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The world faces increasing challenges related to climate change and shifting geopolitical situations. The COVID 19 pandemic clearly illustrated the need for resilience not just within civil engineering but also within social and economic systems. While significant work has focused on applying digital technologies to solve the Sustainable Development Goals (SDGs), less effort has been placed on the ability of digital technologies to enable humanity to continue to live effectively within the Planetary Boundaries. Within this paper, we perform a Systematic Literature Review using the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) methodology. Nine hundred and twenty papers were reviewed. Based on the Systematic Literature Review (SLR), we have outlined specific methods to apply digital technologies to enable humanity to live within PB. Critical capabilities provided by digital technologies, specifically the ability to create dynamic networks for enabling humanity to live within PB, are identified and illustrated in food and agriculture. The paper closes with a brief assessment of the concept's applicability to other Critical National Infrastructure., Comment: 16 pages, 10 figures, pre print

We study a flaring activity of the HSP Mrk421 that was characterized from radio to very-high-energy (VHE; E $>0.1$TeV) gamma rays with MAGIC, Fermi-LAT, Swift, XMM-Newton and several optical and radio telescopes. These observations included, for the first time for a gamma-ray flare of a blazar, simultaneous X-ray polarization measurements with IXPE. We find substantial variability in both X-rays and VHE gamma rays throughout the campaign, with the highest VHE flux above 0.2 TeV occurring during the IXPE observing window, and exceeding twice the flux of the Crab Nebula. However, the VHE and X-ray spectra are on average softer, and the correlation between these two bands weaker that those reported in previous flares of Mrk421. IXPE reveals an X-ray polarization degree significantly higher than that at radio and optical frequencies. The X-ray polarization angle varies by $\sim$100$^\circ$ on timescales of days, and the polarization degree changes by more than a factor 4. The highest X-ray polarization degree reaches 26%, around which a X-ray counter-clockwise hysteresis loop is measured with XMM-Newton. It suggests that the X-ray emission comes from particles close to the high-energy cutoff, hence possibly probing an extreme case of the Turbulent Extreme Multi-Zone model. We model the broadband emission with a simplified stratified jet model throughout the flare. The polarization measurements imply an electron distribution in the X-ray emitting region with a very high minimum Lorentz factor, which is expected in electron-ion plasma, as well as a variation of the emitting region size up to a factor of three during the flaring activity. We find no correlation between the fluxes and the evolution of the model parameters, which indicates a stochastic nature of the underlying physical mechanism. Such behaviour would be expected in a highly turbulent electron-ion plasma crossing a shock front., Comment: Submitted to Astronomy and Astrophysics. Corresponding authors: Axel Arbet-Engels, Lea Heckmann, David Paneque

OT 081 is a well-known, luminous blazar that is remarkably variable in many energy bands. We present the first broadband study of the source which includes very-high-energy (VHE, $E>$100\,GeV) $\gamma$-ray data taken by the MAGIC and H.E.S.S. imaging Cherenkov telescopes. The discovery of VHE $\gamma$-ray emission happened during a high state of $\gamma$-ray activity in July 2016, observed by many instruments from radio to VHE $\gamma$-rays. We identify four states of activity of the source, one of which includes VHE $\gamma$-ray emission. Variability in the VHE domain is found on daily timescales. The intrinsic VHE spectrum can be described by a power-law with index $3.27\pm0.44_{\rm stat}\pm0.15_{\rm sys}$ (MAGIC) and $3.39\pm0.58_{\rm stat}\pm0.64_{\rm sys}$ (H.E.S.S.) in the energy range of 55--300\,GeV and 120--500\,GeV, respectively. The broadband emission cannot be sucessfully reproduced by a simple one-zone synchrotron self-Compton model. Instead, an additional external Compton component is required. We test a lepto-hadronic model that reproduces the dataset well and a proton-synchrotron dominated model that requires an extreme proton luminosity. Emission models that are able to successfully represent the data place the emitting region well outside of the Broad Line Region (BLR) to a location at which the radiative environment is dominated by the infrared thermal radiation field of the dusty torus. In the scenario described by this flaring activity, the source appears to be an FSRQ, in contrast with past categorizations. This suggests that the source can be considered to be a transitional blazar, intermediate between BL~Lac and FSRQ objects., Comment: Accepted on MNRAS Corresponding authors: M. Manganaro, J. Becerra Gonz\'alez, M. Seglar-Arroyo, D. A. Sanchez

Imaging atmospheric Cherenkov telescopes (IACTs) are used to observe very high-energy photons from the ground. Gamma rays are indirectly detected through the Cherenkov light emitted by the air showers they induce. The new generation of experiments, in particular the Cherenkov Telescope Array Observatory (CTAO), sets ambitious goals for discoveries of new gamma-ray sources and precise measurements of the already discovered ones. To achieve these goals, both hardware and data analysis must employ cutting-edge techniques. This also applies to the LST-1, the first IACT built for the CTAO, which is currently taking data on the Canary island of La Palma. This paper introduces a new event reconstruction technique for IACT data, aiming to improve the image reconstruction quality and the discrimination between the signal and the background from misidentified hadrons and electrons. The technique models the development of the extensive air shower signal, recorded as a waveform per pixel, seen by CTAO telescopes' cameras. Model parameters are subsequently passed to random forest regressors and classifiers to extract information on the primary particle. The new reconstruction was applied to simulated data and to data from observations of the Crab Nebula performed by the LST-1. The event reconstruction method presented here shows promising performance improvements. The angular and energy resolution, and the sensitivity, are improved by 10 to 20% over most of the energy range. At low energy, improvements reach up to 22%, 47%, and 50%, respectively. A future extension of the method to stereoscopic analysis for telescope arrays will be the next important step., Comment: Accepted in A&A

