Variable Representation Research Articles

Improving effort-aware just-in-time defect prediction with weighted code churn and multi-objective slime mold algorithm

Effort-aware just-in-time software defect prediction (JIT-SDP) aims to effectively utilize the limited resources of software quality assurance (SQA) to detect more software defects. This improves the efficiency of SQA work and the quality of the software. However, there is disagreement regarding the representation of the key feature variable, SQA effort, in the field of effort-aware JIT-SDP. Additionally, the most recent metaheuristic optimization algorithms (MOAs) have not yet been effectively integrated with multi-objective effort-aware JIT-SDP tasks. These deficiencies, in both feature representation and model optimization (MO), result in a significant disparity between the performance of effort-aware JIT-SDP techniques and the expectations of the industry. In this study, we present a novel method called weighted code churn (CC) and improved multi-objective slime mold algorithm (SMA) (WCMS) for effort-aware JIT-SDP. It comprises two stages: feature improvement (FI) and MO. In the FI phase, we normalize the two feature variables: number of modified files (NF) and distribution of modified code across each file (Entropy). We then use an exponential function to quantify the level of difficulty of the change. The equation is as follows: DD = NFEntropy, where DD is an acronym for the degree of difficulty, NF denotes the base number, and Entropy denotes the index. We define change effort as the product of the difficulty degree in implementing the change and CC, with weighted CC representing the change effort. During the MO stage, we improve the SMA by incorporating multi-objective handling capabilities and devising mechanisms for multi-objective synchronization and conflict resolution. We develop a multi-objective optimization algorithm for hyperparameter optimization (HPO) of the JIT-SDP model in WCMS. To evaluate the performance of our method, we conducted experiments using six well-known open-source projects and employed two effort-aware performance evaluation metrics. We evaluated our method based on three scenarios: cross-validation, time-wise cross-validation, and across-project prediction. The experimental results indicate that the proposed method outperforms the benchmark method. Furthermore, the proposed method demonstrates greater scalability and generalization capabilities.

Makkan Arabic in the digital age: A sociolinguistic analysis of the representation of fricative, stop, and sibilant variation in WhatsApp text messages

This study examines Makkan Arabic speakers’ orthographic representation of standard and colloquial variants in their WhatsApp text messages. In particular, we examine the role that speaker gender, speaker age, gender composition of conversations, and topic of discussions play in Hadari Makkans’ representation of standard and colloquial variants of the variables (th), (dh), and (Dh). Statistical analyses reveal that women favor colloquial variant stops [t] and [d], while men exhibit a preference for standard variants [θ] and [ð], particularly when conversing with other men. For (Dh), however, both women and men favor the standard variant [ðˤ]. Age also plays a role in the distribution of variants, with speakers favoring standard variants as they age. The use of fricatives [θ] and [ð] also increases when participants discuss formal topics, which suggests an implicit association between standard language and formality, despite the inherent informality of WhatsApp interactions. This study provides insights into how phonological variation is orthographically represented within a written genre designed to mimic spontaneous conversation and enriches the broader discourse on Arabic language variation and digital communication.

Bayesian L 1/2 Regression

It is well known that Bridge regression enjoys superior theoretical properties when compared to traditional LASSO. However, the current latent variable representation of its Bayesian counterpart, based on the exponential power prior, is computationally expensive in higher dimensions. In this article, we show that the exponential power prior has a closed form scale mixture of normal decomposition for α = ( 1 2 ) γ , γ ∈ { 1 , 2 , … } . We call these types of priors L 1 2 prior for short. We develop an efficient partially collapsed Gibbs sampling scheme for computation using the L 1 2 prior and study theoretical properties when p > n . In addition, we introduce a non-separable Bridge penalty function inspired by the fully Bayesian formulation and a novel, efficient coordinate descent algorithm. We prove the algorithm’s convergence and show that the local minimizer from our optimization algorithm has an oracle property. Finally, simulation studies were carried out to illustrate the performance of the new algorithms. Supplementary materials for this article are available online.

Electronic properties and vibrationally averaged structures of X̃ 2Σ+ MgOH: a computational molecular spectroscopy study.

