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Graphs have emerged as an important foundation for a variety of applications, including capturing and reasoning over factual knowledge, semantic data integration, social networks, and providing factual knowledge for machine learning algorithms. To formalise certain properties of the data and to ensure data quality, there is a need to describe the schema of such graphs. Because of the breadth of applications and availability of different data models, such as RDF and property graphs, both the Semantic Web and the database community have independently developed graph schema languages: SHACL, ShEx, and PG-Schema. Each language has its unique approach to defining constraints and validating graph data, leaving potential users in the dark about their commonalities and differences. In this paper, we provide formal, concise definitions of the core components of each of these schema languages. We employ a uniform framework to facilitate a comprehensive comparison between the languages and identify a common set of functionalities, shedding light on both overlapping and distinctive features of the three languages., Comment: To be published at WWW 2025

The COVID-19 pandemic caused widespread disruptions to education, with school closures affecting over one billion children. These closures, aimed at reducing virus transmission, resulted in significant learning losses, particularly in mathematics and science. Using data from TIMSS 2023, which assesses fourth and eighth-grade achievements across 71 education systems, this study analyzes the impact of school closure duration on learning outcomes. Mixed-effect models estimate deviations from pre-pandemic trends, adjusting for demographic factors. Results show a global average decline of 0.11 standard deviations (SD) in student achievement, with longer closures linked to more severe losses. The effects on low performers, girls, and linguistic minorities show effect sizes up to 0.22 SD. These findings highlight the lasting impact of school closures and emphasize the need for targeted recovery strategies and international cooperation to promote equitable educational outcomes post-pandemic.

It is well known that the usefulness of a machine learning model is due to its ability to generalize to unseen data. This study uses three popular cyberbullying datasets to explore the effects of data, how it's collected, and how it's labeled, on the resulting machine learning models. The bias introduced from differing definitions of cyberbullying and from data collection is discussed in detail. An emphasis is made on the impact of dataset expansion methods, which utilize current data points to fetch and label new ones. Furthermore, explicit testing is performed to evaluate the ability of a model to generalize to unseen datasets through cross-dataset evaluation. As hypothesized, the models have a significant drop in the Macro F1 Score, with an average drop of 0.222. As such, this study effectively highlights the importance of dataset curation and cross-dataset testing for creating models with real-world applicability. The experiments and other code can be found at https://github.com/rootdrew27/cyberbullying-ml., Comment: 8 pages, 6 figures

Motivated by the spectral theory of relativistic atoms, we prove matching upper and lower bounds for the transition density of Hardy perturbations of subordinated Bessel heat kernels. The analysis is based on suitable supermedian functions, in particular invariant functions., Comment: 59 pages

Modelling the scrape-off layer of a stellarator is challenging due to the complex magnetic 3D geometry. The here presented study analyses simulations of the scrape-off layer (SOL) of the stellarator Wendelstein 7-X (W7-X) using spatially varying diffusion coefficients for the magnetic standard configuration, extending our previous study. Comparing the EMC3-Eirene simulations with experimental observations, an inconsistency between the strike-line width (SLW) and the upstream parameters was observed. While to match the experimental SLW a particle diffusion coefficient $D \approx 0.2$ is needed, $D \approx 1$ is needed to get experimental separatrix temperatures of 50\,eV at the given experimental heating power. We asses the impact of physically motivated spatially varying transport coeffients. Agreement with experimental data can be improved, but various differences remain. We show that drifts are expected to help overcome the discrepancies and, thus, the development of SOL transport models including drifts is a necessary next step to study the SOL transport of the W7-X stellarator., Comment: 15 pages, 13 figures. arXiv admin note: substantial text overlap with arXiv:2201.06341

Transparency rendering is problematic and can be considered an open problem in real-time graphics. There are many different algorithms currently available, but handling complex scenes and achieving accurate, glitch-free results is still costly. This paper describes LucidRaster: a software rasterizer running on a GPU which allows for efficient exact rendering of complex transparent scenes. It uses a new two-stage sorting technique and sample accumulation method. On average it's faster than high-quality OIT approximations and only about 3x slower than hardware alpha blending. It can be very efficient especially when rendering scenes with high triangle density or high depth complexity., Comment: 15 pages, 21 figures

Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan of equipment, and prevent failures. A wide range of PdM tasks can be performed using Artificial Intelligence (AI) methods, which often use data generated from industrial sensors. The steel industry, which is an important branch of the global economy, is one of the potential beneficiaries of this trend, given its large environmental footprint, the globalized nature of the market, and the demanding working conditions. This survey synthesizes the current state of knowledge in the field of AI-based PdM within the steel industry and is addressed to researchers and practitioners. We identified 219 articles related to this topic and formulated five research questions, allowing us to gain a global perspective on current trends and the main research gaps. We examined equipment and facilities subjected to PdM, determined common PdM approaches, and identified trends in the AI methods used to develop these solutions. We explored the characteristics of the data used in the surveyed articles and assessed the practical implications of the research presented there. Most of the research focuses on the blast furnace or hot rolling, using data from industrial sensors. Current trends show increasing interest in the domain, especially in the use of deep learning. The main challenges include implementing the proposed methods in a production environment, incorporating them into maintenance plans, and enhancing the accessibility and reproducibility of the research., Comment: Preprint submitted to Engineering Applications of Artificial Intelligence

This paper analyzes the neutral fluxes in the divertor region of the W7-X standard configuration for different input powers, both under attached and detached conditions. The performed analysis is conducted through EMC3-EIRENE simulations. They show the importance of the horizontal divertor to generate neutrals, and resolve the neutral plugging in the divertor region. Simulations of detached cases show a decrease in the number of generated neutrals compared to the attached simulations, in addition to a higher fraction of the ion flux arriving on the baffles during detachment. As the ionization takes place further inside the plasma during detachment, a larger percentage of the generated neutral particles leave the divertor as neutrals. The leakage in the poloidal and toroidal direction increases, just as the fraction of collected particles at the pumping gap. The fraction of pumped particles increases with a factor two, but stays below one percent. This demonstrates that detachment with the current target geometry, although it improves the power exhaust, is not yet leading to an increased particle exhaust.

We provide sharp two-sided estimates of the heat kernel of the Dirichlet fractional Laplacian on the half-line perturbed by the Hardy potential., Comment: 20 pages

As software projects rapidly evolve, software artifacts become more complex and defects behind get harder to identify. The emerging Transformer-based approaches, though achieving remarkable performance, struggle with long code sequences due to their self-attention mechanism, which scales quadratically with the sequence length. This paper introduces SparseCoder, an innovative approach incorporating sparse attention and learned token pruning (LTP) method (adapted from natural language processing) to address this limitation. Compared to previous state-of-the-art models CodeBERT, RoBERTa, and CodeT5, our experiments demonstrate that SparseCoder can handle significantly longer input sequences--at least twice as long, within the limits of our hardware resources and data statistics. Additionally, SparseCoder is four times faster than other methods measured in runtime, achieving a 50% reduction in floating point operations per second (FLOPs) with a negligible performance drop of less than 1% compared to Transformers using sparse attention (Sparse Atten). Plotting FLOPs of model inference against token lengths reveals that SparseCoder scales linearly, whereas other methods, including the current state-of-the-art model CodeT5, scale quadratically. Moreover, SparseCoder enhances interpretability by visualizing non-trivial tokens layer-wise., Comment: 34 pages, 9 figures, pre-print

Biology is perhaps the most complex of the sciences, given the incredible variety of chemical species that are interconnected in spatial and temporal pathways that are daunting to understand. Their interconnections lead to emergent properties such as memory, consciousness, and recognition of self and non-self. To understand how these interconnected reactions lead to cellular life characterized by activation, inhibition, regulation, homeostasis, and adaptation, computational analyses and simulations are essential, a fact recognized by the biological communities. At the same time, students struggle to understand and apply binding and kinetic analyses for the simplest reactions such as the irreversible first-order conversion of a single reactant to a product. This likely results from cognitive difficulties in combining structural, chemical, mathematical, and textual descriptions of binding and catalytic reactions. To help students better understand dynamic reactions and their analyses, we have introduced two kinds of interactive graphs and simulations into the online educational resource, Fundamentals of Biochemistry, a multivolume biochemistry textbook that is part of the LibreText collection. One type is available for simple binding and kinetic reactions. The other displays progress curves (concentrations vs time) for both simple reactions and more complex metabolic and signal transduction pathways, including those available through databases using systems biology markup language (SBML) files. Users can move sliders to change dissociation and kinetic constants as well as initial concentrations and see instantaneous changes in the graphs. They can also export data into a spreadsheet for further processing, such as producing derivative Lineweaver-Burk and traditional Michaelis-Menten graphs of initial velocity (v0) vs substrate concentration., Comment: 17 pages, 2 tables, 8 figures. Submitted to Biochemistry and Molecular Biology Education. Funding: MiniSidewinder: NIH/NIGMS (Grant R01-GM123032-04) LibreText: Department of Education Open Textbook Pilot Project, the UC Davis Office of the Provost, the UC Davis Library, the California State University Affordable Learning Solutions Program, and Merlot

