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Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The challenge of finding anomalies can be confounding since it requires codifying a complete knowledge of the known scientific behaviors and then projecting these known behaviors on the data to look for deviations. When utilizing machine learning, this presents a particular challenge since we require that the model not only understands scientific data perfectly but also recognizes when the data is inconsistent and out of the scope of its trained behavior. In this paper, we present three datasets aimed at developing machine learning-based anomaly detection for disparate scientific domains covering astrophysics, genomics, and polar science. We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR). Furthermore, we present an approach that generalizes to future machine learning challenges, enabling the possibility of large, more compute-intensive challenges that can ultimately lead to scientific discovery., Comment: 18 pages 6 figures to be submitted to Nature Communications

Clustering is a fundamental technique in machine learning and data analysis, widely used across various domains. Internal clustering validation measures, such as the Average Silhouette Width, Calinski-Harabasz, and Davies-Bouldin indices, play a crucial role in assessing clustering quality when external ground truth labels are unavailable. However, these measures can be affected by feature relevance, potentially leading to unreliable evaluations in high-dimensional or noisy data sets. In this paper, we introduce a Feature Importance Rescaling (FIR) method designed to enhance internal clustering validation by adjusting feature contributions based on their dispersion. Our method systematically attenuates noise features making clustering compactness and separation clearer, and by consequence aligning internal validation measures more closely with the ground truth. Through extensive experiments on synthetic data sets under different configurations, we demonstrate that FIR consistently improves the correlation between internal validation indices and the ground truth, particularly in settings with noisy or irrelevant features. The results show that FIR increases the robustness of clustering evaluation, reduces variability in performance across different data sets, and remains effective even when clusters exhibit significant overlap. These findings highlight the potential of FIR as a valuable enhancement for internal clustering validation, making it a practical tool for unsupervised learning tasks where labelled data is not available.

We adapt Goldie's concept of uniform dimensions from module theory over rings to $\Gamma$-monoids. A $\Gamma$-monoid $M$ is said to have uniform dimension $n$ if $n$ is the largest number of pairwise incomparable nonzero $\Gamma$-order ideals contained in $M$. Specializing to the talented monoid of a graph, we show that the uniform dimension provides a rough measure of how the graph branches out. Since for any order ideal $I$, its orthogonal ideal $I^\perp$ is the largest ideal incomparable to $I$, we study the notions of orthogonality and regularity, particularly when $I^{\perp\perp}=I$. We show that the freeness of the action of $\mathbb Z$ on the talented monoid of a graph is preserved under quotienting by a regular ideal. Furthermore, we determine the underlying hereditary and saturated sets that generate these ideals. These results unify recent studies on regular ideals of the corresponding Leavitt path algebras and graph $C^*$-algebras. We conclude that for graphs $E$ and $F$, if there is a $\mathbb Z$-monoid isomorphism $T_E\cong T_F$, then there is a one-to-one correspondence between the regular ideals of the associated Leavitt path algebras $L_K(E)$ and $L_K(F)$ (and similarly, $C^*(E)$ and $C^*(F)$). Since the talented monoid $T_E$ is the positive cone of the graded Grothendieck group $K_0^{gr}(L_K(E))$, this provides further evidence supporting the Graded Classification Conjecture.

Neural network verification is a new and rapidly developing field of research. So far, the main priority has been establishing efficient verification algorithms and tools, while proper support from the programming language perspective has been considered secondary or unimportant. Yet, there is mounting evidence that insights from the programming language community may make a difference in the future development of this domain. In this paper, we formulate neural network verification challenges as programming language challenges and suggest possible future solutions., Comment: Accepted at ESOP 2025, European Symposium on Programming Languages