The gravitational physics landscape is evolving rapidly, driven by our ability to study strong-field regions, in particular black holes. Black Holes Inside and Out gathered world experts to discuss the status of the field and prospects ahead. We hope that the ideas and perspectives are a source of inspiration. Structure: Black Hole Evaporation - 50 Years by William Unruh The Stability Problem for Extremal Black Holes by Mihalis Dafermos The Entropy of Black Holes by Robert M. Wald The Non-linear Regime of Gravity by Luis Lehner Black Holes Galore in D > 4 by Roberto Emparan Same as Ever: Looking for (In)variants in the Black Holes Landscape by Carlos A. R. Herdeiro Black Holes, Cauchy Horizons, and Mass Inflation by Matt Visser The Backreaction Problem for Black Holes in Semiclassical Gravity by Adrian del Rio Black Holes Beyond General Relativity by Enrico Barausse and Jutta Kunz Black Holes as Laboratories: Searching for Ultralight Fields by Richard Brito Primordial Black Holes from Inflation by Misao Sasaki Tests of General Relativity with Future Detectors by Emanuele Berti Black Holes as Laboratories: Tests of General Relativity by Ruth Gregory and Samaya Nissanke Simulating Black Hole Imposters by Frans Pretorius Black Hole Spectroscopy: Status Report by Gregorio Carullo VLBI as a Precision Strong Gravity Instrument by Paul Tiede Testing the nature of compact objects and the black hole paradigm by Mariafelicia De Laurentis and Paolo Pani Some Thoughts about Black Holes in Asymptotic Safety by Alessia Platania Black Hole Evaporation in Loop Quantum Gravity by Abhay Ashtekar How the Black Hole Puzzles are Resolved in String Theory by Samir D. Mathur Quantum Black Holes: From Regularization to Information Paradoxes by Niayesh Afshordi and Stefano Liberati, Comment: 221 pages, 21 contributions

In recent years, Full-Waveform Inversion (FWI) has been extensively used to derive high-resolution subsurface velocity models from seismic data. However, due to the nonlinearity and ill-posed nature of the problem, FWI requires a good starting model to avoid producing non-physical solutions. Moreover, conventional optimization methods fail to quantify the uncertainty associated with the recovered solution, which is critical for decision-making processes. Bayesian inference offers an alternative approach as it directly or indirectly evaluates the posterior probability density function. For example, Markov Chain Monte Carlo (MCMC) methods generate multiple sample chains to characterize the solution's uncertainty. Despite their ability to theoretically handle any form of distribution, MCMC methods require many sampling steps; this limits their usage in high-dimensional problems with computationally intensive forward modeling, as is the FWI case. Variational Inference (VI), on the other hand, provides an approximate solution to the posterior distribution in the form of a parametric or non-parametric proposal distribution. Among the various algorithms used in VI, Stein Variational Gradient Descent (SVGD) is recognized for its ability to iteratively refine a set of samples to approximate the target distribution. However, mode and variance-collapse issues affect SVGD in high-dimensional inverse problems. This study aims to improve the performance of SVGD within the context of FWI by utilizing, for the first time, an annealed variant of SVGD and combining it with a multi-scale strategy. Additionally, we demonstrate that Principal Component Analysis (PCA) can be used to evaluate the performance of the optimization process. Clustering techniques are also employed to provide more rigorous and meaningful statistical analysis of the particles in the presence of multi-modal distributions.

We study the robustness of parametrized tests of General Relativity (GR) with gravitational waves due to waveform inaccuracy. In particular, we determine the properties of the signal (signal-to-noise ratio (SNR) and source parameters) such that we are led to falsely identify a GR deviation due to neglecting spin precession or higher models in the recovery model. To characterize the statistical significance of the biases, we compute the Bayes factor between the non-GR and GR models, and the fitting factor of the non-GR model. For highly-precessing, edge-on signals, we find that mismodeling the signal leads to a significant systematic bias in the recovery of the non-GR parameter, even at an SNR of 30. However, these biased inferences are characterized by a significant loss of SNR and a weak preference for the non-GR model (over the GR model). At a higher SNR, the biased inferences display a strong preference for the non-GR model (over the GR model) and a significant loss of SNR. For edge-on signals containing asymmetric masses, at an SNR of 30, we find that excluding higher modes does not impact the ppE tests as much as excluding spin precession. Our analysis, therefore, identifies the spin-precessing and mass-asymmetric systems for which parametrized tests of GR are robust. With a toy model and using the linear signal approximation, we illustrate these regimes of bias and characterize them by obtaining bounds on the ratio of systematic to statistical error and the effective cycles incurred due to mismodeling. As a by-product of our analysis, we explicitly connect different measures and techniques commonly used to estimate systematic errors -- linear-signal approximation, Laplace approximation, fitting factor, effective cycles, and Bayes factor -- that are generally applicable to all studies of systematic uncertainties in gravitational wave parameter estimation., Comment: 51 pages, 22 figures, 5 tables

The measurement of cosmic rays at energies exceeding 100 TeV per nucleon is crucial for enhancing the understanding of high-energy particle propagation and acceleration models in the Galaxy. HERD is a space-borne calorimetric experiment that aims to extend the current direct measurements of cosmic rays to unexplored energies. The payload is scheduled to be installed on the Chinese Space Station in 2027. The primary peculiarity of the instrument is its capability to measure particles coming from all directions, with the main detector being a deep, homogeneous, 3D calorimeter. The active elements are read out using two independent systems: one based on wavelength shifter fibers coupled to CMOS cameras, and the other based on photo-diodes read-out with custom front-end electronics. A large calorimeter prototype was tested in 2023 during an extensive beam test campaign at CERN. In this paper, the performance of the calorimeter for high-energy electron beams, as obtained from the photo-diode system data, is presented. The prototype demonstrated excellent performance, e.g., an energy resolution better than 1% for electrons at 250 GeV. A comparison between beam test data and Monte Carlo simulation data is also presented.

