Unknown Correlation Research Articles

A Large-Scale Study of Failures on Petascale Supercomputers

With the rapid development of supercomputers, the scale and complexity are ever increasing, and the reliability and resilience are faced with larger challenges. There are many important technologies in fault tolerance, such as proactive failure avoidance technologies based on fault prediction, reactive fault tolerance based on checkpoint, and scheduling technologies to improve reliability. Both qualitative and quantitative descriptions on characteristics of system faults are very critical for these technologies. This study analyzes the source of failures on two typical petascale supercomputers called Sunway BlueLight (based on multi-core CPUs) and Sunway TaihuLight (based on heterogeneous manycore CPUs). It uncovers some interesting fault characteristics and finds unknown correlation relationship among main components’ faults. Finally the paper analyzes the failure time of the two supercomputers in various grains of resource and different time spans, and builds a uniform multi-dimensional failure time model for petascale supercomputers.

Quantum Turbo Decoding for Quantum Channels Exhibiting Memory

Inspired by the success of classical turbo codes, quantum turbo codes (QTCs) have also been conceived for near-hashing-bound transmission of quantum information over memoryless quantum channels. However, in real physical situations, the memoryless channel assumption may not be well justified, since the channel often exhibits memory of previous error events. Here, we investigate the performance of QTCs over depolarizing channels exhibiting memory and we show that they suffer from a performance degradation at low depolarizing probability values. In order to circumvent the performance degradation issue, we conceive a new coding scheme termed quantum turbo coding scheme exploiting error-correlation (QTC-EEC) that is capable of utilizing the error-correlation while performing the iterative decoding at the receiver. The proposed QTC-EEC can achieve convergence threshold at a higher depolarizing probability for channels with a higher value of correlation parameter and achieve performance near to the capacity. Finally, we propose a joint decoding and estimation scheme for our QTC-EEC relying on the correlation estimation (QTC-EEC-E) designed for more realistic quantum systems with unknown correlation parameter. Simulation results reveal that the proposed QTC-EEC-E can achieve the same performance as that of the ideal system of known correlation parameter and hence demonstrate the accurate estimation of the proposed QTC-EEC-E.

An Unknown Correlation in Hexametric Poetry and the Interpretation of the Brevis in Longo Principle

The goal of this paper is to show that (some) Ancient Greek hexametric poets were not indifferent to the quantity of the final syllable of the verse, by studying the correlation between that quantity and the different possible word ends in the fourth foot of the verse. The results of the study suggest that in some poets there was a certain preference for “rhythmic coherence” within the second colon, which indicates that, even if there was “compositional indifference” regarding the quantity of the last syllable, there was not “actual indifference”.

Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency.

The paradigm of multisensor data fusion has been evolved from a centralized architecture to a decentralized or distributed architecture along with the advancement in sensor and communication technologies. These days, distributed state estimation and data fusion has been widely explored in diverse fields of engineering and control due to its superior performance over the centralized one in terms of flexibility, robustness to failure and cost effectiveness in infrastructure and communication. However, distributed multisensor data fusion is not without technical challenges to overcome: namely, dealing with cross-correlation and inconsistency among state estimates and sensor data. In this paper, we review the key theories and methodologies of distributed multisensor data fusion available to date with a specific focus on handling unknown correlation and data inconsistency. We aim at providing readers with a unifying view out of individual theories and methodologies by presenting a formal analysis of their implications. Finally, several directions of future research are highlighted.

Disparities in Pediatric Palliative Care: An Opportunity to Strive for Equity.

* Abbreviations: EOL — : end of life PPC — : pediatric palliative care The importance of pediatric palliative care (PPC) for children with cancer has been broadly embraced over the last 2 decades in response to compelling evidence of high symptom burden and suffering throughout the continuum of care.1–3 Yet, how best to evaluate the impact of PPC remains a methodological challenge in a field focused on patient-reported outcomes, including symptom alleviation and goal-concordant care. In adult oncology, the benchmarking of palliative care has been facilitated by data demonstrating that end-of-life (EOL) care intensity (which is evaluable with administrative data, such as hospitalization and receipt of ICU care) is discordant with the wishes of adult patients with cancer.4,5 Similar benchmarking in pediatrics has been limited by an unknown correlation of care intensity with family goals and an absence of methodology for pediatric-specific administrative data. In this issue of Pediatrics , Johnston et al6 provide the first US population-based data … Address correspondence to Kira Bona, MD, MPH, Department of Pediatric Oncology, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA 02115. E-mail: kira.bona{at}childrens.harvard.edu

Simultaneous confidence regions for multivariate bioequivalence.

