Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding Software Vulnerabilities in Open-Source C Projects via Bounded Model Checking

Computer-based systems have solved several domain problems, including industrial, military, education, and wearable. Nevertheless, such arrangements need high-quality software to guarantee security and safety as both are mandatory for modern software products. We advocate that bounded model-checking techniques can efficiently detect vulnerabilities in general software systems. However, such an approach struggles to scale up and verify extensive code bases. Consequently, we have developed and evaluated a methodology to verify large software systems using a state-of-the-art bounded model checker. In particular, we pre-process input source-code files and guide the respective model checker to explore them systematically. Moreover, the proposed scheme includes a function-wise prioritization strategy, which readily provides results for code entities according to a scale of importance. Experimental results using a real implementation of the proposed methodology show that it can efficiently verify large software systems. Besides, it presented low peak memory allocation when executed. We have evaluated our approach by verifying twelve popular open-source C projects, where we have found real software vulnerabilities that their developers confirmed.
RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information

As the IT industry advances, system log data becomes increasingly crucial. Many computer systems rely on log texts for management due to restricted access to source code. The need for log anomaly detection is growing, especially in real-world applications, but identifying anomalies in rapidly accumulating logs remains a challenging task. Traditional deep learning-based anomaly detection models require dataset-specific training, leading to corresponding delays. Notably, most methods only focus on sequence-level log information, which makes the detection of subtle anomalies harder, and often involve inference processes that are difficult to utilize in real-time. We introduce RAPID, a model that capitalizes on the inherent features of log data to enable anomaly detection without training delays, ensuring real-time capability. RAPID treats logs as natural language, extracting representations using pre-trained language models. Given that logs can be categorized based on system context, we implement a retrieval-based technique to contrast test logs with the most similar normal logs. This strategy not only obviates the need for log-specific training but also adeptly incorporates token-level information, ensuring refined and robust detection, particularly for unseen logs. We also propose the core set technique, which can reduce the computational cost needed for comparison. Experimental results show that even without training on log data, RAPID demonstrates competitive performance compared to prior models and achieves the best performance on certain datasets. Through various research questions, we verified its capability for real-time detection without delay.
Solving Combinatorial Optimization Problems in Parallel - Methods and Techniques

SCOOP: Solving Combinatorial Optimization problems in parallel. - Parallel approximation of optimization problems. - Randomized parallel algorithms. - Automatic synthesis of parallel algorithms. - An introduction to parallel dynamic programming. - Mapping tree-structured combinatorial optimization problems onto parallel computers. - Towards an abstract parallel branch and bound machine. - Parallel best-first branch- and-bound in discrete optimization: A framework. - Building a parallel branch and bound library. - Parallel algorithms for global optimization problems. - Parallel heuristic search - Introductions and a new approach.
Campostriniite, (Bi3+,Na)3(NH4,K)2Na2(SO4)6·H2O, a new sulfate isostructural with görgeyite, from La Fossa Crater, Vulcano, Aeolian Islands, Italy

Abstract The new mineral campostriniite, (Bi3+,Na)3(NH4,K)2Na2(SO4)6·H2O, was found in an active fumarole (fumarole FA, temperature ~350°C) at La Fossa Crater, Vulcano, Aeolian Islands, Sicily, Italy. It occurs on a pyroclastic breccia as white prismatic crystals up to 0.2 mm long, in association with adranosite, demicheleite-(Br), demicheleite-(I), argesite and sassolite. The mineral is monoclinic, space group: C2/c (no. 15) with a = 17.748(3), b = 6.982(1) c = 18.221(3) Å, β = 113.97(1)°, V = 2063(1) Å3 and Z = 4. The six strongest reflections in the powder X-ray diffraction pattern are: [dobs in Å (I)(hkl)] 6.396(100)(110), 7.507(75)(2̄02), 2.766(60)(3̄16), 3.380(57)(312), 5.677(55)(111), 3.166(50)(4 0 2). The empirical formula (based on 25 anions p.f.u.) is Bi2.41N1. 52Na2.41K0. 48 S6.07H8. 08O25. The calculated density is 3.87 g cm-3. Using single-crystal diffraction data, the structure was refined to a final R(F) = 0.051 for 3025 independent observed reflections [I > 2σ(I)]. Campostriniite is isostructural with görgeyite and belongs to the 7.CD group of the Strunz classification system. The structure contains two independent nine-fold coordinated sites, one of them located on a two-fold axis (M1) and the other one in general position (M2) essentially occupied by Bi3+ atoms and minor amounts of Na+ ions, an eight-fold coordinated site fully occupied by Na+ ions and another eight-fold coordinated site occupied by NH+4 and K+ ions; three independent sulfate anions in a general position and a water molecule coordinated to M1 and located on a two-fold axis are also present.
Conformal diagrams for stationary and dynamical strong-field hyperboloidal slices