Instruments for gamma-ray astronomy at Very High Energies ($E>100\,{\rm GeV}$) have traditionally derived their scientific results through proprietary data and software. Data standardisation has become a prominent issue in this field both as a requirement for the dissemination of data from the next generation of gamma-ray observatories and as an effective solution to realise public data legacies of current-generation instruments. Specifications for a standardised gamma-ray data format have been proposed as a community effort and have already been successfully adopted by several instruments. We present the first production of standardised data from the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes. We converted $166\,{\rm h}$ of observations from different sources and validated their analysis with the open-source software Gammapy. We consider six data sets representing different scientific and technical analysis cases and compare the results obtained analysing the standardised data with open-source software against those produced with the MAGIC proprietary data and software. Aiming at a systematic production of MAGIC data in this standardised format, we also present the implementation of a database-driven pipeline automatically performing the MAGIC data reduction from the calibrated down to the standardised data level. In all the cases selected for the validation, we obtain results compatible with the MAGIC proprietary software, both for the manual and for the automatic data productions. Part of the validation data set is also made publicly available, thus representing the first large public release of MAGIC data. This effort and this first data release represent a technical milestone toward the realisation of a public MAGIC data legacy., Comment: Accepted for publication in the Journal of High Energy Astrophysics

The spin of intermediate-mass black holes (IMBHs) growing through repeated black hole mergers in stellar clusters statistically asymptotes to zero. Putative observations of IMBHs with dimensionless spin parameter $\chi\gtrsim 0.6$ would require a phase of coherent gas accretion to spin up the black hole. We estimate the amount of gas necessary to produce a given IMBH spin. If the observed IMBH mass and spin are $M\gtrsim 1000~M_\odot$ and $\chi\gtrsim 0.6$, respectively, the IMBH must have coherently accreted at least $\sim 100~M_\odot$ of gas. In this scenario, as long as the spin is not maximal, the IMBH can only accrete at most half of its mass. Our estimates can constrain the relative contribution of accretion and mergers to the growth of IMBHs in dense stellar environments., Comment: 10 pages, 7 figures

Understanding and optimizing the design of helical micro-swimmers is crucial for advancing their application in various fields. This study presents an innovative approach combining Free-Form Deformation with Bayesian Optimization to enhance the shape of these swimmers. Our method facilitates the computation of generic swimmer shapes that achieve optimal average speed and efficiency. Applied to both monoflagellated and biflagellated swimmers, our optimization framework has led to the identification of new optimal shapes. These shapes are compared with biological counterparts, highlighting a diverse range of swimmers, including both pushers and pullers.

Black hole demographics in different environments is critical in view of recent results on massive-stars binarity, and of the multi-messenger detectability of compact objects mergers. But the identification and characterization of non-interacting black holes is elusive, especially in the sparse field stellar population. A candidate non-interactive black hole (BH)+red giant (RG) binary system, 2MASSJ05215658+4359220, was identified by Thompson et al.(2019). We obtained Astrosat/UVIT Far-Ultraviolet (FUV) imaging and Hubble Space Telescope (HST) UV-optical imaging and spectroscopy of the source, to test possible scenarios for the optically-elusive companion. HST/STIS spectra from about 1,600 to 10,230Ang are best fit by the combination of two stellar sources, a red giant with Teff=4250 (uncertainty 150K), logg=2.0, Radius_RG=27.8Rsun (assuming a single-temperature atmosphere), and a subgiant companion with Teff=6,000K, Radius_comp=2.7Rsun, or Teff=5,270K, Radius_comp=4.2Rsun using models with one-tenth or one-third solar metallicity respectively, logg=3.0, extinction E(B-V)=0.50(uncertainty 0.2), adopting the DR3 Gaia distance D=2,463pc (uncertainty 120pc). No FUV data existed prior to our programs. STIS spectra give an upper limit of 10e-17ergs cm-2 s-1 Ang-1 shortward of 2300Ang; an upper limit of >25.7ABmag was obtained in two UVIT FUV broad-bands. The non-detection of FUV flux rules out a compact companion such as a hot WD. The STIS spectrum shows strong MgII lambda2800Ang emission, typical of chromospherically active red giants. The masses inferred by comparison with evolutionary tracks, about 1 Msun for the red giant and between 1.1 and 1.6Msun for the subgiant companion, suggest past mass transfer, although the red giant currently does not fill its Roche lobe. WFC3 imaging in F218W, F275W, F336W, F475W, and F606W shows an unresolved source in all filters., Comment: ApJ, in press, DOI: 10.3847/1538-4357/ad712f

We theoretically study stimulated and spontaneous Hawking emission from an analog horizon for spin modes in a two-component Bose-Einstein condensate, both with and without a coherent coupling between the two components. We highlight the conceptual and practical advantages that these systems offer to the experimental observation of the phenomenon, namely the massive nature of elementary excitations and the experimental accessibility of the different quadratures of the spin excitations. In particular, we go beyond the relativistic regimes previously addressed in the literature, and identify various observables that show a signature of the Hawking process, as well as additional features associated with the massive nature of the modes, such as undulations. Semi-analytical calculations of the scattering properties of the horizon and of two-point correlation functions of the emitted radiation in an ideal stationary setup are supported by time-dependent numerical simulations based on Gross-Pitaevskii and Bogoliubov theory.