For X̃ 2Σ+ MgOH, we have calculated the 3D potential energy surface (PES) at the MR-SDCI+Q/[cc-pCV5Z (Mg), aug-cc-pV5Z (O), cc-pV5Z (H)] level and derived the vibrational properties from there using the discrete variable representation (DVR) method. The PES minimum is at the linear structure; hence, MgOH is a "linear molecule." The 3D PES is shallow, and MgOH tends to bend in the region immediately when either or both Mg-O and O-H bonds become longer than those of the equilibrium structure (re(Mg-O) = 1.7614 Å, re(O-H) = 0.9453 Å, and ∠e(Mg-O-H) = 180°). The zero-point structure, determined as the expectation values over the DVR3D wavefunctions, has 〈r(Mg-O)〉0 = 1.7837 Å and 〈r(O-H)〉0 = 0.9948 Å, and the deviation angle from linearity 〈〉0 = 26.4°. The harmonic frequencies ωe for the Mg-O stretching, bending, and O-H stretching modes are 768, 142, and 4060 cm-1, respectively, and the corresponding term values ν1, ν2, and ν3 are 752, 156, and 3867 cm-1. All the vibrational behaviors, such as quasi-linear features, unusual relationship ν2 > ω2, a large amplitude bending motion, etc., are elucidated in terms of the ab initio electronic wavefunctions and the DVR3D vibrational wavefunctions. We have another piece of evidence to support our postulation that a linear molecule is observed as being bent.

A Girsanov transformed Clark-Ocone-Haussmann type formula for $$L^1$$-pure jump additive processes and its application to portfolio optimization

AbstractWe derive a Clark-Ocone-Haussmann (COH) type formula under a change of measure for $$ L^1 $$ L 1 -canonical additive processes, providing a tool for representing financial derivatives under a risk-neutral probability measure. COH formulas are fundamental in stochastic analysis, providing explicit martingale representations of random variables in terms of their Malliavin derivatives. In mathematical finance, the COH formula under a change of measure is crucial for representing financial derivatives under a risk-neutral probability measure. To prove our main results, we use the Malliavin-Skorohod calculus in $$ L^0 $$ L 0 and $$ L^1 $$ L 1 for additive processes, as developed by Di Nunno and Vives (2017). An application of our results is solving the local risk minimization (LRM) problem in financial markets driven by pure jump additive processes. LRM, a prominent hedging approach in incomplete markets, seeks strategies that minimize the conditional variance of the hedging error. By applying our COH formula, we obtain explicit expressions for locally risk-minimizing hedging strategies in terms of Malliavin derivatives under the market model underlying the additive process. These formulas provide practical tools for managing risks in financial market price fluctuations with $$L^1$$ L 1 -additive processes.

Retrieving compressive sea ice strength in the Beaufort Sea using the inverse visco-plastic model

We apply a simplified 2d Visco-Plastic (VP) sea ice model with a spatially variable representation of the sea ice rheological parameters for retrieving maximum compressive sea ice strength from satellite and in situ observations. A set of Observing System Simulation Experiments (OSSEs) demonstrates feasibility of optimizing rheological parameter of the VP sea ice model through the variational data assimilation approach during the periods of strong sea ice convergence if accurate sea ice observations are available. Following this strategy, the developed variational data assimilation VP model was applied to the sea ice velocity (https://nsidc.org/data/nsidc-0116/versions/4), sea ice concentration (https://nsidc.org/data/) and CryoSat-2 sea ice thickness observations collected in the vicinity of three moorings in the Beaufort Sea during periods of intensive sea ice convergence. Ice velocities from moorings and atmospheric wind speed (NCEP-NCAR) were used as well. Our results show that conventional maximum compressive sea ice strength (Hibler, 1979) may depend on sea ice thickness or other parameters partly controlled by the sea ice thickness, which is driven by the seasonal cycle.

