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Much research effort is invested in attempting to determine causal influences on disease onset and progression to inform prevention and treatment efforts. However, this is often dependent on observational data that are prone to well-known limitations, particularly residual confounding and reverse causality. Several statistical methods have been developed to support stronger causal inference. However, a complementary approach is to use design-based methods for causal inference, which acknowledge sources of bias and attempt to mitigate these through the design of the study rather than solely through statistical adjustment. Genetically informed methods provide a novel and potentially powerful extension to this approach, accounting by design for unobserved genetic and environmental confounding. No single approach will be absent from bias. Instead, we should seek and combine evidence from multiple methodologies that each bring different (and ideally uncorrelated) sources of bias. If the results of these different methodologies align-or triangulate-then we can be more confident in our causal inference. To be truly effective, this should ideally be done prospectively, with the sources of evidence specified in advance, to protect against one final source of bias-our own cognitions, expectations, and fondly held beliefs.

Parents are key protective systems in the lives of children experiencing homelessness. Evidence suggests that parent emotional reactivity, expression, and regulation play a critical role in promoting adaptive parenting behaviors. Studies of emotional reactivity in parents utilize different methods, including self-report, observations, and physiological measures. However, these methods are rarely evaluated together, particularly among disadvantaged families. The present study examined how subjective (i.e., self-report), observed, and physiological measures of parent emotional reactivity relate to each other and to observed parenting behaviors in problem-solving and teaching parent-child interaction tasks. Participants included fifty 4- to 7-year-old children and their caregivers staying in an emergency homeless shelter. Observed and subjective emotional reactivity were largely uncorrelated, except for positive emotions during problem-solving tasks. Adaptive parenting behavior was related to lower scores on measures of subjective and observed negative emotions and higher observed scores for positive emotions during problem-solving tasks, as well as higher observed scores of positive emotions during teaching tasks. Physiological reactivity was not related to parenting behaviors. Results suggest that associations of emotional reactivity with parenting behavior depend on the context of the parent-child interaction and how emotional reactivity is measured. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Traditional single-ended fault location method is influenced by the fault resistance and the remote in feed, which reflects as the two equations built by fault phase have four unknowns. And in the grid-connected converter system, the weak feedback characteristic further reduces the performance of the method. To solve it, a single-ended fault location method based on control and protection coordination is proposed in this paper. First, using the high controlled-ability of the converter, the control strategy for fault ride-through (FRT) operation is divided into two stages by switching control modes. Then, according to the electrical quantities under two stages, four uncorrelated equations can be established, and the equations still have four unknown parameters. The fault distance can be calculated by solving them. The simulation results show that the proposed method has high accuracy and is immune to fault resistance and fault location, it solves the problem of insufficient equations in principle.

Our primary objective in this article is to compare the spectral densities of some cyclostationary time series. By using the limiting distributions of the discrete Fourier transform, a novel approach is introduced to determine whether the spectral densities of some uncorrelated cyclostationary time series are the same or not. Also, the ability of the proposed technique is examined by employing simulated and real datasets.

In reality, symmetric relations cannot informatively explain the causal complexity due to non-100% linear observable relationships. However, asymmetric relations are seldom discussed in crowdfunding fundraising. Based on the signaling theory and start-up quality, this article uses symmetric (logistics regression and negative binomial regression) and asymmetric analyses (fuzzy-set qualitative comparative) to explain the causal complexity of intellectual, human, and social capital signals in technology project fundraising. This article tests data collected from 516 technology projects in reward-based crowdfunding in China. As a result, symmetric analysis partly supports hypotheses from symmetric relations. However, asymmetric analysis indicates that uncorrelated capital signals in the symmetric analysis are active conditions in configurations to funding success and performance. Furthermore, asymmetric analysis supports that social capital signals are more important than intellectual capital signals and human capital signals. Non-third-party authorized signals are more important than third-party authorized signals in fundraising. Social connection between investors and start-ups is also the necessary social capital signal for funding success. These findings highlight the importance of joint effects of capital signals on crowdfunding fundraising, thus enriching the understanding of start-up quality and signals, third-party endorsement, unprofessional investors, and the high-noise environment of crowdfunding.

A natural approach to enhance portfolio diversification is to rely on factor-risk parity, which yields the portfolio whose risk is equally spread among a set of uncorrelated factors. The standard choice is to take the variance as risk measure, and the principal components (PCs) of asset returns as factors. Although PCs are unique and useful for dimension reduction, they are an arbitrary choice: any rotation of the PCs results in uncorrelated factors. This is problematic because we demonstrate that any portfolio is a factor-variance-parity portfolio for some rotation of the PCs. More importantly, choosing the PCs does not account for the higher moments of asset returns. To overcome these issues, we propose using the independent components (ICs) as factors, which are the rotation of the PCs that are maximally independent, and care about higher moments of asset returns. We demonstrate that using the IC-variance-parity portfolio helps to reduce the return kurtosis. We also show how to exploit the near independence of the ICs to parsimoniously estimate the factor-risk-parity portfolio based on value at risk. Finally, we empirically demonstrate that portfolios based on ICs outperform those based on PCs, and several state-of-the-art benchmarks.