Cross-assignment of directional wave spectra is a critical task in wave data assimilation. Traditionally, most methods rely on two-parameter spectral distances or energy ranking approaches, which often fail to account for the complexities of the wave field, leading to inaccuracies. To address these limitations, we propose the Controlled Four-Parameter Method (C4PM), which independently considers four integrated wave parameters. This method enhances the accuracy and robustness of cross-assignment by offering flexibility in assigning weights and controls to each wave parameter. We compare C4PM with a two-parameter spectral distance method using data from two buoys moored 13 km apart in deep water. Although both methods produce negligible bias and high correlation, C4PM demonstrates superior performance by preventing the occurrence of outliers and achieving a lower root mean square error across all parameters. The negligible computational cost and customization make C4PM a valuable tool for wave data assimilation, improving the reliability of forecasts and model validations.

This work presents a set of experiments conducted to predict the gender of Twitter users based on language-independent features extracted from the text of the users' tweets. The experiments were performed on a version of TwiSty dataset including tweets written by the users of six different languages: Portuguese, French, Dutch, English, German, and Italian. Logistic regression (LR), and feed-forward neural networks (FFNN) with back-propagation were used to build models in two different settings: Inter-Lingual (IL) and Cross-Lingual (CL). In the IL setting, the training and testing were performed on the same language whereas in the CL, Italian and German datasets were set aside and only used as test sets and the rest were combined to compose training and development sets. In the IL, the highest accuracy score belongs to LR whereas in the CL, FFNN with three hidden layers yields the highest score. The results show that neural network based models underperform traditional models when the size of the training set is small; however, they beat traditional models by a non-trivial margin, when they are fed with large enough data. Finally, the feature analysis confirms that men and women have different writing styles independent of their language.

Context. Spectroscopic surveys like APOGEE, GALAH, and LAMOST have significantly advanced our understanding of the Milky Way by providing extensive stellar parameters and chemical abundances. Complementing these, photometric surveys with narrow/medium-band filters, such as the Southern Photometric Local Universe Survey (S-PLUS), offer the potential to estimate stellar parameters and abundances for a much larger number of stars. Aims. This work develops methodologies to extract stellar atmospheric parameters and selected chemical abundances from S-PLUS photometric data, which spans ~3000 square degrees using seven narrowband and five broadband filters. Methods. Using 66 S-PLUS colors, we estimated parameters based on training samples from LAMOST, APOGEE, and GALAH, applying Cost-Sensitive Neural Networks (NN) and Random Forests (RF). We tested for spurious correlations by including abundances not covered by the S-PLUS filters and evaluated NN and RF performance, with NN consistently outperforming RF. Including Teff and log g as features improved accuracy by ~3%. We retained only parameters with a goodness-of-fit above 50%. Results. Our approach provides reliable estimates of fundamental parameters (Teff, log g, [Fe/H]) and abundance ratios such as [{\alpha}/Fe], [Al/Fe], [C/Fe], [Li/Fe], and [Mg/Fe] for ~5 million stars, with goodness-of-fit >60%. Additional ratios like [Cu/Fe], [O/Fe], and [Si/Fe] were derived but are less accurate. Validation using star clusters, TESS, and J-PLUS data confirmed the robustness of our methodology. Conclusions. By leveraging S-PLUS photometry and machine learning, we present a cost-effective alternative to high-resolution spectroscopy for deriving stellar parameters and abundances, enabling insights into Milky Way stellar populations and supporting future classification efforts., Comment: 23 pages, 14 Figures

We present a brief introduction to a class of interactive fuzzy numbers, called f correlated fuzzy numbers, which consist of pairs of fuzzy numbers where one is dependent on the other by a continuous montone injector function. We present some results of operations with these numbers, obtaining equations that can directly calculate the results of these operations using only basic operations with real numbers, intervals on the real line and the function that relates the fuzzy numbers being considered., Comment: 5 pages, no figures