Conformal Carter-Penrose diagrams are used for the visualization of hyperboloidal slices, which are smooth spacelike slices reaching null infinity. The focus is on the Schwarzschild black hole geometry in spherical symmetry, whose Penrose diagrams are introduced in a pedagogical way. The stationary regime involves time-independent slices. In this case, different options are given for integrating the height function -- the main ingredient for constructing hyperboloidal foliations. The dynamical regime considers slices changing in time, which are evolved together with the spacetime using the eikonal equation. It includes the relaxation of hyperboloidal Schwarzschild trumpet slices and the collapse of a massless scalar field into a black hole, for which Penrose diagrams are presented.
The Complexity of Checking Non-Emptiness in Symbolic Tree Automata

We study the satisfiability problem of symbolic tree automata and decompose it into the satisfiability problem of the existential first-order theory of the input characters and the existential monadic second-order theory of the indices of the accepted words. We use our decomposition to obtain tight computational complexity bounds on the decision problem for this automata class and an extension that considers linear arithmetic constraints on the underlying effective Boolean algebra
Outlier-Robust Wasserstein DRO

Distributionally robust optimization (DRO) is an effective approach for data-driven decision-making in the presence of uncertainty. Geometric uncertainty due to sampling or localized perturbations of data points is captured by Wasserstein DRO (WDRO), which seeks to learn a model that performs uniformly well over a Wasserstein ball centered around the observed data distribution. However, WDRO fails to account for non-geometric perturbations such as adversarial outliers, which can greatly distort the Wasserstein distance measurement and impede the learned model. We address this gap by proposing a novel outlier-robust WDRO framework for decision-making under both geometric (Wasserstein) perturbations and non-geometric (total variation (TV)) contamination that allows an $\varepsilon$-fraction of data to be arbitrarily corrupted. We design an uncertainty set using a certain robust Wasserstein ball that accounts for both perturbation types and derive minimax optimal excess risk bounds for this procedure that explicitly capture the Wasserstein and TV risks. We prove a strong duality result that enables tractable convex reformulations and efficient computation of our outlier-robust WDRO problem. When the loss function depends only on low-dimensional features of the data, we eliminate certain dimension dependencies from the risk bounds that are unavoidable in the general setting. Finally, we present experiments validating our theory on standard regression and classification tasks.
The Glass Transition of Quantum Hard Spheres in High Dimensions

We study the equilibrium thermodynamics of quantum hard spheres in the infinite-dimensional limit, determining the boundary between liquid and glass phases in the temperature-density plane by means of the Franz-Parisi potential. We find that as the temperature decreases from high values, the effective radius of the spheres is enhanced by a multiple of the thermal de Broglie wavelength, thus increasing the effective filling fraction and decreasing the critical density for the glass phase. Numerical calculations show that the critical density continues to decrease monotonically as the temperature decreases further, suggesting that the system will form a glass at sufficiently low temperatures for any density.
Splitting the Conditional Gradient Algorithm

We propose a novel generalization of the conditional gradient (CG / Frank-Wolfe) algorithm for minimizing a smooth function $f$ under an intersection of compact convex sets, using a first-order oracle for $\nabla f$ and linear minimization oracles (LMOs) for the individual sets. Although this computational framework presents many advantages, there are only a small number of algorithms which require one LMO evaluation per set per iteration; furthermore, these algorithms require $f$ to be convex. Our algorithm appears to be the first in this class which is proven to also converge in the nonconvex setting. Our approach combines a penalty method and a product-space relaxation. We show that one conditional gradient step is a sufficient subroutine for our penalty method to converge, and we provide several analytical results on the product-space relaxation's properties and connections to other problems in optimization. We prove that our average Frank-Wolfe gap converges at a rate of $\mathcal{O}(\ln t/\sqrt{t})$, -- only a log factor worse than the vanilla CG algorithm with one set.
Measuring the Prevalence of WiFi Bottlenecks in Home Access Networks

As broadband Internet speeds continue to increase, the home wireless ("WiFi") network may more frequently become a performance bottleneck. Past research, now nearly a decade old, initially documented this phenomenon through indirect inference techniques, noting the prevalence of WiFi bottlenecks but never directly measuring them. In the intervening years, access network (and WiFi) speeds have increased, warranting a re-appraisal of this important question, particularly with renewed private and federal investment in access network infrastructure. This paper studies this question, developing a new system and measurement technique to perform direct measurements of WiFi and access network performance, ultimately collecting and analyzing a first-of-its-kind dataset of more than 13,000 joint measurements of WiFi and access network throughputs, in a real-world deployment spanning more than 50 homes, for nearly two years. Using this dataset, we re-examine the question of whether, when, and to what extent a user's home wireless network may be a performance bottleneck, particularly relative to their access connection. We do so by directly and continuously measuring the user's Internet performance along two separate components of the Internet path -- from a wireless client inside the home network to the wired point of access (e.g., the cable modem), and from the wired point of access to the user's ISP. Confirming and revising results from more than a decade ago, we find that a user's home wireless network is often the throughput bottleneck. In particular, for users with access links that exceed 800~Mbps, the user's home wireless network was the performance bottleneck 100% of the time.
Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot

Addressee Estimation is the ability to understand to whom a person is talking, a skill essential for social robots to interact smoothly with humans. In this sense, it is one of the problems that must be tackled to develop effective conversational agents in multi-party and unstructured scenarios. As humans, one of the channels that mainly lead us to such estimation is the non-verbal behavior of speakers: first of all, their gaze and body pose. Inspired by human perceptual skills, in the present work, a deep-learning model for Addressee Estimation relying on these two non-verbal features is designed, trained, and deployed on an iCub robot. The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction compared to previous tests on the dataset used for the training.
Turning food into a gastronomic experience: Olive oil tourism

Nowadays, gastronomy has taken on a new role associated with social, cultural, pleasure and entertainment needs and has become one of the main motivations for travel. Therefore, agriculture and food have become extremely important new drivers of tour ism in production areas. The positive relationship between gastronomy and tourist activity is an economic catalyst for growth in specific territories, promoting local products and providing added value to the tourist. In this context, gastronomic tourism is an excellent business opportunity for Spain if the country is able to capitalize on the development of high-quality agriculture and food, especially related to olive oil. This paper analyses the state of olive oil tourism in Spain. Based on qualitative interviews with gastronomic tourism experts, and specifically olive oil tourism experts, this paper proposes a series of recommendations to guide the design and improvement of olive oil based tourism products and aid the creation of related experiences.
A Framework to Assess (Dis)agreement Among Diverse Rater Groups

Recent advancements in conversational AI have created an urgent need for safety guardrails that prevent users from being exposed to offensive and dangerous content. Much of this work relies on human ratings and feedback, but does not account for the fact that perceptions of offense and safety are inherently subjective and that there may be systematic disagreements between raters that align with their socio-demographic identities. Instead, current machine learning approaches largely ignore rater subjectivity and use gold standards that obscure disagreements (e.g., through majority voting). In order to better understand the socio-cultural leanings of such tasks, we propose a comprehensive disagreement analysis framework to measure systematic diversity in perspectives among different rater subgroups. We then demonstrate its utility by applying this framework to a dataset of human-chatbot conversations rated by a demographically diverse pool of raters. Our analysis reveals specific rater groups that have more diverse perspectives than the rest, and informs demographic axes that are crucial to consider for safety annotations.
From Stability to Change: The Potential Application of Bifurcation Theory to Opinion Dynamics Considerations

This study adopts a nonlinear dynamics approach, specifically using bifurcation theory, to analyze social interactions and behavior in online communities. Referencing key works by Steven Strogatz and others, the paper explores the application of pitchfork, saddle node, and transcritical bifurcations to model collective opinion shifts and trend diffusion in social media. By integrating Strogatz's insights on complex networks and synchronization with the foundational theories of Guckenheimer, Holmes, Kuznetsov, and Wiggins, the study examines the role of small-world network effects and synchronization in collective behavior. Seidel's practical take on bifurcation theory helps apply these mathematical concepts to social science research, aiming to shed light on the dynamics of digital communication and the rapid spread of information. The research also touches on the use of Melnikov's method for analyzing the stability of homoclinic and heteroclinic orbits, and the onset of chaos, reflecting on the broader implications for understanding complex systems in nature and society. The goal is to provide a model that captures the swift and intricate cognitive processes of individuals in digital ecosystems, offering a fresh perspective on the evolution of online collective opinion, the rise and spread of fads, and behavioral changes in digital contexts.
Lithium-ion battery performance model including solvent segregation effects

A model of a lithium-ion battery containing a cosolvent electrolyte is developed and implemented within the open-source PyBaMM platform. Lithium-ion electrolytes are essential to battery operation and normally contain at least two solvents to satisfy performance requirements. The widely used Doyle-Fuller-Newman battery model assumes that the electrolyte comprises a salt dissolved in a single effective solvent, however. This single-solvent approximation has been disproved experimentally and may hinder accurate battery modelling. Here, we present a two-solvent model that resolves the transport of ethylene carbonate (EC) and lithium salt in a background linear carbonate. EC concentration polarization opposes that of Li+ during cycling, affecting local electrolyte properties and cell-level overpotentials. Concentration gradients of Li+ can be affected by cross-diffusion, whereby EC gradients enhance or impede salt flux. A rationally parametrized model that includes EC transport predicts 6% more power loss at 4.5C discharge and ~0.32% more capacity loss after a thousand 1C cycles than its single-solvent equivalent. This work provides a tool to model more transport behaviour in the electrolyte that may affect degradation and enables the transfer of microscopic knowledge about solvation structure-dependent performance to the macroscale.
Object-centric Cross-modal Feature Distillation for Event-based Object Detection

