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The growing capabilities of AI models are leading to their wider use, including in safety-critical domains. Explainable AI (XAI) aims to make these models safer to use by making their inference process more transparent. However, current explainability methods are seldom evaluated in the way they are intended to be used: by real-world end users. To address this, we conducted a large-scale user study with 85 healthcare practitioners in the context of human-AI collaborative chest X-ray analysis. We evaluated three types of explanations: visual explanations (saliency maps), natural language explanations, and a combination of both modalities. We specifically examined how different explanation types influence users depending on whether the AI advice and explanations are factually correct. We find that text-based explanations lead to significant over-reliance, which is alleviated by combining them with saliency maps. We also observe that the quality of explanations, that is, how much factually correct information they entail, and how much this aligns with AI correctness, significantly impacts the usefulness of the different explanation types., Comment: EMNLP 2024

In the pursuit of alternative and less invasive medical treatments, magnetic nanoparticles (NPs) have gained significant relevance. Iron carbides NPs stand out for their higher saturation magnetizations compared to iron oxides, while maintaining a suitable biocompatibility. In this work, high control is achieved over the composition and morphology of Fe3C/C encapsulated core/shell nanoparticles through fine-tuning of the sol-gel synthesis parameters. Specifically, the impact of decreasing each surfactant concentration added, nt, the same both for oleylamine (ON) and oleic acid (OA), has been explored. A minimum value for such parameter denoted by nt,min. was required to produce pure Fe3C@C NP-composites. For nt < 4 mmol, some minor {\alpha}-Fe impurities arise, and the effective carburization becomes unstable due to insufficient carbon. The magnetic properties of the materials prepared were optimized by reducing the excess carbon from surfactants, resulting in saturation magnetization values of 86 emu/g. (for pure Fe3C at nt = 5 mmol) and 102 emu/g (for Fe3C and <2 % w.t. of {\alpha}-Fe impurity at nt = 4 mmol). In view of this, several cytotoxicity studies for different Fe3C@C samples were conducted, exhibiting excellent biocompatibility in cell-based assays, which could lead to potential application at the forefront of biomedical fields.

As small radiation fields play an ever-increasing role in radiation therapy, accurate dosimetry of these fields becomes critical to ensure high quality dose calculation and treatment optimization. The PRB-0002, a new plastic scintillation detector (PSD) part of the Hyperscint RP-200 dosimetric platform from Medscint, can potentially facilitate small field measurements. In this work our objective is twofold: first we perform a detailed characterization of the field output correction factors of the PRB-0002 over a wide range of field sizes; second we demonstrate how this PSD can be used to determine the field output correction factors for other small field detectors. Uncertainties were carefully considered and a detailed uncertainty budget is provided. EGSnrc Monte Carlo simulations were conducted to determine the impact of detector composition, surrounding materials, dose averaging within the sensitive volume as well as ionization quenching. From these simulations, the field output correction factors of this PSD were determined. Then, by experimental comparisons, field output correction factors for 2 solid state detectors and 3 small volume ion chambers were. With PRB-0002, the material composition was found to be balanced with the ionization quenching making the field output correction factor near unity. For fields between 0.6 $\times$ 0.6 and 30 $\times$ 30~cm$^2$, the field output correction factors of the PRB-0002 were between 1.002 and 0.999 with a total uncertainty of 0.5 %. Analysis of the uncertainty budget showed that an overall uncertainty of 0.59% can be achieved for a 1 $\times$ 1~cm$^2$ using PRB-0002. We conclude that PRB-0002 is a near-ideal detector for small field dosimetry. Furthermore, it can be used to experimentally determine the field output correction factors of other dosimeters with great accuracy.

Accurate depth estimation in endoscopy is vital for successfully implementing computer vision pipelines for various medical procedures and CAD tools. In this paper, we present the EndoDepth benchmark, an evaluation framework designed to assess the robustness of monocular depth prediction models in endoscopic scenarios. Unlike traditional datasets, the EndoDepth benchmark incorporates common challenges encountered during endoscopic procedures. We present an evaluation approach that is consistent and specifically designed to evaluate the robustness performance of the model in endoscopic scenarios. Among these is a novel composite metric called the mean Depth Estimation Robustness Score (mDERS), which offers an in-depth evaluation of a model's accuracy against errors brought on by endoscopic image corruptions. Moreover, we present SCARED-C, a new dataset designed specifically to assess endoscopy robustness. Through extensive experimentation, we evaluate state-of-the-art depth prediction architectures on the EndoDepth benchmark, revealing their strengths and weaknesses in handling endoscopic challenging imaging artifacts. Our results demonstrate the importance of specialized techniques for accurate depth estimation in endoscopy and provide valuable insights for future research directions.