Impacts of forest canopy heterogeneity on plot-scale hydrometeorological variables - Insights from an experiment in the humid boreal forest with the Canadian Land Surface Scheme

High latitude regions, including the circumpolar boreal biome, are experiencing important changes in the availability of usable surface water because of climate change. In this context, an adequate representation of the land-atmosphere interaction is critical to ensure optimal management of current and future water resources, forest management, and climate prediction. However, the task is particularly intricate in high-latitude boreal forest, as land surface model faces several challenges due to the unique environmental conditions and ecological characteristics. The objective of this study is to quantify the impact of forest landscape heterogeneity, specifically stand leaf-area index (LAI), soil texture, and drainage regime, on surface water and energy balance in a small boreal high-latitude sub-catchment. To this end, hydrometeorological conditions at seventeen 20×20 m plots in a 1-km2 boreal forest sub-basin are simulated using the Canadian Land Surface Scheme (CLASS), a land surface model, at the point scale. The subplot-scale soil texture, drainage regime, and vegetation characteristics and type are based as closely as possible on field measurements and observations for the 17 plots. The model-driven experiment comprises two sets of simulations using CLASS, each employing the same model setup and run for the 17 experimental plots. The main set employs meteorological forcing from a local micrometeorological tower within the sub-basin to investigate the plot-to-plot variability of albedo, energy fluxes, and soil state variables. A second set of simulations is conducted using meteorological forcing from the ERA5-Land reanalysis, which spans from 1986 to 2022. This data provides a longer time series, enabling a more accurate representation of the interannual climatic variability in the sub-basin. The results of the main and secondary sets of CLASS simulations are used to assess the plot-to-plot and temporal variability of several key hydrometeorological variables by calculating a monthly spread. In brief, the following conclusions and broader implications can be drawn from the findings: i) The simulated total annual evapotranspiration remains relatively uniform between plots despite notable variation in its partitioning from plot to plot. ii) In the presence of a full snowpack, the albedo exhibits substantial heterogeneity at the subplot scale, linked to the canopy's LAI. iii) Local soil properties, drainage regime, and vegetation structure and type exhibit substantial influence on the plot-to-plot variability in soil water content. iv) When parameterized with localized observations and measurements, CLASS can represent and be responsive to the complex dynamics of energy and water fluxes at the plot scale within the heterogeneous surface of boreal forests.

Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations

Driven by the urgent necessity for accurate environmental data in urban settings, this research leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) as a machine learning-based approach to refine SPS30 low-cost sensor data influenced by hygroscopicity in Turin, Italy. Employing ANFIS offers several advantages: it enhances clarity regarding the correspondence between output and input values and rules, improves system interpretability, and facilitates the representation of linguistic variables and rules, thereby encouraging domain experts' involvement in enhancing the system's performance as needed. This paper illustrates the utility of ANFIS in adjusting the detected particulate matter (PM) concentration and compares its effectiveness with other established machine-learning techniques, including linear regression, decision trees, random forest, SVR and a multilayer perceptron (MLP). These methods are chosen as benchmarks owing to their established effectiveness in calibration procedures.We propose certain preprocessing steps for detecting and rectifying anomalies, alongside introducing two distinct data-splitting methodologies. Additionally, a discussion about feature selection is presented to elucidate the impact of specific features on performance enhancement. The efficacy of ANFIS in refining PM data is demonstrated through a comparative assessment, where it outperforms all the established machine-learning techniques. Notably, incorporating only PM2.5, relative humidity and temperature as features yields optimal performance while mitigating overfitting issues. The paper also explores various ANFIS configurations, including two distinct optimization algorithms, and investigates the impact of the number and type of membership functions on the fuzzy system's performance. Our study highlights the potential of the Adaptive Neuro-Fuzzy Inference System as a versatile and effective tool for addressing real-world challenges in environmental sensing.

A collaborative planning model of IES containing EHs and multi-energy grids under the premise of demand response as well as wind power uncertainty