This paper presents some concepts of the theory of interactive fuzzy numbers, and mainly, a class of interactive fuzzy numbers, called $f$-correlated fuzzy numbers. We start from the foundations of general fuzzy mathematics and go through operations and the notion of interactivity for fuzzy numbers. The main result is that $f$-correlation preserve the shape of certains fuzzy numbers. More specificaly, if two fuzzy numbers are $f$ correlated, and one is a LR-type fuzzy number, the other is also a LR-type fuzzy number. This paper also presents some operations with the $f$-correlated fuzzy numbers wich are interesting to applications like biomathematics., Comment: 6 pages, no figures

Large Language Models (LLMs) have shown potential to enhance software development through automated code generation and refactoring, reducing development time and improving code quality. This study empirically evaluates StarCoder2, an LLM optimized for code generation, in refactoring code across 30 open-source Java projects. We compare StarCoder2's performance against human developers, focusing on (1) code quality improvements, (2) types and effectiveness of refactorings, and (3) enhancements through one-shot and chain-of-thought prompting. Our results indicate that StarCoder2 reduces code smells by 20.1% more than developers, excelling in systematic issues like Long Statement and Magic Number, while developers handle complex, context-dependent issues better. One-shot prompting increases the unit test pass rate by 6.15% and improves code smell reduction by 3.52%. Generating five refactorings per input further increases the pass rate by 28.8%, suggesting that combining one-shot prompting with multiple refactorings optimizes performance. These findings provide insights into StarCoder2's potential and best practices for integrating LLMs into software refactoring, supporting more efficient and effective code improvement in real-world applications.

An important stage of most state-of-the-art (SOTA) noisy-label learning methods consists of a sample selection procedure that classifies samples from the noisy-label training set into noisy-label or clean-label subsets. The process of sample selection typically consists of one of the two approaches: loss-based sampling, where high-loss samples are considered to have noisy labels, or feature-based sampling, where samples from the same class tend to cluster together in the feature space and noisy-label samples are identified as anomalies within those clusters. Empirically, loss-based sampling is robust to a wide range of noise rates, while feature-based sampling tends to work effectively in particular scenarios, e.g., the filtering of noisy instances via their eigenvectors (FINE) sampling exhibits greater robustness in scenarios with low noise rates, and the K nearest neighbor (KNN) sampling mitigates better high noise-rate problems. This paper introduces the Adaptive Nearest Neighbors and Eigenvector-based (ANNE) sample selection methodology, a novel approach that integrates loss-based sampling with the feature-based sampling methods FINE and Adaptive KNN to optimize performance across a wide range of noise rate scenarios. ANNE achieves this integration by first partitioning the training set into high-loss and low-loss sub-groups using loss-based sampling. Subsequently, within the low-loss subset, sample selection is performed using FINE, while the high-loss subset employs Adaptive KNN for effective sample selection. We integrate ANNE into the noisy-label learning state of the art (SOTA) method SSR+, and test it on CIFAR-10/-100 (with symmetric, asymmetric and instance-dependent noise), Webvision and ANIMAL-10, where our method shows better accuracy than the SOTA in most experiments, with a competitive training time., Comment: Accepted at Pattern Recognition

With over a billion active users, TikTok's video-sharing service is currently one of the largest social media websites. This rise in TikTok's popularity has made the website a central platform for music discovery. In this paper, we analyze how TikTok helps to revitalize older songs. To do so, we use both the popularity of songs shared on TikTok and how the platform allows songs to propagate to other places on the Web. We analyze data from TokBoard, a website measuring such popularity over time, and Google Trends, which captures songs' overall Web search interest. Our analysis initially focuses on whether TokBoard can cause (Granger Causality) popularity on Google Trends. Next, we examine whether TikTok and Google Trends share the same virality patterns (via a Bass Model). To our knowledge, we are one of the first works to study song re-popularization via TikTok., Comment: To appear in the 1st Latin American Music Information Retrieval Workshop

