Simulated Real Data Research Articles

How to Detect an Astrophysical Nanohertz Gravitational Wave Background

Analyses of pulsar timing data have provided evidence for a stochastic gravitational wave background in the nanohertz frequency band. The most plausible source of this background is the superposition of signals from millions of supermassive black hole binaries. The standard statistical techniques used to search for this background and assess its significance make several simplifying assumptions, namely (i) Gaussianity, (ii) isotropy, and most often, (iii) a power-law spectrum. However, a stochastic background from a finite collection of binaries does not exactly satisfy any of these assumptions. To understand the effect of these assumptions, we test standard analysis techniques on a large collection of realistic simulated data sets. The data-set length, observing schedule, and noise levels were chosen to emulate the NANOGrav 15 yr data set. Simulated signals from millions of binaries drawn from models based on the Illustris cosmological hydrodynamical simulation were added to the data. We find that the standard statistical methods perform remarkably well on these simulated data sets, even though their fundamental assumptions are not strictly met. They are able to achieve a confident detection of the background. However, even for a fixed set of astrophysical parameters, different realizations of the universe result in a large variance in the significance and recovered parameters of the background. We also find that the presence of loud individual binaries can bias the spectral recovery of the background if we do not account for them.

Model-based characterization of the equilibrium dynamics of transcription initiation and promoter-proximal pausing in human cells.

In metazoans, both transcription initiation and the escape of RNA polymerase (RNAP) from promoter-proximal pausing are key rate-limiting steps in gene expression. These processes play out at physically proximal sites on the DNA template and appear to influence one another through steric interactions. Here, we examine the dynamics of these processes using a combination of statistical modeling, simulation, and analysis of real nascent RNA sequencing data. We develop a simple probabilistic model that jointly describes the kinetics of transcription initiation, pause-escape, and elongation, and the generation of nascent RNA sequencing read counts under steady-state conditions. We then extend this initial model to allow for variability across cells in promoter-proximal pause site locations and steric hindrance of transcription initiation from paused RNAPs. In an extensive series of simulations, we show that this model enables accurate estimation of initiation and pause-escape rates. Furthermore, we show by simulation and analysis of real data that pause-escape is often strongly rate-limiting and that steric hindrance can dramatically reduce initiation rates. Our modeling framework is applicable to a variety of inference problems, and our software for estimation and simulation is freely available.

Inverse Lindley distribution: different methods for estimating their PDF and CDF

The probability density and cumulative distribution functions are essential statistical forms in modelling data and characterizing their respective distributions. This study addresses the estimation problem of these basic functions for the inverse Lindley distribution. The maximum likelihood, uniformly minimum variance unbiased and Bayes estimators are considered here. The exact confidence intervals for the probability density and cumulative distribution functions of the inverse Lindley distribution are also derived. A real data application and simulation studies are performed for illustrating and comparing among the so developed methods.

Waste bunker size optimization under uncertainty: Application of inventory models with real data simulation

Waste-to-Energy (WtE) technologies play a prominent role in recovering energy from waste. One of the aspects ensuring the operability and economic sustainability of WtE plants is the size optimization of a waste bunker assigned to each WtE plant, whose main target is to ensure waste homogeneity for effective operation. Each WtE plant faces various uncertain challenges, such as planned and unplanned shutdowns during which the plant is inactive and not ideally coordinated with waste collection companies, further importing the collected waste into the waste bunker, mainly due to existing contracts. This work aims to develop a scenario-based model to support strategic capacity planning by capturing various uncertain attributes and optimizing the waste bunker size while minimizing the total costs composing the investment, inventory, and waste shortage costs. The model is tested on a case study from the Czech Republic. The main result can be summarized such that for a specific case of a newly situated WtE facility with a considered capacity of 100kt/y, the model suggests a bunker of size ca. 4,200m3 for a middle scenario. However, this is strongly related to other attributes of a particular WtE project (e.g., waste properties or cost parameters).