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research., Comment: 51 pages, 9 figures

Dzyaloshinskii-Moriya interaction (DMI) manifesting in asymmetric layered ferromagnetic films gives rise to non-colinear spin structures stabilizing magnetization configurations with nontrivial topology. In this work magnetization reversal, domain structure, and strength of DMI are related with the structure of W/Co/Pt multilayers grown by molecular beam epitaxy. Applied growth method enables fabrication of layered systems with higher crystalline quality than commonly applied sputtering techniques. As a result, a high value of D coefficient was determined from the aligned magnetic domain stripe structure, substantially exceeding 2 mJ/m2. The highest value of DMI value D$_{eff}$ = 2.64mj/m2 and strength of surface DMI parameter DS = 1.83pJ/m for N=10 has been observed. Experimental results coincide precisely with those obtained from structure based micromagnetic modelling and density functional theory calculations performed for well-defined layered stacks. This high value of DMI strength originates from dominating contributions of the interfacial atomic Co layers and additive character from both interface types.

SHACL is a W3C-proposed schema language for expressing structural constraints on RDF graphs. Recent work on formalizing this language has revealed a striking relationship to description logics. SHACL expressions can use three fundamental features that are not so common in description logics. These features are equality tests; disjointness tests; and closure constraints. Moreover, SHACL is peculiar in allowing only a restricted form of expressions (so-called targets) on the left-hand side of inclusion constraints. The goal of this paper is to obtain a clear picture of the impact and expressiveness of these features and restrictions. We show that each of the four features is primitive: using the feature, one can express boolean queries that are not expressible without using the feature. We also show that the restriction that SHACL imposes on allowed targets is inessential, as long as closure constraints are not used. In addition, we show that enriching SHACL with "full" versions of equality tests, or disjointness tests, results in a strictly more powerful language.

The formation of the radial electric field, $E_{\rm r}$ in the SOL has been experimentally studied for attached divertor conditions in stellarator W7-X. The main objective of this study is to test the validity in a complex 3D island divertor of simple models, typically developed in tokamaks, relating $E_{\rm r}$ to the sheath potential drop gradient at the target. Additionally, we investigate the effect of the edge $E_{\rm r}$ shear on the reduction of density fluctuation amplitude, a well-established phenomenon according to the existing bibliography. The main diagnostic for measurements in the SOL is a V-band Doppler reflectometer that can provide the measurement of the $E_{\rm r}$ and density fluctuations with good spatial resolution. Three-dimensional measurements of divertor parameters has been carried out using infrared cameras, with $\lambda_{\rm q}$ resulting a suitable proxy for the model-relevant $\lambda_{\rm T}$. In the investigated attached regimes, it is shown for the first time that the formation of the $E_{\rm r}$ in the SOL depends on parameters at the divertor, following a $E_{\rm r} \propto T_{\rm e} /\lambda_q$ qualitatively similar to that found in a tokamak. Then, from the analyzed plasmas, the observed $E_{\rm r}$ shear at the edge is linked to a moderate local reduction of the amplitude of density fluctuations.

In this study, we develop a multi criteria model to identify dengue outbreak periods. To validate the model, we performed a simulation using dengue transmission-related data in Sri Lanka's Western Province. Our results indicated that the developed model can be used to predict a dengue outbreak situation in a given region up to one month in advance.

We obtain Gr\"onwall type estimates for the gradient of the harmonic functions for a L\'evy operator with order strictly larger than 1 and minimal assumptions of its L\'evy measure.

We give local in time sharp two sided estimates of the heat kernel associated with the relativistic stable operator perturbed by a critical (Hardy) potential.

We consider non-local Schr\"odinger operators $H=-L-V$ in $L^2(\mathbf{R}^d)$, $d \geq 1$, where the kinetic terms $L$ are pseudo-differential operators which are perturbations of the fractional Laplacian by bounded non-local operators and $V$ is the fractional Hardy potential. We prove pointwise estimates of eigenfunctions corresponding to negative eigenvalues and upper finite-time horizon estimates for heat kernels. We also analyze the relation between the matching lower estimates of the heat kernel and the ground state near the origin. Our results cover the relativistic Schr\"odinger operator with Coulomb potential., Comment: 28 pages

We consider the fractional Burgers equation $ \Delta^{\alpha/2} u + b\cdot \nabla (u|u|^{(\alpha-1)/\beta})$ on ${\mathbf R}^d$, $d\geq2$, with {$\alpha \in (1,2)$ and} $\beta>1$ and prove the existence of a solution for a large class of initial conditions, which contains functions that do not belong to any $L^p({\mathbf R}^d)$, $1\leq p\leq\infty$. Next, we apply the general results to the initial condition $u_0(x)=M|x|^{-\beta}$, $1<\beta