As machine intelligence evolves, the need to test and compare the problem-solving abilities of different AI models grows. However, current benchmarks are often simplistic, allowing models to perform uniformly well and making it difficult to distinguish their capabilities. Additionally, benchmarks typically rely on static question-answer pairs that the models might memorize or guess. To address these limitations, we introduce Dynamic Intelligence Assessment (DIA), a novel methodology for testing AI models using dynamic question templates and improved metrics across multiple disciplines such as mathematics, cryptography, cybersecurity, and computer science. The accompanying dataset, DIA-Bench, contains a diverse collection of challenge templates with mutable parameters presented in various formats, including text, PDFs, compiled binaries, visual puzzles, and CTF-style cybersecurity challenges. Our framework introduces four new metrics to assess a model's reliability and confidence across multiple attempts. These metrics revealed that even simple questions are frequently answered incorrectly when posed in varying forms, highlighting significant gaps in models' reliability. Notably, API models like GPT-4o often overestimated their mathematical capabilities, while ChatGPT-4o demonstrated better performance due to effective tool usage. In self-assessment, OpenAI's o1-mini proved to have the best judgement on what tasks it should attempt to solve. We evaluated 25 state-of-the-art LLMs using DIA-Bench, showing that current models struggle with complex tasks and often display unexpectedly low confidence, even with simpler questions. The DIA framework sets a new standard for assessing not only problem-solving but also a model's adaptive intelligence and ability to assess its limitations. The dataset is publicly available on the project's page: https://github.com/DIA-Bench.

This paper assesses the uncertainties inherent to parton shower simulations at double logarithmic accuracy, with a focus on their impact on jet quenching studies in high-energy heavy-ion collisions. For that purpose, we developed a massless quark-initiated vacuum parton shower toy-model with different evolution variables, such as inverse formation time, invariant squared mass, and squared opening angle. In addition to the effects of varying the ordering variable we further examine their corresponding kinematic reconstructions. The results highlight how these variations influence key distributions, including the number of splittings, angular and transverse momentum distribution of subsequent emissions. We also analyse the Lund distributions and their average trajectories, revealing that the choice of ordering variable has a significantly greater impact on the vacuum parton shower evolution than the kinematic scheme, particularly in large-angle emission regions. When a simple jet quenching model based on decoherence is implemented, we observe that the fraction of quenched events is sensitive to the ordering prescription, especially for the first splitting and thin media, highlighting the need for a deeper understanding of the branching process in the presence of an extended QCD media., Comment: 47 pages, 27 figures

An all-fiber dual Mach-Zehnder interferometer (MZI) based on an acoustically modulated hollow-core fiber (HCF) is experimentally demonstrated for the first time. By attaching an acoustic driver in between the fixed ends of an HCF, we fabricated two acousto-optic modulators (AOMs) with distinct driver positions, allowing for synchronizing two in-line MZIs inside the HCF. The first MZI is set by two acoustic long-period gratings separated by a second MZI formed at the fiber and driver attaching region. We show that this setup enables frequency-tuning of the coupling between the fundamental and higher-order modes in the HCF. Additionally, we simulate and analyze the HCF modal couplings and MZIs' modulated spectra under distinct device parameters using the transfer matrix method. The new AOM-MZI enables tuning of the MZIs free spectral range by adjusting 1 Hz of the electrical frequency, which is promising to modulate multiwavelength filters, sensors and fiber lasers.

This study investigates vulnerabilities in dependencies of sampled open-source software (OSS) projects, the relationship between these and overall project security, and how developers' behaviors and practices influence their mitigation. Through analysis of OSS projects, we have identified common issues in outdated or unmaintained dependencies, including pointer dereferences and array bounds violations, that pose significant security risks. We have also examined developer responses to formal verifier reports, noting a tendency to dismiss potential issues as false positives, which can lead to overlooked vulnerabilities. Our results suggest that reducing the number of direct dependencies and prioritizing well-established libraries with strong security records are effective strategies for enhancing the software security landscape. Notably, four vulnerabilities were fixed as a result of this study, demonstrating the effectiveness of our mitigation strategies., Comment: 16 pages

