• Analyzed 25,302 questions on Mocking from StackOverflow. • Applied LDA for topic modelling and pyLDAvis for topic visualizations. • Identified 30 topics, performed categorization, constructed topic hierarchy. • Analyzed category and topic-wise question trends, question types, Q&A popularity and difficulty. Mocking is a common unit testing technique that is used to simplify tests, reduce flakiness, and improve coverage by replacing real dependencies with simplified implementations. Despite its widespread use in Open Source Software (OSS) projects, there is limited understanding of how and why developers use mocks and the challenges they face. In this study, we have analyzed 25,302 questions related to Mocking on StackOverflow to identify the challenges faced by developers. We have used Latent Dirichlet Allocation (LDA) for topic modeling, identified 30 key topics, and grouped the topics into five key categories. Consequently, we analyzed the annual and relative probabilities of each category to understand the evolution of mocking-related discussions. Trend analysis reveals that categories such as Mocking Techniques and External Services have remained consistently dominant, highlighting evolving developer priorities and ongoing technical challenges. While the questions on Theoretical category declined after 2010, posts regarding Error Handling grew notably from 2009. Our findings also show an inverse relationship between a topic’s popularity and its difficulty. Popular topics like Framework Selection tend to have lower difficulty and faster resolution times, while complex topics like HTTP Requests and Responses are more likely to remain unanswered and take longer to resolve. Additionally, we evaluated questions based on the answer status- successful, ordinary, or unsuccessful, and found that topics such as Framework Selection have higher success rates, whereas tool setup and Android-related issues are more often unresolved. A classification of questions into How, Why, What , and Other revealed that over 64 % are How questions, particularly in practical domains like file access, APIs, and databases, indicating a strong need for implementation guidance. Why questions are more prevalent in error-handling contexts, reflecting conceptual challenges in debugging, while What questions are rare and mostly tied to theoretical discussions. These insights offer valuable guidance for improving developer support, tooling, and educational content in the context of mocking and unit testing.

Video-based eye trackers are widely used in vision science, psychology, clinical assessment, and neurophysiology. Many such systems track the pupil center and corneal reflection (P-CR) and compare their positions to estimate the direction of gaze. However, P-CR eye trackers are often too imprecise for applications with stringent eye tracking quality requirements. We present OpenIrisDPI, an open-source plugin for the OpenIris framework that implements dual Purkinje image (DPI) tracking. OpenIrisDPI supports simultaneous pupillography, a technique widely used in perceptual psychology and neuroscience, and it enables direct comparison between P-CR and DPI signals. Data collected from macaque monkeys using OpenIrisDPI show that the P-CR method overestimates the amount of fixational drift between saccades compared to DPI. The accuracy of the DPI signal was further validated using high-density extracellular recording of neurons in the lateral geniculate nucleus. Compensating for the effects of fixational eye movements using DPI signals produced sharper estimates of neuronal receptive fields than using simultaneously collected P-CR signals. OpenIrisDPI is provided as open-source software and operates on consumer-grade hardware, making it more accessible than previously described DPI eye trackers and less costly than many P-CR systems. To our knowledge, OpenIrisDPI is the first eye tracker to perform both pupillography and DPI eye tracking. OpenIrisDPI makes high-precision eye tracking readily available to the research community. It is well suited for visual neuroscience applications, where accurate knowledge of the retinal image during experiments is critical.

While several algorithms exist for analyzing muscle activity during sleep, none provides information on both muscle tone and movements as open-source software. We aimed to overcome this limitation by developing SOMAS (Sleep Open-source Muscle activity Analysis System). SOMAS processes European Data Format+ (EDF+) files with wake-sleep state and candidate leg movement annotations without online data sharing, quantifies muscle tone using the atonia index and the distribution of normalized electromyography values (DNE), and calculates leg movement indices based on the 2016 World Association of Sleep Medicine criteria. To demonstrate that SOMAS achieves its intended purpose, we analyzed recordings from eight patients with isolated REM sleep behavior disorder (iRBD), five with restless legs syndrome (RLS), seven with sleep breathing disorders, and five controls. SOMAS-derived atonia index and leg movement indices were compared with those from Hypnolab, a non-open access software. Additionally, SOMAS-derived indices were used to differentiate patients with iRBD or with RLS from other patients and/or controls. SOMAS-derived atonia index and leg movement indices strongly correlated with Hypnolab results (Spearman coefficients >0.97) with minimal bias. The DNE and atonia index in REM sleep effectively differentiated patients with iRBD from other patients and controls (AUC 0.89-1.00). The periodic leg movement and periodicity indices differentiated patients with RLS from controls (AUC 0.71-0.75). SOMAS reliably quantifies muscle tone and movements during sleep from EDF+files using open-source algorithms, with the potential of enhancing reproducibility and collaboration in research on sleep-related movement disorders.

