Non-zero Correlation Research Articles

A Generalized Configuration Model With Triadic Closure

In this paper we present a generalized configuration model with random triadic closure (GCTC). This model possesses five fundamental properties: large clustering coefficient, power law degree distribution, short path length, non-zero Pearson degree correlation, and existence of community structures. We analytically derive the Pearson degree correlation coefficient and the clustering coefficient of the proposed model. We select a few datasets of real-world networks. By simulation, we show that the GCTC model matches very well with the datasets in terms of Pearson degree correlations and clustering coefficients. We also test three well-known community detection algorithms on our model, the datasets and other three prevalent benchmark models. We show that the GCTC model performs equally well as the other three benchmark models. Finally, we perform influence diffusion on the GCTC model using the independent cascade model and the linear threshold model. We show that the influence spreads of the GCTC model are much closer to those of the datasets than the other benchmark models. This suggests that the GCTC model is a suitable tool to study network science problems where degree correlation or clustering plays an important role.

Prior choice and data requirements of Bayesian multivariate hierarchical models fit to tag-recovery data: The need for power analyses.

Recent empirical studies have quantified correlation between survival and recovery by estimating these parameters as correlated random effects with hierarchical Bayesian multivariate models fit to tag-recovery data. In these applications, increasingly negative correlation between survival and recovery has been interpreted as evidence for increasingly additive harvest mortality. The power of these hierarchal models to detect nonzero correlations has rarely been evaluated, and these few studies have not focused on tag-recovery data, which is a common data type. We assessed the power of multivariate hierarchical models to detect negative correlation between annual survival and recovery. Using three priors for multivariate normal distributions, we fit hierarchical effects models to a mallard (Anas platyrhychos) tag-recovery data set and to simulated data with sample sizes corresponding to different levels of monitoring intensity. We also demonstrate more robust summary statistics for tag-recovery data sets than total individuals tagged. Different priors led to substantially different estimates of correlation from the mallard data. Our power analysis of simulated data indicated most prior distribution and sample size combinations could not estimate strongly negative correlation with useful precision or accuracy. Many correlation estimates spanned the available parameter space (-1,1) and underestimated the magnitude of negative correlation. Only one prior combined with our most intensive monitoring scenario provided reliable results. Underestimating the magnitude of correlation coincided with overestimating the variability of annual survival, but not annual recovery. The inadequacy of prior distributions and sample size combinations previously assumed adequate for obtaining robust inference from tag-recovery data represents a concern in the application of Bayesian hierarchical models to tag-recovery data. Our analysis approach provides a means for examining prior influence and sample size on hierarchical models fit to capture-recapture data while emphasizing transferability of results between empirical and simulation studies.

Decision-making factors for renovation of old residential areas in Chinese cities under the concept of sustainable development.

The renovation problem of old residential areas is not only a city management task, but also a major engineering of benefiting the people, which is related to the people living and working in peace and contentment and the improvement of the overall image of the city. In order to improve the appearance of old residential areas in Chinese cities and the living conditions of their residents, city planners must take a scientific and structured approach to renovation. After analyzing current status of the old residential in several major Chinese cities, consulting the literature, and undertaking field investigations, this paper summarizes the main influencing factors on renovation decision-making. We propose five hypotheses, which we test using questionnaire survey data, sample analysis of these influencing factors, and a structural equation model (SEM). The results show that the degree of influence of each factor on renovation decisions ranks as technology being the most influential, followed by the economy, policy, the environment, and society. In addition, we find that there is nonzero correlation between these various factors.

