Miracle-dz/UniFAR-FTUs huggingface dataset:
How are Software Repositories Mined? A Systematic Literature Review of Workflows, Methodologies, Reproducibility, and Tools

With the advent of open source software, a veritable treasure trove of previously proprietary software development data was made available. This opened the field of empirical software engineering research to anyone in academia. Data that is mined from software projects, however, requires extensive processing and needs to be handled with utmost care to ensure valid conclusions. Since the software development practices and tools have changed over two decades, we aim to understand the state-of-the-art research workflows and to highlight potential challenges. We employ a systematic literature review by sampling over one thousand papers from leading conferences and by analyzing the 286 most relevant papers from the perspective of data workflows, methodologies, reproducibility, and tools. We found that an important part of the research workflow involving dataset selection was particularly problematic, which raises questions about the generality of the results in existing literature. Furthermore, we found a considerable number of papers provide little or no reproducibility instructions -- a substantial deficiency for a data-intensive field. In fact, 33% of papers provide no information on how their data was retrieved. Based on these findings, we propose ways to address these shortcomings via existing tools and also provide recommendations to improve research workflows and the reproducibility of research.
Anomalous Hall conductivity control in Mn$_3$NiN antiperovskite by epitaxial strain along the kagome plane

Antiferromagnetic manganese-based nitride antiperovskites, such as Mn$_3$NiN, hold a triangular frustrated magnetic ordering over their kagome lattice formed by the Mn atoms along the (111)-plane. As such, frustration imposes a non-trivial interplay between the symmetric and asymmetric magnetic interactions, which can only reach equilibrium in a noncollinear magnetic configuration. Consequently, the associated electronic interactions and their possible tuning by external constraints, such as applied epitaxial strain, play a crucial role in defining the microscopic and macroscopic properties of such topological condensed matter systems. Thus, in the present work, we explored and explained the effect of the epitaxial strain imposed within the (111)-plane, in which the magnetic and crystallographic symmetry operations are kept fixed, and only the magnitude of the ionic and electronic interactions are tuned. We found a linear shifting in the energy of the band structure and a linear increase/decrease of the available states near the Fermi level with the applied strain. Concretely, the compression strain reduces the Mn-Mn distances in the (111) kagome plane but linearly increases the separation between the stacked kagome lattices and the available states near the Fermi level. Despite the linear controlling of the available states across the Fermi energy, the anomalous Hall conductivity shows a non-linear behavior where the $\sigma_{111}$ conductivity nearly vanishes for tensile strain. On the other hand, $\sigma_{111}$ fetches a maximum increase of 26\% about the unstrained structure for a compression value close to $-$1.5\%. This behavior found an explanation in the non-divergent Berry curvature within the kagome plane, which is increased for constraining but significantly reduced for expansion strain values.
Hole Spin Qubits in Thin Curved Quantum Wells

Hole spin qubits are frontrunner platforms for scalable quantum computers because of their large spin-orbit interaction which enables ultrafast all-electric qubit control at low power. The fastest spin qubits to date are defined in long quantum dots with two tight confinement directions, when the driving field is aligned to the smooth direction. However, in these systems the lifetime of the qubit is strongly limited by charge noise, a major issue in hole qubits. We propose here a different, scalable qubit design, compatible with planar CMOS technology, where the hole is confined in a curved germanium quantum well surrounded by silicon. This design takes full advantage of the strong spin-orbit interaction of holes, and at the same time suppresses charge noise in a wide range of configurations, enabling highly coherent, ultrafast qubit gates. While here we focus on a Si/Ge/Si curved quantum well, our design is also applicable to different semiconductors. Strikingly, these devices allow for ultrafast operations even in short quantum dots by a transversal electric field. This additional driving mechanism relaxes the demanding design constraints, and opens up a new way to reliably interface spin qubits in a single quantum dot to microwave photons. By considering state-of-the-art high-impedance resonators and realistic qubit designs, we estimate interaction strengths of a few hundreds of MHz, largely exceeding the decay rate of spins and photons. Reaching such a strong coupling regime in hole spin qubits will be a significant step towards high-fidelity entangling operations between distant qubits, as well as fast single-shot readout, and will pave the way towards the implementation of a large-scale semiconducting quantum processor.
In Defense of Subspace Tracker: Orthogonal Embedding for Visual Tracking