Dynamic prediction of spatial dose rate distribution during nuclear severe accidents by means of active machine learning and mobile sensors

The prediction of atmospheric dispersion of nuclear material is crucial to support emergency response during nuclear accidents, due to the potential severity and far-reaching consequences of such accidents. This importance is particularly evident in the context of severe accidents, like the incident that occurred at the Fukushima Daiichi Nuclear Power Plant (NPP). NPP are commonly supported by atmospheric dispersion systems (ADS), which estimate the spatial dose rates distribution, referred to as the dose rate map (DRM), by means of physical model simulations, including source term (ST), wind fields, atmospheric transport and diffusion, and dose calculations. However, it is well-known that a real accident scenario may differ from those modeled especially under severe conditions, and this fact may lead to erroneous predictions resulting in poor or catastrophic decision making. Aiming to minimize this problem, several efforts have been made to improve predictions based on field measurements, such as inverse problems and other methods to correct/adjust ST parameters. Such methods, however, complement the physical model calculations. This paper proposes a novel approach to predict DRM at ground level, based only upon field measurements (avoiding execution of physical model simulations). The idea is to dynamically adapt a set of mobile sensors (for example monitoring drones swarm) to follow the radioactive plume. To achieve this, an active machine learning (AML) approach was developed based on Gaussian process regression (GRP) and swarm intelligence. To test the approach, computational experiments using realistic simulated data (generated on the ADS simulator of a Brazilian NPP) has been used. As result, DRMs have been reproduced with good qualitative (visual) representation and satisfactory quantitative metrics. The average correlation coefficient between predicted and real DRMs was approximately 0.72 (ranging from 0.64 to 0.97 during plume evolution), demonstrating to be a promising approach to predict DRMs.

Critical appraisal of two Box-Cox formulae for their utility in determining reference intervals by realistic simulation and extensive real-world data analyses

BackgroundThe reference interval (RI) is defined as the central 95 % range of reference values (RVs) from healthy individuals. The ideal method for determining RIs is to transform RV distribution into Gaussian and estimate its 95 % range parametrically. One-parameter Box-Cox formula (1pBC) is widely used for correcting skewness (Sk) or kurtosis (Kt) in data distribution. However, 1pBC is not popular for computing RIs due to its unreliability in Gaussian transformation. While its two-parameter version (2pBC) is not used due to a challenge in fitting power (λ) and shift (α) parameters simultaneously. In this study, technical issues in fitting both formulae are assessed, and an alternative algorithm for successful use of 2pBC is proposed. MethodsFor fitting 1pBC, optimal λ was determined by stepwise linear search. For 2pBC, optimal [λ, α] combination was pursued in two ways: by grid search of λ and α (2pBCgrid) or by using the grid search but keeping α-range close to the reference distribution (2pBCopt). Their accuracy and precision in determining RIs were compared by generating power-normal distributions simulating RVs of 23 major chemistry analytes. Additionally, their practical utilities were compared by analyzing 776 real-world datasets comprising test results of 25 analytes that were obtained from the global multicenter RV study of IFCC. For comparison, the performance of nonparametric method was evaluated in both settings. ResultsFor analytes with not-much-skewed distributions, unbiased estimation of RIs was accomplished by all methods. Nevertheless, when reference distributions are located far from zero, λ estimated by1pBC and 2pBCgrid fluctuated widely, which was attributable to virtually flat goodness-of-fit profile for [λ, α]. For highly skewed distributions, 1pBC caused bias in estimating RI and λ due to remotely peaked goodness-of-fit profile. Real-world data analyses revealed that 2pBCopt and 1pBC achieved Gaussian transformation (|Sk|<0.1 and |Kt|<0.3) in 82.4 % and 66.9 % among 776 datasets, respectively. Fitting bias signified by Kt<−0.4 was more common to 1pBC. The practical utility of 2pBCopt was unbiased prediction of analyte-specific distribution-shape (λ). Whereas nonparametric method gave highly variable RIs for both simulated and real-world datasets. Conclusions2pBCopt is suitable for calculating RIs and grasping distribution-shape from the estimated λ.

How to analyze continuous and discrete repeated measures in small-sample cross-over trials?

To optimize the use of data from a small number of subjects in rare disease trials, an at first sight advantageous design is the repeated measures cross-over design. However, it is unclear how these within-treatment period and within-subject clustered data are best analyzed in small-sample trials. In a real-data simulation study based upon a recent epidermolysis bullosa simplex trial using this design, we compare non-parametric marginal models, generalized pairwise comparison models, GEE-type models and parametric model averaging for both repeated binary and count data. The recommendation of which methodology to use in rare disease trials with a repeated measures cross-over design depends on the type of outcome and the number of time points the treatment has an effect on. The non-parametric marginal model testing the treatment-time-interaction effect is suitable for detecting between group differences in the shapes of the longitudinal profiles. For binary outcomes with the treatment effect on a single time point, the parametric model averaging method is recommended, while in the other cases the unmatched generalized pairwise comparison methodology is recommended. Both provide an easily interpretable effect size measure, and do not require exclusion of periods or subjects due toincompleteness.

