Spurious Factors Research Articles

Unveiling intra-person fingerprint similarity via deep contrastive learning.

Fingerprint biometrics are integral to digital authentication and forensic science. However, they are based on the unproven assumption that no two fingerprints, even from different fingers of the same person, are alike. This renders them useless in scenarios where the presented fingerprints are from different fingers than those on record. Contrary to this prevailing assumption, we show above 99.99% confidence that fingerprints from different fingers of the same person share very strong similarities. Using deep twin neural networks to extract fingerprint representation vectors, we find that these similarities hold across all pairs of fingers within the same person, even when controlling for spurious factors like sensor modality. We also find evidence that ridge orientation, especially near the fingerprint center, explains a substantial part of this similarity, whereas minutiae used in traditional methods are almost nonpredictive. Our experiments suggest that, in some situations, this relationship can increase forensic investigation efficiency by almost two orders of magnitude.

Distorted correlations among censored data: causes, effects, and correction.

Data censoring occurs when researchers do not know precise values of data points (e.g., age is 55+ or concentration ≤ .001). Censoring is frequent within psychology but typically unrecognized outside of longitudinal studies. We describe five circumstances when censoring may occur, demonstrate censoring distorts correlations, and discuss how censoring can create spurious factors. Next, we explain how to use R package lava to calculate maximum likelihood estimates (Holst and Budtz-JørgensenComputational Statistics, 28(4), 1385-1452,2013) of correlations between uncensored variables based upon censored variables. Previous research demonstrated these estimates were more accurate than Muthén's (1984) estimate for one particular model, but no research has systematically examined their accuracy. We therefore conducted a simulation study exploring the effects of the correlation, sample size, and censoring on point and interval estimates of correlations. Based upon 80 cells in which low values of normally distributed variables were censored, we recommend the constrained regression model with Wald confidence intervals. These methods were precise and unbiased unless both variables had 70% censoring and the correlation was large and negative (e.g., -.9), in which case estimates were closer to -1 than they should be. Opposite results would occur if low values of one variable and high values of the other were censored: Estimates would be precise and unbiased unless censoring was extreme and correlations were large and positive. To estimate large correlations accurately, we recommend researchers reduce censoring by using longer longitudinal studies, using scales with more response options, and matching measures to populations to reduce floor and ceiling effects.

Deep subclass reconstruction network for fault diagnosis of rotating machinery under various operating conditions

In the real-world industrial application, all measured vibration signals are usually suffered from substantial spurious factors and characterized as large intra-class and intra-subclass variations due to changes in the operating conditions (i.e., working loads, shaft speeds), which seriously deteriorates most of the machine learning methods’ ability to learn the discriminative feature representations. In this work, we propose a novel subclass reconstruction network (SCRN) to learn discriminative feature representations from raw vibration signals under different working conditions by suppressing the intra-class and intra-subclass variations in the feature space. Specifically, a novel and simple average strategy is developed to represent the cluster centroid of each subclass information as an effective supervised representation which is effectively embedded into the SCRN model. Furthermore, a new cost function of SCRN is formulated by jointly minimizing the basic and subclass-level reconstruction errors. To better exploit the discriminative information and improve classification performance, we further develop a deep subclass reconstruction network (DSCRN) model by stacking multiple SCRN models together through non-linear transformations to learn better deep feature representations. Extensive comparative evaluations on three benchmarks CWRU, 2009 PHM, and MFPT demonstrate that the proposed methods achieve consistently better diagnosis performance than existing state-of-the-art methods. Especially, when only 1% training samples from CWRU are used, the proposed DSCRN has significant improvement with classification performance of 94.55% over compared method with a gain of at least 9%.