In the field of microdroplet manipulation, optical tweezers have been used to form and grow droplets, to transport them, or to measure forces between droplet pairs. However, the exploration of out-of-equilibrium phenomena in optically trapped droplets remains largely uncharted. Here, we report the rotation of biphasic droplets fabricated by co-emulsifying two immiscible liquids (i.e., hydrocarbon and fluorocarbon oils) with a refractive index mismatch in water. When trapped, droplets of a specific geometry rotate around the axis of the laser beam, in what appears to be a dissipative, out-of-equilibrium phenomenon. The rotational frequency, obtained from image analysis is stable and proportional to the beam power. Remarkably, droplets that do not interact with the trapping beam can also be rotated indirectly. This is achieved by positioning the droplets at the center of a circular arrangement of multiple, sequentially activated traps. In this case, the droplet orients towards the location of the active trap by a mechanism yet to be understood. Altogether, our results demonstrate out-from-equilibrium phenomenology in optically trapped biphase droplets, which could inspire the development of devices based on them (e.g., optically induced mixing, etc.). In addition, they may shed light on fundamental principles of optical manipulation of asymmetric particles., Comment: 15 pages, 5 figures, 3 supplementary figures

Deep neural networks trained via empirical risk minimisation often exhibit significant performance disparities across groups, particularly when group and task labels are spuriously correlated (e.g., "grassy background" and "cows"). Existing bias mitigation methods that aim to address this issue often either rely on group labels for training or validation, or require an extensive hyperparameter search. Such data and computational requirements hinder the practical deployment of these methods, especially when datasets are too large to be group-annotated, computational resources are limited, and models are trained through already complex pipelines. In this paper, we propose Targeted Augmentations for Bias Mitigation (TAB), a simple hyperparameter-free framework that leverages the entire training history of a helper model to identify spurious samples, and generate a group-balanced training set from which a robust model can be trained. We show that TAB improves worst-group performance without any group information or model selection, outperforming existing methods while maintaining overall accuracy., Comment: Accepted at the 18th European Conference on Computer Vision (ECCV 2024) as an Oral presentation

A key challenge for computationally intensive state-of-the-art Earth-system models is to distinguish global warming signals from interannual variability. Recently machine learning models have performed better than state-of-the-art numerical weather prediction models for medium-range forecasting. Here we introduce DLESyM, a parsimonious deep learning model that accurately simulates the Earth's current climate over 1000-year periods with negligible drift. DLESyM simulations equal or exceed key metrics of seasonal and interannual variability--such as tropical cyclone genesis and intensity, and mid-latitude blocking frequency--for historical simulations from four leading models from the 6th Climate Model Intercomparison Project. DLESyM, trained on both historical reanalysis data and satellite observations, is a key step toward an accurate highly efficient model of the coupled Earth system, empowering long-range sub-seasonal and seasonal forecasts., Comment: 24 Pages, 20 figures

The way in which the large-scale cosmic environment affects galactic properties is not yet understood. Dark matter halos, which embed galaxies, initially evolve following linear theory. Their subsequent evolution is driven by non-linear structure formation in the halo region and in its outer environment. In this work, we present the first study where we explicitly control the linear part of the evolution of the halo, thus revealing the role of non-linear effects on halo formation. We focus specifically on the effect of proximity to a large cosmological filament. We employ the splicing method to keep fixed the initial density, velocity, and potential fields where a halo will form while changing its outer environment, from an isolated state to one where the halo is near a large filament. In the regime of Milky Way-mass halos, we find that mass and virial radius of such halos are not affected by even drastic changes of environment, whereas halo spin and shape orientation with respect to a massive filament is largely impacted, with fluctuations of up to 80 % around the mean value. Our results suggest that halo orientation and shape cannot be predicted accurately from a local analysis in the initial conditions alone. This has direct consequences on the modeling of intrinsic alignment for cosmic shear surveys, like Euclid. Our results highlight that non-linear couplings to the large-scale environment may have an amplitude comparable to linear effects, and should thus be treated explicitly in analytical models of dark matter halo formation., Comment: 14 pages, 10 figures, submitted to MNRAS

In this article we present UNED-ACCESS 2024, a bilingual dataset that consists of 1003 multiple-choice questions of university entrance level exams in Spanish and English. Questions are originally formulated in Spanish and translated manually into English, and have not ever been publicly released. A selection of current open-source and proprietary models are evaluated in a uniform zero-shot experimental setting both on the UNED-ACCESS 2024 dataset and on an equivalent subset of MMLU questions. Results show that (i) reasoning questions are challenging for models, (ii) smaller models perform worse than larger models and degrade faster in Spanish than in English and (iii) the performance gap between languages is negligible for the best models and grows up to 37% for smaller models. Model ranking on UNED-ACCESS 2024 is almost identical in English and Spanish, and has also a high correlation (0.98 Pearson) with ranking on MMLU, suggesting that a small dataset is sufficiently diverse and representative to measure performance by discipline.

