fronts. The spectrum has a characteristic strain $h_c(f)~10^{-15} (f/yr^{-1})^{-2/3}$, just below the detection limit from recent analysis of precision pulsar timing measurements. However, the amplitude of the spectrum is still very uncertain owing to approximations in the theoretical formulation of the model, to our lack of knowledge of the merger rate and MBH population at high redshift, and to the dynamical problem of removing enough angular momentum from the MBH binary to reach a GW-dominated regime.Comment: 31 Pages, 8 Figures, small changes to match the published versio

Optimal Restricted Estimation for More Efficient Longitudinal Causal Inference

Efficient semiparametric estimation of longitudinal causal effects is often analytically or computationally intractable. We propose a novel restricted estimation approach for increasing efficiency, which can be used with other techniques, is straightforward to implement, and requires no additional modeling assumptions

Performance of an emergency cold weld repair on a 2.25Cr-1Mo longitudinally seam-welded pressure vessel.

This is an overview of a current three-year project for the Cooperative Research Centre for Welded Structures entitled “Integrity of High Energy Piping”. The results of a performance evaluation conducted on an emergency cold weld (controlled deposition temperbead, TB) repair applied to a 2.25Cr-1Mo steel header using the manual metal arc welding (MMAW) process are described. With repair rather than replace being a far more viable option, welding is increasingly used for performing repairs, replacements, retrofits and modifications to elevated temperature plants. However, with the considerable cost and time involved with performing conventional post weld heat-treatment (PWHT) repairs, in today’s economic environment utility owners are increasingly forced to turn toward other alternatives, such as cold weld repairs. These require no PWHT and rely on a controlled deposition process – precise weld bead placement and heat inputs etc to achieve tempering of the HAZ. However, much of the research conducted on these repair techniques has used accelerated high temperature creep testing to demonstrate their integrity. How well this reflects their real-life performance is unknown. Therefore this study provides an opportunity to evaluate the effects of service exposure on the performance of an emergency cold weld repair. © 2003, The Institute of Materials Engineering Australasia Ltd

Accommodating error analysis in comparison and clustering of molecular fingerprints.

Molecular epidemiologic studies of infectious diseases rely on pathogen genotype comparisons, which usually yield patterns comprising sets of DNA fragments (DNA fingerprints). We use a highly developed genotyping system, IS6110-based restriction fragment length polymorphism analysis of Mycobacterium tuberculosis, to develop a computational method that automates comparison of large numbers of fingerprints. Because error in fragment length measurements is proportional to fragment length and is positively correlated for fragments within a lane, an align-and-count method that compensates for relative scaling of lanes reliably counts matching fragments between lanes. Results of a two-step method we developed to cluster identical fingerprints agree closely with 5 years of computer-assisted visual matching among 1,335 M. tuberculosis fingerprints. Fully documented and validated methods of automated comparison and clustering will greatly expand the scope of molecular epidemiology

Separating Stimulus-Induced and Background Components of Dynamic Functional Connectivity in Naturalistic fMRI

We consider the challenges in extracting stimulus-related neural dynamics from other intrinsic processes and noise in naturalistic functional magnetic resonance imaging (fMRI). Most studies rely on inter-subject correlations (ISC) of low-level regional activity and neglect varying responses in individuals. We propose a novel, data-driven approach based on low-rank plus sparse (L+S) decomposition to isolate stimulus-driven dynamic changes in brain functional connectivity (FC) from the background noise, by exploiting shared network structure among subjects receiving the same naturalistic stimuli. The time-resolved multi-subject FC matrices are modeled as a sum of a low-rank component of correlated FC patterns across subjects, and a sparse component of subject-specific, idiosyncratic background activities. To recover the shared low-rank subspace, we introduce a fused version of principal component pursuit (PCP) by adding a fusion-type penalty on the differences between the columns of the low-rank matrix. The method improves the detection of stimulus-induced group-level homogeneity in the FC profile while capturing inter-subject variability. We develop an efficient algorithm via a linearized alternating direction method of multipliers to solve the fused-PCP. Simulations show accurate recovery by the fused-PCP even when a large fraction of FC edges are severely corrupted. When applied to natural fMRI data, our method reveals FC changes that were time-locked to auditory processing during movie watching, with dynamic engagement of sensorimotor systems for speech-in-noise. It also provides a better mapping to auditory content in the movie than ISC