СМЕРТНОСТЬ ОТ COVID-19 КАК ПОКАЗАТЕЛЬ РЕГИОНАЛЬНЫХ РАЗЛИЧИЙ КАЧЕСТВА ЖИЗНИ НАСЕЛЕНИЯ

Significance. The multifaceted nature of the novel coronavirus pandemic makes it difficult to determine the factors leading to an extremely wide variation in regional COVID-19 mortality rates. The purpose of the study is: to determine whether life quality affects mortality from COVID-19. Quality of life may be one of the factors. Material and methods. The average values of mortality from COVID-19 and its growth rate from 2020 to 2021 were compared in groups of regions with the highest and lowest indicators: conditions and quality of life, financial status, population per hospital bed and per doctor, standardized mortality from homicides, suicide and self-harm, alcohol and drug-related causes. A correlation analysis was implemented. Results. In the first year of the pandemic, significant differences in average mortality from COVID-19 were detected between groups of regions that differ in mortality from causes associated with alcohol consumption, while in the second year of the pandemic significant differences were detected between groups of regions that differ in terms of conditions and quality of life, and mortality from suicide and self-harm. Differences in mortality growth rates were registered between the same groups of regions, as well as between groups by financial status of the population and population per bed ratio. For 2020, but not for 2021, there are weak, although significantly non-zero correlations between standardized COVID-19 mortality and population per hospital bed ratio (direct), population per doctor ratio (reverse), and alcohol-related mortality (reverse). Conclusion. Life quality of the population is a factor of regional differentiation of mortality from COVID-19. In the “objective” model of life quality, mortality from COVID-19 is higher in prosperous regions, while in the “subjective” model it is lower. The growth rate of mortality in the second year of the pandemic was higher in the regions with a high standard of living. The population per bed ratio had an effect on mortality from COVID-19 only in the first year of the pandemic. Scope of application. The results obtained can be used for both planning anti-epidemic measures and developing measures to equalize the living conditions in different regions.

Matrix treatment for the correlation between intensity fluctuations of light waves on weak scattering from a collection of particles with L types.

We report a new approach to the correlation between intensity fluctuations (CIF) of light waves on weak scattering from a collection of particles with L types. Two L×L matrices called a pair-potential matrix (PPM) and a pair-structure matrix (PSM) are introduced to jointly formulate the CIF of the scattered field for the first time. We show that the CIF equals the squared modulus of the trace of the product of the PSM and the transpose of the PPM, and thus these two matrices provide sufficient amount of information to determine the CIF of the scattered field. Based on this, we further analyze the normalized version of the CIF of the scattered field. It is found that the expression of the normalized CIF can have pretty compact and profound forms in three special cases: (I) the spatial distributions of the scattering potentials of particles of different types are similar (II) the spatial distributions of the densities of particles of different types are similar (III) both the scattering potentials and the densities of particles of different types are similarly distributed in space. Finally, the effects of the off-diagonal elements of the PPM and the PSM on the normalized CIF of the scattered field are illustrated by two examples. The results show that the non-zero cross correlation between particles of different types can induce intense changes in the normalized CIF of the scattered field.

Systematic enumeration of two-level even-odd designs of strength 3

The first dedicated algorithm to enumerate even-odd designs of strength 3 is presented. Such designs cannot be constructed by folding over smaller designs, but they may permit simultaneous estimation of many more two-factor interactions than designs that can be constructed by folding over. In the algorithm, enumeration is restricted to the computationally convenient class of designs with at least one nonzero correlation between a two-factor and a three-factor interaction contrast vector. All such designs with up to 56 runs, all those with 64 runs and up to 13 factors, and a specific subclass of those with 64 runs and more than 13 factors have been enumerated.1 The best ranked 64-run designs substantially improve on benchmark designs from the literature.

Tsallis entropy based uncertainty relations on sparse representation for vector and matrix signals

In this paper, the Tsallis entropy based novel uncertainty relations on vector signals and matrix signals in terms of sparse representation are deduced for the first time. These new uncertainty bounds are not only related with the entropy parameter, but also related with the vector length, the min non-zero correlation between standard orthogonal basis and the given signals, the max correlation between the two given orthogonal basis and even the eigenvalues along with eigenvectors. Especially, the relationship between uncertainty bounds and matrix eigenvalues is discussed as well, as discloses the new interesting interpretation on sparse representation. In addition, the theoretical analysis and numerical examples have been shown to verify these newly proposed uncertainty principles, e.g., under the case of special parameters of Tsallis entropy, the uncertainty bound reaches its peak value for the sparsest representation of matrix (i.e., only one eigenvalue is not zero). Moreover, various numerical relations between uncertainty bounds and Tsallis entropy parameters are shown in perceptual form, as maybe give us the possible enlightenment or guidance in future sparse representation analysis.

A Machine Learning-Based Predictive Model of Epidermal Growth Factor Mutations in Lung Adenocarcinomas.