Early detection of chronic and Non-Communicable Diseases (NCDs) is crucial for effective treatment during the initial stages. This study explores the application of wearable devices and Artificial Intelligence (AI) in order to predict weight loss changes in overweight and obese individuals. Using wearable data from a 1-month trial involving around 100 subjects from the AI4FoodDB database, including biomarkers, vital signs, and behavioral data, we identify key differences between those achieving weight loss (>= 2% of their initial weight) and those who do not. Feature selection techniques and classification algorithms reveal promising results, with the Gradient Boosting classifier achieving 84.44% Area Under the Curve (AUC). The integration of multiple data sources (e.g., vital signs, physical and sleep activity, etc.) enhances performance, suggesting the potential of wearable devices and AI in personalized healthcare., Comment: 15 pages, 5 figures, 6 tables, 1 appendix

Context. On 2022 January 20, the Energetic Particle Detector (EPD) on board Solar Orbiter measured a solar energetic particle (SEP) event showing unusual first arriving particles from the anti-Sun direction. Near-Earth spacecraft separated 17{\deg} in longitude to the west from Solar Orbiter measured classic antisunward-directed fluxes. STEREO-A and MAVEN, separated 18{\deg} to the east and 143{\deg} to the west from Solar Orbiter respectively, also observed the event, suggesting that particles spread over at least 160{\deg} in the heliosphere. Results. Solar Orbiter was embedded in a MC erupting on 16 January from the same active region as the one related to the SEP event on 20 January. The SEP event is related to a M5.5 flare and a fast CME-driven shock of 1433 km/s, which injected particles within and outside the MC. The hard SEP spectra, the presence of a Type II radio burst, and the co-temporal Type III radio bursts being observed from 80 MHz that seems to emanate from the Type II, points to the shock as the relevant accelerator of the particles. Conclusions. The detailed analysis of the SEP event strongly suggest that the energetic particles are injected mainly by a CME-driven shock into and outside of a previous MC present in the heliosphere at the time of the particle onset. The sunward propagating SEPs measured by Solar Orbiter are produced by the injection of particles along the longer (western) leg of the MC still connected to the Sun at the time of the release of the particles. The determined electron propagation path length inside the MC is around 30% longer than the estimated length of the loop leg of the MC itself (based on the graduated cylindrical shell model) consistent with a low number of field line rotations., Comment: 23 pages, 19 figures

We prove that all knots can be embedded into the Menger Sponge fractal. We prove that all Pretzel knots can be embedded into the Sierpinski Tetrahedron. Then we compare the number of iterations of each of these fractals needed to produce a given knot as a mean to compare the complexity of the two fractals., Comment: 14 pages

We consider the {\em correlated knapsack orienteering} (CSKO) problem: we are given a travel budget $B$, processing-time budget $W$, finite metric space $(V,d)$ with root $\rho\in V$, where each vertex is associated with a job with possibly correlated random size and random reward that become known only when the job completes. Random variables are independent across different vertices. The goal is to compute a $\rho$-rooted path of length at most $B$, in a possibly adaptive fashion, that maximizes the reward collected from jobs that processed by time $W$. To our knowledge, CSKO has not been considered before, though prior work has considered the uncorrelated problem, {\em stochastic knapsack orienteering}, and {\em correlated orienteering}, which features only one budget constraint on the {\em sum} of travel-time and processing-times. We show that the {\em adaptivity gap of CSKO is not a constant, and is at least $\Omega\bigl(\max\sqrt{\log{B}},\sqrt{\log\log{W}}\}\bigr)$}. Complementing this, we devise {\em non-adaptive} algorithms that obtain: (a) $O(\log\log W)$-approximation in quasi-polytime; and (b) $O(\log W)$-approximation in polytime. We obtain similar guarantees for CSKO with cancellations, wherein a job can be cancelled before its completion time, foregoing its reward. We also consider the special case of CSKO, wherein job sizes are weighted Bernoulli distributions, and more generally where the distributions are supported on at most two points (2-CSKO). Although weighted Bernoulli distributions suffice to yield an $\Omega(\sqrt{\log\log B})$ adaptivity-gap lower bound for (uncorrelated) {\em stochastic orienteering}, we show that they are easy instances for CSKO. We develop non-adaptive algorithms that achieve $O(1)$-approximation in polytime for weighted Bernoulli distributions, and in $(n+\log B)^{O(\log W)}$-time for the more general case of 2-CSKO., Comment: Full version of APPROX 2024 paper