Swimming involves a body's capability to navigate through a fluid by undergoing self-deformations. Typically, fluid dynamics are described by the Navier-Stokes equations, and when integrated with a swimming body, it results in a highly intricate model. This paper introduces a computational framework for simulating the movement of multiple swimmers with various geometries immersed in a Navier-Stokes fluid. The approach relies on the finite element method with an Arbitrary Lagrangian-Eulerian (ALE) framework to handle swimmer displacements. Numerous numerical experiments demonstrate the adaptability of the computational framework across various scenarios.All the implementations are made using the Feel++ finite element library.

As the demand for lithium-ion batteries rapidly increases there is a need to design these cells in a safe manner to mitigate thermal runaway. Thermal runaway in batteries leads to an uncontrollable temperature rise and potentially fires, which is a major safety concern. Typically, when modelling the chemical kinetics of thermal runaway calorimetry data ( e.g. Accelerating Rate Calorimetry (ARC)) is needed to determine the temperature-driven decomposition kinetics. Conventional methods of fitting Arrhenius Ordinary Differential Equation (ODE) thermal runaway models to Accelerated Rate Calorimetry (ARC) data make several assumptions that reduce the fidelity and generalizability of the obtained model. In this paper, Chemical Reaction Neural Networks (CRNNs) are trained to fit the kinetic parameters of N-equation Arrhenius ODEs to ARC data obtained from a Molicel 21700 P45B. The models are found to be better approximations of the experimental data. The flexibility of the method is demonstrated by experimenting with two-equation and four-equation models. Thermal runaway simulations are conducted in 3D using the obtained kinetic parameters, showing the applicability of the obtained thermal runaway models to large-scale simulations.

Large Language Models (LLMs) have emerged as powerful conversational interfaces, and their application in process mining (PM) tasks has shown promising results. However, state-of-the-art LLMs struggle with complex scenarios that demand advanced reasoning capabilities. In the literature, two primary approaches have been proposed for implementing PM using LLMs: providing textual insights based on a textual abstraction of the process mining artifact, and generating code executable on the original artifact. This paper proposes utilizing the AI-Based Agents Workflow (AgWf) paradigm to enhance the effectiveness of PM on LLMs. This approach allows for: i) the decomposition of complex tasks into simpler workflows, and ii) the integration of deterministic tools with the domain knowledge of LLMs. We examine various implementations of AgWf and the types of AI-based tasks involved. Additionally, we discuss the CrewAI implementation framework and present examples related to process mining.

I developed a neural audio codec model based on the residual quantized variational autoencoder architecture. I train the model on the Slakh2100 dataset, a standard dataset for musical source separation, composed of multi-track audio. The model can separate audio sources, achieving almost SoTA results with much less computing power. The code is publicly available at github.com/LeonardoBerti00/Source-Separation-of-Multi-source-Music-using-Residual-Quantizad-Variational-Autoencoder, Comment: 9 pages

In Business Process Management (BPM), effectively comprehending process models is crucial yet poses significant challenges, particularly as organizations scale and processes become more complex. This paper introduces a novel framework utilizing the advanced capabilities of Large Language Models (LLMs) to enhance the interpretability of complex process models. We present different methods for abstracting business process models into a format accessible to LLMs, and we implement advanced prompting strategies specifically designed to optimize LLM performance within our framework. Additionally, we present a tool, AIPA, that implements our proposed framework and allows for conversational process querying. We evaluate our framework and tool by i) an automatic evaluation comparing different LLMs, model abstractions, and prompting strategies and ii) a user study designed to assess AIPA's effectiveness comprehensively. Results demonstrate our framework's ability to improve the accessibility and interpretability of process models, pioneering new pathways for integrating AI technologies into the BPM field.

We contrast high effectiveness of state of the art deep learning architectures designed for general audio classification tasks, refined for respiratory insufficiency (RI) detection and blood oxygen saturation (SpO$_2$) estimation and classification through automated audio analysis. Recently, multiple deep learning architectures have been proposed to detect RI in COVID patients through audio analysis, achieving accuracy above 95% and F1-score above 0.93. RI is a condition associated with low SpO$_2$ levels, commonly defined as the threshold SpO$_2$ <92%. While SpO$_2$ serves as a crucial determinant of RI, a medical doctor's diagnosis typically relies on multiple factors. These include respiratory frequency, heart rate, SpO$_2$ levels, among others. Here we study pretrained audio neural networks (CNN6, CNN10 and CNN14) and the Masked Autoencoder (Audio-MAE) for RI detection, where these models achieve near perfect accuracy, surpassing previous results. Yet, for the regression task of estimating SpO$_2$ levels, the models achieve root mean square error values exceeding the accepted clinical range of 3.5% for finger oximeters. Additionally, Pearson correlation coefficients fail to surpass 0.3. As deep learning models perform better in classification than regression, we transform SpO$_2$-regression into a SpO$_2$-threshold binary classification problem, with a threshold of 92%. However, this task still yields an F1-score below 0.65. Thus, audio analysis offers valuable insights into a patient's RI status, but does not provide accurate information about actual SpO$_2$ levels, indicating a separation of domains in which voice and speech biomarkers may and may not be useful in medical diagnostics under current technologies., Comment: 23 pages, 4 figures, in review at Journal of Biomedical Signal Processing and Control