The Southern Photometric Local Universe Survey (S-PLUS) is a project to map $\sim9300$ sq deg of the sky using twelve bands (seven narrow and five broadbands). Observations are performed with the T80-South telescope, a robotic telescope located at the Cerro Tololo Observatory in Chile. The survey footprint consists of several large contiguous areas, including fields at high and low galactic latitudes, and towards the Magellanic Clouds. S-PLUS uses fixed exposure times to reach point source depths of about $21$ mag in the $griz$ and $20$ mag in the $u$ and the narrow filters. This paper describes the S-PLUS Data Release 4 (DR4), which includes calibrated images and derived catalogues for over 3000 sq deg, covering the aforementioned area. The catalogues provide multi-band photometry performed with the tools \texttt{DoPHOT} and \texttt{SExtractor} -- point spread function (\PSF) and aperture photometry, respectively. In addition to the characterization, we also present the scientific potential of the data. We use statistical tools to present and compare the photometry obtained through different methods. Overall we find good agreement between the different methods, with a slight systematic offset of 0.05\,mag between our \PSF and aperture photometry. We show that the astrometry accuracy is equivalent to that obtained in previous S-PLUS data releases, even in very crowded fields where photometric extraction is challenging. The depths of main survey (MS) photometry for a minimum signal-to-noise ratio $S/N = 3$ reach from $\sim19.5$ for the bluer bands to $\sim21.5$ mag on the red. The range of magnitudes over which accurate \PSF photometry is obtained is shallower, reaching $\sim19$ to $\sim20.5$ mag depending on the filter. Based on these photometric data, we provide star-galaxy-quasar classification and photometric redshift for millions of objects., Comment: 26 pages, 17 figures, 14 tables, accepted for A&A

In the world of embedded systems, optimizing actions with the uncertain costs of multiple resources is a complex challenge. Existing methods include plan building based on Monte Carlo Tree Search (MCTS), an approach that thrives in multiple online planning scenarios. However, these methods often overlook uncertainty in worst-case cost estimations. A system can fail to operate before achieving a critical objective when actual costs exceed optimistic worst-case estimates. Conversely, a system based on pessimistic worst-case estimates would lead to resource over-provisioning even for less critical objectives. To solve similar issues, the Mixed Criticality (MC) approach has been developed in the real-time systems community. In this paper, we propose to extend the MCTS heuristic in three directions. Firstly, we reformulate the concept of MC to account for uncertain worst-case costs. High-criticality tasks must be executed regardless of their uncertain costs. Low-criticality tasks are either executed in low-criticality mode utilizing resources up-to their optimistic worst-case estimates, or executed in high-criticality mode by degrading them, or discarded when resources are scarce. Secondly, although the MC approach was originally developed for real-time systems, focusing primarily on worst-case execution time as the only uncertain resource, our approach extends the concept of resources to deal with several resources at once, such as the time and energy required to perform an action. Finally, we propose an extension of MCTS with MC concepts, which we refer to as $(MC)^2TS$, to efficiently adjust resource allocation to uncertain costs according to the criticality of actions. We demonstrate our approach in an active perception scenario. Our evaluation shows $(MC)^2TS$ outperforms the traditional MCTS regardless of whether the worst case estimates are optimistic or pessimistic.