Simple SummaryTargeted therapy against epidermal growth factor (EGFR) mutations has become the standard of care for non-small cell lung cancer, and there has not been an efficient genetic test for non-small cell lung cancer patients. The present study aims to find a novel data-driven genetic testing method that can effectively predict the mutation status of EGFR based on a prediction model combining clinical features. The results of this study provide a powerful theoretical basis for the establishment of an effective mutation prediction model. The prediction model can provide a high reference value aiding in EGFR mutation diagnosis and subsequent treatment course.Data from 758 patients with lung adenocarcinoma were retrospectively collected. All patients had undergone computed tomography imaging and EGFR gene testing. Radiomic features were extracted using the medical imaging tool 3D-Slicer and were combined with the clinical features to build a machine learning prediction model. The high-dimensional feature set was screened for optimal feature subsets using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO). Model prediction of EGFR mutation status in the validation group was evaluated using multiple classifiers. We showed that six clinical features and 622 radiomic features were initially collected. Thirty-one radiomic features with non-zero correlation coefficients were obtained by LASSO regression, and 24 features correlated with label values were obtained by PCA. The shared radiomic features determined by these two methods were selected and combined with the clinical features of the respective patient to form a subset of features related to EGFR mutations. The full dataset was partitioned into training and test sets at a ratio of 7:3 using 10-fold cross-validation. The area under the curve (AUC) of the four classifiers with cross-validations was: (1) K-nearest neighbor (AUCmean = 0.83, Acc = 81%); (2) random forest (AUCmean = 0.91, Acc = 83%); (3) LGBM (AUCmean = 0.94, Acc = 88%); and (4) support vector machine (AUCmean = 0.79, Acc = 83%). In summary, the subset of radiographic and clinical features selected by feature engineering effectively predicted the EGFR mutation status of this NSCLC patient cohort.

Conservative Potentials for a Lattice-Mapped, Coarse-Grain Scheme with Fuzzy Switching Functions.

We extend our previous work (Luo, S.; Thachuk, M. J. Phys. Chem. A 2021, 125, 64866497) on determining conservative potentials for lattice-like, coarse-grain (CG) mapping schemes to the case where the boundaries between different spatial regions are not sharply defined but are fuzzy. In other words, the system is divided into interpenetrating "subcells" such that atomistic particles continuously change their memberships as they move through space. This is done by using fuzzy switching functions to define overlapping regions between subcells with fractional particle occupations. In this case, a full mass matrix is required to describe the system, and its off-diagonal elements are nonzero and contribute to the CG potential. As the overlapping region increases in size, we observe the mass distribution transitions from a discrete spectrum, through an intermediate state, and finally to a continuous Gaussian-like function. We interpret this as a quantitative measure for signaling when a continuum-theory description of the system is appropriate. Nonzero correlations among all CG variables are calculated and are found to depend strongly on the degree of overlap. In particular, those for the diagonal mass elements decrease in magnitude, and there exists a specific value of the overlap for which the correlations are zero. Other correlations are strong only when the overlap is quite large, so there is a trade-off between the complexity of the interactions in the system and the degree of fuzziness between the subcells. However, if the number of particles in a subcell is large enough and the overlap is moderate, then the CG potential is found to be well-approximated by a generalized quadratic function. These results demonstrate the transition between atomistic and continuum resolutions in a system and have implications for designing CG schemes with mixed atomistic and continuum character.

Impact of spill-in counts from off-target regions on [18F]Flortaucipir PET quantification

Background[18F]Flortaucipir (FTP) PET quantification is usually hindered by spill-in counts from off-target binding (OFF) regions. The present work aims to provide an in-depth analysis of the impact of OFF in FTP PET quantification, as well as to identify optimal partial volume correction (PVC) strategies to minimize this problem. Methods309 amyloid-beta (Aβ) negative cognitively normal subjects were included in the study. Additionally, 510 realistic FTP images with different levels of OFF were generated using Monte Carlo simulation (MC). Images were corrected for PVC using both a simple two-compartment and a multi-region method including OFF regions. FTP standardized uptake value ratio (SUVR) was quantified in Braak Areas (BA), the hippocampus (which was not included in Braak I/II) and different OFF regions (caudate, putamen, pallidum, thalamus, choroid plexus (ChPlex), cerebellar white matter (cerebWM), hemispheric white matter (hemisWM) and cerebrospinal fluid (CSF)) using the lower portion of the cerebellum as a reference region. The correlations between OFF and cortical SUVRs were studied both in real and in simulated PET images, with and without PVC. ResultsIn-vivo, we found correlations between all OFF and target regions, especially strong for the hemisWM (slope>0.63, R2>0.4). All the correlations were attenuated but remained significant after applying PVC, except for the ChPlex. In MC simulations, the hemisWM and CSF were the main contributors to PVE in all BA (slopes 0.15-0.26 and 0.13-0.21 respectively). The hemisWM (slope=0.2), as well as the ChPlex (slope=0.02), influenced SUVRs in the hippocampus. The CerebWM was negatively correlated with all target regions (slope<-0.02, R2>0.8). While no other correlations between OFF and target regions were found, hemisWM was correlated with all OFF regions but the cerebWM (slopes 0.06-0.33). HemisWM correlations attenuated (slopes<0.06) when applying two-compartment PVC, but the hippocampus-ChPlex and the cerebWM correlations required more complex PVC with dedicated compartments for these regions. In-vivo, PVC removed a notably higher fraction of the correlation between OFF regions found to be affected by PVE in the simulation studies and BA (≈50%) than for OFF regions not affected by PVE (16%). ConclusionHemisWM is the main driver of spill-in effects in FTP PET, affecting both target regions and the rest of OFF regions. PVC successfully reduces PVE, even when using a simple two-compartment method. Despite PVC, non-zero correlations were still observed between target and OFF regions in vivo, which suggests the existence of biological or tracer-related contributions to these correlations.