We shall prove a weak law of large numbers for the uncorrelated (see Definition 3.1) fuzzy random variable sequence with respect to the uniform Hausdorff metric $d_H^{\infty}$, which is an extension of weak law of large numbers for independent fuzzy random variables., 9 pages

While digital multi-user (MU) maximal ratio combining (MRC) is well understood, relatively few analytical results exist for analog MU-MRC. For example, it has recently been shown that MU system performance is highly dependent on the correlation model used, but the scope is limited to digital processing. Thus, in this paper we compare the performance of analog and digital MRC, focusing on the effects of correlation. We begin by deriving the expected signal and interference powers, demonstrating that the signal-to-interference ratio decreases with correlation when users have the same correlation matrices, while it increases when their correlation matrices are different. These finite system results are then extended by deriving asymptotic signal-to-interference-and-noise ratio expressions for both analog and digital MRC for the benchmark scenarios of uncorrelated and perfectly correlated Rayleigh channels. Here, we once again demonstrate that the performance is critically dependent on the correlation scenario.

Digital games are one of the potential pedagogical approaches within STEM teaching and learning. The main idea behind using digital games in STEM contexts is to enhance the children’s conceptual understanding and to increase their interest in STEM. The interest in STEM is still an unsolved global issue. Most studies looking into digital games show there to be positive implications when it comes to STEM learning. This gap and the uncorrelated scenario from both the theoretical and practical perspectives need to both be solved. Here we conceptualise the usefulness of STEM digital games for stimulating an interest in STEM, particularly among children from a design perspective. The previous studies are the main qualitative sources for the interpretation of the main argument in this paper. As result, digital games have a strong empirically supportive foundational basis that acts as a medium to stimulate interest. To achieve this, STEM digital games should be designed to be of better quality. Good STEM digital games will contribute to better STEM learning outcomes and meaningful gaming experiences that can contribute to the interest in STEM. This study proposes a comprehensive framework to understand the purpose of the design and development of STEM digital games. https://doi.org/10.26803/ijlter.20.10.13

Effective suppression of inadvertent or deliberate jamming signals is crucial to ensure reliable wireless communication. However, as demonstrated in this paper, when the transmitted jamming signals are highly correlated, and especially when the correlation coefficient varies, nullifying the jamming signals can be challenging. Unlike existing techniques that often assume uncorrelated jamming signals or non-zero but constant correlation, we analyze the impact of the non-zero and varying correlations between transmitted jamming signals on the suppression of the jamming signals. Specifically, we observe that by varying the correlation coefficients between transmitted jamming signals, jammers can “virtually change” the jamming channels hence their nullspace, even when these channels do not physically change. This makes most jamming suppression techniques that rely on steering receiving beams towards the nullspace of jamming channels no longer applicable. To tackle the problem, we develop techniques to effectively track the jamming nullspace and correspondingly update receiving beams. Monte Carlo simulations show that our proposed techniques can suppress/nullify jamming signals for all considered scenarios with non-zero and varying correlation coefficients amongst transmitted jamming signals.

Rotating machines are commonly used in industrial applications. Mechanical faults such as rotor unbalance, shaft misalignment, pulley misalignment, structural looseness, and bearing faults leading to unplanned shutdown based on the severity of these faults. The condition monitoring technique based on vibration analysis has the potential to detect and diagnose a great number of early stage faults. However, some mechanical faults have correlated vibration features leading to ambiguous diagnosis to identify and distinguish these faults. In this paper, a proposed method based on the Principal Component Analysis (PCA) is presented to produce uncorrelated Principal Components (PCs) to identify the healthy and different faulty cases. A test rig was prepared to simulate a group of mechanical faults such as rotor unbalance, pulley misalignment, belt damage, combined unbalance with pulley misalignment, and combined unbalance with belt damage. The conventional vibration measurements were collected for each case and their features were extracted and used to produce the equivalent PCs. It was found that the produced uncorrelated PCs have the superior to distinguish the majority of simulated faults which have correlated vibration features as presented in the rest of paper.

This paper is mainly concerned with sets which do not contain four-term arithmetic progressions, but are still very rich in three term arithmetic progressions, in the sense that all sufficiently large subsets contain at least one such progression. We prove that there exists a positive constant c and a set A ⊂ F^n_q which does not contain a four-term arithmetic progression, with the property that for every subset A′ ⊂ A with |A′| ≥ |A|^(1−c), A′ contains a nontrivial three term arithmetic progression. We derive this from a more general quantitative Roth-type theorem in random subsets of F^n_q, which improves a result of Kohayakawa-Luczak-Rodl/Tao-Vu. We also discuss a similar phenomenon over the integers, where we show that for all ϵ > 0, and all sufficiently large N ∈ N, there exists a four-term progression-free set A of size N with the property that for every subset A′ ⊂ A with |A′| ≫ 1/(logN)^(1−ϵ)⋅ N contains a nontrivial three term arithmetic progression. Finally, we include another application of our methods, showing that for sets in F^n_q or Z the property of "having nontrivial three-term progressions in all large subsets" is almost entirely uncorrelated with the property of "having large additive energy".