Recent work has shown that the quasinormal mode spectrum of black holes is unstable under small perturbations (of order $\epsilon$) of the radial potential, while the early time-domain ringdown waveform is only marginally affected. In this paper we provide further insight into the apparent tension between the frequency-domain and the time-domain descriptions by analyzing the scattering properties of the problem. In the frequency domain, we study analytically the solutions corresponding to the perturbed potential. We show that there are two qualitatively different classes of instabilities, and that both Schwarzschild and Kerr black holes are affected by what we call a "Type II" instability, i.e., an exponential migration of the mode frequencies away from their unperturbed value as the perturbing "bump" moves away from the peak of the unperturbed potential. In the time domain, we elucidate the effect of the spectral instability in terms of the causal structure of the Green's function. By using an equivalent scattering problem we confirm analytically (and show numerically) that the deviation from the unperturbed waveform in the early ringdown stage is proportional to $\epsilon$ when $\epsilon\lesssim10^{-2}$., Comment: 18 pages, 8 figures

We introduce modified energies that are suitable to get upper bounds on the high Sobolev norms for solutions to the $1$D periodic NLS. Our strategy is rather flexible and allows us to get a new and simpler proof of the bounds obtained by Bourgain in the case of the quintic nonlinearity, as well as its extension to the case of higher order nonlinearities. Our main ingredients are a combination of integration by parts and classical dispersive estimates., Comment: 29 pages

Large Language Models (LLMs) have the potential to semi-automate some process mining (PM) analyses. While commercial models are already adequate for many analytics tasks, the competitive level of open-source LLMs in PM tasks is unknown. In this paper, we propose PM-LLM-Benchmark, the first comprehensive benchmark for PM focusing on domain knowledge (process-mining-specific and process-specific) and on different implementation strategies. We focus also on the challenges in creating such a benchmark, related to the public availability of the data and on evaluation biases by the LLMs. Overall, we observe that most of the considered LLMs can perform some process mining tasks at a satisfactory level, but tiny models that would run on edge devices are still inadequate. We also conclude that while the proposed benchmark is useful for identifying LLMs that are adequate for process mining tasks, further research is needed to overcome the evaluation biases and perform a more thorough ranking of the competitive LLMs.

The building sector in the European Union significantly impacts energy consumption and greenhouse gas emissions. The EU's Horizon 2050 initiative sets ambitious goals to reduce these impacts through enhanced building renovation rates. The CoE HiDALGO2 supports this initiative by developing high-performance computing solutions, specifically through the Urban Building pilot application, which utilizes advanced CI/CD methodologies to streamline simulation and deployment across various computational platforms, such as the EuroHPC JU supercomputers. The present work provides an overview of the Ktirio Urban Building framework (KUB), starting with an overview of the workflow and a description of some of the main ingredients of the software stack and discusses some current results performed on EuroHPC JU supercomputers using an innovative CI/CD pipeline.

Object-centric event logs, allowing events related to different objects of different object types, represent naturally the execution of business processes, such as ERP (O2C and P2P) and CRM. However, modeling such complex information requires novel process mining techniques and might result in complex sets of constraints. Object-centric anomaly detection exploits both the lifecycle and the interactions between the different objects. Therefore, anomalous patterns are proposed to the user without requiring the definition of object-centric process models. This paper proposes different methodologies for object-centric anomaly detection and discusses the role of domain knowledge for these methodologies. We discuss the advantages and limitations of Large Language Models (LLMs) in the provision of such domain knowledge. Following our experience in a real-life P2P process, we also discuss the role of algorithms (dimensionality reduction+anomaly detection), suggest some pre-processing steps, and discuss the role of feature propagation.

Context: There are currently three pulsars firmly detected by imaging atmospheric Cherenkov telescopes (IACTs), two of them reaching TeV energies, challenging models of very-high-energy (VHE) emission in pulsars. More precise observations are needed to better characterize pulsar emission at these energies. The LST-1 is the prototype of the Large-Sized Telescope, that will be part of the Cherenkov Telescope Array Observatory (CTAO). Its improved performance over previous IACTs makes it well suited for studying pulsars. Aims: To study the Crab pulsar emission with the LST-1, improving and complementing the results from other telescopes. These observations can also be used to characterize the potential of the LST-1 to study other pulsars and detect new ones. Methods: We analyzed a total of $\sim$103 hours of gamma-ray observations of the Crab pulsar conducted with the LST-1 in the period from September 2020 to January 2023. The observations were carried out at zenith angles less than 50 degrees. A new analysis of the Fermi-LAT data was also performed, including $\sim$14 years of observations. Results: The Crab pulsar phaseogram, long-term light-curve, and phase-resolved spectra are reconstructed with the LST-1 from 20 GeV to 450 GeV for P1 and up to 700 GeV for P2. The pulsed emission is detected with a significance of 15.2$\sigma$. The two characteristic emission peaks of the Crab pulsar are clearly detected (>10$\sigma$), as well as the so-called bridge emission (5.7$\sigma$). We find that both peaks are well described by power laws, with spectral indices of $\sim$3.44 and $\sim$3.03 respectively. The joint analysis of Fermi-LAT and LST-1 data shows a good agreement between both instruments in the overlapping energy range. The detailed results obtained in the first observations of the Crab pulsar with LST-1 show the potential that CTAO will have to study this type of sources., Comment: Accepted by A&A

Stochastic gravitational-wave backgrounds can be of either cosmological or astrophysical origin. The detection of an astrophysical stochastic gravitational-wave background with ground-based interferometers is expected in the near future. Perhaps even more excitingly, the detection of stochastic backgrounds of cosmological origin by future ground-based interferometers could reveal invaluable information about the early Universe. From this perspective, the astrophysical background is a {\it foreground} that can prevent the extraction of this information from the data. In this paper, we revisit a time-frequency domain notching procedure previously proposed to remove the astrophysical foreground in the context of next-generation ground-based detectors, but we consider the more realistic scenario where we remove individually detectable signals by taking into account the uncertainty in the estimation of their parameters. We find that time-frequency domain masks can still efficiently remove the astrophysical foreground and suppress it to about $5\%$ of its original level. Further removal of the foreground formed by unresolvable events (in particular, unresolvable binary neutron stars), which is about $10$ times larger than the residual foreground from realistic notching, would require detector sensitivity improvements. Therefore, the main limitation in the search for a cosmological background is the unresolvable foreground itself, and not the residual of the notching procedure., Comment: 13 pages, 6 figures