The paper focuses on a classical tracking model, subspace learning, grounded on the fact that the targets in successive frames are considered to reside in a low-dimensional subspace or manifold due to the similarity in their appearances. In recent years, a number of subspace trackers have been proposed and obtained impressive results. Inspired by the most recent results that the tracking performance is boosted by the subspace with discrimination capability learned over the recently localized targets and their immediately surrounding background, this work aims at solving such a problem: how to learn a robust low-dimensional subspace to accurately and discriminatively represent these target and background samples. To this end, a discriminative approach, which reliably separates the target from its surrounding background, is injected into the subspace learning by means of joint learning, achieving a dimension-adaptive subspace with superior discrimination capability. The proposed approach is extensively evaluated and compared with the state-of-the-art trackers on four popular tracking benchmarks. The experimental results demonstrate that the proposed tracker performs competitively against its counterparts. In particular, it achieves more than 9% performance increase compared with the state-of-the-art subspace trackers.
Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning

Bayesian policy reuse (BPR) is a general policy transfer framework for selecting a source policy from an offline library by inferring the task belief based on some observation signals and a trained observation model. In this article, we propose an improved BPR method to achieve more efficient policy transfer in deep reinforcement learning (DRL). First, most BPR algorithms use the episodic return as the observation signal that contains limited information and cannot be obtained until the end of an episode. Instead, we employ the state transition sample, which is informative and instantaneous, as the observation signal for faster and more accurate task inference. Second, BPR algorithms usually require numerous samples to estimate the probability distribution of the tabular-based observation model, which may be expensive and even infeasible to learn and maintain, especially when using the state transition sample as the signal. Hence, we propose a scalable observation model based on fitting state transition functions of source tasks from only a small number of samples, which can generalize to any signals observed in the target task. Moreover, we extend the offline-mode BPR to the continual learning setting by expanding the scalable observation model in a plug-and-play fashion, which can avoid negative transfer when faced with new unknown tasks. Experimental results show that our method can consistently facilitate faster and more efficient policy transfer.
Multi-organ Segmentation Network with Adversarial Performance Validator

CT organ segmentation on computed tomography (CT) images becomes a significant brick for modern medical image analysis, supporting clinic workflows in multiple domains. Previous segmentation methods include 2D convolution neural networks (CNN) based approaches, fed by CT image slices that lack the structural knowledge in axial view, and 3D CNN-based methods with the expensive computation cost in multi-organ segmentation applications. This paper introduces an adversarial performance validation network into a 2D-to-3D segmentation framework. The classifier and performance validator competition contribute to accurate segmentation results via back-propagation. The proposed network organically converts the 2D-coarse result to 3D high-quality segmentation masks in a coarse-to-fine manner, allowing joint optimization to improve segmentation accuracy. Besides, the structural information of one specific organ is depicted by a statistics-meaningful prior bounding box, which is transformed into a global feature leveraging the learning process in 3D fine segmentation. The experiments on the NIH pancreas segmentation dataset demonstrate the proposed network achieves state-of-the-art accuracy on small organ segmentation and outperforms the previous best. High accuracy is also reported on multi-organ segmentation in a dataset collected by ourselves.
Researcher Views on Changes in Personality, Mood, and Behavior in Next-Generation Deep Brain Stimulation

Abstract The literature on deep brain stimulation (DBS) and adaptive DBS (aDBS) raises concerns that these technologies may affect personality, mood, and behavior. We conducted semi-structured interviews with researchers (n = 23) involved in developing next-generation DBS systems, exploring their perspectives on ethics and policy topics including whether DBS/aDBS can cause such changes. The majority of researchers reported being aware of personality, mood, or behavioral (PMB) changes in recipients of DBS/aDBS. Researchers offered varying estimates of the frequency of PMB changes. A smaller majority reported changes in personality specifically. Some expressed reservations about the scientific status of the term ‘personality,’ while others used it freely. Most researchers discussed negative PMB changes, but a majority said that DBS/aDBS can also result in positive changes. Several researchers viewed positive PMB changes as part of the therapeutic goal in psychiatric applications of DBS/aDBS. Finally, several discussed potential causes of PMB changes other than the device itself.
Application electrochemical sensor based on nanosheets G-C3N4/CPE by square-wave anodic stripping voltammetric for measure amounts of toxic tartrazine color residual in different drink and foodstuffs

Abstract The present work describes a method (SWASV) techniques for measure of tartrazine color a harmful compound present in real samples, and the extremely harmful to humans and animals even at low concentrations using G-C3N4 nanosheets sensor. Here, we report the use of an electrochemical approach for analytical determination of toxic tartrazine that takes 150 s. The calibration curve was linear in range of the (0.02–18.0 µmol L−1). The current response was linearly proportional to the tartrazine concentration with a R 2∼ 0.999. We demonstrated a sensitivity a limit of detection of (0.022 µmol L−1). Finally, sensor nanosheets G-C3N4/CPE introduced to measure toxic tartrazine in different drink and foodstuff samples was used and the chemical nanosheets G-C3N4/CPE sensor made it possible as an excellent sensor with reproducibility for determination other samples.
Development and psychometric testing of a scale measuring caring behaviors for healthcare students and providers