The COVID-19 pandemic is a global crisis that has forced governments around the world to implement large-scale interventions such as school closures and national lockdowns. Previous research has shown that partisanship plays a major role in explaining public attitudes towards these policies and beliefs about the intensity of the crisis. However, it remains unclear whether and how partisan differences in policy support relate to partisan gaps in beliefs about the number of deaths that the pandemic will cause. Do individuals who forecast fewer COVID-19 deaths show less agreement with preventive measures? How does partisanship correlate with people’s beliefs about the intensity of the crisis and their support for COVID-19 policies? Here, we sought to answer these questions by performing a behavioral experiment in Argentina (Experiment 1, N = 640) and three quasi-replication studies in Uruguay (Experiment 2, N = 372), Brazil (Experiment 3, N = 353) and the United States (Experiment 4, N = 630). In all settings, participants forecasted the number of COVID-19 deaths in their country after considering either a high or low number, and then rated their agreement with a series of interventions. This anchoring procedure, which experimentally induced a large variability in the forecasted number of deaths, did not modify policy preferences. Instead, each experiment provided evidence that partisanship was a key indicator of the optimism of forecasts and the degree of support for COVID-19 policies. Remarkably, we found that the number of forecasted deaths was robustly uncorrelated with participants’ agreement with preventive measures designed to prevent those deaths. We discuss these empirical observations in the light of recently proposed theories of tribal partisan behavior. Moreover, we argue that these results may inform policy making as they suggest that even the most effective communication strategy focused on alerting the public about the severity of the pandemic would probably not translate into greater support for COVID-19 preventive measures.

American industries have grown more concentrated over the last 40 years. In the absence of productivity innovation, this should lead to price hikes and output reductions, decreasing consumer welfare. With US census data from 1972 to 2012, I use price data to disentangle revenue from output. Industry-level estimates show that concentration increases are positively correlated to productivity and real output growth, uncorrelated with price changes and overall payroll, and negatively correlated with labor’s revenue share. I rationalize these results in a simple model of competition. Productive industries (with growing oligopolists) expand real output and hold down prices, raising consumer welfare, while maintaining or reducing their workforces, lowering labor’s share of output. (JEL D43, L13, D24, D33, D21, D42)

This article is concerned with a novel data-driven bias-eliminated subspace identification approach for closed-loop systems. Compared with the existing methods, the proposed method first proposes to utilize the coprime factorization of the controller to construct an instrumental variable uncorrelated with noise under closed-loop conditions. Furthermore, it can reliably eliminate the pole estimation bias due to the correlation between inputs and noise under feedback control. More importantly, the proposed method establishes a general framework for both open-loop and closed-loop system identification. Performance comparisons with two other closed-loop methods are made from many different aspects. Finally, the performance of the identified system is again demonstrated in the vehicle lateral dynamic system.

For species primarily regulated by a common predator, the P* rule of Holt and Lawton [1993] predicts that the prey species that supports the highest mean predator density (P*) excludes the other prey species. This prediction is re-examined in the presence of temporal fluctuations in the vital rates of the interacting species including predator attack rates. When the fluctuations in predator attack rates are temporally uncorrelated, the P* rule still holds even when the other vital rates are temporally auto-correlated. However, when temporal auto-correlations in attack rates are positive but not too strong, the prey species can coexist due to the emergence of a positive covariance between predator density and prey vulnerability. This coexistence mechanism is similar to the storage effect for species regulated by a common resource. Negative or strongly positive auto-correlations in attack rates generate a negative covariance between predator density and prey vulnerability and a stochastic priority effect can emerge: with non-zero probability either prey species is excluded. These results highlight how temporally auto-correlated species’ interaction rates impact the structure and dynamics of ecological communities.

This paper extends the principal component analysis (PCA) to moderately non-stationary vector time series. We propose a method that searches for a linear transformation of the original series such that the transformed series is segmented into uncorrelated subseries with lower dimensions. A columns’ rearrangement method is proposed to regroup transformed series based on their relationships. We discuss the theoretical properties of the proposed method for fixed and large dimensional cases. Many simulation studies show our approach is suitable for moderately non-stationary data. Illustrations on real data are provided.