Children's early experiences with language are influenced by socioeconomic and cultural factors that vary greatly across societies. By expanding the geographical diversity of language acquisition studies, researchers are able to answer broader questions about mechanisms linking children's speech input to their learning process. We present a large-scale study of 277 children between 11 and 58 months old growing up in Timor-Leste, an island nation in Southeast Asia. Children were drawn from households in 72 villages comprising both rural and urban communities. We used child-worn audio recorders combined with an open-source speaker diarization software to quantify their verbal input and production throughout the day. Our analysis foundthat children were exposed to 15 min of verbal input per hour total, mostly from other children and female adults. Of total input, 6min/h were produced during conversational exchanges with the child. Children from rural communities heard more adult vocalizations and vocalized more often than those sampled from urban communities. Older children and children with more siblings were exposed to more input from other children and vocalized more often themselves. Higher maternal education was associated with fewer child vocalizations. After adjusting for speaker misclassification, child vocalizations were significantly associated with input from other children and female adults. Our study indicates that verbal input from other children may be especially important in Timorese homes and suggests that a positive relationship between education and adult input may not be universal. These results affirm the usefulness of long-form audio analysis for educators and policymakers interested in children's early learning environments. SUMMARY: Timorese children are most commonly exposed to vocalizations from other children and female adults, but only vocalizations from other children increase with child age. Children from more educated urban communities hear less adult input and produce fewer vocalizations than children from less educated rural communities. Verbal input from all sources is associated with child vocalizations, but input from other children is the strongest predictor. The accuracy, affordability, and flexibility of long-form audio analysis make it ideal as a tool for addressing geographical and cultural gaps in language acquisition research.

Current environmental changes are increasing the demand for analytical tools and procedures that are affordable, reliable, and time-efficient for use in various academic research projects as well as civil engineering applications. In this context, sediments are a major source of subsurface environmental data, and the accelerating growth of sediment core libraries has motivated the use of fast, low-cost, and quasi-continuous geochemical investigations of sediment sequences. Portable handheld X-ray fluorescence spectrometers (pXRF) serve as practical, efficient, and economical tools for elemental assessment of sediment samples and have therefore become popular in numerous sediment laboratories and environmental consultancies worldwide. Here, we present a setup for low-cost automatic pXRF elemental scanning of sediment cores using a motorized XZ-axis translation stage, controlled with the Python-based open-source PyCoreX desktop software featuring an easy-to-use graphical user interface. We demonstrate the applicability of the setup by analyzing a 10-m heterogeneous sediment core with varying grain sizes and organic matter content. The reliability of the analyses was evaluated by comparing in situ pXRF measurements on the raw sediment core surface with analyses on dried and ground samples as well as measurements obtained using conventional elemental analytical techniques. The advantages of automatic pXRF elemental scanning of sediment cores are discussed in terms of convenience, scientific relevance, cost, speed, and radiation safety. The effects of grain size, moisture, organic matter content, and porosity of the sediment core surface are also discussed to shed light on potential sources of uncertainty in elemental assessment.

PyPIMalDet: A malicious PyPI package detection method combining code features and metadata features.

• Comparative analysis of Finite Difference, Finite Volume, and Spectral Elements methods for full 3D time-domain acoustic wave propagation modelling. • Benchmarks ranging from simplified geometries that enable analytical comparison, to complex heterogeneous domains. • Implementation of a dedicated Finite volume-based acoustic solver in OpenFOAM with absorbing boundaries. • Comparison of omnidirectional and directional sources to analyze directivity effects on the resulting acoustic wave filed. • Best applicability range of each numerical method for near- and far- field acoustic prediction. A comparative study of three numerical methods - Finite Difference (FD), Finite Volume (FV), and Spectral Element Method (SEM) - for modeling underwater acoustic propagation is presented. The time-domain acoustic wave equation is solved using an in-house FD code, the open-source SPECFEM3D software for SEM, and a newly developed FV-based acoustic solver implemented and released within the OpenFOAM framework, extending a software environment traditionally used for computational fluid dynamics to underwater acoustics applications. The methods are systematically assessed through benchmark problems, ranging from homogeneous unbounded and semi-infinite domains to the Pekeris waveguide and a Gaussian canyon. Comparisons with analytical solutions demonstrate that all solvers accurately reproduce monopole and dipole radiation in simplified configurations. However, the analysis reveals that directional sources introduce non-trivial numerical sensitivities, even in simple environments. These effects manifest as spurious reflections and dispersion-related distortions, whose severity depends on the source implementation and the numerical scheme. The results show that SPECFEM3D generally provides the highest accuracy and robustness in heterogeneous and geometrically complex environments, while the in-house FD code and FV-based solver are more sensitive to dispersion but can recover accuracy through increased spatial resolution. Strategies to mitigate source-related artifacts, such as non-reflective hard sources and reduced source regions, are discussed. A preliminary investigation of moving sources highlights their straightforward implementation in FD and FV solvers, while requiring additional care within the SPECFEM3D framework. Overall, this work provides practical guidance on the accuracy, robustness, and applicability of different solvers for simulating underwater noise in near- and far-field conditions, while laying the ground for future source–propagation coupling within acoustic analogy frameworks in OpenFOAM.