The proliferation of renewable energy resources and the incorporation of various types of energy bring opportunities as well as new challenges to the planning and operations within integrated energy systems (IESs). To better characterize multiple uncertain variables within IES, reducing wind power curtailment and minimizing costs, this paper proposes an IES collaborative planning model with EHs, in which uncertain variables are depicted by Z-numbers. Firstly, Z-number incorporates both fuzzy and probabilistic uncertainties, allowing the representation of uncertain variables with credibility information in constraints. Secondly, simplifying Z-numbers into an engineering-applicable method reduces the complexity of model resolution. Thirdly, to fully leverage the complementary transformation characteristics among different energy sources, the optimization model includes decisions on the capacity of energy conversion and energy storage components within EHs. Moreover, outside the EHs, conventional generator dispatches, and the hardening of electric-gas grid decisions are also included. Finally, the model is verified on an IES that involves the coupling of a six-node power system and a six-node gas system, and numerical results validate that wind power output values represented by Z-numbers have a standard deviation of only 0.33 from actual values. Furthermore, different credibility information in Z-numbers significantly impacts planning outcomes; an increased DR credibility can be conducive to reducing wind curtailment with 45KW and help cost-savings with 4.05%.

Analyzing Genetic Variation and Graphical Representation (Wr-Vr) in Brinjal (Solanum melongena L.) using the Hayman Approach in the Northwestern Region of India

Background: Brinjal, also known as eggplant, is a resilient and widely cultivated vegetable crop in the Solanaceae family. It plays a crucial role in diets due to its rich supply of vitamins, minerals and dietary fiber, helping address the deficit in vegetable consumption recommended by the World Health Organization (WHO), which advises a daily intake of at least 400 grams of fruits and vegetables. Estimating genetic parameters is essential for understanding the genetic architecture of yield and its determining factors. Hayman’s diallel cross analysis method is extensively utilized in plant breeding due to its capacity to provide comprehensive insights into diverse genetic parameters, including gene action, degree of dominance and heritability. Methods: Nine parents with thirty six F1 hybrids of brinjal derived using a half diallel mating design with nine brinjal genotypes, namely JBL-1, JBR-1, JBR-2, JBR-3, JBL-2, JBL-3, JBR-4, JBR-5 and JBR-6 were evaluated for yield and other yield attributing traits during kharif season 2023) using randomized block design (RBD) with three replications. Result: The dominance effect (H1) was significant and displayed a greater magnitude, indicating dominance gene action for yield per plant. The regression coefficient slope showed a significant deviation from zero, but not from unity, indicating the absence of non-allelic interaction. Furthermore, the regression line intersected the Wr axis below the origin, suggesting the presence of over-dominance for days to 50% flowering, fruit length (cm), number of primary branches per plant, plant height (cm), total fruit yield per plant (kg) and fruit borer infestation (%). The mean degree of dominance exceeding one indicated the prevalence of over-dominance for all traits except days to first picking, average fruit weight (gm), fruit girth (cm) and number of fruits per plant. These findings emphasize the importance of dominance variation in eggplant improvement, enabling successful heterosis breeding for advancing these populations and development of hybrids.

Magneto-thermoelastic responses in an unbounded porous body with a spherical cavity subjected to laser pulse heating via an Atangana-Baleanu fractional operator

Poro-thermoelasticity models are crucial for analyzing porous materials in geotechnical engineering, such as how soil and rock respond to thermal and mechanical loads, fluid flow, and heat transmission. This study introduces a fractional dual-phase lag (DPL) porous thermoelasticity model using the Atangana-Baleanu fractional derivative to analyze thermoelastic responses in a material with unlimited porosity containing a spherical cavity under a pulsed magnetic field. The governing equations were solved using Laplace transforms, and numerical solutions were obtained by inverting the transforms. Graphical representations of deformation, temperature variation, and thermal stresses were generated for analysis. The results reveal significant insights into the behavior of porous materials like concrete and masonry under thermal and mechanical stress, aiding the development of more robust and flexible structures. For example, the proposed model predicts a 20 % reduction in the amount of thermal stress compared to conventional models, highlighting the influence of phase lag and fractional derivatives in accurately simulating realistic conditions.

Differential stability of task variable representations in retrosplenial cortex

Cortical neurons store information across different timescales, from seconds to years. Although information stability is variable across regions, it can vary within a region as well. Association areas are known to multiplex behaviorally relevant variables, but the stability of their representations is not well understood. Here, we longitudinally recorded the activity of neuronal populations in the mouse retrosplenial cortex (RSC) during the performance of a context-choice association task. We found that the activity of neurons exhibits different levels of stability across days. Using linear classifiers, we quantified the stability of three task-relevant variables. We find that RSC representations of context and trial outcome display higher stability than motor choice, both at the single cell and population levels. Together, our findings show an important characteristic of association areas, where diverse streams of information are stored with varying levels of stability, which may balance representational reliability and flexibility according to behavioral demands.