Rural Small-Scale Women Farmers and Preference for Family Size in South-East Nigeria

Among other things, fertility control in Nigeria may not be feasible without recourse to socioeconomic issues such as micro-occupation classification and dominant family cultural traditions facing women and their relationship to fertility behaviour. Rural small-scale women farmers are a relatively closed group with some uniqueness for the understanding of the value of children (VOC) in socioeconomic and cultural contexts and its relationship with fertility behaviour in developing nations. This study, guided by the VOC model, focuses on determining factors for preference for family size (0-4 children) among rural small-scale women farmers in Eha-Amufun in Enugu state. 200 married women (mean age = 33.9; mean age at marriage = 24.5) from 20 agricultural co-operative societies were selected for the study. The study adopted a survey and quantitative research design. Besides the sociodemographic information of the study participants, the study elicited from the respondents information on their choice of family size, the connection of family size with their occupation and the circumstances surrounding son preference and son adoption in the family and rural contexts. The collected data were analysed using the ordinal logistic regression model. The findings show that economic independence, son preference and male child adoption negates limiting family sizes to 0-4 (p<.05) however, age, formal education, children as source of labour and economic independence were positively correlated with the desire for a family size of 4 children and above. In view of the limitations of the study and the ability of the VOC model to unveil spurious factors for fertility behaviour among women, there is a need for comparative studies of rural closed groups in developing nations and their fertility behaviour.Among other things, fertility control in Nigeria may not be feasible without recourse to socioeconomic issues such as micro-occupation classification and dominant family cultural traditions facing women and their relationship to fertility behaviour. Rural small-scale women farmers are a relatively closed group with some uniqueness for the understanding of the value of children (VOC) in socioeconomic and cultural contexts and its relationship with fertility behaviour in developing nations. This study, guided by the VOC model, focuses on determining factors for preference for family size (0-4 children) among rural small-scale women farmers in Eha-Amufun in Enugu state. 200 married women (mean age = 33.9; mean age at marriage = 24.5) from 20 agricultural co-operative societies were selected for the study. The study adopted a survey and quantitative research design. Besides the sociodemographic information of the study participants, the study elicited from the respondents information on their choice of family size, the connection of family size with their occupation and the circumstances surrounding son preference and son adoption in the family and rural contexts. The collected data were analysed using the ordinal logistic regression model. The findings show that economic independence, son preference and male child adoption negates limiting family sizes to 0-4 (p<.05) however, age, formal education, children as source of labour and economic independence were positively correlated with the desire for a family size of 4 children and above. In view of the limitations of the study and the ability of the VOC model to unveil spurious factors for fertility behaviour among women, there is a need for comparative studies of rural closed groups in developing nations and their fertility behaviour.

A Bayesian Regularized Approach to Exploratory Factor Analysis in One Step

ABSTRACT This research proposes a one-step Bayesian regularized approach to exploratory factor analysis (EFA) with an unknown number of factors. The proposed Bayesian regularized exploratory factor analysis (BREFA) model builds on the idea of bi-level Bayesian sparse group selection and can produce exact zero estimates at both the factor and loading levels. It can distinguish true factors from spurious factors and provide estimations of model and tuning parameters simultaneously. In addition to achieving model simplicity at both the factor and item levels, the approach provides interval estimates that can be used for significance testing, making it capable of addressing both uncorrelated and correlated factors. The Bayesian hierarchical formulation is implemented using Markov chain Monte Carlo estimation with the multivariate spike and slab priors and posterior median estimator. Based on simulated and real data analysis, BREFA demonstrates clear advantages or flexibility compared with traditional and Bayesian EFA, in terms of factor extraction, parameter estimation, and model interpretation.

Stock Market Information and Innovative Investment in the Supply Chain

Abstract We investigate whether suppliers adjust innovative supply chain investment following stock market signals about customers’ economic prospects. We show that suppliers increase R&D and investment in customer-related patents after positive market reactions to customers’ new product announcements. A battery of falsification tests suggest that spurious factors are unlikely to explain our results. Market signals about customers appear more important when information asymmetry is greater, when suppliers face greater competitive threats, and when suppliers are financially unconstrained. Our evidence suggests that the stock market can be a viable source of information to mitigate supply chain frictions. (JEL G14, G30, L14)

Changes in supramaximal M-wave amplitude at different regions of biceps brachii following eccentric exercise of the elbow flexors.