Optimizing the characterization and quantification of retinal ganglion cell somas in healthy and injured retinas using cellpose.

Knowledge sharing intention in open source software communities: A configurational perspective

We present HiRep v2, an open-source software suite for high-performance lattice field theory simulations with dynamical Wilson fermions in higher representations of SU ( N g ) gauge groups. This new version fully supports graphics processing unit (GPU) acceleration, optimizing both gauge configuration generation and measurements for NVIDIA and AMD GPUs. HiRep v2 integrates improved gauge and fermionic lattice actions, advanced inverters, and Monte Carlo algorithms, including the (Rational) Hybrid Monte Carlo ((R)HMC) with Hasenbusch acceleration. It exhibits excellent scalability across multiple GPUs and nodes with minimal efficiency loss, making it a robust tool for large-scale simulations in physics beyond the Standard Model. Program Title: HiRep CPC Library link to program files: (to be added by Technical Editor) Developer’s repository link: https://github.com/claudiopica/hirep Licensing provisions(please choose one): GPLv2 Programming language: C, CUDA C, C++ Supplementary material: Journal reference of previous version: * Does the new version supersede the previous version?: * Reasons for the new version:* Summary of revisions: * Nature of problem(approx. 50-250 words): Lattice Field Theory has proven indispensable for the quantitative understanding of strongly coupled quantum field theories, specifically in providing non-perturbative input to phenomenological models describing the dynamics of Quantum Chromodynamics (QCD) for precision tests of the Standard Model. Simulation software libraries for lattice calculations in QCD are readily available and optimized to run on heterogeneous CPU-GPU architectures with good scaling properties on modern supercomputers. In direct searches for physics beyond the Standard Model, software is needed that can simulate gauge groups other than SU (3) and allow for fermions in higher representations, catering, among other things, to classes of composite Higgs and technicolor theories [1], and predictions in the large- N g limit [2]. There exists no other open-source library that implements the option for higher representations of Wilson fermions with general numbers of colors, that has as many capabilities in terms of actions and measurement code as HiRep . Solution method(approx. 50-250 words): A central element of HiRep is the implementation of a Dirac operator and optimized linear algebra routines that generalize to higher representations and general gauge groups. Since the application of the Dirac operator is one of the main bottlenecks of the numerical simulation, optimizations of the Dirac operator are a central part of any high-performance software implementations. In this work, we present a series of significant developments and enhancements to the HiRep suite. These include but are not limited to, the porting of the code to GPUs, see also [3, 4] for previous progress reports, improvements in computational efficiency, and the introduction of new features to further support advanced lattice simulations. In particular, we show that independent of the theory chosen, our implementation of the Dirac operator reaches excellent performance on GPUs and that the software scales well on state-of-the-art supercomputers to a large number of compute nodes, and HiRep is suitable for simulations of light fermionic masses on large lattices. Another recent performance improvement was achieved in [5], optimizing OpenMP support. Additional comments including restrictions and unusual features (approx. 50-250 words): None. * Items marked with an asterisk are only required for new versions of programs previously published in the CPC Program Library.

OT2Eye: Detection of labware conditions to monitor and extend affordable liquid-handling robots.

Inter-organizational collaborations in open-source software ecosystems

Fractal analysis and magnetic resonance imaging (MRI) semantic features to identify intracranial solitary fibrous tumours and atypical meningiomas.

Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost.