Training large language models (LLMs) requires a substantial investment of time and money. To get a good return on investment, the developers spend considerable effort ensuring that the model never produces harmful and offensive outputs. However, bad-faith actors may still try to slander the reputation of an LLM by publicly reporting a forged output. In this paper, we show that defending against such slander attacks requires reconstructing the input of the forged output or proving that it does not exist. To do so, we propose and evaluate a search based approach for targeted adversarial attacks for LLMs. Our experiments show that we are rarely able to reconstruct the exact input of an arbitrary output, thus demonstrating that LLMs are still vulnerable to slander attacks., Comment: 4 pages, 3 figures

A framework with sets of attacking arguments (SETAF) is an extension of the well-known Dung's Abstract Argumentation Frameworks (AAFs) that allows joint attacks on arguments. In this paper, we provide a translation from Normal Logic Programs (NLPs) to SETAFs and vice versa, from SETAFs to NLPs. We show that there is pairwise equivalence between their semantics, including the equivalence between L-stable and semi-stable semantics. Furthermore, for a class of NLPs called Redundancy-Free Atomic Logic Programs (RFALPs), there is also a structural equivalence as these back-and-forth translations are each other's inverse. Then, we show that RFALPs are as expressive as NLPs by transforming any NLP into an equivalent RFALP through a series of program transformations already known in the literature. We also show that these program transformations are confluent, meaning that every NLP will be transformed into a unique RFALP. The results presented in this paper enhance our understanding that NLPs and SETAFs are essentially the same formalism. Under consideration in Theory and Practice of Logic Programming (TPLP)., Comment: 44 pages, 5 figures. Under consideration in Theory and Practice of Logic Programming (TPLP)

In heterogeneous scenarios where the data distribution amongst the Federated Learning (FL) participants is Non-Independent and Identically distributed (Non-IID), FL suffers from the well known problem of data heterogeneity. This leads the performance of FL to be significantly degraded, as the global model tends to struggle to converge. To solve this problem, we propose Differentially Private Synthetic Data Aided Federated Learning Using Foundation Models (DPSDA-FL), a novel data augmentation strategy that aids in homogenizing the local data present on the clients' side. DPSDA-FL improves the training of the local models by leveraging differentially private synthetic data generated from foundation models. We demonstrate the effectiveness of our approach by evaluating it on the benchmark image dataset: CIFAR-10. Our experimental results have shown that DPSDA-FL can improve class recall and classification accuracy of the global model by up to 26% and 9%, respectively, in FL with Non-IID issues.

This paper introduces a tool for verifying Python programs, which, using type annotation and front-end processing, can harness the capabilities of a bounded model-checking (BMC) pipeline. It transforms an input program into an abstract syntax tree to infer and add type information. Then, it translates Python expressions and statements into an intermediate representation. Finally, it converts this description into formulae evaluated with satisfiability modulo theories (SMT) solvers. The proposed approach was realized with the efficient SMT-based bounded model checker (ESBMC), which resulted in a tool called ESBMC-Python, the first BMC-based Python-code verifier. Experimental results, with a test suite specifically developed for this purpose, showed its effectiveness, where successful and failed tests were correctly evaluated. Moreover, it found a real problem in the Ethereum Consensus Specification.

This paper presents Efficient SMT-Based Context-Bounded Model Checker (ESBMC) v7.6, an extended version based on previous work on ESBMC v7.3 by K. Song et al. The v7.3 introduced a new Clang-based C++ front-end to address the challenges posed by modern C++ programs. Although the new front-end has demonstrated significant potential in previous studies, it remains in the developmental stage and lacks several essential features. ESBMC v7.6 further enhanced this foundation by adding and extending features based on the Clang AST, such as 1) exception handling, 2) extended memory management and memory safety verification, including dangling pointers, duplicate deallocation, memory leaks and rvalue references and 3) new operational models for STL updating the outdated C++ operational models. Our extensive experiments demonstrate that ESBMC v7.6 can handle a significantly broader range of C++ features introduced in recent versions of the C++ standard., Comment: 27 pages, 2 figures. arXiv admin note: substantial text overlap with arXiv:2308.05649