Demonstrating bioequivalence of several pharmacokinetic (PK) parameters, such as AUC and Cmax , that are calculated from the same biological sample measurements is in fact a multivariate problem, even though this is neglected by most practitioners and regulatory bodies, who typically settle for separate univariate analyses. We believe, however, that a truly multivariate evaluation of all PK measures simultaneously is clearly more adequate. In this paper, we review methods to construct joint confidence regions around multivariate normal means and investigate their usefulness in simultaneous bioequivalence problems via simulation. Some of them work well for idealised scenarios but break down when faced with real-data challenges such as unknown variance and correlation among the PK parameters. We study the shapes of the confidence regions resulting from different methods, discuss how marginal simultaneous confidence intervals for the individual PK measures can be derived, and illustrate the application to data from a trial on ticlopidine hydrochloride. An R package is available.

Temporal Factor-Aware Video Affective Analysis and Recommendation for Cyber-Based Social Media

As an important cyber-enabled application, online video recommendation is seeing significant interest from both industry and academia. To effectively recommend video content becomes a popular research topic. However, it has been found that existing recommendation methods based on video affective analysis ignore the temporal factor, leading to poor performance especially when the order of emotion components does affect the recommendation quality. This motivates us to study the feature of emotion fluctuation, which we call Temporal Factor of Emotion (TFE). In this paper, a novel recommendation method based on the Grey Relational Analysis (GRA) is proposed to tackle this problem. GRA preserves the temporal factor of objects during analysis and is suitable for analyzing systems with unknown correlation (a set of independent videos). In our work, first, specific video features are extracted and mapped to the well-known Lovheim emotion-space, through the SVMs (Support Vector Machine). Then, GRA is applied to compute the quantitative relation among videos by using extracted emotions as factors. Finally, a pick-filter pattern and GRA-based recommendation method under the Fisher model are proposed. To evaluate the performance of our method, an online video recommendation system is developed. Experimental results of both user study and parameter evaluation demonstrate that the GRA-based method can improve accuracy of video affective analysis and performance of video recommendation.

Noise Correlation Effect on Detection: Signals in Equicorrelated or Autoregressive(1) Gaussian.

In this letter, we consider the effect of noise correlation on the error performance of binary hypothesis signal detection, when one of two deterministic signals is received in correlated Gaussian noise. For the likelihood ratio detection scheme, analytical performance results are derived for equicorrelated and autoregressive order one models. Although it is known previously that the best signal lies in the direction of eigenvector corresponding to the minimum eigenvalue of the noise covariance matrix, our investigation of the variation of mean signal-to-noise power ratio as a function of correlation parameter (i) shows how correlation leads to increased probability of error up to a point, beyond which monotonic decrease in error probability with increasing correlation is possible and (ii) provides a max-min signal design solution for the unknown correlation parameter case. Numerical results are also included for some specific signals.

The role of firms' qualification in public contracts execution: An empirical assessment

According to the Italian regulation firms must qualify to bid in auctions for public work contracts worth more than 150,000 euros. In this paper, we investigate the link between the efficiency of infrastructure provision, and the Italian regulation concerning the firm's entry and qualification system, employing a large dataset on Italian public works contracts for roads and highways. First, firm's efficiency in public contracts' execution is estimated using a smoothed data envelopment analysis (DEA) bootstrap procedure. Then, the effects of the qualification system on firm's efficiency is evaluated using a semi-parametric technique that produces a robust inference for an unknown serial correlation between efficiency scores. Our analysis shows that fully qualified firms perform better than partially qualified firms.

Sequence robust association test for familial data.

Genome-wide association studies (GWAS) and next generation sequencing studies (NGSS) are often performed in family studies to improve power in identifying genetic variants that are associated with clinical phenotypes. Efficient analysis of genome-wide studies with familial data is challenging due to the difficulty in modeling shared but unmeasured genetic and/or environmental factors that cause dependencies among family members. Existing genetic association testing procedures for family studies largely rely on generalized estimating equations (GEE) or linear mixed-effects (LME) models. These procedures may fail to properly control for type I errors when the imposed model assumptions fail. In this article, we propose the Sequence Robust Association Test (SRAT), a fully rank-based, flexible approach that tests for association between a set of genetic variants and an outcome, while accounting for within-family correlation and adjusting for covariates. Comparing to existing methods, SRAT has the advantages of allowing for unknown correlation structures and weaker assumptions about the outcome distribution. We provide theoretical justifications for SRAT and show that SRAT includes the well-known Wilcoxon rank sum test as a special case. Extensive simulation studies suggest that SRAT provides better protection against type I error rate inflation, and could be much more powerful for settings with skewed outcome distribution than existing methods. For illustration, we also apply SRAT to the familial data from the Framingham Heart Study and Offspring Study to examine the association between an inflammatory marker and a few sets of genetic variants.