The calculation of the interfacial free energy between two thermodynamic phases is crucial across different fields including materials science, chemistry, and condensed matter physics. This study delves into a thermodynamic approach to predict interfacial free energy along a coexistence line. For that purpose, we utilize Gibbs-Cahn integration method to accurately determine the interfacial free energy along various coexistence conditions, relying solely on data obtained from a single-state interfacial free energy calculation at given conditions of pressure and temperature. Unlike traditional methods that require individual measurements at each thermodynamic state, this approach significantly reduces computational demand and enhances efficiency. The integration scheme requires the computation of the excess interfacial energy (by means of unbiased NVT computer simulations in which the two phases involved coexist) at different points along the coexistence line, for later use this information as an input in our calculations. We apply this method for the Lennard-Jones and mW water model for the case of the liquid-solid interface, as well as Lennard-Jones and TIP4P/2005 models for liquid-vapor interfaces. Our results highlight the accuracy and effectiveness of this thermodynamic integration route for estimating the interfacial free energy along a coexistence line., Comment: Main text: 13 pages and 9 figures. Supporting material: 7 pages and 4 figures

Content moderation in online platforms is crucial for ensuring activity therein adheres to existing policies, especially as these platforms grow. NLP research in this area has typically focused on automating some part of it given that it is not feasible to monitor all active discussions effectively. Past works have focused on revealing deletion patterns with like sentiment analysis, or on developing platform-specific models such as Wikipedia policy or stance detectors. Unsurprisingly, however, this valuable body of work is rather scattered, with little to no agreement with regards to e.g., the deletion discussions corpora used for training or the number of stance labels. Moreover, while efforts have been made to connect stance with rationales (e.g., to ground a deletion decision on the relevant policy), there is little explanability work beyond that. In this paper, we introduce a suite of experiments on Wikipedia deletion discussions and wide-analyis (Wikipedia Deletion Analysis), a Python package aimed at providing one click analysis to content moderation discussions. We release all assets associated with wide-analysis, including data, models and the Python package, and a HuggingFace space with the goal to accelerate research on automating content moderation in Wikipedia and beyond., Comment: System Demonstration

In this work, we aim to advance the development of a fractal theory for sets of integers. The core idea is to utilize the fractal structure of $p$-adic integers, where $p$ is a prime number, and compare this with conventional densities and counting measures for integers. Our approach yields some results in combinatorial number theory. The results show how the local fractal structure of a set in $\mathbb{Z}_p$ can provide bounds for the counting measure for its projection onto $\mathbb{Z}$. Additionally, we establish a relationship between the counting dimension of a set of integers and its box-counting dimension in $\mathbb{Z}_p$. Since our results pertain to sets that are projections of closed sets in $\mathbb{Z}_p$, we also provide both necessary and sufficient combinatorial conditions for a set $E\subset \mathbb{Z}$ to be the projection of a closed set in $\mathbb{Z}_p$., Comment: Comments are welcome, 24 pages

We developed SaludConectaMX as a comprehensive system to track and understand the determinants of complications throughout chemotherapy treatment for children with cancer in Mexico. SaludConectaMX is unique in that it integrates patient clinical indicators with social determinants and caregiver mental health, forming a social-clinical perspective of the patient's evolving health trajectory. The system is composed of a web application (for hospital staff) and a mobile application (for family caregivers), providing the opportunity for cooperative patient monitoring in both hospital and home settings. This paper presents the system's preliminary design and usability evaluation results from a 1.5-year pilot study. Our findings indicate that while the hospital web app demonstrates high completion rates and user satisfaction, the family mobile app requires additional improvements for optimal accessibility; statistical and qualitative data analysis illuminate pathways for system improvement. Based on this evidence, we formalize suggestions for health system development in LMICs, which HCI researchers may leverage in future work.

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

Understanding why researchers cite each other has been a longstanding conjecture in studying scientific networks. Prior research suggests relevance, group cohesion, or honest source crediting as possible factors. However, the dual nature of cognitive and social dimensions underlying citation is often overlooked by not considering the intermediary steps leading up to a citation. For one work to be cited by another, it must first be published by a set of authors. Therefore, we investigate the reasons behind researchers' citations, explicitly examining the interplay of socio-cognitive ties through the interdependence of coauthorship and citation networks. We assess our claims in an empirical analysis by employing the Author-Oriented Relational HyperEvent Model (AuthRHEM) to study Chilean astronomers' citation and collaboration behavior between 2013 and 2015 in a joint framework. We find evidence that when deciding which work to cite, authors prefer other work with novelty and cognitive ties, such as work-to-work relations. At the same time, coherent groups are relevant because coauthors are cocited more frequently in subsequent publications.