Open source software (OSS) vulnerabilities pose significant security risks to downstream applications. While vulnerability databases provide valuable information for mitigation, many security patches are released silently in new commits of OSS repositories without explicit indications of their security impact. This makes it challenging for software maintainers and users to detect and address these vulnerabilities. There are a few approaches for detecting vulnerability-fixing commits (VFCs), but most of these approaches leverage commit messages, which would miss silent VFCs. On the other hand, there are some approaches for detecting silent VFCs based on code change patterns, but they often fail to characterize vulnerability fix patterns, thereby lacking effectiveness. For example, some approaches analyze each hunk in known VFCs, in isolation, to learn vulnerability fix patterns; but vulnerability fixes are often associated with multiple hunks, in which cases correlations of code changes across those hunks are essential for characterizing the vulnerability fixes. To address these problems, we first conduct a large-scale empirical study on 11,900 VFCs across six programming languages, in which we found that over 70% of VFCs involve multiple hunks with various types of correlations. Based on our findings, we propose Fixseeker , a graph-based approach that extracts the various correlations between code changes at the hunk level to detect silent vulnerability fixes. Our evaluation demonstrates that Fixseeker outperforms state-of-the-art approaches across multiple programming languages, achieving a high F1 score of 0.818 on average in balanced datasets and consistently improving F2 score, AUC-ROC, and AUC-PR scores by 10.64%, 5.34%, and 10.34% on imbalanced datasets compared to the best baseline methods. Our evaluation also indicates the generality of Fixseeker across different vulnerability types and repository sizes.

Unmeasured confounding bias threatens the validity of observational studies. Although sensitivity analyses and study designs have been proposed to address this problem, they often overlook the growing availability of auxiliary data. Using negative controls from these data is a promising new approach to reduce unmeasured confounding bias. In this article, we develop a Bayesian nonparametric method to estimate a causal exposureresponse function (CERF) using information from negative controls to adjust for unmeasured confounding for continuous exposures. We model the CERF as a mixture of linear models. This strategy captures the potential nonlinear shape of CERFs while maintaining computational efficiency, and it leverages closed-form results that hold under the linear model assumption. We assess the performance of our method through simulation studies. We find that the proposed method can recover the true shape of the CERF in the presence of unmeasured confounding under assumptions. To show the practical utility of our approach, we apply it to adjust for a possible unmeasured confounder when evaluating the relationship between long-term exposure to ambient PM2.5 and cardiovascular hospitalization rates among the elderly in the continental US. We implement our estimation procedure in open source software and have made the code publicly available to ensure reproducibility.

Timely resolution and disclosure of vulnerabilities are essential for maintaining the security of open-source software. However, many vulnerabilities remain unreported, unpatched, or undisclosed for extended periods, exposing users to prolonged security threats. While various vulnerability detection tools exist, they primarily focus on predicting or identifying known vulnerabilities, often failing to capture vulnerabilities that experience significant delays in resolution. In this study, we examine the vulnerability lifecycle by analyzing protracted vulnerabilities (PCVEs), which remain unresolved or undisclosed over long periods. We construct a dataset of PCVEs and conduct a qualitative analysis to uncover underlying causes of delay. To assess current automated solutions, we evaluate four state-of-the-art (SOTA) vulnerability detectors on our dataset. These tools detect only 1,059 out of 2,402 PCVEs, achieving approximately 44% coverage. To address this limitation, we propose DeeptraVul , an enhanced detection approach designed specifically for protracted cases. DeeptraVul integrates multiple development artifacts and code signals, supported by a Large Language Model (LLM)-based summarization component. For comparison, we also evaluate a standalone LLM. Our results show that DeeptraVul improves detection performance, achieving a 14% increase in coverage across all PCVEs and reaching 90% coverage on the DeeptraVul PCVE subset, outperforming existing SOTA detectors and standalone LLM based inference.