Multimodal Deep Learning enhances decision-making by integrating diverse information sources, such as texts, images, audio, and videos. To develop trustworthy multimodal approaches, it is essential to understand how uncertainty impacts these models. We propose LUMA, a unique benchmark dataset, featuring audio, image, and textual data from 50 classes, for learning from uncertain and multimodal data. It extends the well-known CIFAR 10/100 dataset with audio samples extracted from three audio corpora, and text data generated using the Gemma-7B Large Language Model (LLM). The LUMA dataset enables the controlled injection of varying types and degrees of uncertainty to achieve and tailor specific experiments and benchmarking initiatives. LUMA is also available as a Python package including the functions for generating multiple variants of the dataset with controlling the diversity of the data, the amount of noise for each modality, and adding out-of-distribution samples. A baseline pre-trained model is also provided alongside three uncertainty quantification methods: Monte-Carlo Dropout, Deep Ensemble, and Reliable Conflictive Multi-View Learning. This comprehensive dataset and its benchmarking tools are intended to promote and support the development, evaluation, and benchmarking of trustworthy and robust multimodal deep learning approaches. We anticipate that the LUMA dataset will help the ICLR community to design more trustworthy and robust machine learning approaches for safety critical applications.

The Lorentz Invariance Violation (LIV), a proposed consequence of certain quantum gravity (QG) scenarios, could instigate an energy-dependent group velocity for ultra-relativistic particles. This energy dependence, although suppressed by the massive QG energy scale $E_\mathrm{QG}$, expected to be on the level of the Planck energy $1.22 \times 10^{19}$ GeV, is potentially detectable in astrophysical observations. In this scenario, the cosmological distances traversed by photons act as an amplifier for this effect. By leveraging the observation of a remarkable flare from the blazar Mrk\,421, recorded at energies above 100 GeV by the MAGIC telescopes on the night of April 25 to 26, 2014, we look for time delays scaling linearly and quadratically with the photon energies. Using for the first time in LIV studies a binned-likelihood approach we set constraints on the QG energy scale. For the linear scenario, we set $95\%$ lower limits $E_\mathrm{QG}>2.7\times10^{17}$ GeV for the subluminal case and $E_\mathrm{QG}> 3.6 \times10^{17}$ GeV for the superluminal case. For the quadratic scenario, the $95\%$ lower limits for the subluminal and superluminal cases are $E_\mathrm{QG}>2.6 \times10^{10}$ GeV and $E_\mathrm{QG}>2.5\times10^{10}$ GeV, respectively.

Turbulent flows at the surface of the ocean deviate from geostrophic equilibrium on scales smaller than about 10 km. These scales are associated with important vertical transport of active and passive tracers, and should play a prominent role in the heat transport at climatic scales and for plankton dynamics. Measuring velocity fields on such small scales is notoriously difficult but new, high-resolution satellite altimetry is starting to reveal them. However, the satellite-derived velocities essentially represent the geostrophic flow component, and the impact of unresolved ageostrophic motions on particle dispersion needs to be understood to properly characterize transport properties. Here, we investigate ocean fine-scale turbulence using a model that represents some of the processes due to ageostrophic dynamics. We take a Lagrangian approach and focus on the predictability of the particle dynamics, comparing trajectories advected by either the full flow or by its geostrophic component only. Our results indicate that, over long times, relative dispersion is marginally affected by the filtering of the ageostrophic component. Nevertheless, advection by the filtered flow leads to an overestimation of the typical pair-separation rate, and to a bias on trajectories (in terms of displacement from the actual ones), whose importance grows with the Rossby number. We further explore the intensity of the transient particle clustering induced by ageostrophic motions and find that it can be significant, even for small flow compressibility. Indeed, we show that clustering is here due to the interplay between compressibility and persistent flow structures that trap particles, enhancing their aggregation., Comment: 19 pages, 10 figures

Astrophysical scenarios for the formation and evolution of intermediate-mass black holes (IMBHs) in the mass range $10^2 M_\odot \lesssim M \lesssim 10^6 M_\odot$ remain uncertain, but future ground-based gravitational-wave (GW) interferometers will probe the lower end of the IMBH mass range. We study the detectability of IMBH binary mergers and the measurability of their parameters with next-generation ground-based detector networks consisting of various combinations of Cosmic Explorer (CE) and Einstein Telescope (ET) interferometers. We find that, for binaries with component masses $m_{1,2}\sim 1000\,M_\odot$, an optimal 3-detector network can constrain the masses with errors $\lesssim 0.1\%$ ($\lesssim 1\%$) at $z=0.5$ ($z=2$), and the source redshift can be measured with percent-level accuracy or better at $z\lesssim 2$. The redshift of lighter binaries ($m_{1,2}\lesssim 300\,M_\odot$) can still be measured with $O(10)\%$ accuracy even at $z=10$. Binaries with $z\lesssim 0.5$ can be localized within $1\,\rm{deg}^2$ for $m_{1,2}\lesssim 1000\,M_\odot$, and within $0.1\,\rm{deg}^2$ for comparable mass systems. The sky localization is good enough that it may be possible to cross-correlate GW searches with galaxy catalogs and to search for electromagnetic counterparts to IMBH mergers. We also point out that the low-frequency sensitivity of the detectors is crucial for IMBH detection and parameter estimation. It will be interesting to use our results in conjunction with population synthesis codes to constrain astrophysical IMBH formation models., Comment: 18 pages, 11 figures. All data is available at https://zenodo.org/records/13829385