A study of time homogeneous, real valued Markov processes with a special property and a non-atomic initial distribution is provided. The new notion of a function of evolution of distribution which determines the dependency between one dimensional distributions of a process is introduced. This, along with the notion of bridge operators which determine the backward structure, as opposed to the forward structure determined by the usual semi-group operators, paves a way to the new approach for dealing with finite-dimensional distributions of Markov processes. This, in particular, produces explicit formulas which effectively simplify the computations of finite-dimensional distributions, giving an alternative to the standard approach based on computations using the chain rule of transition densities. Various examples illustrating the new approach are presented.

We give ground-state representation for the fractional Laplacian with Dirichlet condition on the half-line

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line, and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML, and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations., Comment: 6 pages, 2 figures

We describe the large-time asymptotics of solutions to the heat equation for the fractional Laplacian with added subcritical or even critical Hardy-type potential. The asymptotics is governed by a self-similar solution of the equation, obtained as a normalized limit at the origin of the kernel of the corresponding Feynman-Kac semigroup., Comment: 27 pages, corrected exponent in Theorem 4.6

Modelling the scrape-off layer of a stellarator is challenging due to the complex magnetic 3D geometry. The here presented study analyses simulations of the scrape-off layer (SOL) of the stellarator Wendelstein 7-X (W7-X) using the EMC3-EIRENE code for the magnetic standard configuration. Comparing with experimental observations, the transport model is validated. Based on the experimentally observed strike line width, the anomalous transport coefficients, used as input to the code are determined to around $0.2\,$m$^2/$s. This is however in disagreement with upstream measurements, where such small cross-field transport leads to temperatures higher than measured experimentally.

In constraint languages for RDF graphs, such as ShEx and SHACL, constraints on nodes and their properties in RDF graphs are known as "shapes". Schemas in these languages list the various shapes that certain targeted nodes must satisfy for the graph to conform to the schema. Using SHACL, we propose in this paper a novel use of shapes, by which a set of shapes is used to extract a subgraph from an RDF graph, the so-called shape fragment. Our proposed mechanism fits in the framework of Linked Data Fragments. In this paper, (i) we define our extraction mechanism formally, building on recently proposed SHACL formalizations; (ii) we establish correctness properties, which relate shape fragments to notions of provenance for database queries; (iii) we compare shape fragments with SPARQL queries; (iv) we discuss implementation options; and (v) we present initial experiments demonstrating that shape fragments are a feasible new idea.

A recent characterization of core turbulence carried out with a Doppler reflectometer in the optimized stellarator Wendelstein 7-X (W7-X) found that discharges achieving high ion temperatures at the core featured an ITG-like suppression of density fluctuations driven by a reduction of the gradient ratio $\eta_i = L_n/L_{T_i}$ [D. Carralero et al., Nucl. Fusion, 2021]. In order to confirm the role of ITG turbulence in this process, we set out to establish experimentally the relation between core density fluctuations, turbulent heat flux and global confinement. With this aim, we consider the scenarios found in the previous work and carry out power balance analysis for a number of representative ones, including some featuring high ion temperature. As well, we evaluate the global energy confinement time and discuss it in the context of the ISS04 inter-stellarator scaling. We find that, when turbulence is suppressed as a result of a reduction of $\eta_i$, there is a reduction of ion turbulent transport, and global performance is improved as a result. This is consistent with ITG turbulence limiting the ion temperature at the core of W7-X. In contrast, when turbulence is reduced following a decrease in collisionality, no changes are observed in transport or confinement. This could be explained by ITG modes being combined with TEM turbulence when the later is destabilized at low collisionalities., Comment: Submitted to PPCF, 47th EPS special issue

SHACL is a W3C-proposed language for expressing structural constraints on RDF graphs. The recommendation only specifies semantics for non-recursive SHACL; recently, some efforts have been made to allow recursive SHACL schemas. In this paper, we argue that for defining and studying semantics of recursive SHACL, lessons can be learned from years of research in non-monotonic reasoning. We show that from a SHACL schema, a three-valued semantic operator can directly be obtained. Building on Approximation Fixpoint Theory (AFT), this operator immediately induces a wide variety of semantics, including a supported, stable, and well-founded semantics, related in the expected ways. By building on AFT, a rich body of theoretical results becomes directly available for SHACL. As such, the main contribution of this short paper is providing theoretical foundations for the study of recursive SHACL, which can later enable an informed decision for an extension of the W3C recommendation., Comment: In Proceedings ICLP 2021, arXiv:2109.07914