Bounded Model Checking (BMC) is a widely used software verification technique. Despite its successes, the technique has several limiting factors, from state-space explosion to lack of completeness. Over the years, interval analysis has repeatedly been proposed as a partial solution to these limitations. In this work, we evaluate whether the computational cost of interval analysis yields significant enough improvements in BMC's performance to justify its use. In more detail, we quantify the benefits of interval analysis on two benchmarks: the Intel Core Power Management firmware and 9537 programs in the ReachSafety category of the International Competition on Software Verification. Our results show that interval analysis is essential in solving 203 unique benchmarks., Comment: Submitted to IFM

Realm Management Monitor (RMM) is an essential firmware component within the recent Arm Confidential Computing Architecture (Arm CCA). Previous work applies formal techniques to verify the specification and prototype reference implementation of RMM. However, relying solely on a single verification tool may lead to the oversight of certain bugs or vulnerabilities. This paper discusses the application of ESBMC, a state-of-the-art Satisfiability Modulo Theories (SMT)-based software model checker, to further enhance RRM verification. We demonstrate ESBMC's ability to precisely parse the source code and identify specification failures within a reasonable time frame. Moreover, we propose potential improvements for ESBMC to enhance its efficiency for industry engineers. This work contributes to exploring the capabilities of formal verification techniques in real-world scenarios and suggests avenues for further improvements to better meet industrial verification needs.

We prove that local stable/unstable sets of homeomorphisms of an infinite compact metric space satisfying the gluing-orbit property always contain compact and perfect subsets of the space. As a consequence, we prove that if a positively countably expansive homeomorphism satisfies the gluing-orbit property, then the space is a single periodic orbit. We also prove that there are homeomorphisms with gluing-orbit such that its induced homeomorphism on the hyperspace of compact subsets does not have gluing-orbit, contrasting with the case of the shadowing and specification properties, proving that if the induced map has gluing-orbit, then the base map has gluing-orbit and is topologically mixing., Comment: 13 pages, 1 figure

The next generation of AI systems requires strong safety guarantees. This report looks at the software implementation of neural networks and related memory safety properties, including NULL pointer deference, out-of-bound access, double-free, and memory leaks. Our goal is to detect these vulnerabilities, and automatically repair them with the help of large language models. To this end, we first expand the size of NeuroCodeBench, an existing dataset of neural network code, to about 81k programs via an automated process of program mutation. Then, we verify the memory safety of the mutated neural network implementations with ESBMC, a state-of-the-art software verifier. Whenever ESBMC spots a vulnerability, we invoke a large language model to repair the source code. For the latest task, we compare the performance of various state-of-the-art prompt engineering techniques, and an iterative approach that repeatedly calls the large language model.

We demonstrate the acousto-optic modulation of a hybrid-lattice hollow core fiber (HL-HCF) for the first time. For many years, optical fibers with reduced diameters have been the main solution to increase the interaction of acoustic and optical waves. However, the high drive voltages and large modulator components still employed drastically affect the efficiency and miniaturization of these devices. Here, we experimentally show that combining Kagom\'e and tubular lattices in HL-HCFs allows for enhancing the amplification of the acoustic waves and the modulation of the guided optical modes thus providing high modulation efficiency even when using a fiber with a 240 um diameter. To the best of our knowledge, the measured HL-HCF's modulation efficiency (1.3 dB/V) is the highest compared to devices employing reduced diameter fibers. Additionally, we demonstrate a compact acousto-optic modulator with driver dimensions smaller than the HL-HCF diameter. Overall, our results show a promising alternative to solve the compromise of speed, efficiency, and compactness for integration with microscale all-fiber photonic devices.