Individual variability in neural representations of mind-wandering.

Mind-wandering is a frequent, daily mental activity, experienced in unique ways in each person. Yet neuroimaging evidence relating mind-wandering to brain activity, for example in the default mode network (DMN), has relied on population- rather than individual-based inferences owing to limited within-person sampling. Here, three densely sampled individuals each reported hundreds of mind-wandering episodes while undergoing multi-session functional magnetic resonance imaging. We found reliable associations between mind-wandering and DMN activation when estimating brain networks within individuals using precision functional mapping. However, the timing of spontaneous DMN activity relative to subjective reports, and the networks beyond DMN that were activated and deactivated during mind-wandering, were distinct across individuals. Connectome-based predictive modeling further revealed idiosyncratic, whole-brain functional connectivity patterns that consistently predicted mind-wandering within individuals but did not fully generalize across individuals. Predictive models of mind-wandering and attention that were derived from larger-scale neuroimaging datasets largely failed when applied to densely sampled individuals, further highlighting the need for personalized models. Our work offers novel evidence for both conserved and variable neural representations of self-reported mind-wandering in different individuals. The previously unrecognized interindividual variations reported here underscore the broader scientific value and potential clinical utility of idiographic approaches to brain-experience associations.

Implementation and assessment of a model including mixotrophs and the carbonate cycle (Eco3M_MIX-CarbOx v1.0) in a highly dynamic Mediterranean coastal environment (Bay of Marseille, France) – Part 2: Towards a better representation of total alkalinity when modeling the carbonate system and air–sea CO2 fluxes

Abstract. The Bay of Marseille (BoM), located in the northwestern Mediterranean Sea, is affected by various hydrodynamic processes (e.g., Rhône River intrusion and upwelling events) that result in a highly complex local carbonate system. In any complex environment, the use of models is advantageous since it allows us to identify the different environmental forcings, thereby facilitating a better understanding. By combining approaches from two biogeochemical ocean models and improving the formulation of total alkalinity, we develop a more realistic representation of the carbonate system variables at high temporal resolution, which enables us to study air–sea CO2 fluxes and seawater pCO2 variations more reliably. We apply this new formulation to two particular scenarios that are typical for the BoM: (i) summer upwelling and (ii) Rhône River intrusion events. In both scenarios, our model was able to correctly reproduce the observed patterns of pCO2 variability. Summer upwelling events are typically associated with a pCO2 decrease that mainly results from decreasing near-surface temperatures. Furthermore, Rhône River intrusion events are typically associated with a pCO2 decrease, although, in this case, the pCO2 decrease results from a decrease in salinity and an overall increase in total alkalinity. While we were able to correctly represent the daily range of air–sea CO2 fluxes, the present configuration of Eco3M_MIX-CarbOx does not allow us to correctly reproduce the annual cycle of air–sea CO2 fluxes observed in the area. This pattern directly impacts our estimates of the overall yearly air–sea CO2 flux as, even if the model clearly identifies the bay as a CO2 sink, its magnitude was underestimated, which may be an indication of the limitations inherent in dimensionless models for representing air–sea CO2 fluxes.

Numerical solution by finite element method for time Caputo-Fabrizio fractional partial diffusion equation

The foundation of this study lies in the fractional order derivative definition pioneered by Caputo and Fabrizio, with distinct representations for temporal and spatial variables. Our focus is on presenting a finite element scheme to address the time-fractional diffusion equation. This approach contrasts with traditional finite difference methods and variational solutions. We rigorously establish the stability and convergence order of our proposed method. To validate our theoretical assertions, we provide a numerical example demonstrating its efficacy.

Representation of random variables as Lebesgue integrals

Representation of random variables as Lebesgue integrals

Active mutual conjoint estimation of multiple contrast sensitivity functions.