Large language models (LLMs) have been shown to propagate and amplify harmful stereotypes, particularly those that disproportionately affect marginalised communities. To understand the effect of these stereotypes more comprehensively, we introduce GlobalBias, a dataset of 876k sentences incorporating 40 distinct gender-by-ethnicity groups alongside descriptors typically used in bias literature, which enables us to study a broad set of stereotypes from around the world. We use GlobalBias to directly probe a suite of LMs via perplexity, which we use as a proxy to determine how certain stereotypes are represented in the model's internal representations. Following this, we generate character profiles based on given names and evaluate the prevalence of stereotypes in model outputs. We find that the demographic groups associated with various stereotypes remain consistent across model likelihoods and model outputs. Furthermore, larger models consistently display higher levels of stereotypical outputs, even when explicitly instructed not to., Comment: Accepted to EMNLP Main 2024

High kinetic inductance superconductors are gaining increasing interest for the realisation of qubits, amplifiers and detectors. Moreover, thanks to their high impedance, quantum buses made of such materials enable large zero-point fluctuations of the voltage, boosting the coupling rates to spin and charge qubits. However, fully exploiting the potential of disordered or granular superconductors is challenging, as their inductance and, therefore, impedance at high values are difficult to control. Here we have integrated a granular aluminium resonator, having a characteristic impedance exceeding the resistance quantum, with a germanium double quantum dot and demonstrate strong charge-photon coupling with a rate of $g_\text{c}/2\pi= (566 \pm 2)$ MHz. This was achieved due to the realisation of a wireless ohmmeter, which allows \emph{in situ} measurements during film deposition and, therefore, control of the kinetic inductance of granular aluminium films. Reproducible fabrication of circuits with impedances (inductances) exceeding 13 k$\Omega$ (1 nH per square) is now possible. This broadly applicable method opens the path for novel qubits and high-fidelity, long-distance two-qubit gates.

The advent of the sixth-generation (6G) networks presents another round of revolution for the mobile communication landscape, promising an immersive experience, robust reliability, minimal latency, extreme connectivity, ubiquitous coverage, and capabilities beyond communication, including intelligence and sensing. To achieve these ambitious goals, it is apparent that 6G networks need to incorporate the state-of-the-art technologies. One of the technologies that has garnered rising interest is fluid antenna system (FAS) which represents any software-controllable fluidic, conductive, or dielectric structure capable of dynamically changing its shape and position to reconfigure essential radio-frequency (RF) characteristics. Compared to traditional antenna systems (TASs) with fixed-position radiating elements, the core idea of FAS revolves around the unique flexibility of reconfiguring the radiating elements within a given space. One recent driver of FAS is the recognition of its position-flexibility as a new degree of freedom (dof) to harness diversity and multiplexing gains. In this paper, we provide a comprehensive tutorial, covering channel modeling, signal processing and estimation methods, information-theoretic insights, new multiple access techniques, and hardware designs. Moreover, we delineate the challenges of FAS and explore the potential of using FAS to improve the performance of other contemporary technologies. By providing insights and guidance, this tutorial paper serves to inspire researchers to explore new horizons and fully unleash the potential of FAS., Comment: 50 pages, 45 figures, 5 tables. Submitted for potential publication

Estimating the probability of rare channel conditions is a central challenge in ultra-reliable wireless communication, where random events, such as deep fades, can cause sudden variations in the channel quality. This paper proposes a sample-efficient framework for predicting the statistics of such events by utilizing spatial dependency between channel measurements acquired from various locations. The proposed framework combines radio maps with non-parametric models and extreme value theory (EVT) to estimate rare-event channel statistics under a Bayesian formulation. The framework can be applied to a wide range of problems in wireless communication and is exemplified by rate selection in ultra-reliable communications. Notably, besides simulated data, the proposed framework is also validated with experimental measurements. The results in both cases show that the Bayesian formulation provides significantly better results in terms of throughput compared to baselines that do not leverage measurements from surrounding locations. It is also observed that the models based on EVT are generally more accurate in predicting rare-event statistics than non-parametric models, especially when only a limited number of channel samples are available. Overall, the proposed methods can significantly reduce the number of measurements required to predict rare channel conditions and guarantee reliability., Comment: Submitted for IEEE Transaction on Communications

We introduce CHEW, a novel dataset of changing events in Wikipedia expressed in naturally occurring text. We use CHEW for probing LLMs for their timeline understanding of Wikipedia entities and events in generative and classification experiments. Our results suggest that LLMs, despite having temporal information available, struggle to construct accurate timelines. We further show the usefulness of CHEW-derived embeddings for identifying meaning shift., Comment: Short Paper

