Pairs Of Variables Research Articles

An automated detection of confusing variable pairs with highly similar compound names in Java and Python programs

Variable names represent a significant source of information regarding the source code, and a successful naming of variables is key to producing readable code. Programmers often use a compound variable name by concatenating two or more words to make it more informative and enhance the code readability. While each compound variable name is descriptive, a collection of them sometimes produces “confusing” variable pairs if their names are highly similar, e.g., “shippingHeight,” vs. “shippingWeight.” A confusing variable pair would adversely affect the code readability because it can cause a misreading or mix-up of variables during the programming or code review activities. Toward automated support for enhancing code readability, this paper conducts a large-scale investigation of compound variable names in Java and Python programs. The investigation collects 116,921,127 pairs of compound-named variables from 1,876 open-source Java projects and 106,943,523 pairs of such variables from 2,427 open-source Python projects. Then, this study analyzes those variable pairs from two perspectives of name similarity: string similarity and semantic similarity. Through an evaluation study with 30 human participants, the data analyses show that both string and semantic similarity can help detect confusing variable pairs in Java and Python programs. In order to distill confusing variable pairs automatically, support tools for detecting confusing variable pairs are also developed in this study.

Temporal changes in dependence between compound coastal and inland flooding drivers around the contiguous United States coastline

Flooding in low-lying coastal zones arises from coastal (storm surge, tides, and waves), fluvial (excessive river discharge), and pluvial (excessive surface runoff) drivers. We analyse changes in compound flooding potential around the contiguous United States (CONUS) coastline stemming from select combinations of these flooding drivers using long observational records with at least 55 years of data. We assess temporal changes in the tail (extremal) dependence (χ) using a 30-year sliding time window. Periods of strong tail dependence are found for the windows centered between the 1960s and 1980s/1990s at several locations for surge-discharge (S-Q) and surge-precipitation (S–P) combinations. Changes in dependence are associated with large-scale climate indices such as the Arctic Oscillation (AO) and El Nino Southern Oscillation indices (Niño 1.2 and Niño 3), among others. The significance of potential changes in the dependence structure is subsequently tested using Kullback–Leibler (KL) divergence. We find that changes are mostly not significant. Finally, we perform a complete multivariate statistical analysis exemplarily for one selected pair of variables at one location (S-Q in Washington, DC), allowing for varying dependence strength and structure as well as changes in the marginal distributions. Combined changes with increase in the dependence and marginals exacerbate the predicted compound flood potential. The comprehensive analysis presented here provides new insights into how and where compound flooding potential has changed with time, demonstrates associated links with large-scale climate indices, and highlights the effects of changes in the dependence and marginals in a multivariate statistical framework.

Quantile-Based Multivariate Log-Normal Distribution

We introduce a quantile-based multivariate log-normal distribution, providing a new multivariate skewed distribution with positive support. The parameters of this distribution are interpretable in terms of quantiles of marginal distributions and associations between pairs of variables, a desirable feature for statistical modeling purposes. We derive statistical properties of the quantile-based multivariate log-normal distribution involving the transformations, closed-form expressions for the mixed moments, expected value, covariance matrix, mode, Shannon entropy, and Kullback–Leibler divergence. We also present results on marginalization, conditioning, and independence. Additionally, we discuss parameter estimation and verify its performance through simulation studies. We evaluate the model fitting based on Mahalanobis-type distances. An application to children data is presented.

Uncertainty principle and complementary variables

The two most important areas in modern physics are quantum mechanics and the theory of relativity. Unlike classical physics, where Newton's mechanics dominates, these two areas change human beings' fundamental view of the universe. One of the theories that build up the base of quantum mechanics is Heisenberg's Uncertainty Principle. Starting from a thought experiment, Heisenberg's microscope in the setting of classical physics, Werner Heisenberg built a bridge between classical and quantum physics by presenting a counterintuitive outcome in the thought experiment. Since then, the observer of a physics phenomenon is no longer a bystander. The behavior of observation became a part of the physical experiment. To come up with a mathematical expression that can describe such a new discovery, Heisenberg came up with matrix mechanics and the concept of complementary variables. There is a trade-off between a pair of complementary variables. When one of them is measured precisely, meaning the information of that variable is known on a large scale, the other variable can not be measured precisely, meaning there is no way to know enough information about the other variable. The principle indicates a fundamental limit on what human beings can know about the unknown variables. The discoveries of other complementary variables help physicists know the new image of the physics world under new rules.