PURPOSE To compare the double-Maddox rod test with other methods of measuring cyclodeviation DESIGN: Retrospective cohort study. METHODS We retrospectively identified 153 adults in a clinical practice with cyclodeviation assessed using double-Maddox rods, of whom 105 were also assessed using fusible synoptophore targets, 73 using nonfusible synoptophore targets, 118 using single-Maddox rod, and 43 using fundus photography. Relationships between double-Maddox rod and other tests were evaluated by calculating mean differences with 95% confidence intervals (CI), intraclass correlation coefficients (ICC), and Bland-Altman plots with linear regression. RESULTS Synoptophore cross-in-circle targets and the largest (of right or left) single-Maddox rod values were similar to double-Maddox values (mean differences: -1.2° and 0.1°, respectively; ICC: 0.79 and 0.82, respectively). Synoptophore house targets measured less excyclodeviation (mean difference: -2.7°; ICC: 0.71). Mean summed single-Maddox rod values were somewhat similar to double-Maddox values (mean difference: 1.5°; ICC: 0.85), but differences increased with greater cyclodeviation (r2 = 0.2678; P < .001). Fundus photographs showed large, uncorrelated differences compared with double-Maddox rod test, when summing right and left eyes and when using the largest of right or left (mean differences: 12.2° and 6.2°; ICC: -0.02 and 0.21, respectively), and differences increased with greater cyclodeviation (r2 = 0.4094; P < .001 and r2 = .1143; P= .03, respectively). CONCLUSIONS There was good agreement between double-Maddox and the largest single- Maddox test values and synoptophore cross-in-circle targets but poorer agreement with other tests. Further study is needed to understand which measurements best reflect true cyclodeviation and relationships with symptoms.

Gulisashvili et al. [Quant. Finance, 2018, 18(10), 1753-1765] provide a small-time asymptotics for the mass at zero under the uncorrelated stochastic-alpha-beta-rho (SABR) model by approximating the integrated variance with a moment-matched lognormal distribution. We improve the accuracy of the numerical integration by using the Gauss--Hermite quadrature. We further obtain the option price by integrating the constant elasticity of variance (CEV) option prices in the same manner without resorting to the small-strike volatility smile asymptotics of De Marco et al. [SIAM J. Financ. Math., 2017, 8(1), 709-737]. For the uncorrelated SABR model, the new option pricing method is accurate and arbitrage-free across all strike prices.

Glacier mass-balance observations at seasonal resolution have been performed since 1914 at two sites on Claridenfirn, Switzerland. The measurements are the longest uninterrupted records of glacier mass balance worldwide. Here, we provide a complete re-analysis of the 106-year series (1914–2020), focusing on both point and glacier-wide mass balance. The approaches to evaluate and homogenize the direct observations are described in detail. Based on conservative assumptions, average uncertainties of$\pm$0.25 m w.e. are estimated for glacier-wide mass balances at the annual scale. It is demonstrated that long-term variations in mass balance are clearly driven by melting, whereas decadal changes in accumulation are uncorrelated with mass balance and can only be relevant in short periods. Mass change of Claridenfirn is impacted by dry calving at a frontal ice cliff. Considerations of ice volume flux at a cross-profile reveal long-term variations in frontal ice loss accounting for$\sim$9% of total annual ablation on average. The effect of changes in frontal ablation mostly explains$\lt$10% of the mass-balance difference relative to the period 1960–1990, but accounts for$\sim$20% in 2010–2020. Glacier mass changes are discussed in the context of observations throughout the European Alps indicating that Claridenfirn is regionally representative.

When there is the coexistence of uncorrelated and coherent signals in sparse arrays, the conventional algorithms using coarray are fail. In order to solve this problem, the letter proposes a novel method based on compressed sensing. Firstly, the authors vectorize the covariance matrix and establish a sparse representation model through constructing a two‐dimensional redundant dictionary. Then, the authors use an improved orthogonal matching pursuit algorithm for off‐grid sources to recover the sparse vector. Through analysing location of non‐zero elements in sparse vector, the direction‐of‐arrivals of both uncorrelated and coherent signals can be obtained. The proposed method has no strict limitation by the structure of the existing sparse arrays. Moreover, it makes full use of vectorized data and can estimate more number of signals than that of sensors. Numerical experiments prove the effectiveness and favourable performance of the proposed method.

Larger array aperture is provided by sparse arrays than uniform ones, which can improve the angle estimation resolution and reduce the cost of system evidently. However, manifold ambiguity is introduced due to the array sparsity. In this paper, a Power Estimation Multiple-Signal Classification (PE-MUSIC) algorithm is proposed to solve the manifold ambiguity of arbitrary sparse arrays for uncorrelated sources in Multiple-Input Multiple-Output (MIMO) radar. First, the paired direction of departure (DOD) and direction of arrival (DOA) are obtained for all targets by MUSIC algorithm, including the true and spurious ones; then, the well-known Davidon–Fletcher–Powell (DFP) algorithm is applied to estimate all targets’ power values, among which the value of a spurious target trends to zero. Therefore, the ambiguity of sparse array in MIMO radar can be cleared. Simulation results verify the effectiveness and feasibility of the method.