Recent advances in nonparametric contrast sensitivity function (CSF) estimation have yielded a new tradeoff between accuracy and efficiency not available to classical parametric estimators. An additional advantage of this new framework is the ability to independently tune multiple aspects of the estimator to seek further improvements. Machine learning CSF estimation with Gaussian processes allows for design optimization in the kernel, acquisition function, and underlying task representation, to name a few. This article describes a novel kernel for CSF estimation that is more flexible than a kernel based on strictly functional forms. Despite being more flexible, it can result in a more efficient estimator. Further, trial selection for data acquisition that is generalized beyond pure information gain can also improve estimator quality. Finally, introducing latent variable representations underlying general CSF shapes can enable simultaneous estimation of multiple CSFs, such as from different eyes, eccentricities, or luminances. The conditions under which the new procedures perform better than previous nonparametric estimation procedures are presented and quantified.

Temporal and spatial changes in the environmental lapse rate distribution over the Arctic

Abstract The Environmental Lapse Rate (ELR) depicts how the temperature near the surface varies with altitude and can be used for temperature downscaling coarse resolution data and for understanding boundary layer processes. We calculated the ELR using ERA5 reanalysis data, examined its temporal and larger-scale spatial variability, and found a prevalent seasonal ELR cycle over the Arctic. There are extensive positive ELR values resulting from pervasive inversions over most of the Arctic in winter; hence, we also explored the possible factors that lead to inversions in polar regions. Our results can serve as a reference for future research on the inversions in different morphological regions at different pressure levels. By improving the characterization of the ELR, we obtain a more explicit representation of the vertical temperature variation across the Arctic region and examine potential trends in ELR over time. Our results challenge the commonly assumed fixed ELR values that are typically used in the Arctic region in, for example, correcting ice-core temperature reconstructions or estimating higher-resolution runoff from land ice.

Single-Trial Representations of Decision-Related Variables by Decomposed Frontal Corticostriatal Ensemble Activity.

The frontal cortex-striatum circuit plays a pivotal role in adaptive goal-directed behaviors. However, it remains unclear how decision-related signals are mediated through cross-regional transmission between the medial frontal cortex and the striatum by neuronal ensembles in making decision based on outcomes of past action. Here, we analyzed neuronal ensemble activity obtained through simultaneous multiunit recordings in the secondary motor cortex (M2) and dorsal striatum (DS) in rats performing an outcome-based left-or-right choice task. By adopting tensor component analysis (TCA), a single-trial-based unsupervised dimensionality reduction approach, for concatenated ensembles of M2 and DS neurons, we identified distinct three spatiotemporal neural dynamics (TCA components) at the single-trial level specific to task-relevant variables. Choice-position-selective neural dynamics reflected the positions chosen and was correlated with the trial-to-trial fluctuation of behavioral variables. Intriguingly, choice-pattern-selective neural dynamics distinguished whether the incoming choice was a repetition or a switch from the previous choice before a response choice. Other neural dynamics was selective to outcome and increased within-trial activity following response. Our results demonstrate how the concatenated ensembles of M2 and DS process distinct features of decision-related signals at various points in time. Thereby, the M2 and DS collaboratively monitor action outcomes and determine the subsequent choice, whether to repeat or switch, for action selection.

Examining site intervention efficacy and uncertainties with conceptual Bayesian networks: preventing offsite migration of DNAPL and contaminated groundwater.

For contaminated sites, conceptual site models (CSMs) guide the assessment and management of risks, including remediation strategies. Recent research has expanded diagrammatic CSMs with structural causal modeling to develop what are nominally called conceptual Bayesian networks (CBNs) for environmental risk assessment. These CBNs may also be useful for problems of controlling and preventing offsite contaminant migration, especially for sites containing dense nonaqueous phase liquids (DNAPLs). In particular, the CBNs provide greater clarity on the causal relationships between source term, onsite and offsite migration, and remediation effectiveness characterization for contaminated DNAPL sites compared to traditional CSMs. These ideas are demonstrated by the inclusion of modifying variables, causal pathway analysis, and interventions in CBNs. Additionally, several new extensions of the CBN concept are explored including the representation of measurement variables as lines of evidence and alignment with conventional pictorial CSMs for groundwater modeling. Taken as a whole, the CBNs provide a powerful and adaptable knowledge representation tool for remediating subsurface systems contaminated by DNAPL.