Previous evidence from surface electromyograms (EMGs) suggests that exercise-induced muscle damage (EIMD) may manifest unevenly within the muscle. Here we investigated whether these regional changes were indeed associated with EIMD or if they were attributed to spurious factors often affecting EMGs. Ten healthy male subjects performed 3 × 10 eccentric elbow flexions. Maximal voluntary contraction (MVC), muscle soreness and ultrasound images from biceps brachii distal and proximal regions were measured immediately before (baseline) and during each of the following 4days after the exercise. Moreover, 64 monopolar surface EMGs were detected while 10 supramaximal pulses were applied to the musculocutaneous nerve. The innervation zone (IZ), the number of electrodes detecting largest M-waves and their centroid longitudinal coordinates were assessed to characterize the spatial distribution of the M-waves amplitude. The MVC torque decreased (~ 25%; P < 0.001) while the perceived muscle soreness scale increased (~ 4cm; 0cm for no soreness and 10cm for highest imaginable soreness; P < 0.005) across days. The echo intensity of the ultrasound images increased at 48h (71%), 72h (95%) and 96h (112%) for both muscle regions (P < 0.005), while no differences between regions were observed (P = 0.136). The IZ location did not change (P = 0.283). The number of channels detecting the greatest M-waves significantly decreased (up to 10.7%; P < 0.027) and the centroid longitudinal coordinate shifted distally at 24, 48 and 72h after EIMD (P < 0.041). EIMD consistently changed supramaximal M-waves that were detected mainly proximally from the biceps brachii, suggesting that EIMD takes place locally within the biceps brachii.

Hidden in Plain Sight: The Role of Corporate Board of Directors in Public Charity Lobbying

We show that public charities with corporate directors on their boards spend more on lobbying for connected firms' industry interests. The effects of board connections are stronger when firms have greater lobbying expenditures themselves or when charities are constrained on funding. We rule out spurious factors with firm-charity pair fixed effects, and address reverse causality concerns using director turnovers as shocks to connections. Consistent with quid-pro-quo relationships, we find that connected firms benefit from increased procurement contracts, and that connected charities receive more grants and donations. Our results highlight executive charitable engagement as a complementary avenue for corporate political activities.

Assurance of learning and accreditations in business schools: an AACSB perspective

PurposeThe post-globalization epoch has placed academic institutions internationally in competitive situations where knowledgeable, effective and capable decisions have come to provide the comparative edge. Academia has turned to explicit – and even conceptualizing on tacit – knowledge management to elaborate a systematic approach to develop and sustain the intellectual capital needed to succeed, in response to the employment market demand for its products. To be able to do that, you must be able to visualize your organization as consisting of nothing but knowledge and knowledge flows. The use of web-based technologies in academic institutions for their diverse practices has been widespread in colleges and universities for several decades. These applications include surveying stakeholders, assessing classes, reporting on faculty development and assurance of learning (AoL) data to mention a few. Further advances have led to the integration of applications that not only enable the sharing of knowledge but which also support the reporting requirements necessary to obtain and retain accreditation, for example; likewise, satisfy the supply of intellectual capital to the employment marketplace. The purpose of this paper is to portray the relationship between AoL and accreditations at large in business schools, with the particular articulation of a modus operandi and relevant model that could facilitate curriculum improvement likewise.Design/methodology/approachObservational research (or field research) is a type of correlational (i.e. non-experimental) research in which a researcher observes ongoing behavior. There are a variety of types of observational research, each of which has both strengths and weaknesses. A select set of business schools and leading accreditation agencies have been observed and reported upon in pertinence with the expected practices and modus operandi toward assuring learning.FindingsThe use of web-based technologies in academic institutions for their diverse practices has been widespread in colleges and universities for several decades. These applications include surveying stakeholders, assessing classes, reporting on faculty development and AoL data to mention a few. Further advances have led to the integration of applications that not only enable the sharing of knowledge but which also support the reporting requirements necessary to obtain and retain accreditation; likewise, satisfy the supply of intellectual capital to the employment marketplace. In this paper, the author aims to portray the relationship between AoL and assessment at large with real-life examples and approaches.Research limitations/implicationsObservational research types are organized by the extent to which an experimenter intrudes upon or controls the environment. Observational research is particularly prevalent in the social sciences. It is a social research technique that involves the direct observation of phenomena in their natural setting. This differentiates it from experimental research in which a quasi-artificial environment is created to control for spurious factors, and where at least one of the variables is manipulated as part of the experiment. Henceforth, other research methods may be engaged in to quantify and investigate the phenomenon of AoL vs international practices reported upon as per internal and external forces acting on business schools.Practical implicationsThe diversity of approaches followed by business schools and encouraged by accreditations agencies is immense and at times may be connected to the choices such schools make as to how they ought to measure the learning curves of their constituents. Herein, a practical AoL approach is transcribed, as liaised with assessment and evaluation data.Social implicationsAcademia has turned to explicit – and even conceptualizing on tacit – knowledge management to elaborate a systematic approach to develop and sustain the intellectual capital needed to succeed, in response to the employment market demand for its academic products and services. To be able to do that, you must be able to visualize your organization as consisting of nothing but knowledge and knowledge flows.Originality/valueThis research is conceptualized upon as per the international standards and expectations from the field with an Association to Advance Collegiate Schools of Business framework in mind and aims to link AoL to curriculum management and through e-Assessment in a practical manner.