A Fast Density Peak Clustering Method for Power Data Security Detection Based on Local Outlier Factors

The basic work of power data research is anomaly detection. It is necessary to find a method suitable for processing current power system data. Research proposes an algorithm of fast density peak clustering with Local Outlier Factor (LOF). The algorithm has poor performance in processing datasets with irregular shapes and significant local density changes, and has the disadvantage of strong dependence on truncation distance. This study provides the decision rules for outliers incorporating the idea of LOF. The improved algorithm can fully consider the characteristics of power data and reduce the dependence on truncation distance. In anomaly detection based on the simulation of real power data, the classification accuracy of the improved CFSFDP algorithm is 4.87% higher than that of the traditional algorithm, and the accuracy rate is 97.41%. The missed and false detection rates of the LOF-CFSFDP algorithm are decreased by 2.23% and 2.64%, respectively, compared to the traditional algorithm, and it is ultimately able to reach rates of 1.26% and 1.33%. These results indicate that the algorithm proposed in this study can better describe the characteristics of power data, making the features of outliers and cluster center points more obvious.

Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach

The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe. However, observations of the CMB are contaminated by foreground emissions, obscuring the CMB signal and reducing its efficacy in constraining cosmological parameters. We employ deep learning as a data-driven approach to CMB cleaning from multi-frequency full-sky maps. In particular, we develop a graph-based Bayesian convolutional neural network based on the U-Net architecture that predicts cleaned CMB with pixel-wise uncertainty estimates. We demonstrate the potential of this technique on realistic simulated data based on the Planck mission. We show that our model ac- accurately recovers the cleaned CMB sky map and resulting angular power spectrum while identifying regions of uncertainty. Finally, we discuss the current challenges and the path forward for deploying our model for CMB recovery on real observations.

SmartSPEC: A framework to generate customizable, semantics-based smart space datasets

This paper presents SmartSPEC, an approach to generate customizable synthetic smart space datasets using sensorized spaces in which people and events are embedded. Smart space datasets are critical to design, deploy and evaluate systems and applications under issues of heterogeneity, scalability and robustness, leading to cost-effective operation which improves the safety, comfort and convenience experienced by space occupants. However, many challenges exist in obtaining realistic smart space datasets for testing and validation, from a lack of fine-grained sensing to privacy/security concerns. SmartSPEC is a smart space simulator and data generator that leverages a semantic model augmented with user-defined constraints to represent important attributes, relationships, and external domain knowledge for a smart space. We employ machine learning (ML) approaches to extract relevant patterns from a sensorized space, which are used in an event-driven simulation strategy to generate realistic simulated data about the space (events, trajectories, sensor observation datasets, etc.). To evaluate the realism of the generated data, we develop a structured methodology and metrics to assess various aspects of smart space datasets, including trajectories of people and occupancy of spaces. Our experimental study looks at two real-world settings/datasets: an instrumented smart campus building and a city-wide GPS dataset. Our results show the realism of trajectories produced by SmartSPEC (1.4x to 4.4x more realistic than the best synthetic data baseline when compared to real-world data, depending on the scenario and configuration), as well as sensor data derived from such trajectories which adhere to the underlying semantics of the smart space as compared to synthetic sensor data baselines, even under hypothetical changes.

Data analysis for accelerated life tests via Weibull‐gamma frailty regression models

AbstractIn this article, we study data analysis methods for accelerated life test (ALT) with blocking. Unlike the previous assumption of normal distribution for random block effects, we advocate the use of Weibull regression model with gamma random effects for making statistical inference of ALT data. To estimate the unknown parameters in the proposed model, maximum likelihood estimation and Bayesian estimation methods are provided. We illustrate the proposed methods using real data examples and simulation examples. Numerical results suggest that distribution of random effects has minimal impact on the estimation of fixed effects in the Weibull regression models. Furthermore, to demonstrate the advantage of our proposed model, we also provide methods to compare ALT plans and thus identify the optimal ALT plans.