The Hurwitz-Hopf map and harmonic wave functions for integer and half-integer angular momentum

Harmonic wave functions for integer and half-integer angular momentum are given in terms of the Euler angles (θ, ϕ, ψ) that define a rotation in SO(3), and the Euclidean norm r in , keeping the usual meaning of the spherical coordinates (r, θ, ϕ). They form a Hilbert (super)-space decomposed in the form . Following a classical work by Schwinger, 2-dimensional harmonic oscillators are used to produce raising and lowering operators that change the total angular momentum eigenvalue of the wave functions in half units. The nature of the representation space is approached from the double covering group homomorphism SU(2) → SO(3) and the topology involved is taken care of by using the Hurwitz-Hopf map . It is shown how to reconsider H as a 2-to-1 group map, , translating it into an assignment (z 1, z 2) ↦ (r, θ, ϕ, ψ) whose domain consists of pairs (z 1, z 2) of complex variables, under the appropriate identification of with . It is shown how the Lie algebra of G 0 is coupled with two Heisenberg Lie algebras of 2-dimensional (Schwinger’s) harmonic oscillators generated by the operators and their adjoints. The whole set of operators gets algebraically closed either into a 13-dimensional Lie algebra or into a (4∣8)-dimensional Lie superalgebra. The wave functions in can be written in terms of polynomials in the complex coordinates (z 1, z 2) and their complex conjugates and the representations are explicitly constructed via the various highest weight (or lowest weight) vector representations of G 0. Finally, a new nonrelativistic quantum (Schrödinger-like) equation for the hydrogen atom that takes into account the electron spin is introduced and expressed in terms of (r, θ, ϕ, ψ) and the time t. The equation is succeptible to be solved exactly in terms of the harmonic wave functions hereby introduced.

Correlations with Congenital Syphilis in the United States: A Cross-Sectional Study

Syphilis is caused by the bacterium Treponema pallidum and is transmitted from human to human through sexual contact. Congenital syphilis (CS) occurs when the mother transmits the infection to the fetus. Clinical manifestations of CS include anemia, hepatosplenomegaly, blindness, deafness, meningitis, and deformities in bone structure. The number of cases of CS have increased over the past decade in the United States according to the CDC. A study was conducted correlating the number of Medicaid enrollees in 2020, the number of uninsured persons in 2020, and the number of cases of COVID-19 in 2020 to cases of CS in the United States in 2021. A Spearman rank correlation analysis was done using SPSS. Results were statistically significant for all three pairs of variables with positive correlations; Medicaid enrollment and CS cases (r = 0.735, P<.05), uninsured persons with CS cases (r = 0.713, P<.05), COVID-19 cases and CS cases (r = 0.689, P<.05). Reasons for the increase in CS cases are multifactorial, including variations in state laws regarding syphilis screening in the prenatal period, differences in provider processes for persons on Medicaid, persons uninsured, and restrictions to accessing healthcare providers during the COVID-19 pandemic. Future studies should include questionnaires and interviews with women on their experiences during prenatal visits in regards to syphilis screening, particularly women covered by Medicaid, and surveys completed by healthcare providers to gain insight and to identify factors that affect a woman not being tested for syphilis during her pregnancy.

Sitting vs. supine ultrasound measurements of the vastus medialis: correlations with MRI measurements and age considerations

BackgroundMuscle thickness (MT) and echo intensity (EI) measurements are ultrasound alternatives to magnetic resonance imaging (MRI) for evaluating muscle quantity and quality. The vastus medialis (VM) is a clinically important muscle, and assessment methods that most accurately reflect its quantity and quality are required. This study aimed to examine the correlation between MT and EI measured in the supine and sitting postures with corresponding MRI-measured muscle quantity and quality indices.MethodsIn total, 134 adults (91 older and 43 young) participated in this study. Ultrasound images of the VM were acquired in the supine and sitting postures, and MT and EI were measured. The cross-sectional area (CSA), muscle volume (MV), and intramuscular adipose tissue (intraMAT) of the VM were evaluated from MRI images using T1-weighted and Dixon methods. Pearson’s coefficients were used to quantify the correlation strength amongst pairs of dependent variables. Meng’s test was used to test for correlation coefficient differences between the two measurement postures (supine and sitting).ResultsThe correlation coefficients amongst MT, CSA, and MV were significantly higher in the sitting posture than in the supine posture. EI measured in the supine and sitting postures correlated significantly with intraMAT, and in young individuals, these correlation coefficients were significantly higher in the sitting posture than in the supine posture.ConclusionsThese findings suggest that assessment of VM muscle quantity in the sitting posture is superior for young and older individuals, and assessment of VM muscle quality in the sitting posture is most effective in younger individuals.