This work investigates Artificial Intelligence (AI) systems that detect respiratory insufficiency (RI) by analyzing speech audios, thus treating speech as a RI biomarker. Previous works collected RI data (P1) from COVID-19 patients during the first phase of the pandemic and trained modern AI models, such as CNNs and Transformers, which achieved $96.5\%$ accuracy, showing the feasibility of RI detection via AI. Here, we collect RI patient data (P2) with several causes besides COVID-19, aiming at extending AI-based RI detection. We also collected control data from hospital patients without RI. We show that the considered models, when trained on P1, do not generalize to P2, indicating that COVID-19 RI has features that may not be found in all RI types., Comment: 5 pages, 2 figures, 1 table. Published in Artificial Intelligence in Medicine (AIME) 2023

Recent studies have shown the ability of large language models to perform a variety of tasks, including time series forecasting. The flexible nature of these models allows them to be used for many applications. In this paper, we present a novel study of large language models used for the challenging task of time series anomaly detection. This problem entails two aspects novel for LLMs: the need for the model to identify part of the input sequence (or multiple parts) as anomalous; and the need for it to work with time series data rather than the traditional text input. We introduce sigllm, a framework for time series anomaly detection using large language models. Our framework includes a time-series-to-text conversion module, as well as end-to-end pipelines that prompt language models to perform time series anomaly detection. We investigate two paradigms for testing the abilities of large language models to perform the detection task. First, we present a prompt-based detection method that directly asks a language model to indicate which elements of the input are anomalies. Second, we leverage the forecasting capability of a large language model to guide the anomaly detection process. We evaluated our framework on 11 datasets spanning various sources and 10 pipelines. We show that the forecasting method significantly outperformed the prompting method in all 11 datasets with respect to the F1 score. Moreover, while large language models are capable of finding anomalies, state-of-the-art deep learning models are still superior in performance, achieving results 30% better than large language models., Comment: This work is accepted by IEEE International Conference on Data Science and Advanced Analytics (DSAA 2024)

Galactic double white dwarf (DWD) binaries are among the guaranteed sources for the Laser Interferometer Space Antenna (LISA), an upcoming space-based gravitational wave (GW) detector. Most DWDs in the LISA band are far from merging and emit quasimonochromatic GWs. As these sources are distributed throughout the Milky Way, they experience different accelerations in the Galactic gravitational potential, and therefore each DWD exhibits an apparent GW frequency chirp due to differential acceleration between the source and LISA. We examine how Galactic acceleration influences parameter estimation for these sources; and investigate how LISA observations could provide insight into the distribution of matter in the Galaxy., Comment: 20 pages, 7 figures

Addressing multi-label action recognition in videos represents a significant challenge for robotic applications in dynamic environments, especially when the robot is required to cooperate with humans in tasks that involve objects. Existing methods still struggle to recognize unseen actions or require extensive training data. To overcome these problems, we propose Dual-VCLIP, a unified approach for zero-shot multi-label action recognition. Dual-VCLIP enhances VCLIP, a zero-shot action recognition method, with the DualCoOp method for multi-label image classification. The strength of our method is that at training time it only learns two prompts, and it is therefore much simpler than other methods. We validate our method on the Charades dataset that includes a majority of object-based actions, demonstrating that -- despite its simplicity -- our method performs favorably with respect to existing methods on the complete dataset, and promising performance when tested on unseen actions. Our contribution emphasizes the impact of verb-object class-splits during robots' training for new cooperative tasks, highlighting the influence on the performance and giving insights into mitigating biases.

We prove the long-standing conjecture regarding the existence of infinitely many high-frequency modulational instability ``isolas" for a Stokes wave in arbitrary depth $ \mathtt{h} > 0 $, subject to longitudinal perturbations. We completely describe the spectral bands with non-zero real part away from the origin of the $L^2(\mathbb{R})$-spectrum of the water waves system linearized at a Stokes waves of small amplitude $ \epsilon > 0 $. The unstable spectrum is the union of isolas of elliptical shape, parameterized by integers $ \mathtt{p}\geq 2 $, with semiaxis of size $ |\beta_1^{(\mathtt{p})} (\mathtt{h})| \epsilon^\mathtt{p}+ O(\epsilon^{\mathtt{p}+1} )$ where $\beta_1^{( \mathtt{p})} (\mathtt{h})$ is a nonzero analytic function of the depth $ \mathtt{h} $ that depends on the Taylor coefficients of the Stokes waves up to order $\mathtt{p}$., Comment: 58 pages, 10 figures, companion material at https://git-scm.sissa.it/amaspero/isolas/

General relativity (GR) has proven to be a highly successful theory of gravity since its inception. The theory has thrivingly passed numerous experimental tests, predominantly in weak gravity, low relative speeds, and linear regimes, but also in the strong-field and very low-speed regimes with binary pulsars. Observable gravitational waves (GWs) originate from regions of spacetime where gravity is extremely strong, making them a unique tool for testing GR, in previously inaccessible regions of large curvature, relativistic speeds, and strong gravity. Since their first detection, GWs have been extensively used to test GR, but no deviations have been found so far. Given GR's tremendous success in explaining current astronomical observations and laboratory experiments, accepting any deviation from it requires a very high level of statistical confidence and consistency of the deviation across GW sources. In this paper, we compile a comprehensive list of potential causes that can lead to a false identification of a GR violation in standard tests of GR on data from current and future ground-based GW detectors. These causes include detector noise, signal overlaps, gaps in the data, detector calibration, source model inaccuracy, missing physics in the source and in the underlying environment model, source misidentification, and mismodeling of the astrophysical population. We also provide a rough estimate of when each of these causes will become important for tests of GR for different detector sensitivities. We argue that each of these causes should be thoroughly investigated, quantified, and ruled out before claiming a GR violation in GW observations., Comment: Review article; 1 figure; 1 table; comments welcome