Unified model for the LISA measurements and instrument simulations

LISA is a space-based mHz gravitational-wave observatory, with a planned launch in 2034. It is expected to be the first detector of its kind, and will present unique challenges in instrumentation and data analysis. An accurate preflight simulation of LISA data is a vital part of the development of both the instrument and the analysis methods. The simulation must include a detailed model of the full measurement and analysis chain, capturing the main features that affect the instrument performance and processing algorithms. Here, we propose a new model that includes, for the first time, proper relativistic treatment of reference frames with realistic orbits; a model for onboard clocks and clock synchronization measurements; proper modeling of total laser frequencies, including laser locking, frequency planning and Doppler shifts; better treatment of onboard processing and updated noise models. We then introduce two implementations of this model, LISANode and LISA Instrument. We demonstrate that TDI processing successfully recovers gravitational-wave signals from the significantly more realistic and complex simulated data. LISANode and LISA Instrument are already widely used by the LISA community and, for example, currently provide the mock data for the LISA Data Challenges.

A STAP anti-interference technology with zero phase bias in wireless IoT systems based on high-precision positioning

Fog computing has been applied to the data processing for the Internet of Things (IoT) based on distributed high-precision Global Navigation Satellite Systems (GNSS). However, the space-time adaptive processing (STAP) interference suppression technology in the system will cause fog computing data deviation that includes carrier phase bias and pseudocode offset. An unbiased STAP technique is proposed to eliminate these deviations. First, it is analyzed that the carrier phase bias and pseudocode offset are caused by the non-linear phase response of the STAP equivalent filter. Then, a coefficient-constrained method based on practical engineering processing is proposed, which can eliminate these deviations by restricting the tap coefficients to be symmetrically equal around the center-tap. Moreover, by analyzing the coherent integral function of the pseudocode after filtering, the tap structure of STAP is modified to eliminate the group offset of the pseudocode without increasing the computational complexity and hardware resources. Finally, the unbiased performance and anti-interference performance of the system are verified by numerical and real data simulations.

Prospects for Higgs boson and new scalar resonant production searches in ttbb final state at the LHC

In this article we probe resonant associated production of a Standard Model Higgs boson with new heavy scalar resonance in proton-proton collisions at a center-of-mass energy s=13 TeV. The Higgs boson and new scalar resonant are required to decay into a pair of bottom quarks and a pair of top quarks, respectively. Semileptonic decay of top quarks is considered. The searches are projected into operation conditions of the Large Hadron Collider during Run II data taking period at a center-of-mass energy of 13 TeV using Monte Carlo generated events, realistic detector response simulation and available Open Data samples. Analysis strategies are presented and machine learning approach using Deep Neural Network is proposed to resolve ambiguous in jets assignment and improve kinematic reconstruction of signal events. Sensitivity of the CMS detector is estimated as 95% expected upper limits on the product of the production cross section and the branching fractions of the searched particles.

Regularization and variable selection with triple shrinkage in linear regression: a generalization of lasso

We propose a new shrinkage and variable selection method in linear regression, which is based on triple shrinkage on the regression coefficients. The new estimation method contains the ridge, lasso and elastic net as special cases. The term based on the shrunken estimator in the new method can provide estimates with a smaller length depending on the size of a new tuning parameter compared to the elastic net, maintaining the variable selection feature in the case of multicollinearity. The new estimator has the property of the grouping effect similar to that of the elastic net. The well-known coordinate descent algorithm is used to compute the coefficient path of the new estimator, efficiently. We conduct real data analysis and simulation studies to compare the new estimator with several methods including the lasso and elastic net.

On some pitfalls of the log-linear modeling framework for capture-recapture studies in disease surveillance

Abstract In epidemiological studies, the capture-recapture (CRC) method is a powerful tool that can be used to estimate the number of diseased cases or potentially disease prevalence based on data from overlapping surveillance systems. Estimators derived from log-linear models are widely applied by epidemiologists when analyzing CRC data. The popularity of the log-linear model framework is largely associated with its accessibility and the fact that interaction terms can allow for certain types of dependency among data streams. In this work, we shed new light on significant pitfalls associated with the log-linear model framework in the context of CRC using real data examples and simulation studies. First, we demonstrate that the log-linear model paradigm is highly exclusionary. That is, it can exclude, by design, many possible estimates that are potentially consistent with the observed data. Second, we clarify the ways in which regularly used model selection metrics (e.g., information criteria) are fundamentally deceiving in the effort to select a “best” model in this setting. By focusing attention on these important cautionary points and on the fundamental untestable dependency assumption made when fitting a log-linear model to CRC data, we hope to improve the quality of and transparency associated with subsequent surveillance-based CRC estimates of case counts.