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal component analysis is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning principal component analysis is not well understood by many applied analytical scientists and spectroscopists who use principal component analysis. The meaning of features identified through principal component analysis is often unclear. This manuscript traces the journey of the spectra themselves through the operations behind principal component analysis, with each step illustrated by simulated spectra. Principal component analysis relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of principal component analysis , such as the scores representing “concentration” or “weights". The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a principal component analysis model shows how to interpret application specific chemical meaning of the principal component analysis loadings and how to analyze scores. A critical benefit of principal component analysis is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

Ridge regression is frequently utilized by both supervised and semisupervised learnings. However, the trivial solution might occur, when ridge regression is directly applied for clustering. To address this issue, an uncorrelated constraint is introduced to the ridge regression with embedding the manifold structure. In particular, we choose uncorrelated constraint over orthogonal constraint, since the closed-form solution can be obtained correspondingly. In addition to the proposed uncorrelated ridge regression, a soft pseudo label is utilized with $\ell _{1}$ ball constraint for clustering. Moreover, a brand new strategy, i.e., a rescaled technique, is proposed such that optimal scaling within the uncorrelated constraint can be achieved automatically to avoid the inconvenience of tuning it manually. Equipped with the rescaled uncorrelated ridge regression with the soft label, a novel clustering method can be developed based on solving the related clustering model. Consequently, extensive experiments are provided to illustrate the effectiveness of the proposed method.

Measurements of skeletal muscle cross-sectional area (SMA) at the level of the third lumbar (L3) vertebra derived from clinical computed tomography (CT) scans are commonly used in assessments of sarcopenia, the loss of skeletal muscle mass and function associated with aging. As SMA is correlated with height and Body Mass Index (BMI), body size adjustment is necessary to fairly assess sarcopenic low muscle mass in individuals of different height and BMI. The skeletal muscle index, a widely used measure, adjusts for height as $$(SMA/height^2)$$ ( S M A / h e i g h t 2 ) but uses no BMI adjustment. There is no agreed upon standard for body size adjustment. We extracted L3 SMA using non-contrast-enhanced CT scans from healthy adults, split into ‘Under-40’ and ‘Over-40’ cohorts. Sex-specific allometric analysis showed that height to the power of one was the optimal integer coefficient for height adjusted SMA in both males and females. We computed two height-adjusted measures $$SMA_{HT}=SMA/height$$ S M A HT = S M A / h e i g h t and $$SMA_{HT2}=SMA/height^2$$ S M A H T 2 = S M A / h e i g h t 2 , comparing their Pearson correlations versus age, height, weight, and BMI separately by sex and cohort. Finally, in the ‘Under-40’ cohort, we used linear regression to convert each height-adjusted measure into a z-score ($$z(SMA_{HT})$$ z ( S M A HT ) , $$z(SMA_{HT2})$$ z ( S M A H T 2 ) ) adjusted for BMI. $$SMA_{HT}$$ S M A HT was less correlated with height in both males and females ($$r=0.005$$ r = 0.005 , $$p=0.91$$ p = 0.91 and $$r=0.1$$ r = 0.1 , $$p=0.01$$ p = 0.01 ) than $$SMA_{HT2}$$ S M A H T 2 ($$r=-\,0.30$$ r = - 0.30 and $$r=-\,0.21$$ r = - 0.21 , $$p p < 0.001 ). $$z(SMA_{HT})$$ z ( S M A HT ) was uncorrelated with BMI and weight, and minimally correlated with height in males and females ($$r=-\,0.01$$ r = - 0.01 , $$p=0.85$$ p = 0.85 and $$r=0.15$$ r = 0.15 , $$p p < 0.001 ). The final $$z(SMA_{HT})$$ z ( S M A HT ) equation was: $$z = (I - {\widehat{I}}) / SD(I)$$ z = ( I - I ^ ) / S D ( I ) , where $$I = SMA/height$$ I = S M A / h e i g h t , $${\widehat{I}} = 50 + BMI + 13 \times sex + 0.6 \times BMI \times sex$$ I ^ = 50 + B M I + 13 × s e x + 0.6 × B M I × s e x , $$SD(I) = 8.8 + 2.6 \times sex$$ S D ( I ) = 8.8 + 2.6 × s e x , and sex = 1 if male, 0 if female. We propose $$SMA_{HT}$$ S M A HT for optimal height adjustment and the $$z(SMA_{HT})$$ z ( S M A HT ) score for optimal height and BMI adjustment. By minimizing correlations with height and BMI, the $$z(SMA_{HT})$$ z ( S M A HT ) score produces unbiased assessments of relative L3 skeletal muscle area across the full range of body sizes.

In this article, we consider the problem of downlink power allocation in a cell-free massive multiple-input multiple-output (m-MIMO) communication system under spectral efficiency (SE) constraints for the users. From the perspective of green communications, the power allocation is formulated as an optimization problem where the aim is to maximize the sum SE as the objective function, while limiting the transmission power of each access point (AP) and imposing lower and upper bounds on the achievable SEs of different users. The resulting optimization problem is non-convex since the objective function is non-concave and the upper bounding constraints on user SEs are non-convex. To address these difficulties, we first derive a closed-form lower bound on the sum SE (objective function) and prove that it is a quasi-concave function. Then, we relax the unwieldy upper bounding constraints on the user SEs by replacing them with linear functions, which renders the optimization problem convex. An optimal solution to the relaxed problem is finally obtained by solving a sequence of convex feasibility programs. We evaluate the performance of the proposed downlink power allocation scheme through Monte Carlo simulations under the uncorrelated and correlated shadow fading models. The results show that for both models, the proposed algorithm can lead to a significant reduction in total power consumption compared to a benchmark approach, while accurately allocating power to the APs so that the SE constraint of each user is satisfied within the imposed bounds.