ADAPTIVE COHERENT SIGNAL PROCESSING ON THE BACKGROUND OF PASSIVE INTERFERENCE

Principles of optimization of band elimination of passive interference in conditions of a priori parametric uncertainty of their spectral & correlation characteristics have been reviewed. A procedure is proposed for optimizing of systems for coherent signal processing based on a combination of a band elimination and a multichannel filter, such procedure in conditions of a priori uncertainty entails adaptive rearrangement of the weight coefficients of the filters and makes it possible to approach the maximum efficiency of the system. The analysis of the processing systems efficiency, which was performed on the basis of asymptotic properties of evaluations of the maximum likelihood, establishes the relationship between interference cancellation in the system and errors in adaptation to unknown correlation parameters of the interference. The relationships obtained as a result of the analysis make it possible to select the scope of the learning sample depending on the preset value of the adaptation loss, the spectral & correlation properties of the passive interference and the structure of the coherent signal processing system.

Multiantenna GLR Detection of Rank-One Signals With Known Power Spectral Shape Under Spatially Uncorrelated Noise

We establish the generalized likelihood ratio (GLR) test for a Gaussian signal of known power spectral shape and unknown rank-one spatial signature in additive white Gaussian noise with an unknown diagonal spatial correlation matrix. This is motivated by spectrum sensing problems in dynamic spectrum access, in which the temporal correlation of the primary signal can be assumed known up to a scaling, and where the noise is due to an uncalibrated receive array. For spatially independent identically distributed (i.i.d.) noise, the corresponding GLR test reduces to a scalar optimization problem, whereas the GLR detector in the general non-i.i.d. case yields a more involved expression, which can be computed via alternating optimization methods. Low signal-to-noise ratio (SNR) approximations to the detectors are given, together with an asymptotic analysis showing the influence on detection performance of the signal power spectrum and SNR distribution across antennas. Under spatial rank- $P$ conditions, we show that the rank-one GLR detectors are consistent with a statistical criterion that maximizes the output energy of a beamformer operating on filtered data. Simulation results support our theoretical findings in that exploiting prior knowledge on the signal power spectrum can result in significant performance improvement.

Bank efficiency in Malaysia: a use of malmquist meta-frontier analysis

This paper uses a two-stage data envelopment analysis to examine bank efficiency in Malaysia. In the first stage, we use meta-frontier technology to address bank heterogeneity-bank nature (Islamic vs. conventional banks) and bank ownership (local vs. foreign banks). Using a data set of 43 Malaysian commercial banks, the application of meta-frontier enables us to compare the existence of inefficiency among Malaysian banks because of their business nature and ownership. In doing so, we empirically demonstrate that using different approaches (i.e., production, profitability, and intermediation) for calculating bank efficiency can give significant contradictory results in efficiency scores. In the second stage, we use Simar and Wilson’s double bootstrap regression to estimate the determinants of efficiency in Malaysian banks. This helps to achieve valid inference even in the presence of unknown serial correlation in the meta-frontier efficiency scores. The results from double bootstrap regression confirm that bank ownership, bank nature and gross domestic product have significant influence on bank efficiency. This study reveals that Islamic banks have outperformed. The frontier results reveal that local Islamic banks have moved towards the group technology and foreign Islamic banks have taken the lead in country frontier technology.

A prognostics approach based on the evolution of damage precursors using dynamic Bayesian networks

During the lifetime of a component, microstructural changes emerge at its material level and evolve through time. Classical empirical degradation models (e.g. Paris Law in fatigue crack growth) are usually established based on monitoring and estimating well-known direct damage indicators such as crack size. However, by the time the usual inspection techniques efficiently identify such damage indicators, most of the life of the component would have been expended, and usually it would be too late to save the component. Therefore, it is important to detect damage at the earliest possible time. This article presents a new structural health monitoring and damage prognostics framework based on evolution of damage precursors representing the indirect damage indicators, when conventional direct damage indicator, such as a crack, is unobservable, inaccessible, or difficult to measure. Dynamic Bayesian network is employed to represent all the related variables as well as their causal or correlation relationships. Since the degradation model based on damage precursor evolution is not fully recognized, the methodology needs to be capable of online-learning the degradation process as well as estimating the damage state. Therefore, the joint particle filtering technique is implemented as an inference method inside the dynamic Bayesian network to assess both model parameters and damage states simultaneously. The proposed framework allows the integration of any related sources of information in order to reduce the inherent uncertainties. Incorporating different types of evidences in dynamic Bayesian network entails advance techniques to identify and formulate the possible interaction between potentially nonhomogenous variables. This article uses the support vector regression in order to define generally unknown nonparametric and nonlinear correlation between the input variables. The methodology is successfully applied to damage estimation and prediction of crack initiation in a metallic alloy under fatigue. The proposed framework is intended to be general and comprehensive so that it can be implemented in different applications.