Background: Computer models for simulating cardiac electrophysiology are valuable tools for research and clinical applications. Traditional reaction-diffusion (RD) models used for these purposes are computationally expensive. While eikonal models offer a faster alternative, they are not well-suited to study cardiac arrhythmias driven by reentrant activity. The present work extends the diffusion-reaction eikonal alternant model (DREAM), incorporating conduction velocity (CV) restitution for simulating complex cardiac arrhythmias. Methods: The DREAM modifies the fast iterative method to model cyclical behavior, dynamic boundary conditions, and frequency-dependent anisotropic CV. Additionally, the model alternates with an approximated RD model, using a detailed ionic model for the reaction term and a triple-Gaussian to approximate the diffusion term. The DREAM and monodomain models were compared, simulating reentries in 2D manifolds with different resolutions. Results: The DREAM produced similar results across all resolutions, while experiments with the monodomain model failed at lower resolutions. CV restitution curves obtained using the DREAM closely approximated those produced by the monodomain simulations. Reentry in 2D slabs yielded similar results in vulnerable window and mean reentry duration for low CV in both models. In the left atrium, most inducing points identified by the DREAM were also present in the high-resolution monodomain model. DREAM's reentry simulations on meshes with an average edge length of 1600$\mu$m were 40x faster than monodomain simulations at 200$\mu$m. Conclusion: This work establishes the mathematical foundation for using the accelerated DREAM simulation method for cardiac electrophysiology. Cardiac research applications are enabled by a publicly available implementation in the openCARP simulator., Comment: 39 pages, 9 figures

We propose several hierarchical graphs that represent the semantic relations between physical theories, their fundamental constants and units of measurement. We begin with an alternative representation of Zelmanov's cube of fundamental constants as a concept lattice. We then propose the inclusion of a new fundamental constant: Milgrom's critical acceleration and discuss the implications of such analysis. We then look for the same fundamental constants in a graph that relates magnitudes and units of measurement in the International System of Units. This exercise shows the potential of visualizing hierarchical networks as a tool to better comprehend the interrelations and dependencies of physical magnitudes, units and theories. New regimes of application may be deduced, as well as an interesting reflection on our ontologies and corresponding theoretical objects., Comment: 12 pages, 9 figures, Accepted for publication in the journal Symmetry

In healthcare there is a pursuit for employing interpretable algorithms to assist healthcare professionals in several decision scenarios. Following the Predictive, Descriptive and Relevant (PDR) framework, the definition of interpretable machine learning as a machine-learning model that explicitly and in a simple frame determines relationships either contained in data or learned by the model that are relevant for its functioning and the categorization of models by post-hoc, acquiring interpretability after training, or model-based, being intrinsically embedded in the algorithm design. We overview a selection of eight algorithms, both post-hoc and model-based, that can be used for such purposes., Comment: 26 pages, 5 figures

Neural architecture search (NAS) finds high performing networks for a given task. Yet the results of NAS are fairly prosaic; they did not e.g. create a shift from convolutional structures to transformers. This is not least because the search spaces in NAS often aren't diverse enough to include such transformations a priori. Instead, for NAS to provide greater potential for fundamental design shifts, we need a novel expressive search space design which is built from more fundamental operations. To this end, we introduce einspace, a search space based on a parameterised probabilistic context-free grammar. Our space is versatile, supporting architectures of various sizes and complexities, while also containing diverse network operations which allow it to model convolutions, attention components and more. It contains many existing competitive architectures, and provides flexibility for discovering new ones. Using this search space, we perform experiments to find novel architectures as well as improvements on existing ones on the diverse Unseen NAS datasets. We show that competitive architectures can be obtained by searching from scratch, and we consistently find large improvements when initialising the search with strong baselines. We believe that this work is an important advancement towards a transformative NAS paradigm where search space expressivity and strategic search initialisation play key roles., Comment: Project page at https://linusericsson.github.io/einspace/

Matrix scaling problems with sparse cost matrices arise frequently in various domains, such as optimal transport, image processing, and machine learning. The Sinkhorn-Knopp algorithm is a popular iterative method for solving these problems, but its convergence properties in the presence of sparsity have not been thoroughly analyzed. This paper presents a theoretical analysis of the convergence rate of the Sinkhorn-Knopp algorithm specifically for sparse cost matrices. We derive novel bounds on the convergence rate that explicitly depend on the sparsity pattern and the degree of nonsparsity of the cost matrix. These bounds provide new insights into the behavior of the algorithm and highlight the potential for exploiting sparsity to develop more efficient solvers. We also explore connections between our sparse convergence results and existing convergence results for dense matrices, showing that our bounds generalize the dense case. Our analysis reveals that the convergence rate improves as the matrix becomes less sparse and as the minimum entry of the cost matrix increases relative to its maximum entry. These findings have important practical implications, suggesting that the Sinkhorn-Knopp algorithm may be particularly well-suited for large-scale matrix scaling problems with sparse cost matrices arising in real-world applications. Future research directions include investigating tighter bounds based on more sophisticated sparsity patterns, developing algorithm variants that actively exploit sparsity, and empirically validating the benefits of our theoretical results on real-world datasets. This work advances our understanding of the Sinkhorn-Knopp algorithm for an important class of matrix scaling problems and lays the foundation for designing more efficient and scalable solutions in practice., Comment: Incorrect Mathematical Theorem