Consistency between Treatment Effects on Clinical and Brain Atrophy Outcomes in Alzheimer’s Disease Trials

BackgroundLongitudinal changes in volumetric MRI outcome measures have been shown to correlate well with longitudinal changes in clinical instruments and have been widely used as biomarker outcomes in clinical trials for Alzheimer’s disease (AD). While instances of discordant findings have been noted in some trials, especially the recent amyloid-removing therapies, the overall relationship between treatment effects on brain atrophy and clinical outcomes, and how it might depend on treatment target or mechanism, clinical instrument or imaging variable is not yet clear.ObjectiveTo systematically assess the consistency and therapeutic class-dependence of treatment effects on clinical outcomes and on brain atrophy in published reports of clinical trials conducted in mild cognitive impairment (MCI) and/or AD.DesignQuantitative review of the published literature. The consistency of treatment effects on clinical and brain atrophy outcomes was assessed in terms of statistical agreement with hypothesized equal magnitude effects (e.g., 30% slowing of both) and nominal directional concordance, as a function of therapeutic class.SettingInterventional randomized clinical trials.ParticipantsMCI or AD trial participants.InterventionTreatments included were those that involved ingestion or injection of a putatively active substance into the body, encompassing both pharmacological and controlled dietary interventions.MeasurementsEach trial included in the analysis reported at least one of the required clinical outcomes (ADAS-Cog, CDR-SB or MMSE) and at least one of the required imaging outcomes (whole brain, ventricular or hippocampal volume).ResultsData from 35 trials, comprising 185 pairwise comparisons, were included. Overall, the 95% confidence bounds overlapped with the line of identity for 150/185 (81%) of the imaging-clinical variable pairs. The greatest proportion of outliers was found in trials of anti-amyloid antibodies that have been shown to dramatically reduce the level of PET-detectable amyloid plaques, for which only 13/33 (39%) of observations overlapped the identity line. A Deming regression calculated using all data points yielded a slope of 0.54, whereas if data points from the amyloid remover class were excluded, the Deming regression line had a slope of 0.92. Directional discordance of treatment effects was also most pronounced for the amyloid-removing class, and for comparisons involving ventricular volume.ConclusionOur results provide a frame of reference for the interpretation of clinical and brain atrophy results from future clinical trials and highlight the importance of mechanism of action in the interpretation of imaging results.

Social integration, physical and mental health and subjective well-being in the floating population-a moderated mediation analysis.

Individuals of domestic migrant populations in China (specifically, migration that is economically driven) often face difficulties in social integration. They are suffering from discrimination and unfair treatment in work and life, which do harm to their physical/mental health and Subjective Well-Being (SWB). The current study utilized a stratified sampling survey in the Yangtze River Delta region of China, in October and November 2022. Six hundred and eleven useful self-reported questionnaires were collected. Questionnaires include questions about social integration, social capital, physical/mental health, and SWB; Bootstrapping method was used to test the mediating effect of physical health and mental health. Multiple hierarchical regression was used to test the moderating effect of social capital. Social integration had positive impact on the SWB (r = 0.523, p < 0.01). Bootstrap analysis showed that physical health and mental health partially mediated the correlation between social integration and SWB of Floating Population with a mediation effect of 0.149 and 0.192. Social capital can positively moderate the relationship between two pair of variables: social integration and SWB (β = 0.152, t = 4.42, p < 0.001), physical health and SWB (β = 0.148, t = 4.39, p < 0.01). However, social capital does not play a significant moderating role in the association between the effect of mental health on SWB (β = 0.032, t = 0.973, p > 0.05). This study proved a significant correlation between social integration and SWB of Floating Population, with physical/mental health playing a mediating role. Enhancing the social integration of floating population and keeping healthy physically and mentally are key to improving their SWB.