Too good to be true? Fallacies in evaluating risk factor models

This paper is concerned with statistical inference and model evaluation in possibly misspecified and unidentified linear asset pricing models estimated by maximum likelihood. Strikingly, when spurious factors (that is, factors that are uncorrelated with the returns on the test assets) are present, the model exhibits perfect fit, as measured by the squared correlation between the model’s fitted expected returns and the average realized returns. Furthermore, factors that are spurious are selected with high probability, and factors that are useful are driven out of the model. While ignoring potential misspecification and lack of identification can be very problematic for models with macroeconomic factors, empirical specifications with traded factors (e.g., Fama and French, 1993; Hou et al., 2015) do not suffer from the identification problems shown in this study.

Polynomial Greatest Common Divisor as a Solution of System of Linear Equations

In this article we present a new algebraic approach to the greatest common divisor (GCD) computation of two polynomials based on Bezout’s identity. This approach is based on the solution of system of linear equations. Also we introduce the dmod operation for polynomials. This operation on polynomials f, g is used to reduce the degree of the larger polynomial f in a finite field Fp. This operation saves GCD(f, g). Also we present some ideas how to reduce spurious factors that arise at the procedure.

Evaluating the Effect of Law Clerk Gender on Voting at the United States Supreme Court

ABSTRACTScholars have analyzed how those with close ties to Supreme Court justices—including family, friends, and political and legal elites—influence judicial behavior, but there are still questions about how law clerks’ attributes affect their relationship to their justice. This is important because clerks’ genders may affect their credibility and their ability to influence their justice’s behavior when a case involves a clear gender dimension. Scholars have uncovered a great deal about the determinants and consequences of the credibility of a different set of attorneys—those who present at oral argument. I apply insights from the literature on attorney credibility to the context of law clerks and analyze whether women enjoy greater credibility and influence in cases involving sex discrimination and abortion. I find that women influence their justice’s vote on the merits, but this influence is conditional on the number of women a justice hires and the justice’s ideology. This finding is robust to accounting for potential spurious factors and to balancing covariates via matching. This analysis has implications for how the justices acquire and use information from their environment to aid in their decision making.

Forecasting With Nonspurious Factors in U.S. Macroeconomic Time Series

This study examines the practical implications of the fact that structural changes in factor loadings can produce spurious factors (or irrelevant factors) in forecasting exercises. These spurious factors can induce an overfitting problem in factor-augmented forecasting models. To address this concern, we propose a method to estimate nonspurious factors by identifying the set of response variables that have no structural changes in their factor loadings. Our theoretical results show that the obtained set may include a fraction of unstable response variables. However, the fraction is so small that the original factors are able to be identified and estimated consistently. Moreover, using this approach, we find that a significant portion of 132 U.S. macroeconomic time series have structural changes in their factor loadings. Although traditional principal components provide eight or more factors, there are significantly fewer nonspurious factors. The forecasts using the nonspurious factors can significantly improve out-of-sample performance.

Testing for factor loading structural change under common breaks

This paper proposes a new test for factor loading structural change in dynamic factor models. We first show that the leading test proposed by Breitung and Eickmeier (2011) exhibits nonmonotonic power, essentially because the breaks are considered as spurious factors with stable factor loadings. The proposed test eliminates the effects of the spurious factors by maximizing the test statistic over possible numbers of the original factors. Monte Carlo simulations and an empirical example using U.S. Treasury yield curve data clearly illustrate the usefulness of the proposed test.

Protein-protein interfaces from cytochrome c oxidase I evolve faster than nonbinding surfaces, yet negative selection is the driving force.