Event cameras are gaining popularity due to their unique properties, such as their low latency and high dynamic range. One task where these benefits can be crucial is real-time object detection. However, RGB detectors still outperform event-based detectors due to the sparsity of the event data and missing visual details. In this paper, we develop a novel knowledge distillation approach to shrink the performance gap between these two modalities. To this end, we propose a cross-modality object detection distillation method that by design can focus on regions where the knowledge distillation works best. We achieve this by using an object-centric slot attention mechanism that can iteratively decouple features maps into object-centric features and corresponding pixel-features used for distillation. We evaluate our novel distillation approach on a synthetic and a real event dataset with aligned grayscale images as a teacher modality. We show that object-centric distillation allows to significantly improve the performance of the event-based student object detector, nearly halving the performance gap with respect to the teacher.
Single-shot Tomography of Discrete Dynamic Objects

This paper presents a novel method for the reconstruction of high-resolution temporal images in dynamic tomographic imaging, particularly for discrete objects with smooth boundaries that vary over time. Addressing the challenge of limited measurements per time point, we propose a technique that synergistically incorporates spatial and temporal information of the dynamic objects. This is achieved through the application of the level-set method for image segmentation and the representation of motion via a sinusoidal basis. The result is a computationally efficient and easily optimizable variational framework that enables the reconstruction of high-quality 2D or 3D image sequences with a single projection per frame. Compared to current methods, our proposed approach demonstrates superior performance on both synthetic and pseudo-dynamic real X-ray tomography datasets. The implications of this research extend to improved visualization and analysis of dynamic processes in tomographic imaging, finding potential applications in diverse scientific and industrial domains.
Improved DDIM Sampling with Moment Matching Gaussian Mixtures

We propose using a Gaussian Mixture Model (GMM) as reverse transition operator (kernel) within the Denoising Diffusion Implicit Models (DDIM) framework, which is one of the most widely used approaches for accelerated sampling from pre-trained Denoising Diffusion Probabilistic Models (DDPM). Specifically we match the first and second order central moments of the DDPM forward marginals by constraining the parameters of the GMM. We see that moment matching is sufficient to obtain samples with equal or better quality than the original DDIM with Gaussian kernels. We provide experimental results with unconditional models trained on CelebAHQ and FFHQ and class-conditional models trained on ImageNet datasets respectively. Our results suggest that using the GMM kernel leads to significant improvements in the quality of the generated samples when the number of sampling steps is small, as measured by FID and IS metrics. For example on ImageNet 256x256, using 10 sampling steps, we achieve a FID of 6.94 and IS of 207.85 with a GMM kernel compared to 10.15 and 196.73 respectively with a Gaussian kernel.
The Strength and Variability of the Helium 10830 Å Triplet in Young Stars, with Implications for Exosphere Detection

Young exoplanets trace planetary evolution, in particular the atmospheric mass loss that is most dynamic in youth. However, the high activity level of young stars can mask or mimic the spectroscopic signals of atmospheric mass loss. This includes the activity-sensitive He 10830 Å triplet, which is an increasingly important exospheric probe. To characterize the He-10830 triplet at young ages, we present time-series NIR spectra for young transiting planet hosts taken with the Habitable-zone Planet Finder. The He-10830 absorption strength is similar across our sample, except at the fastest and slowest rotations, indicating that young chromospheres are dense and populate metastable helium via collisions. Photoionization and recombination by coronal radiation only dominates metastable helium population at the active and inactive extremes. Volatile stellar activity, such as flares and changing surface features, drives variability in the He-10830 triplet. Variability is largest at the youngest ages before decreasing to ≲5–10 mÅ (or 3%) at ages above 300 Myr, with six of eight stars in this age range agreeing with there being no intrinsic variability. He-10830 triplet variability is smallest and age-independent at the shortest timescales. Intrinsic stellar variability should not preclude detection of young exospheres, except at the youngest ages. We recommend out-of-transit comparison observations taken directly surrounding transit and observation of multiple transits to minimize activity’s effect. Regardless, caution is necessary when interpreting transit observations in the context of stellar activity, as many scenarios can lead to enhanced stellar variability even on timescales of an hour.