T cell receptor β repertoires in patients with COVID-19 reveal disease severity signatures.

The immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are crucial in maintaining a delicate balance between protective effects and harmful pathological reactions that drive the progression of coronavirus disease 2019 (COVID-19). T cells play a significant role in adaptive antiviral immune responses, making it valuable to investigate the heterogeneity and diversity of SARS-CoV-2-specific T cell responses in COVID-19 patients with varying disease severity. In this study, we employed high-throughput T cell receptor (TCR) β repertoire sequencing to analyze TCR profiles in the peripheral blood of 192 patients with COVID-19, including those with moderate, severe, or critical symptoms, and compared them with 81 healthy controls. We specifically focused on SARS-CoV-2-associated TCR clonotypes. We observed a decrease in the diversity of TCR clonotypes in COVID-19 patients compared to healthy controls. However, the overall abundance of dominant clones increased with disease severity. Additionally, we identified significant differences in the genomic rearrangement of variable (V), joining (J), and VJ pairings between the patient groups. Furthermore, the SARS-CoV-2-associated TCRs we identified enabled accurate differentiation between COVID-19 patients and healthy controls (AUC > 0.98) and distinguished those with moderate symptoms from those with more severe forms of the disease (AUC > 0.8). These findings suggest that TCR repertoires can serve as informative biomarkers for monitoring COVID-19 progression. Our study provides valuable insights into TCR repertoire signatures that can be utilized to assess host immunity to COVID-19. These findings have important implications for the use of TCR β repertoires in monitoring disease development and indicating disease severity.

Pelvic floor ultrasound versus urodynamics in evaluating insensible urinary incontinence: A retrospective study

Insensible Urinary Incontinence (IUI) is a situation when you complain of urinary incontinence but are unaware of how it occurred. Therefore, it is necessary to apply highly specific diagnostic methods to promote accuracy in the diagnosis of IUI, including pelvic floor ultrasound (PFU) and urodynamic studies (UDS). A total of 41 women with IUI were retrospectively included. Patients were categorized into two groups: the urodynamic urinary incontinence group (UUI group, n=20) and the non-urodynamic urinary incontinence group (NUUI group, n=21), according to the urine leakage during UDS. The baseline clinical characteristics, UDS results, and PFU parameters were collected. Compared with the NUUI group, the UUI group had a smaller maximum cystometric capacity (P=0.008), lower maximum urethral closure pressure (P=0.005), shorter functional urethral length (FUL) (P=0.01), more bladder neck funneling (BNF) (P=0.02), greater BNF depth (P=0.04), and larger BNF area (P=0.01). The area and depth of BNF were negatively correlated with maximum urethral closure pressure (r=-0.42, P=0.01), FUL (r=-0.36, P=0.02 versus r=-0.39, P=0.01), and maximum cystometric capacity (r=-0.35, P=0.03), but positively correlated with maximum urinary flow rate (r=0.33, P=0.04 versus r=0.36, P=0.02). The canonical correlation analysis of the ultrasound parameters and UDS parameters shows that the first pair of canonical variables was statistically significant (r1=0.9, P<0.001). The PFU is associated with UDS in evaluating IUI. It has the advantages of low cost and high comfort, thus should be used as an auxiliary examination for IUI.

Causal Recurrent Variational Autoencoder for Medical Time Series Generation

We propose causal recurrent variational autoencoder (CR-VAE), a novel generative model that is able to learn a Granger causal graph from a multivariate time series x and incorporates the underlying causal mechanism into its data generation process. Distinct to the classical recurrent VAEs, our CR-VAE uses a multi-head decoder, in which the p-th head is responsible for generating the p-th dimension of x (i.e., x^p). By imposing a sparsity-inducing penalty on the weights (of the decoder) and encouraging specific sets of weights to be zero, our CR-VAE learns a sparse adjacency matrix that encodes causal relations between all pairs of variables. Thanks to this causal matrix, our decoder strictly obeys the underlying principles of Granger causality, thereby making the data generating process transparent. We develop a two-stage approach to train the overall objective. Empirically, we evaluate the behavior of our model in synthetic data and two real-world human brain datasets involving, respectively, the electroencephalography (EEG) signals and the functional magnetic resonance imaging (fMRI) data. Our model consistently outperforms state-of-the-art time series generative models both qualitatively and quantitatively. Moreover, it also discovers a faithful causal graph with similar or improved accuracy over existing Granger causality-based causal inference methods. Code of CR-VAE is publicly available at https://github.com/hongmingli1995/CR-VAE.