Turbulence-induced bias in time-averaged laser absorption tomography of correlated concentration and temperature fields with a first-order correction

The influence of correlated scalar fluctuations on time-averaged laser absorption tomography measurements of temperature and species in a piloted turbulent premixed flame was examined using a coupled spectroscopic and fluid-dynamic analysis. To understand bias associated with turbulence, spatio-temporally resolved temperature and species mole fraction profiles predicted by large eddy simulations (LES) were used to synthetically generate time-resolved line-of-sight absorption measurements at short time scales (microsecond) to reflect the unsteady nature of a canonical jet burner across various transverse measurement planes. Inversion methods were employed on the time-averaged line-of-sight data to produce radially-resolved temperature and mole fraction profiles, analogous to those produced by laser absorption tomography performed on a time-averaged axisymmetric flowfield. It is shown that bias in the measurements compared to true time-averaged scalar fields is a function primarily of temperature dependence in absorptivity and non-zero correlation between temperature and species concentration scalars. A first-order correction to tomography measurements is proposed to account for the bias based on estimated correlations and the known spectroscopic parameters of the probed absorption transitions.

American options and stochastic interest rates

We study finite-maturity American equity options in a stochastic mean-reverting diffusive interest rate framework. We allow for a non-zero correlation between the innovations driving the equity price and the interest rate. Importantly, we also allow for the interest rate to assume negative values, which is the case for some investment grade government bonds in Europe in recent years. In this setting we focus on American equity call and put options and characterize analytically their two-dimensional free boundary, i.e. the underlying equity and the interest rate values that trigger the optimal exercise of the option before maturity. We show that non-standard double continuation regions may appear, extending the findings documented in the literature in a constant interest rate framework. Moreover, we contribute by developing a bivariate discretization of the equity price and interest rate processes that converges in distribution as the time step shrinks. This discretization, described by a recombining quadrinomial tree, allows us to compute American equity options’ prices and to analyze their free boundaries with respect to time and current interest rate. Finally, we document the existence of non-standard optimal exercise policies for American call options on a non-dividend-paying equity.

Throughput Multiplexing Efficiency for High-Order Handset MIMO Antennas

In multipath environments, multiple-input multiple-output (MIMO) terminals typically suffer from non-zero correlations due to limited handset space and power imbalances due to different antenna efficiencies or partial hand blockage. These antenna–channel impairments can lead to significant throughput performance degradation of MIMO terminals. The multiplexing efficiency was proposed to quantitatively assess this performance degradation. Previous work only derived the analytical expression of the throughput-based multiplexing efficiency of two-port MIMO antennas for user equipment (UE). However, the fifth-generation (5G) UE dictates four or more antennas. In this paper, we extend the multiplexing efficiency metric to high-order MIMO UEs. Both correlation-based channel models and geometry-based stochastic channel models were used for validation. Besides dipole antennas, two representative terminal antennas were also employed for simulation verifications in this work.

Opinion polarization in social networks.

In this paper, we propose a Boltzmann-type kinetic description of opinion formation on social networks, which takes into account a general connectivity distribution of the individuals. We consider opinion exchange processes inspired by the Sznajd model and related simplifications but we do not assume that individuals interact on a regular lattice. Instead, we describe the structure of the social network statistically, assuming that the number of contacts of a given individual determines the probability that their opinion reaches and influences the opinion of another individual. From the kinetic description of the system, we study the evolution of the mean opinion, whence we find precise analytical conditions under which a polarization switch of the opinions, i.e. a change of sign between the initial and the asymptotic mean opinions, occurs. In particular, we show that a non-zero correlation between the initial opinions and the connectivity of the individuals is necessary to observe polarization switch. Finally, we validate our analytical results through Monte Carlo simulations of the stochastic opinion exchange processes on the social network. This article is part of the theme issue 'Kinetic exchange models of societies and economies'.