Extented Reality (XR) refers to a class of contemporary services that are intertwined with a plethora of rather demanding Quality of Service (QoS) and functional requirements. Despite Kubernetes being the de-facto standard in terms of deploying and managing contemporary containerized microservices, it lacks adequate support for cross-cluster networking, hindering service-to-service communication across diverse cloud domains. Although there are tools that may be leveraged alongside Kubernetes in order to establish multi-cluster deployments, each one of them comes with its drawbacks and limitations. The purpose of this article is to explore the various potential technologies that may facilitate multi-cluster deployments and to propose how they may be leveraged to provide a cross-cluster connectivity solution that caters to the intricacies of XR services. The proposed solution is based on the use of two open source frameworks, namely Cluster API for multi-cluster management, and Liqo for multi-cluster interconnectivity. The efficiency of this approach is evaluated in the context of two experiments. This work is the first attempt at proposing a solution for supporting multi-cluster deployments in a manner that is aligned with the requirements of XR services

This study compares state-of-the-art Large Language Models (LLMs) on their tendency to generate vulnerabilities when writing C programs using a neutral zero-shot prompt. Tihanyi et al. introduced the FormAI dataset at PROMISE'23, featuring 112,000 C programs generated by GPT-3.5-turbo, with over 51.24% identified as vulnerable. We extended that research with a large-scale study involving 9 state-of-the-art models such as OpenAI's GPT-4o-mini, Google's Gemini Pro 1.0, TII's 180 billion-parameter Falcon, Meta's 13 billion-parameter Code Llama, and several other compact models. Additionally, we introduce the FormAI-v2 dataset, which comprises 331 000 compilable C programs generated by these LLMs. Each program in the dataset is labeled based on the vulnerabilities detected in its source code through formal verification, using the Efficient SMT-based Context-Bounded Model Checker (ESBMC). This technique minimizes false positives by providing a counterexample for the specific vulnerability and reduces false negatives by thoroughly completing the verification process. Our study reveals that at least 62.07% of the generated programs are vulnerable. The differences between the models are minor, as they all show similar coding errors with slight variations. Our research highlights that while LLMs offer promising capabilities for code generation, deploying their output in a production environment requires proper risk assessment and validation., Comment: Accepted and will be shortly published at Empirical Software Engineering (EMSE). Journal Impact Factor: 3.5 (2023)

Prompting has become one of the main approaches to leverage emergent capabilities of Large Language Models [Brown et al. NeurIPS 2020, Wei et al. TMLR 2022, Wei et al. NeurIPS 2022]. Recently, researchers and practitioners have been "playing" with prompts (e.g., In-Context Learning) to see how to make the most of pre-trained Language Models. By homogeneously dissecting more than a hundred articles, we investigate how software testing and verification research communities have leveraged LLMs capabilities. First, we validate that downstream tasks are adequate to convey a nontrivial modular blueprint of prompt-based proposals in scope. Moreover, we name and classify the concrete downstream tasks we recover in both validation research papers and solution proposals. In order to perform classification, mapping, and analysis, we also develop a novel downstream-task taxonomy. The main taxonomy requirement is to highlight commonalities while exhibiting variation points of task types that enable pinpointing emerging patterns in a varied spectrum of Software Engineering problems that encompasses testing, fuzzing, fault localization, vulnerability detection, static analysis, and program verification approaches. Avenues for future research are also discussed based on conceptual clusters induced by the taxonomy.

This paper is addressing the quantification and the comparison of pumped storage power plants, PSPP, contribution to synchronous inertia and synthetic inertia when fixed speed and variable speed motor-generators technologies are considered, respectively. Therefore, a grid stability study was conducted by means of 1D SIMSEN simulation for the 2 x 395 MW PSPP Frades 2 in Portugal with both fixed speed and variable speed technologies in case of operation connected to an infinite power network or to an islanded 4.4 GW synchronous power network., Comment: 20 pages, Proceedings of 21th International Seminar on Hydropower Plants, 9-11 November 2022, Vienna, Austria

We present FuSeBMC-AI, a test generation tool grounded in machine learning techniques. FuSeBMC-AI extracts various features from the program and employs support vector machine and neural network models to predict a hybrid approach optimal configuration. FuSeBMC-AI utilizes Bounded Model Checking and Fuzzing as back-end verification engines. FuSeBMC-AI outperforms the default configuration of the underlying verification engine in certain cases while concurrently diminishing resource consumption.