Farsighted Probabilistic Sampling: A General Strategy for Boosting Local Search MaxSAT Solvers

Local search has been demonstrated as an efficient approach for two practical generalizations of the MaxSAT problem, namely Partial MaxSAT (PMS) and Weighted PMS (WPMS). In this work, we observe that most local search (W)PMS solvers usually flip a single variable per iteration. Such a mechanism may lead to relatively low-quality local optimal solutions, and may limit the diversity of search directions to escape from local optima. To address this issue, we propose a general strategy, called farsighted probabilistic sampling (FPS), to replace the single flipping mechanism so as to boost the local search (W)PMS algorithms. FPS considers the benefit of continuously flipping a pair of variables in order to find higher-quality local optimal solutions. Moreover, FPS proposes an effective approach to escape from local optima by preferring the best to flip among the best sampled single variable and the best sampled variable pair. Extensive experiments demonstrate that our proposed FPS strategy significantly improves the state-of-the-art (W)PMS solvers, and FPS has an excellent generalization capability to various local search MaxSAT solvers.

Assessment of the Spatial Variability and Uncertainty of Shreddable Pruning Biomass in an Olive Grove Based on Canopy Volume and Tree Projected Area

Olive pruning residues are a by-product that can be applied to soil or used for energy production in a circular economy model. Its benefits depend on the amount of pruning, which varies greatly within farms. This study aimed to investigate the spatial variability of shreddable olive pruning in a traditional olive grove in Córdoba (Spain) with an area of 15 ha and trees distanced 12.5 m from each other. To model the spatial variability of shreddable olive pruning, geostatistical methods of stochastic simulation were applied to three correlated variables measured on sampled trees: the crown projected area (n = 928 trees), the crown volume (n = 167) and the amount of shreddable pruning (n = 59). Pearson’s correlation between pairs of variables varied from 0.71 to 0.76. The amount of pruning showed great variability, ranging from 7.6 to 76 kg tree−1, with a mean value of 37 kg tree−1. Using exponential and spherical variogram models, the spatial continuity of the variables under study was established. Shreddable dry pruning weight values showed spatial autocorrelation up to 180 m. The spatial uncertainty of the estimation was obtained using sequential simulation algorithms. Stochastic simulation algorithms provided 150 possible images of the amount of shreddable pruning on the farm, using tree projected area and crown volume as secondary information. The interquartile range and 90% prediction interval were used as indicators of the uncertainty around the mean value. Uncertainty validation was performed using accuracy plots and the associated G-statistic. Results indicate with high confidence (i.e., low uncertainty) that shreddable dry pruning weight in the mid-western area of the farm will be much lower than the rest of the farm. In the same way, results show with high confidence that dry pruning weight will be much higher in a small area in the middle east of the farm. The values of the G-statistic ranged between 0.89 and 0.90 in the tests performed. The joint use of crown volume and projected areas is valuable in estimating the spatial variability of the amount of pruning. The study shows that the use of prediction intervals enables the evaluation of farm areas and informed management decisions with a low level of risk. The methodology proposed in this work can be extrapolated to other 3D crops without requiring modifications. On a larger scale, it can be useful for predicting optimal locations for biomass plants, areas with high potential as carbon sinks or areas requiring special soil protection measures.

Extending compositional data analysis from a graph signal processing perspective

Traditional methods for the analysis of compositional data consider the log-ratios between all different pairs of variables with equal weight, typically in the form of aggregated contributions. This is not meaningful in contexts where it is known that a relationship only exists between very specific variables (e.g. for metabolomic pathways), while for other pairs a relationship does not exist. Modeling absence or presence of relationships is done in graph theory, where the vertices represent the variables, and the connections refer to relations. This paper links compositional data analysis with graph signal processing, and it extends the Aitchison geometry to a setting where only selected log-ratios can be considered. The presented framework retains the desirable properties of scale invariance and compositional coherence. A real data example from bioinformatics underlines the usefulness of this approach.