SHACL is a W3C-proposed language for expressing structural constraints on RDF graphs. In recent years, SHACL's popularity has risen quickly. This rise in popularity comes with questions related to its place in the semantic web, particularly about its relation to OWL (the de facto standard for expressing ontological information on the web) and description logics (which form the formal foundations of OWL). We answer these questions by arguing that SHACL is in fact a description logic. On the one hand, our answer is surprisingly simple, some might even say obvious. But, on the hand, our answer is also controversial. By resolving this issue once and for all, we establish the field of description logics as the solid formal foundations of SHACL., Comment: Presented at LPNRM conference 2022

For the fractional Laplacian we give Hardy inequality which is optimal in $L^p$ for $1

This work introduces interactive traffic scenarios in the CARLA simulator, which are based on real-world traffic. We concentrate on tactical tasks lasting several seconds, which are especially challenging for current control methods. The CARLA Real Traffic Scenarios (CRTS) is intended to be a training and testing ground for autonomous driving systems. To this end, we open-source the code under a permissive license and present a set of baseline policies. CRTS combines the realism of traffic scenarios and the flexibility of simulation. We use it to train agents using a reinforcement learning algorithm. We show how to obtain competitive polices and evaluate experimentally how observation types and reward schemes affect the training process and the resulting agent's behavior.

We investigate whether a fundamental solution of the Schr\"odinger equation $\partial_t u =(\Delta +V)\, u$ has local in time sharp Gaussian estimates. We compare that class with the class of $V$ for which local in time plain Gaussian estimates hold. We concentrate on $V$ that have fixed sign and we present certain conclusions for $V$ in the Kato class.

The manifold hypothesis suggests that word vectors live on a submanifold within their ambient vector space. We argue that we should, more accurately, expect them to live on a pinched manifold: a singular quotient of a manifold obtained by identifying some of its points. The identified, singular points correspond to polysemous words, i.e. words with multiple meanings. Our point of view suggests that monosemous and polysemous words can be distinguished based on the topology of their neighbourhoods. We present two kinds of empirical evidence to support this point of view: (1) We introduce a topological measure of polysemy based on persistent homology that correlates well with the actual number of meanings of a word. (2) We propose a simple, topologically motivated solution to the SemEval-2010 task on Word Sense Induction & Disambiguation that produces competitive results., Comment: Accepted at the 9th Joint Conference on Lexical and Computational Semantics (*SEM 2020)

Many unsupervised representation learning methods belong to the class of similarity learning models. While various modality-specific approaches exist for different types of data, a core property of many methods is that representations of similar inputs are close under some similarity function. We propose EMDE (Efficient Manifold Density Estimator) - a framework utilizing arbitrary vector representations with the property of local similarity to succinctly represent smooth probability densities on Riemannian manifolds. Our approximate representation has the desirable properties of being fixed-size and having simple additive compositionality, thus being especially amenable to treatment with neural networks - both as input and output format, producing efficient conditional estimators. We generalize and reformulate the problem of multi-modal recommendations as conditional, weighted density estimation on manifolds. Our approach allows for trivial inclusion of multiple interaction types, modalities of data as well as interaction strengths for any recommendation setting. Applying EMDE to both top-k and session-based recommendation settings, we establish new state-of-the-art results on multiple open datasets in both uni-modal and multi-modal settings.

In this work we present the first measurements obtained by the V-band Doppler reflectometer during the second operation phase of Wendelstein 7-X to discuss the influence in the velocity shear layer and the radial electric field, E$_r$, of several plasma parameters such as magnetic configuration, rotational transform or degree of detachment. In the first place, we carry out a systematic characterization of the turbulence rotation velocity profile in order to describe the influence of density and heating power on E$_r$ under the four most frequent magnetic configurations. The $|$E$_r|$ value in the edge is found to increase with configurations featuring higher $\iota$, although this does not apply for the high mirror configuration, KJM. As well, the E$_r$ value in the SOL and the velocity shear near the separatrix are found to display a clear dependence on heating power and density for all configurations. For a number of relevant cases, these results are assessed by comparing them to neoclassical predictions obtained from the codes DKES and KNOSOS, finding generally good agreement with experimental results. Finally, the evolution of E$_r$ at the edge is evaluated throughout the island-divertor detachment regime achieved for the first time in the 2018 campaign. After detachment, $|$E$_r|$ is reduced both at the SOL and edge, and the plasma column shrinks, with the shear layer seemingly moving radially inwards from the separatrix., Comment: Sent for publication to Nuclear Fusion