We propose a bootstrap procedure for data that may exhibit cluster‐dependence in two or more dimensions. The asymptotic distribution of the sample mean or other statistics may be non‐Gaussian if observations are dependent but uncorrelated within clusters. We show that there exists no procedure for estimating the limiting distribution of the sample mean under two‐way clustering that achieves uniform consistency. However, we propose bootstrap procedures that achieve adaptivity with respect to different uniformity criteria. Important cases and extensions discussed in the paper include regression inference, U‐ and V‐statistics, subgraph counts for network data, and non‐exhaustive samples of matched data.

Funding Information: N. Lietzén gratefully acknowledges financial support from the Emil Aaltonen Foundation (Grant 180144 N) and from the Academy of Finland (Grant 321968). Funding Information: The authors would like to thank Katariina Kilpinen for providing the photographs to Section 5. The authors would like to thank the two anonymous referees for their insightful comments that helped to improve this paper greatly.N. Lietzen gratefully acknowledges financial support from the Emil Aaltonen Foundation (Grant 180144 N) and from the Academy of Finland (Grant 321968). Publisher Copyright: © 2021 The Author(s). Published by VTeX. Open access article under the CC BY license. A complex-valued linear mixture model is considered for discrete weakly stationary processes. Latent components of interest are recovered, which underwent a linear mixing. Asymptotic properties are studied of a classical unmixing estimator which is based on simultaneous diagonalization of the covariance matrix and an autocovariance matrix with lag τ.The main contributions are asymptotic results that can be applied to a large class of processes. In related literature, the processes are typically assumed to have weak correlations. This class is extended, and the unmixing estimator is considered under stronger dependency structures. In particular, the asymptotic behavior of the unmixing estimator is estimated for both long-and short-range dependent complex-valued processes. Consequently, this theory covers unmixing estimators that converge slower than the usual√T and unmixing estimators that produce non-Gaussian asymptotic distributions. The presented methodology is a powerful preprocessing tool and highly applicable in several fields of statistics.

The purpose of this paper is to classify wines from 4 different countries in South America. Each class of wines is formed by samples considered by experts as representatives of the following commercial categories: “Argentinean Malbec (AM)”, “Brazilian Merlot (BM)”, “Uruguayan Tannat (UT)” and “Chilean Carmenere (CC)”. The 83 samples collected were analyzed according to their composition of volatiles, semi-volatiles and phenolic compounds. We built a decision system for classification based on support vector machines (SVM), along with Correlation-based Feature selection (CFS), and Random Forest Importance (RFI), which measures the relative importance of the input variables. First, we use CFS to select a subset of variables among 190 chemical compounds. Thirteen chemicals were selected as correlated to the category and uncorrelated with each other. Afterwards, these chemical compounds were organized according to the importance ranking given by the RFI and classified with SVM. The study clearly indicated that SVM in combination with feature selection methods was able to identify the most important chemicals to classify the wine samples. Among the compounds identified in the wine samples, the variable subset defined by the feature selection methods, which were catechin, gallic, octanoic acid, myricetin, caffeic, isobutanol, resveratrol, kaempferol, and ORAC, were able to achieve an accuracy of 93.97% in classifying the commercial categories.

Deep neural networks (DNNs) thrive in recent years, wherein batch normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the huge reduction and elementwise operations that are hard to be executed in parallel, which heavily reduces the training speed. To address this issue, in this article, we propose a methodology to alleviate the BN’s cost by using only a few sampled or generated data for mean and variance estimation at each iteration. The key challenge to reach this goal is how to achieve a satisfactory balance between normalization effectiveness and execution efficiency. We identify that the effectiveness expects less data correlation in sampling while the efficiency expects more regular execution patterns. To this end, we design two categories of approach: sampling or creating a few uncorrelated data for statistics’ estimation with certain strategy constraints. The former includes “batch sampling (BS)” that randomly selects a few samples from each batch and “feature sampling (FS)” that randomly selects a small patch from each feature map of all samples, and the latter is “virtual data set normalization (VDN)” that generates a few synthetic random samples to directly create uncorrelated data for statistics’ estimation. Accordingly, multiway strategies are designed to reduce the data correlation for accurate estimation and optimize the execution pattern for running acceleration in the meantime. The proposed methods are comprehensively evaluated on various DNN models, where the loss of model accuracy and the convergence rate are negligible. Without the support of any specialized libraries, $1.98\times $ BN layer acceleration and 23.2% overall training speedup can be practically achieved on modern GPUs. Furthermore, our methods demonstrate powerful performance when solving the well-known “micro-BN” problem in the case of a tiny batch size. This article provides a promising solution for the efficient training of high-performance DNNs.