Concept-based explainability methods provide insight into deep learning systems by constructing explanations using human-understandable concepts. While the literature on human reasoning demonstrates that we exploit relationships between concepts when solving tasks, it is unclear whether concept-based methods incorporate the rich structure of inter-concept relationships. We analyse the concept representations learnt by concept-based models to understand whether these models correctly capture inter-concept relationships. First, we empirically demonstrate that state-of-the-art concept-based models produce representations that lack stability and robustness, and such methods fail to capture inter-concept relationships. Then, we develop a novel algorithm which leverages inter-concept relationships to improve concept intervention accuracy, demonstrating how correctly capturing inter-concept relationships can improve downstream tasks., Comment: Accepted at ICML 2024

Causal opacity denotes the difficulty in understanding the "hidden" causal structure underlying the decisions of deep neural network (DNN) models. This leads to the inability to rely on and verify state-of-the-art DNN-based systems, especially in high-stakes scenarios. For this reason, circumventing causal opacity in DNNs represents a key open challenge at the intersection of deep learning, interpretability, and causality. This work addresses this gap by introducing Causal Concept Graph Models (Causal CGMs), a class of interpretable models whose decision-making process is causally transparent by design. Our experiments show that Causal CGMs can: (i) match the generalisation performance of causally opaque models, (ii) enable human-in-the-loop corrections to mispredicted intermediate reasoning steps, boosting not just downstream accuracy after corrections but also the reliability of the explanations provided for specific instances, and (iii) support the analysis of interventional and counterfactual scenarios, thereby improving the model's causal interpretability and supporting the effective verification of its reliability and fairness.

The Sinkhorn algorithm has emerged as a powerful tool for solving optimal transport problems, finding applications in various domains such as machine learning, image processing, and computational biology. Despite its widespread use, the intricate structure and scaling properties of the coupling matrices generated by the Sinkhorn algorithm remain largely unexplored. In this paper, we delve into the multifractal properties of these coupling matrices, aiming to unravel their complex behavior and shed light on the underlying dynamics of the Sinkhorn algorithm. We prove the existence of the multifractal spectrum and the singularity spectrum for the Sinkhorn coupling matrices. Furthermore, we derive bounds on the generalized dimensions, providing a comprehensive characterization of their scaling properties. Our findings not only deepen our understanding of the Sinkhorn algorithm but also pave the way for novel applications and algorithmic improvements in the realm of optimal transport., Comment: Submitted to the Journal of Fractal Geometry

The propagation of very-high-energy gamma-rays (VHEGRs) in the extragalactic space offers the opportunity to study astrophysical phenomena not reproducible in laboratories. In particular, the deviation from predictions of the observed photon flux from distant sources at the GeV energy scale still represents an open problem. Commonly, this deviation is interpreted as the result of the deflection out of the line of sight of the source of the cascade-produced charged leptons by weak magnetic fields present in the intergalactic medium (IGM). However, plasma instabilities could have an effect on the energy- and momentum-distribution of the secondary electrons and positrons, modifying the development of electromagnetic cascades. In this work, we study the influence of plasma instabilities on the energy spectrum of electromagnetic cascades through a parametric study, performed with the Monte Carlo code CRPropa. We parameterize plasma instabilities with an energy loss length normalisation $\lambda_0$ and a power law index $\alpha$ of its electron/positron energy dependence. We simulate photon spectra at Earth for different blazar scenarios and find that plasma instabilities can reproduce the suppression in the GeV-photon flux of real astrophysical sources, such as 1ES 0229+200, when the energy loss length of electrons/positrons at 1.0 TeV is $\lambda_0 \simeq 100\,\text{kpc}$ and $\alpha\simeq0$. The energy fraction lost by the secondary electron pairs due to the instability is estimated to be about $1\%$ over the typical interaction length of Inverse Compton scattering for these parameter values., Comment: 12 pages, 14 figures