ABSTRACT This study intended to develop and assess the psychometric properties of a caring behavior scale on healthcare students and providers (CBS-HSP) in a Taiwanese population. After a literature review was conducted and an expert panel was consulted for item generation, 104 items for the CBS-HSP scale were derived on a nine-point Likert scale, with 9 indicating ‘extremely important’ and 1 indicating ‘extremely unimportant. ’ A pilot study was then conducted with seven hundred forty-eight healthcare students and providers in Taiwan for further data analysis. The statistic software used in the study was SPSS for the exploratory factor analysis (EFA) and AMOS for the confirmatory factor analysis (CFA). Also, to examine the psychometric properties of the scale, internal consistency, convergent validities, discriminant validities, and model fit indices were calculated in the study. The EFA results derived 31 items in four factors, with 65.742% of the total variance explained: ‘support and attentiveness’ (11 items; 48.714% of the variance explained), ‘professional knowledge and skills’ (8 items; 8.226% of the variance explained), ‘gratifying needs and responsiveness’ (7 items; 5.236% of the variance explained), and ‘confidentiality and trust’ (5 items; 3.566% of the variance explained). The Cronbach’s alphas for the four subscales and the overall scale ranged from 0.894 to 0.964. The CFA results yielded the same 31 items, with the same four factors. The CFA results demonstrated good to excellent model fit in the χ2/df ratio (1.242), GFI (0.988), CFI (0.988), TFI (0.985), and RMSEA (0.031). The Cronbach’s alphas ranged between 0.866 and 0.971; the composite alphas ranged between 0.854 and 0.964. The convergent and discriminant validities also proved the stability of the CBS-HSP scale. The research results indicated that the developed CBS-HSP appeared to be a reliable instrument to measure healthcare students’ and providers’ caring behaviors.
Use of Pre-Enrollment Randomization and Delayed Consent to Maximize Participation in A Clinical Trial of Opt-In versus Opt-Out Tobacco Treatment

Background: Enrollment in smoking cessation trials remain sub-optimal. The aim of this analysis was to determine the effectiveness of a modified Zelen's design in engaging hospitalized patients who smoke in a pragmatic OPT-IN versus OPT-OUT tobacco treatment trial. Methods: At bedside, clinical staff screened smokers for eligibility, randomized eligible into study arms, and delivered the appropriate treatment approach. Study staff called randomized patients at one-month post-discharge, debriefed patients on the study design, and collected consent to participate. We used frequencies and percentages for categorical variables and means and standard deviations for quantitative variables to describe the characteristics of those who consented and were enrolled versus those who did not enroll. We also compared the characteristics of participants who consented and those who were reached and explicitly refused consent at one-month follow-up. We used the Cohen's d measure of effect size to evaluate differences. Results: Of the 1,000 randomized, 741 (74.1%) consented to continue in the study at one-month follow-up. One hundred and twenty-seven (12.7%) refused consent and 132 (13.2%) were unreachable. Cohen's d effect size differences between those who consented/enrolled (n = 741) and those who were not enrolled (n = 259) were negligible (<0.2) for age, gender, race/ethnicity, and most forms of insurance. The effect size was small for Medicaid (0.36), and other public insurance (0.48). After excluding those unreached at 1 month (12.7%), there were medium Cohen's d effect size differences between those who consented to participate (n = 741) and those who explicitly refused (n = 127) with respect to age (0.55) and self-pay or no insurance (0.51). There were small to negligible effect size differences with respect to sex, race/ethnicity, and other forms of health insurance. Conclusions: The modified Zelen's design resulted in successful enrollment of most participants who were initially randomized into the trial, including those not motivated to quit.
Integrated omics reveal novel functions and underlying mechanisms of the receptor kinase FERONIA in Arabidopsis thaliana

Abstract The receptor kinase FERONIA (FER) is a versatile regulator of plant growth and development, biotic and abiotic stress responses, and reproduction. To gain new insights into the molecular interplay of these processes and to identify new FER functions, we carried out quantitative transcriptome, proteome, and phosphoproteome profiling of Arabidopsis (Arabidopsis thaliana) wild-type and fer-4 loss-of-function mutant plants. Gene ontology terms for phytohormone signaling, abiotic stress, and biotic stress were significantly enriched among differentially expressed transcripts, differentially abundant proteins, and/or misphosphorylated proteins, in agreement with the known roles for FER in these processes. Analysis of multiomics data and subsequent experimental evidence revealed previously unknown functions for FER in endoplasmic reticulum (ER) body formation and glucosinolate biosynthesis. FER functions through the transcription factor NAI1 to mediate ER body formation. FER also negatively regulates indole glucosinolate biosynthesis, partially through NAI1. Furthermore, we found that a group of abscisic acid (ABA)-induced transcription factors is hypophosphorylated in the fer-4 mutant and demonstrated that FER acts through the transcription factor ABA INSENSITIVE5 (ABI5) to negatively regulate the ABA response during cotyledon greening. Our integrated omics study, therefore, reveals novel functions for FER and provides new insights into the underlying mechanisms of FER function.
Treatment of Class II division 2 malocclusion with impacted lower canine