Correlated random effects regression analysis for a log-normally distributed variable

ABSTRACTIn regression analysis, it is assumed that the response (or dependent variable) distribution is Normal, and errors are homoscedastic and uncorrelated. However, in practice, these assumptions are rarely satisfied by a real data set. To stabilize the heteroscedastic response variance, generally, log-transformation is suggested. Consequently, the response variable distribution approaches nearer to the Normal distribution. As a result, the model fit of the data is improved. Practically, a proper (seems to be suitable) transformation may not always stabilize the variance, and the response distribution may not reduce to Normal distribution. The present article assumes that the response distribution is log-normal with compound autocorrelated errors. Under these situations, estimation and testing of hypotheses regarding regression parameters have been derived. From a set of reduced data, we have derived the best linear unbiased estimators of all the regression coefficients, except the intercept which is often unimportant in practice. Unknown correlation parameters have been estimated. In this connection, we have derived a test rule for testing any set of linear hypotheses of the unknown regression coefficients. In addition, we have developed the confidence ellipsoids of a set of estimable functions of regression coefficients. For the fitted regression equation, an index of fit has been proposed. A simulated study illustrates the results derived in this report.

Confidence Intervals for the Population Correlation Coefficient 

Computing a confidence interval for a population correlation coefficient is very important for researchers as it gives an estimated range of values which is likely to include an unknown population correlation coefficient. This paper studied some confidence intervals for estimating the population correlation coefficient ρ by means of a Monte Carlo simulation study. Data are randomly generated from several bivariate distributions with a various values of sample sizes. Assessment measures such as coverage probability, mean width and standard deviation of the width are selected for performances evaluation. Two real life data are analyzed to demonstrate the application of the proposed confidence intervals. Based on our findings, some good confidence intervals for a population correlation coefficient are suggested for practitioners and applied researchers.

Novel Closed-Form Expressions for Effective Electromagnetic Parameters of Honeycomb Radar-Absorbing Structure

Novel closed-form expressions for effective material properties of honeycomb radar-absorbing structure (RAS) are proposed. These expressions, which are derived from strong fluctuation theory with anisotropic correlation function, consist of two parts: 1) the initial value part and 2) the dispersion characteristic part. Compared with the classical closed-form formulas, the novel expressions provide for a better formulation of the effective electromagnetic parameters of honeycomb RAS, which are characterized by well-behaved increase in wide frequency band. The good agreement between the theoretical results and the existing experimental data confirms the validity of the proposed expressions. Furthermore, a linear monomial dispersion characteristic function, which argues not for the absolute frequency value, but the relative frequency displacement of a frequency point relative to the frequency of initial value, is introduced to replace the polynomial expansion of the unknown correlation part in strong fluctuation theory. Such replacement reveals the near-linear relationship between the undetermined coefficients of monomial function and the coating thickness of honeycomb RAS. Compared with polynomial fitting method, which is based on polynomial expansion, this technique can further support the prediction of undetermined coefficients, when simulation results or measurement data are not available.

A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects.

For region-based sequencing data, power to detect genetic associations can be improved through analysis of multiple related phenotypes. With this motivation, we propose a novel test to detect association simultaneously between a set of rare variants, such as those obtained by sequencing in a small genomic region, and multiple continuous phenotypes. We allow arbitrary correlations among the phenotypes and build on a linear mixed model by assuming the effects of the variants follow a multivariate normal distribution with a zero mean and a specific covariance matrix structure. In order to account for the unknown correlation parameter in the covariance matrix of the variant effects, a data-adaptive variance component test based on score-type statistics is derived. As our approach can calculate the P-value analytically, the proposed test procedure is computationally efficient. Broad simulations and an application to the UK10K project show that our proposed multivariate test is generally more powerful than univariate tests, especially when there are pleiotropic effects or highly correlated phenotypes.

Bayesian methods for characterizing unknown parameters of material models

A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). The Bayesian method is also employed to characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.

High-resolution determinant analysis of Japanese residential electricity consumption using home energy management system data

This paper examines seasonal panel data analysis at a higher resolution using commercial home energy management system data to identify the parameters that determine Japanese household electricity consumption. The electricity consumption data from 532 detached houses and 208 apartment-style houses is aggregated by use and time period and regressed with explanatory variables to indicate house and occupant attributes. Predictable significant impact factors such as outdoor temperature, floor area, household size, presence of a central air conditioning system, and a variety of appliances are estimated quantitatively. The differences due to appliance possession are estimated as 844kWh/year for a water server, 885kWh/year for an additional refrigerator, 491kWh/year for a portable humidifier, and 443kWh/year for an air purifier. We found previously unknown correlation between variables such as central air conditioning and hot water demand through this model. We also identified several important parameters that explain electricity demand by data collecting at different sites and time periods. Obtained knowledge will contribute to promoting further energy efficiency program in the Japanese residential sector. Households in our study were newer houses and additional data are required for a more stable and reliable model. In addition, a reverse estimation of household attributes from electricity load dynamics is an issue for future study.