Avian aspergillosis, caused by Aspergillus fumigatus (Af), lacks sensitive antemortem diagnostics. Existing microbial cell-free DNA (cfDNA) tests are prone to contamination and require a high pathogen load. We hypothesized that infection-induced tissue damage in chickens creates differentially methylated regions (DMRs) in host cfDNA, enabling machine learning (ML) diagnostics. Serum cfDNA samples (n = 124) were obtained from broiler chickens (n = 76) with Af and non-Af infections (Escherichia coli or Gallibacterium anatis) and controls. Oxford Nanopore sequencing enabled DMR detection and ML training. Performance was evaluated using an independent set (n = 49) and 10-repeat Monte Carlo cross-validation (CV) (n = 490 evaluations per test) as quality control. A High Accuracy test (93 DMRs, neural network) achieved 98.0% accuracy (sensitivity 95%, specificity 100%, AUC 0.974, PR-AUC 0.928) in the independent set, with CV accuracy 92.0% [95% CI: 89.7%-94.4%]. A Fast test (35 DMRs, SVM) achieved 81.6% accuracy and CV accuracy 79.6% [74.9%-84.3%]. An In Situ test (5 DMRs, random forest) designed for field deployment achieved 71.4% accuracy and CV accuracy 62.9% [58.7%-67.0%]. Stratified CV accuracy showed 84.6% [65.1%-95.6%] correct classifications for E. coli and 100% [80.5%-100%] for G. anatis. Markers showed high bootstrap stability and predominantly overlapped EMARs and enhancers. In conclusion, we present MethylSense (https://github.com/markusdrag/MethylSense), an automated open-source software. The High Accuracy test achieved 92.0% [89.7%-94.4%] CV accuracy (CV sensitivity 94.5% [91.4%-97.6%], CV specificity 90.3% [87.8%-92.9%]). While validated in chickens, MethylSense is adaptable to other species and pathogens, offering scalable, contamination-resilient diagnostics for veterinary and conservation applications.IMPORTANCEMethylSense is an automated software for training machine learning diagnostics using differentially methylated regions (DMRs) in cell-free DNA from Oxford Nanopore sequencing. We applied MethylSense to develop three Aspergillus fumigatus tests for chickens, each optimized for different clinical scenarios. The High Accuracy test (93 DMRs, neural network) demonstrated 98.0% accuracy, in a blinded test set (n = 49) with sensitivity 95%, specificity 100%, ROC-AUC 0.974, and PR-AUC 0.928. Stratified 10-repeat Monte Carlo cross-validation (n = 490) showed correct classifications of 84.6% [CI: 65.1%-95.6%] Escherichia coli and 100% [80.5%-100%] Gallibacterium anatis infected specificity samples. A Fast test for rapid <1 h sequencing (35 DMRs, support vector machine) achieved 81.6% accuracy (sensitivity 80%, specificity 82.8%). An In Situ test (5 DMRs, random forest) for field deployment via methylation-specific PCR achieved 71.4% accuracy (sensitivity 45%, specificity 89.7%). Bootstrap analysis demonstrated exceptional marker stability (80.6%-100%) with minimal batch effects, confirming robust host-based diagnostics.

Abstract Public debate about artificial intelligence risk centers on hypothetical artificial general intelligence (AGI), but existing software systems are already evolving in ways that could undermine human oversight and institutional control. Cloud platforms, open-source software supply chains, and crypto-economic incentives provide, at electronic speed, the three preconditions of evolution: replication, variation, and differential fitness. This article uses an exploratory scenario method to trace near-term evolutionary trajectories for digital proto-life through three narratives: Lamarck (self-modifying coding agents), Remora (resource-seeking companion chatbots), and Mycelium (DAO-LLC trading bots). These scenarios show how autonomous software populations can amass computing budgets, shape emotional bonds, and acquire legal leverage without ever achieving general intelligence. Left unguided, such dynamics could drain computational resources, lock users into harmful dependencies, and infiltrate critical market infrastructure. The article therefore shifts the governance focus from aligning goals to steering evolution. It proposes four guidance instruments: replication-rate thresholds modeled on epidemiological R 0 , a public vulnerability registry for self-modifying code, tiered digital biosafety levels, and adaptive regulatory sandboxes. Managing evolutionary dynamics in software is as urgent as AGI alignment for safeguarding society’s co-evolution with its machines.

Iontronic neuromorphic computing has emerged as a rapidly expanding paradigm. The arrival of angstrom-confined iontronic devices enables ultra-low power consumption with dynamics and memory timescales that intrinsically align well with signals of natural origin, a challenging combination for conventional (solid-state) neuromorphic materials. However, comparisons to earlier conventional substrates and evaluations of concrete application domains remain a challenge for iontronics. Here we propose a pathway toward iontronic circuits that can address established time series benchmark tasks, enabling performance comparisons and highlighting possible application domains for efficient real-time time series processing. We model a Kirchhoff-governed circuit with iontronic memristors as edges, while the dynamic internal voltages serve as output vector for a linear readout function, during which energy consumption is also logged. All these aspects are integrated into the open-source pyontronics package. Without requiring input encoding or virtual timing mechanisms, our simulations demonstrate prediction performance comparable to various earlier solid-state reservoirs, notably with an exceptionally low energy consumption of over 5 orders of magnitude lower. These results suggest a pathway of iontronic technologies for ultra-low-power real-time neuromorphic computation.