In this paper we study the space $\mathbb{L}(n)$ of $n$-gons in the plane degenerated to segments. We prove that this space is a smooth real submanifold of $\mathbb{C}^n$, and describe its topology in terms of the manifold $\mathbb{M}(n)$ of $n$-gons degenerated to segments and with the first vertex at 0. We show that $\mathbb{M}(n)$ and $\mathbb{L}(n)$ contain straight lines that form a basis of directions in each one of their tangent spaces, and we compute the geodesic equations in these manifolds. Finally, the quotient of $\mathbb{L}(n)$ by the diagonal action of the affine complex group and the re-enumeration of the vertices is described., Comment: 17 pages, 1 figure

The propagation of light beams in photovoltaic pyroelectric photorefractive crystals is modelled by a specific generalization of the nonlinear Schr\"odinger equation (GNLSE). We use the variational approximation (VA) to predict the propagation of solitary-wave inputs in the crystal, finding that the VA equations involve the dilogarithm special function. The VA predicts that solitons and breathers exist, and the Vakhitov-Kolokolov criterion predicts that the solitons are stable solutions. Direct simulations of the underlying GNLSE corroborates the existence of such stable modes. The numerical solutions produce both regular breathers and ones featuring beats (long-period modulations of fast oscillations). In the latter case, the Fourier transform of amplitude oscillations reveals a nearly discrete spectrum characterizing the beats dynamics. Numerical solutions of another type demonstrate spontaneous splitting of the input pulse in two or several secondary ones., Comment: 34 pages, 28 figures, 56 references. To be published in a special issue of journal AXIOMS on the topic "Nonlinear Schroedinger equations"

Hoaxes are a recognised form of disinformation created deliberately, with potential serious implications in the credibility of reference knowledge resources such as Wikipedia. What makes detecting Wikipedia hoaxes hard is that they often are written according to the official style guidelines. In this work, we first provide a systematic analysis of similarities and discrepancies between legitimate and hoax Wikipedia articles, and introduce Hoaxpedia, a collection of 311 hoax articles (from existing literature and official Wikipedia lists), together with semantically similar legitimate articles, which together form a binary text classification dataset aimed at fostering research in automated hoax detection. In this paper, We report results after analyzing several language models, hoax-to-legit ratios, and the amount of text classifiers are exposed to (full article vs the article's definition alone). Our results suggest that detecting deceitful content in Wikipedia based on content alone is hard but feasible, and complement our analysis with a study on the differences in distributions in edit histories, and find that looking at this feature yields better classification results than context.

To gain insight into the dynamics of the CN + C2H6 gas-phase reaction, quasi-classical trajectory (QCT) calculations were performed on a full-dimensional analytical potential energy surface. This reaction presents very high exothermicity, -22.20 kcal/mol, and it is practically barrierless, with a barrier height of 0.23 kcal/mol, being an early transition state reaction. The V-shape form of the excitation function is characteristic of non-threshold reactions. The pronounced increase observed at lower energies can be attributed to the substantial increase in the impact parameter within this energy regime. Vibrational excitations by one quantum of stretching and bending modes give rise to excitation functions that present a similar V-shaped profile.

The Tunneling Potential Formalism was introduced to calculate the tunneling actions that control vacuum decay as an alternative to the standard Euclidean Formalism. The new approach sets the problem as a simple variational problem in field space with decay described by a tunneling potential function $V_t$ that extremizes a simple action functional $S[V_t]$ and has a number of appealing properties that have been presented elsewhere. In this note I discuss several instances in which this $V_t$ approach seems to give more than one would have expected a priori, as the following: the $V_t$ describing the decay is a minimum of the new action $S[V_t]$ rather than a saddle point; the decay of AdS, dS or Minkowski vacua are governed by a unique universal $S[V_t]$ which also gives the Hawking-Moss instanton in the appropriate limit; physically relevant solutions beyond the Coleman-De Luccia (CdL) bounce, like pseudo-bounces or bubbles of nothing (BoNs), show up in a straightforward way as generalizations of the CdL bounce, with the correct boundary conditions; in cases for which the Euclidean action calculation requires the inclusion of particular boundary terms (like for BoNs or for the decay of AdS maxima above the Breitenlohner-Freedman bound) $S[V_t]$ gives the correct result without the need of including any boundary term., Comment: 22 pages. Contribution to the Proceedings of the Corfu Summer Institute 2023 "School and Workshops on Elementary Particle Physics and Gravity"

Similar to humans, animals make extensive use of verbal and non-verbal forms of communication, including a large range of audio signals. In this paper, we address dog vocalizations and explore the use of self-supervised speech representation models pre-trained on human speech to address dog bark classification tasks that find parallels in human-centered tasks in speech recognition. We specifically address four tasks: dog recognition, breed identification, gender classification, and context grounding. We show that using speech embedding representations significantly improves over simpler classification baselines. Further, we also find that models pre-trained on large human speech acoustics can provide additional performance boosts on several tasks., Comment: to be published in LREC-COLING 2024