Individual bias and fluctuations in collective decision making: from algorithms to Hamiltonians

In this paper, we reconsider the spin model suggested recently to understand some features of collective decision making among higher organisms (Hartnett et al 2016 Phys. Rev. Lett. 116 038701). Within the model, the state of an agent i is described by the pair of variables corresponding to its opinion and a bias ω i toward any of the opposing values of S i . Collective decision making is interpreted as an approach to the equilibrium state within the nonlinear voter model subject to a social pressure and a probabilistic algorithm. Here, we push such a physical analogy further and give the statistical physics interpretation of the model, describing it in terms of the Hamiltonian of interaction and looking for the equilibrium state via explicit calculation of its partition function. We show that, depending on the assumptions about the nature of social interactions, two different Hamiltonians can be formulated, which can be solved using different methods. In such an interpretation the temperature serves as a measure of fluctuations, not considered before in the original model. We find exact solutions for the thermodynamics of the model on the complete graph. The general analytical predictions are confirmed using individual-based simulations. The simulations also allow us to study the impact of system size and initial conditions on the collective decision making in finite-sized systems, in particular, with respect to convergence to metastable states.

Doppler evaluation of testicular arteries of stallions of different ages

Doppler ultrasound is a relevant method of monitoring testicular hemodynamics and widely used in andrological evaluations in humans. Testicular blood flow in men increases with age, while the pediatric phase is characterized by physiological testicular hypo-vascularization (Dudea et al., Medical Ultrasonography. 2010;12:43-51). Doppler ultrasound is rarely used in stallions which explains the lack of information and benchmarks. Variation in blood flow of adult stallions of different ages has, however, been reported (Pozor and McDonnell, Theriogenology. 2004;61:799-810) and should be further explored. The objective of the present study was to evaluate the blood flow pattern of the testicular artery in normal stallions (anatomically positioned testicle) of different ages. Both testicles of 30 Mangalarga Marchador stallions were evaluated by spectral Doppler and subjected to three different groups: Young (30 months-4 years, n=10), Adults (6-9 years, n=10) and Old (10-22 years, n=10). For the spectral mode, resistance and pulsatility indexes (RI and PI, respectively), systolic peak velocity (SPV), end-diastolic velocity (EDV) and timed average mean maximum velocity (TAMMV) were measured from the supra-testicular (Supra) and capsular (Cap) arteries.Stallions were evaluated during the reproductive season and were restrained without sedation. For statistical analysis, overall effect and pairwise testing with Šidák correction was performed. The effect of the predictor variables presence of torsion, age group and side were assessed using mixed models, Pearson correlations were also applied to assess the association between pairs of variables. Results showed higher SPV Supra in Old than in Young stallions (P<0.041) and, when testis were compared, higher PI Supra in left testicles for Old stallions was observed when compared to right testicles for Young and Adult stallions (P<0.001). These findings suggest lower testicular blood flow in Old stallions possible related to degenerative processes resulting in increased blood flow resistance. Higher PI in the left testicles may be related to early development of left testicles and epididymis (Nishikawa, 1959, Tokyo: Shiba Tamuracho Minatoku) and a lesser descent into the scrotum (Bergin et al., Biol. Reprod. 1970;3:82-92). The difference in perfusion between testes may induce more pronounced degenerative processes in the left testis due to its greater exposure to intra-abdominal temperatures.

Cooperative Coevolutionary CMA-ES With Landscape-Aware Grouping in Noisy Environments

Many real-world optimization tasks suffer from noise. So far, the research on noise-tolerant optimization algorithms is still restricted to low-dimensional problems with less than 100 decision variables. In reality, many problems are high-dimensional. Cooperative coevolutionary (CC) algorithms based on a divide-and-conquer strategy are promising in solving complex high-dimensional problems. However, noisy fitness evaluations pose a challenge in problem decomposition for CC. The state-of-the-art grouping methods, such as differential grouping and recursive differential grouping, are unable to work properly in noisy environments. Because it is impossible to distinguish whether the change of one variable&#x2019;s difference value is caused by noise or the perturbation of its interacting variables. As a result, every pair of variables will be identified as nonseparable in these methods. In this paper, we study how to group decision variables with covariance matrix adaptation evolution strategy (CMA-ES) in noisy environments and subsequently propose a landscape-aware grouping (LAG) method. Instead of detecting pairwise interacting variables, we directly identify a nonseparable subcomponent. To this end, we propose to use two convergence features, variable convergence time and accumulative path, to describe variables&#x2019; fitness landscapes; then, variables are clustered according to these two features. Numerical experiments show that LAG can more effectively identify interactive decision variables in the presence of multiplicative noise than the differential grouping and some of its variants. Up to 500 dimensions, the performance of cooperative coevolutionary CMA-ES with landscape-aware grouping (CC-CMAES-LAG) is competitive compared with existing CC algorithms and uncertainty-handling CMA-ES (UH-CMA-ES).