Respiratory complexes are encoded by two genomes (mitochondrial DNA [mtDNA] and nuclear DNA [nDNA]). Although the importance of intergenomic coadaptation is acknowledged, the forces and constraints shaping such coevolution are largely unknown. Previous works using cytochrome c oxidase (COX) as a model enzyme have led to the so-called “optimizing interaction” hypothesis. According to this view, mtDNA-encoded residues close to nDNA-encoded residues evolve faster than the rest of positions, favoring the optimization of protein–protein interfaces. Herein, using evolutionary data in combination with structural information of COX, we show that failing to discern the effects of interaction from other structural and functional effects can lead to deceptive conclusions such as the “optimizing hypothesis.” Once spurious factors have been accounted for, data analysis shows that mtDNA-encoded residues engaged in contacts are, in general, more constrained than their noncontact counterparts. Nevertheless, noncontact residues from the surface of COX I subunit are a remarkable exception, being subjected to an exceptionally high purifying selection that may be related to the maintenance of a suitable heme environment. We also report that mtDNA-encoded residues involved in contacts with other mtDNA-encoded subunits are more constrained than mtDNA-encoded residues interacting with nDNA-encoded polypeptides. This differential behavior cannot be explained on the basis of predicted thermodynamic stability, as interactions between mtDNA-encoded subunits contribute more weakly to the complex stability than those interactions between subunits encoded by different genomes. Therefore, the higher conservation observed among mtDNA-encoded residues involved in intragenome interactions is likely due to factors other than structural stability.

Direct Geometrico-Static Problem of Underconstrained Cable-Driven Parallel Robots With Three Cables

Abstract This paper studies the direct geometrico-static problem (DGP) of underconstrained cable-driven parallel robots (CDPRs) with three cables. The task consists in determining the end-effector pose and the cable tensile forces when the cable lengths are assigned. The problem is challenging, because kinematics and statics are coupled, and they must be tackled simultaneously. An effective elimination procedure is proposed and a least-degree univariate polynomial free of spurious factors is obtained in the ideal governing the problem. This is proven to admit 156 solutions in the complex field. Several approaches for the efficient computation of the complete solution set are presented, including an eigenproblem formulation and homotopy continuation.

Ansoft HFSS Software Application for the Dielectric and Magnetic Measurements of Ferroelectrics and Related Materials in Microwaves

One of the basic problems in measurements of the electrical properties of materials is finding relations between measured electrical values and characteristics of the material, especially when the shape of a sample is complex. Electromagnetic (EM) simulation software calculates electrical parameters of defined structure. However, at measurements, it is necessary to solve a reverse problem, for what the software is not adapted. Using some properties of the program it is possible to find the solution of the reverse problem. Also it allows to take into account the spurious factors arising at connection of a measured sample to a measuring circuit. The method being discussed was tested by calculation of complex dielectric permittivity of Bi1.5ZnNb1.5O7-xF2 (BZN) pyrochlore ceramics as well as calculation of both complex dielectric permittivity and complex dielectric permeability of carbon-coated capsules of Ni (Ni@C) embedded into polyurethane matrix from experimental results.

Applying Rasch analysis to the SF-36 physical function scale: effect of dependent items

Medical outcome studies often use Patient Reported Outcomes (PROs), and these often take the form of administered or self-completed questionnaires. Often, the responses from these scales are simply added up as a total, and this total score is utilised for outcome purposes. However, for this approach to be valid, there are a number of underlying assumptions that are being made. One of these assumptions is that of Local Independence, which comprises two aspects; the items making up the scale should all be unidimensional (trait dependency) and, the response to an item should not directly influence the response to another item within the set (response dependency) [1]. These assumptions apply to the Rasch model (and other IRT models). Thus the process of Rasch analysis provides a means to test these assumptions, along with other key properties such as invariance. Where data satisfy these assumptions, and fit to the model, an interval scale transformation becomes available. A secondary analysis was performed on data from the Physical Function scale (PF-10) of the SF-36 [2] to demonstrate the application of Rasch Analysis and to investigate the influence of response dependency within the dataset. Initial results showed evidence of a lack of unidimensionality (t-tests = 8.39%; lower bound CI = 5.9%), along with apparent response dependencies between the three walking items of the PF-10 (walking 100 yards, walking half a mile & walking more than a mile), the two climbing stairs items of the PF-10 (climbing one flight of stairs & climbing several flights of stairs) and a dependency between the ‘moderate activities’ item and the ‘lifting or carrying groceries’ item. When adjustments had been made for the response dependency within the item set, it then appeared to be unidimensional (t-tests = 2.1%). Also to be noted is the artificial inflation of the reliability index due to the dependency within the item set. After dependency adjustments had been made, the reliability index (PSI) dropped from 0.851 (initially) to 0.789 (Cronbach’s alpha dropped from 0.91 to 0.80). In conclusion, response dependency can inflate reliability, and lead to spurious (dependency) factors. It has also been shown to influence both item and person parameters in IRT analysis. Rasch analysis provides a framework to assess, as well as adjust for, response dependency within an item set.