A 15-year-old female presented unilateral Class II molar relation with 90% overbite, retroclined upper central incisors, and impacted lower right canine. Nonextraction treatment was planned to correct deep bite, retroclination of upper central incisors, and unilateral Class II molar relation. Intrusion arch was used to intrude and procline the upper central incisors. Correcting the axial inclination of retroclined incisors caused unlocking of the mandible. This, in turn, leads to simultaneous correction of class II molar relation. The vertical loop was used to disimpact canine. Posttreatment incisors inclination was corrected, bilateral Class I molar relation was achieved, and canine had erupted in its position. The smile arc was improved along with mentolabial sulcus and nasolabial angle.
Relationships between fear of COVID‐19, cyberchondria, intolerance of uncertainty, and obsessional probabilistic inferences: A structural equation model

The study was set out to explore the structural relationships between fear of COVID‐19, cyberchondria, intolerance of uncertainty, and obsessional probabilistic inferences. The data were recruited online from a community population (n = 1,049) subjected to a confirmatory factor analytic procedure. The structural model specified according to the previous findings in the literature showed that a general tendency to negative expectations in terms of probabilistic thinking was significantly associated with both COVID‐19‐related‐fear and intolerance of uncertainty. Fear of COVID‐19 was significantly associated with cyberchondria. Probabilistic thinking style and intolerance of uncertainty contributed to cyberchondria through fear of COVID‐19 as well. We concluded that a tendency to engage in a probabilistic thinking style and intolerance of uncertainty seems to play role in the etiology of fear of infection and cyberchondria.
Epidemiology and prevalence of somatic symptom disorder at the primary care level in Muscat, Oman: A cross-sectional study

Objectives Somatic symptom disorder (SSD) is a diagnostic classification to describe the occurrence of physical symptoms without organic cause. This study aimed to identify prevalence rates and associated characteristics of SSD among adults in primary care settings. Methods This cross-sectional study took place between July 2020 and March 2021 and included a random selection of 3383 Omani adults attending 12 primary healthcare centres in Muscat Governorate. Screening was conducted using an Arabic version of the Somatic Symptom Scale-8 (SSS-8). Results A total of 2000 adults participated in the study (response rate: 67.3%), of which most were female (71.7%) and under 50 years old (86.2%). Based on their SSS-8 scores, 602 participants (17.8%) had SSD, resulting in an overall prevalence estimate of 30.1% (95% confidence interval: 28.13–32.15). Significant associations were observed between SSD and age (p = 0.002), gender (p < 0.001), marital status (p = 0.030) and chronic comorbidities (p = 0.001). In addition, adjusted odds ratio estimates revealed SSD to be significantly associated with gender (p < 0.001), education level (p < 0.001) and chronic comorbidities (p = 0.001). Conclusion The estimated prevalence of SSD in primary care settings is high compared to reports from elsewhere in the Gulf region. There is an urgent need to enhance the diagnosis of SSD at the primary care level in order to reduce healthcare service overutilisation and patient dissatisfaction. Moreover, healthcare practitioners should be aware of the effect of age, gender, educational status and chronic comorbidities on somatic symptoms.
Reward reduces the fission illusion in the sound-induced flash illusion

Pairing a single visual stimulus with multiple auditory stimuli will lead to the illusory perception of multiple visual stimuli, which is known as sound-induced flash illusion (SIFI). The present study adopted the classic SIFI paradigm to investigate whether value-associated tasks could affect the SIFI. By adjusting the sequence of reward and nonreward conditions, we also examined the effect of reward history on SIFI. The results showed that the fission illusion was reduced when associated with momentary reward, demonstrating significantly higher accuracy and discriminability than the nonreward condition. However, the fusion illusion was not affected by the momentary reward, and the explanation was that the fusion illusion was not as stable as the fission illusion and disappeared across different trials and conditions. Moreover, the robustness of reward history in the present study was not as strong as previous studies have suggested, indicating that the effect of sound on the perceptual representation of visual stimuli is strong and robust to reward history. These findings demonstrated that the reward could reduce the SIFI and broaden the existing dichotomy of SIFI. New evidence for the operation of value-driven attention mechanisms is also provided, suggesting that the underlying value-driven attention operates across multiple sensory systems.