Bus network decomposition for fault detection and isolation through power line communication

This paper deals with fault detection and isolation (FDI) in complex embedded wired communication networks. The considered faults are soft faults which do not prevent the communication, but may evolve into hard faults, i.e. short or open circuit. A novel FDI method based on power line communication (PLC) transmission systems is proposed. In these PLC systems, the transmission coefficients between the source and each receiver are estimated for communication purposes using orthogonal frequency division multiplexing (OFDM). Health indicators and residuals are computed by comparing the online estimated transmission coefficients with the reference coefficients. A methodology for dealing with complex networks, such as bus networks, is proposed. It is based on the decomposition of the network into several Y-shaped sub-networks. Each of these sub-networks is monitored to detect the presence of a fault. The FDI method is first validated using real data extracted from a Y-shaped network test bench. Then, the proposed approach is validated on a more complex network using realistic simulated data.

Poisson-Lognormal model with measurement error in covariate for small area estimation of count data

Small Area Estimation (SAE) is a statistical technique to estimate parameters of subpopulation containing small-size of samples. SAE is an indirect estimation method by utilizing the strength of the neighbor area and data sources outside the area so that the sample becomes more effective and reduces the variance of parameter estimators. This paper deals with non-symmetrical count data in SAE which can be modelled based on Poisson-Lognormal distribution using hierarchical Bayesian (HB) approach and information on covariate contains measurement error. We develop the Poisson-Lognormal model for predicting small area counts with structural measurement error in the area-specific covariate. To get the HB Bayes estimator for Poisson-Lognormal model with measurement error in its covariates, the Metropolis-Hastings (MH) algorithm from the Markov Chain Monte Carlo (MCMC) technique is used. The HB estimator performance is studied through simulation and implementation of real data to predict the illiteracy rate at the sub-district level in Kepulauan Riau Province Indonesia based on The National Socio-Economic Survey (Susenas) data in March 2020.

Моделювання перколяційної поведінки властивостей полімерних нанокомпозитів, наповнених вуглецевими нанотрубками

The paper considers the application of the scaling approach of the percola-tion theory to describe the step-like change in electrical and thermal conductivi-ty of polymer nanocomposites containing carbon nanotubes. In connection with the use of nano-sized fillers for the creation of polymer composite materials, the problem of mathematical description of the behavior of the properties of such systems at critical filling has arisen. The increasing difficulty of describing a sudden change in properties is due to the fact that the described phenomena, due to the fluctuating processes of aggregation of nanotubes and the formation of an infinite cluster that permeates the entire volume of the material, are essen-tially critical, and therefore cannot be described by traditional mathematical models. In order to solve the problem considered in the publication, it is pro-posed to use universal scaling equations. In this case, an analogy is used be-tween the behavior of a ferromagnet in an external electric field and the behav-ior of the properties of a polymer nanocomposite with non-zero conductivity when it is critically filled with nanotubes. According to the above analogy, a number of equations are written to describe the percolation behavior of both electrical conductivity and thermal conductivity of polymer-filler systems. The results of simulation of real data for systems of this type are given and it is shown that the proposed equations allow to describe the step-like behavior of electrical and thermal conductivity of polymer-nanotube systems with high ac-curacy. The critical exponents of the scaling equations were determined and the behavior of the properties when these indices were varied was modeled. When describing the section of the graph before the percolation threshold, the increase in critical exponents leads to an increase in conductivity, while in the section after the percolation threshold, the increase in critical exponents leads to a de-crease in conductivity

Lasso regression under stochastic restrictions in linear regression: An application to genomic data

Variable selection approaches are often employed in high-dimensionality and multicollinearity problems. Since lasso selects variables by shrinking the coefficients, it has extensive use in many fields. On the other, we may sometime have extra information on the model. In this case, the extra information should be considered in the estimation procedure. In this paper, we propose a stochastic restricted lasso estimator in linear regression model which uses the extra information as stochastic linear restrictions. The estimator is a generalization of mixed estimator with L 1 type penalization. We give the coordinate descent algorithm to estimate the coefficient vector of the proposed method and strong rules for the coordinate descent algorithm to discard variables from the model. Also, we propose a method to estimate the tuning parameter. We conduct two real data analyses and simulation studies to compare the new estimator with several estimators including the ridge, lasso and stochastic restricted ridge. The real data analyses and simulation studies show that the new estimator enjoys the automatic variable selection property of the lasso while outperforms standard methods, achieving lower test mean squared error.