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Evaluating Stan’s Variational Bayes Algorithm for Estimating Multidimensional IRT Models

Bayesian estimation of multidimensional item response theory (IRT) models in large data sets may come with impractical computational burdens when general-purpose Markov chain Monte Carlo (MCMC) samplers are employed. Variational Bayes (VB)—a method for approximating the posterior distribution—poses a potential remedy. Stan’s general-purpose VB algorithms have drastically improved the accessibility of VB methods for a wide psychometric audience. Using marginal maximum likelihood (MML) and MCMC as benchmarks, the present simulation study investigates the utility of Stan’s built-in VB function for estimating multidimensional IRT models with between-item dimensionality. VB yielded a marked speed-up in comparison to MCMC, but did not generally outperform MML in terms of run time. VB estimates were trustworthy only for item difficulties, while bias in item discriminations depended on the model’s dimensionality. Under realistic conditions of non-zero correlations between dimensions, VB correlation estimates were subject to severe bias. The practical relevance of performance differences is illustrated with data from PISA 2018. We conclude that in its current form, Stan’s built-in VB algorithm does not pose a viable alternative for estimating multidimensional IRT models.

Inference for nonparanormal partial correlation via regularized rank-based nodewise regression.

Partial correlation is a common tool in studying conditional dependence for Gaussian distributed data. However, partial correlation being zero may not be equivalent to conditional independence under non-Gaussian distributions. In this paper, we propose a statistical inference procedure for partial correlations under the high-dimensional nonparanormal (NPN) model where the observed data are normally distributed after certain monotone transformations. The NPN partial correlation is the partial correlation of the normal transformed data under the NPN model, which is a more general measure of conditional dependence. We estimate the NPN partial correlations by regularized nodewise regression based on the empirical ranks of the original data. A multiple testing procedure is proposed to identify the nonzero NPN partial correlations. The proposed method can be carried out by a simple coordinate descent algorithm for lasso optimization. It is easy-to-implement and computationally more efficient compared to the existing methods for estimating NPN graphical models. Theoretical results are developed to show the asymptotic normality of the proposed estimator and to justify the proposed multiple testing procedure. Numerical simulations and a case study on brain imaging data demonstrate the utility of the proposed procedure and evaluate its performance compared to the existing methods. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI)database.

Estimating Weak Lensing Convergence Correlation of Type-Ia Supernovae From 5-Year Snls Data by Internal Error Estimate Technique

We report non-zero weak lensing convergence correlation signal of Type-Ia supernovae from 5-year Supernovae Legacy Survey data. For our analysis we utilize 296 supernovae magnification data from 5-year SNLS in the weak lensing limit. The data we use consists of measurements from four different patches, each covering 1 square degree of the sky, merged together. We demonstrate that it is possible to have a very good estimate of the two point correlation function from this data using internal error estimate technique. In order to have a good estimate of the corresponding covariance matrix we apply bootstrap spatial re-sampling technique where we reshuffle the original data consisting of 296 data points 100-10000 times and compare the results with that obtained from original data points. We show that this technique helps us arrive at a reliable conclusion on weak lensing convergence even though the original dataset comprises of a small number of data points. This also allows us to compute the corresponding covariance matrix with great accuracy.

A comparison of spectral reconstruction methods applied to non-zero temperature NRQCD meson correlation functions

We present results from the fastsum collaboration’s programme to determine the spectrum of the bottomonium system as a function of temperature. Three different methods of extracting spectral information are discussed: a Maximum Likelihood approach using a Gaussian spectral function for the ground state, the Backus Gilbert method, and the Kernel Ridge Regression machine learning procedure. We employ the fastsum anisotropic lattices with 2+1 dynamical quark flavours, with temperatures ranging from 47 to 375 MeV.

Estimation of direct and maternal genetic parameters for weaning weight in Hungarian Simmental cattle

The aim of the current research was to estimate variance components and genetic parameters of weaning weight in Hungarian Simmental cattle. Weaning weight records were obtained from the Association of Hungarian Simmental Breeders. The dataset comprised of 44,278 animals born from 1975 to 2020. The data was analyzed using the restricted maximum likelihood methodology of the Wombat software. We fitted a total of six models to the weaning weight data of Hungarian Simmental cattle. Models ranged from a simple model with animals as the only random effect to a model that had maternal environmental effects as additional random effects as well as direct maternal genetic covariance. Fixed effects in the model comprised of herd, birth year, calving order and sex. Likelihood ratio test was used to determine the best fit model for the data. Results indicated that allowing for direct-maternal genetic covariance increases the direct and maternal effect dramatically. The best fit model had direct and maternal genetic effects as the only random effect with non-zero direct-maternal genetic correlation. Direct heritability, maternal heritability and direct maternal correlation of the best fit model was 0.57, 0.16 and -0.78 respectively. The result indicates that problem of (co-)sampling variation occurs when attempting to partition additive genetic variance into direct and maternal components.