Our understanding of risk preferences can be sharpened by considering their evolutionary basis. The existing literature has focused on two sources of risk: idiosyncratic risk and aggregate risk. We introduce a new source of risk, heritable risk, in which there is a positive correlation between the fitness of a newborn agent and the fitness of her parent. Heritable risk was plausibly common in our evolutionary past and it leads to a strictly higher growth rate than the other sources of risk. We show that the presence of heritable risk in the evolutionary past may explain the tendency of people to exhibit skewness loving today., Comment: Manuscript accepted to publication in Theoretical Economics

The effects of dielectric barrier discharge (DBD) plasma treatment on the dielectric properties of Norway spruce wood (Picea abies (L.) Karst.) were investigated using dielectric analysis. Dielectric constant (i.e. permittivity) and loss coefficient were determined at various frequencies. The resulting changes on lamellae specimens of different thicknesses were compared with the change in mass and moisture content. A significant influence of the plasma was found, leading to an increase of the dielectric constant by about 2%, and a decrease of sample mass directly after the plasma treatment by approx. 14%, whereas a reduction in moisture content by only about 0.6% and a corresponding change in loss coefficient were detected. Overall, the mechanisms of the observed changes remain unclear and seem mainly uncorrelated with the hitherto known chemical changes in wood surfaces caused by similar plasma discharges.

This paper makes two contributions. The first is to propose a new family of copulas for which the copula arguments are uncorrelated but dependent. Specifically, if w1 and w2 are the uniform random variables in the copula, they are uncorrelated, but w1 is correlated with |w2 − 1/2|. We show how this family of copulas can be applied to the error structure in an econometric production frontier model. The second contribution is to give some general results on how to extend a two-dimensional copula to three or more dimensions. This extension is necessary in our production frontier model when there are multiple inputs, but our results apply more generally to the extension of arbitrary two-dimensional copulas. We also report the results of some simulations and we give an empirical example.

Solvency is a key issue in the insurance sector. Investments can have significant risks, and a compelling research question is whether the solvency optimizing investment risk corresponds to the lowest possible risk level. This question is even more topical with some interest rates approaching very low levels in many countries. The paper aims to answer this question. Theoretical results suggest that if insurance risk and investment risk are uncorrelated, solvency optimizing investment portfolios with non-risk-free components may exist, and the level of optimal investment risk may inversely depend on the insurance portfolio size.

The El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are two well-known temporal oscillations in sea surface temperature (SST), which are both thought to influence the interannual variability of Indian summer monsoon rainfall (ISMR). Until now, there has been no measure to assess the simultaneous information exchange (IE) from both ENSO and IOD to ISMR. This study explores the information exchange from two source variables (ENSO and IOD) to one target (ISMR). First, in order to illustrate the concepts and quantification of two-source IE to a target, we use idealized test cases consisting of linear and nonlinear dynamical systems. Our results show that these systems exhibit net synergy (i.e., the combined influence of two sources on a target is greater than the sum of their individual contributions), even with uncorrelated sources in both the linear and nonlinear systems. We test IE quantification with various estimators (linear, kernel, and Kraskov estimators) for robustness. Next, the two-source IE from ENSO and IOD to ISMR is investigated in observations, reanalysis, three global climate model (GCM) simulations, and three nested higher-resolution simulations using a regional climate model (RCM). This (1) quantifies IE from ENSO and IOD to ISMR in the natural system and (2) applies IE in the evaluation of the GCM and RCM simulations. The results show that both ENSO and IOD contribute to ISMR interannual variability. Interestingly, significant net synergy is noted in the central parts of the Indian subcontinent, which is India's monsoon core region. This indicates that both ENSO and IOD are synergistic predictors in the monsoon core region. But, they share significant net redundant information in the southern part of the Indian subcontinent. The IE patterns in the GCM simulations differ substantially from the patterns derived from observations and reanalyses. Only one nested RCM simulation IE pattern adds value to the corresponding GCM simulation pattern. Only in this case does the GCM simulation show realistic SST patterns and moisture transport during the various ENSO and IOD phases. This confirms, once again, the importance of the choice of GCM in driving a higher-resolution RCM. This study shows that two-source IE is a useful metric that helps in better understanding the climate system and in process-oriented climate model evaluation., Earth System Dynamics, 11 (4), ISSN:2190-4987, ISSN:2190-4979

In this article, a difference-coarray-based direction of arrival (DOA) method is introduced, which utilizes the uniform linear array (ULA) in a novel fashion to address the problem of DOA estimation for coherent signals. Inspired by the coarray-based estimators employed in cases of sparse arrays, we convert the sample covariance matrix of the observed signals into the difference coarray domain and process the signals using a spatial smoothing technique. The proposed method exhibits good accuracy and robustness in both the uncorrelated and coherent cases. Numerical simulations verify that the ULA difference coarray- (UDC-) based method can achieve good DOA estimation accuracy even when the SNR is very low. In addition, the UDC-based method is insensitive to the number of snapshots. Under extremely challenging conditions, the proposed UDC-ES-DOA method is preferred because of its outstanding robustness, while the UDC-MUSIC method is suitable for most moderate cases of lower complexity. Due to its demonstrated advantages, the proposed method is a promising and competitive solution for practical DOA estimation, especially for low-SNR or snapshot-limited applications.