Training, manpower development and job performance: Perception and relevance among civil servants in Ebonyi State, Nigeria

The paper examined perception and relevance of influence of training and manpower development on employee performance, using civil servants in Ebonyi State, Nigeria as reference. A pre-coded questionnaire was administered on 300 civil servants stratified into three categories: GL 04-06; GL 07-12 and GL 13+. The purpose was to ensure equitable representation of civil servants in the study area and to elicit their opinion on the theme of study. Using chi-square (x2) as a significant test, training was perceived to influence job performance at µ 0.05 and = µ 0.25. Also manpower development was perceived to influence job performance at µ 0.05 and = µ 0.25, but the influence of type of training on job performance was inconclusive. When cross-tabulated, only 49.8% of those that had training and those exposed to manpower development had high job performance as against 21.2% for their counterparts with no training and manpower development. This is suggestive of spurious factors (that may include leadership, organizational climate, management development, salary etc) that may intervene as motivation factors for high job performance. Of significance, is that the primary aim of training and manpower development was rather to meet statutory requirements (60%), as against improvement on the job (31.2%) and has policy implications for training and manpower development relevant to organizational need or goal to be provided to employees. This was a sufficiently promising result to encourage further study, not only to re-visit the theoretical explanation that training and manpower development directly result to improved job performance, but also to verify the circumstance within which training and manpower development enhance job performance. Key words: Training, manpower development, performance, employee, Ebonyi, civil servants.

Ligand–Receptor Binding Affinities from Saturation Transfer Difference (STD) NMR Spectroscopy: The Binding Isotherm of STD Initial Growth Rates

The direct evaluation of dissociation constants (K(D)) from the variation of saturation transfer difference (STD) NMR spectroscopy values with the receptor-ligand ratio is not feasible due to the complex dependence of STD intensities on the spectral properties of the observed signals. Indirect evaluation, by competition experiments, allows the determination of K(D), as long as a ligand of known affinity is available for the protein under study. Herein, we present a novel protocol based on STD NMR spectroscopy for the direct measurements of receptor-ligand dissociation constants (K(D)) from single-ligand titration experiments. The influence of several experimental factors on STD values has been studied in detail, confirming the marked impact on standard determinations of protein-ligand affinities by STD NMR spectroscopy. These factors, namely, STD saturation time, ligand residence time in the complex, and the intensity of the signal, affect the accumulation of saturation in the free ligand by processes closely related to fast protein-ligand rebinding and longitudinal relaxation of the ligand signals. The proposed method avoids the dependence of the magnitudes of ligand STD signals at a given saturation time on spurious factors by constructing the binding isotherms using the initial growth rates of the STD amplification factors, in a similar way to the use of NOE growing rates to estimate cross relaxation rates for distance evaluations. Herein, it is demonstrated that the effects of these factors are cancelled out by analyzing the protein-ligand association curve using STD values at the limit of zero saturation time, when virtually no ligand rebinding or relaxation takes place. The approach is validated for two well-studied protein-ligand systems: the binding of the saccharides GlcNAc and GlcNAcbeta1,4GlcNAc (chitobiose) to the wheat germ agglutinin (WGA) lectin, and the interaction of the amino acid L-tryptophan to bovine serum albumin (BSA). In all cases, the experimental K(D) measured under different experimental conditions converged to the thermodynamic values. The proposed protocol allows accurate determinations of protein-ligand dissociation constants, extending the applicability of the STD NMR spectroscopy for affinity measurements, which is of particular relevance for those proteins for which a ligand of known affinity is not available.