ParaLiNGAM: Parallel causal structure learning for linear non-Gaussian acyclic models

One of the key objectives in many fields in machine learning is to discover causal relationships among a set of variables from observational data. In linear non-Gaussian acyclic models (LiNGAM), it can be shown that the true underlying causal structure can be identified uniquely from merely observational data. The DirectLiNGAM algorithm is a well-known solution to learn the true causal structure in a high dimensional setting. DirectLiNGAM algorithm executes in a sequence of iterations and it performs a set of comparisons between pairs of variables in each iteration. Unfortunately, the runtime of this algorithm grows significantly as the number of variables increases. In this paper, we propose a parallel algorithm, called ParaLiNGAM, to learn casual structures based on DirectLiNGAM algorithm. We propose a threshold mechanism that can reduce the number of comparisons remarkably compared with the sequential solution. Moreover, in order to further reduce runtime, we employ a messaging mechanism between workers. We also present an implementation of ParaLiNGAM on GPU, considering hardware constraints. Experimental results on synthetic and real data show that our proposed solution outperforms DirectLiNGAM by a factor up to 4788X, and by a median of 2344X.

Hemodynamic evaluation of the testicular artery in stallions with spermatic cord torsion

Testicles are supported by spermatic cord and show some mobility within the scrotum. Excessive testicle mobility can result in rotation around its vertical axis, which may result in cord strangulation and reduction of blood perfusion (Filho and Oliveira, ABCMED. 2012). Depending on duration and severity of torsion, a reduction in diastolic velocity or retrograde diastolic flow may happen and lead to the absence of arterial flow, as a result of increased resistance to blood flow (Dogra et al., Journal of Clinical Ultrasound. 2001;29:105-108). Retrograde diastolic blood flow has been detected in stallions with 180° spermatic cord torsion (Pozor and McDonnel, Theriogenology. 2004;61:799-810). The objective of the present study was to evaluate testicular artery blood flow in stallions with 180° spermatic cord torsion in comparison to stallions with testis not affected by spermatic cord torsion. Both testis of 38 Mangalarga Marchador stallions were evaluated by spectral Doppler ultrasound. Animals with normal testis were divided into two experimental groups according to their age to verify if there would be any differences between groups: Normal Young, 30 months-4 years old (NY, n=10) and Normal Adults, 6-9 years old (NA, n=10). Similarly, animals with unilateral 180° spermatic cord torsion were divided into: Young with torsion, 30 months-4 years old (TY, n=10) and Adults with torsion, 6-9 years old (TA, n=8).For Doppler evaluations, spectral mode, resistance and pulsatility indexes (RI and PI, respectively), systolic peak velocity (SPV), end-diastolic velocity (EDV) and timed average mean maximum velocity (TAMMV) were measured at the supratesticular (Supra) and capsular (Cap) arteries. Stallions were evaluated during the reproductive season and were restrained without sedation. For statistical analysis, overall effect and pairwise testing with Šidák correction was performed. The effect of the predictor variables presence of torsion, age group and side were assessed using mixed models, Pearson correlations were also applied to assess the association between pairs of variables. For young stallions, when normal testis (n=20) were compared to rotated testis (n=15) and contralateral (n=5), rotated testis showed lower Supra RI than normal testis (P<0.001). For adult stallions, contralateral testis (n=7) Supra EDV were lower than normal testis (n=20) (P<0.001). Findings showed blood flow changes due to torsion in rotated testis and its contralateral. Therefore, these parameters should be considered to assess damage caused by torsion of the spermatic cord because of impaired testicular perfusion. Findings reinforce the theory of a reduced blood perfusion not only for the rotated testicles, but also for the contralateral one.