This paper examines the relation between productivity and specialisation of firms in business functions. We distinguish between specialisation in R&D, fabrication and marketing based on the firm's employment composition over these functions using unique survey data of Dutch firms. Our results suggest that firms specialised in R&D and marketing are significantly more productive compared to firms specialised in fabrication. We find that measures of upstreamness based on input–output tables do not significantly relate to firm productivity and are uncorrelated to measures of functional specialisation.

Background Emotion-regulation is assumed to be impaired in psychosis: patients with psychotic disorders (PD) self-report to use reappraisal less frequently than healthy controls (HC), but it is uncertain whether they are also less successful in applying reappraisal. Moreover, it has not been investigated whether the habitual use of reappraisal is associated with the ability to use reappraisal successfully. Methods To address these questions, PD (n = 60), a clinical control group of patients with anxiety disorders (AD; n = 40) and HC (n = 40) completed questionnaires on habitual emotion-regulation and alexithymia, performed tests on neurocognitive functioning and jumping-to-conclusion bias, and were subjected to an emotion-regulation paradigm, in which they were asked to down-regulate induced anxiety and sadness. Results No differences were found between PD and HC regarding the habitual use of reappraisal, but PD reported to use reappraisal more frequently than AD. All groups were able to down-regulate anxiety and sadness via reappraisal following instructions. The habitual use of reappraisal and the reduction of anxiety and sadness after instruction were uncorrelated. Conclusion These findings question the common assumption that PD use reappraisal less often or less successfully. At least under laboratory condition, PD do not appear to be impaired in reducing negative affect via reappraisal successfully.

Background and Objectives Although individual age and preexisting health conditions are well-documented risk factors for coronavirus disease 2019 (COVID-19) mortality, it is unclear whether these 2 factors capture unique dimensions of risk for epidemic severity at the national level. In addition, no studies have examined whether national distributions of these factors are associated with epidemic experiences to date. Research Design and Methods Drawing on surveys of older adults from 42 countries and estimated case fatality ratios by age and preexisting health conditions, we document and compare national profiles of COVID-19 mortality risks among older adults. We develop 2 measures of national risk profiles: one based on age structures and another based on distributions of preexisting health conditions. Our analysis compares these constructs and documents their associations with national COVID-19 mortality rates. Results National profiles of COVID-19 mortality risk based on age structure and preexisting health conditions are moderately uncorrelated, capturing different aspects of risk. Both types of national risk profiles correlate meaningfully with countries’ COVID-19 mortality experiences to date. Discussion and Implications Measures of population age structure are readily available for every country in the world, while cross-national measures of older adult population health are more limited. In the COVID-19 crisis, these factors give different pictures of the countries with high and low risks of COVID-19 mortality. Moreover, our results suggest that both types of national risk profiles based on population health reflect current COVID-19 mortality severity in several countries, highlighting the need for more cross-national comparative data on older adult population health.

The calibration technique is the process of adjusting weights for the better enhancement of the estimate of the population parameters using auxiliary information. In this manuscript, we have proposed ratio and regression type calibration estimators for the population mean under both correlated and uncorrelated measurement errors. The variances of the proposed calibrated estimators to the first order approximation are obtained and compared their efficiencies. A Monte Carlo simulation study has been carried out to show the effect of measurement errors on the proposed calibrated estimators.

In this work, a new approach to compare the spectral densities of several uncorrelated almost cyclostationary processes is proposed. The asymptotic distribution of the discrete Fourier transform is used to establish a powerful test statistic. The behavior of the introduced technique is examined in both simulation study and real data case. The results verify the suitability and applicability of the given approach.

In the recent days, several novel and specialized algorithms are coming up for solving particular class of problems. However, their performance on new benchmark or real-world problem remains unsure. This paper proposes a novel Multiobjective Cohort Intelligence (MOCI) algorithm. It is based on Pareto dominance and coevolutionary design principles to achieve efficient, effective, productive and robust performance. The capability of MOCI algorithm is enhanced through use of multiple features for balance of exploration versus exploitation, search towards promising region and avoidance of search stagnation. The performance of MOCI is assessed against the state-of-the-art algorithms, such as: ARMOEA, CMOPSO, hpaEA, LMOCSO, LSMOF, NMPSO and WOFSMPSO across multiple test suites including Classical, ZDT, DTLZ, WFG and UF. The performance assessment is conducted with truly uncorrelated performance metrics. In this regard, an exploratory approach of multiple correlation analysis is proposed. Performance of MOCI algorithm is statistically verified and validated using PROMETHEE-II and nonparametric statistical tests. MOCI is capable of achieving well converged and diversified solutions on most of the test as well as real world problems. The success of MOCI is attributed to multiple features incorporated in the algorithm. In the future, MOCI could be applied to challenging problems in engineering and management.