In this work we compute numerical bounds on the mass $\mu$ of superradiantly unstable scalar fields in a Kerr black hole background using the continued fraction method. We show that the normalized upper bound on the mass $\mu$ increases with the angular momentum number $\ell$ and the azimuthal number $m$, approaching the most stringent analytical bound known to date when $\ell=m \gg 1$. We also provide an analytical fit to the numerically determined mass bound as a function of the dimensionless spin parameter $a/M$ of the black hole with an accuracy of the order $0.1\%$ for the fundamental mode with $\ell=m=1$, and of the order $1\%$ for higher-order modes (up to $\ell=m=20$). We argue that this analytical fit is particularly useful in astrophysical scenarios, since the lowest $\ell=m$ modes are capable of producing the strongest observable imprints of superradiance., Comment: 8 pages, 7 figures. v2: typos corrected, reference added

Rapid formation of supermassive black holes occurs in dense nuclear star clusters that are initially gas-dominated. Stellar-mass black hole remnants of the most massive cluster sink into the core, where a massive runaway black hole forms as a consequence of combined effects of repeated mergers and Eddington-limited gas accretion. The associated gravitational wave signals of high-redshift extreme mass-ratio inspirals are a unique signature of the nuclear star cluster scenario., Comment: 4 pages, 3 figures

Fermion soliton stars are a consistent model of exotic compact objects which involve a nonlinear interaction between a real scalar field and fermions through a Yukawa term. This interaction results in an effective fermion mass that depends upon the vacuum structure in the scalar potential. In this work we investigate the tidal deformations of fermion soliton stars and compute the corresponding tidal Love numbers for different model parameters. Furthermore, we discuss the existence of approximate universal relations for the electric and magnetic tidal deformabilities of these stars, and compare them with other solutions of general relativity, such as neutron stars or boson stars. These relations for fermion soliton stars are less universal than for neutron stars, but they are sufficiently different from the ordinary neutron star case that a measurement of the electric and magnetic tidal Love numbers (as potentially achievable by next-generation gravitational wave detectors) can be used to disentangle these families of compact objects. Finally, we discuss the conditions for tidal disruption of fermion soliton stars in a binary system and estimate the detectability of the electromagnetic signal associated with such tidal disruption events., Comment: 15 pages, 4 figures. v2: new figure added, matches version accepted in PRD

pm4py is a process mining library for Python implementing several process mining (PM) artifacts and algorithms. It also offers methods to integrate PM with large language models (LLMs). This paper examines how the current paradigms of PM on LLM are implemented in pm4py, identifying challenges such as privacy, hallucinations, and the context window limit.

Next-generation gravitational wave detectors such as the Einstein Telescope and Cosmic Explorer will have increased sensitivity and observing volumes, enabling unprecedented precision in parameter estimation. However, this enhanced precision could also reveal systematic biases arising from waveform modeling, which may impact astrophysical inference. We investigate the extent of these biases over a year-long observing run with $10^5$ simulated binary black hole sources using the linear signal approximation. To establish a conservative estimate, we sample binaries from a smoothed truncated power-law population model and compute systematic parameter biases between the IMRPhenomXAS and IMRPhenomD waveform models. For sources with signal-to-noise ratios above 100, we estimate statistically significant parameter biases in $\sim 3\%-20\%$ of the events, depending on the parameter. We find that the average mismatch between waveform models required to achieve a bias of $\leq 1\sigma$ for $99\%$ of detections with signal-to-noise ratios $\geq 100$ should be $\mathcal{O}(10^{-5})$, or at least one order of magnitude better than current levels of waveform accuracy., Comment: 15 pages, 11 figures, 1 table. Matches the version published in Phys. Rev. D 109, 104043

The effect of annealing on the superconducting properties of niobium single crystals cut from the same master boule was studied by local and global magnetic measurements, as well as scanning tunneling microscopy (STM). The formation of large hydride precipitates was observed in unannealed samples. The variation in structural and magnetic properties was studied after annealing under high vacuum at 800 C, 1400 C, and near the melting point of niobium (2477 C) for a few seconds. The initial samples had a high hydrogen content. Polarized optics and magneto-optical studies show that the formation of large niobium hydride precipitates is suppressed already by 800 C annealing. However, the overall superconducting properties in the annealed samples did not improve after annealing, and in fact, worsened. The superconducting transition temperature decreased, the upper critical field increased, and the pinning strength increased. Parallel studies were conducted using STM, where the sample was annealed initially at 400 C, measured, annealed again at 1700 C, and measured again. These studies revealed a ``dirty'' superconducting gap with a significant spatial variation of tunneling conductance after annealing at 400 C. The clean gap was recovered after annealing at 1700 C. It is likely that these results are due to oxygen redistribution near the surface, which is always covered by oxide layers in as-grown crystals. Overall, the results indicate that vacuum annealing at least up to 1400 C, while expected to remove a large amount of hydrogen, introduces additional nanosized defects, perhaps hydride precipitates, that act as efficient pair-breaking and pinning centers.

When gravitational waves pass near a gravitating object, they are deflected, or lensed. If the object is massive, such that the wavelength of the waves is small compared to its gravitational size, lensed gravitational wave events can be identified when multiple signals are detected at different times. However, when the wavelength is long, wave-optics diffraction effects will be important, and a lensed event can be identified by looking for frequency-dependent modulations to the gravitational waveform, without having to associate multiple signals. For current ground-based gravitational wave detectors observing stellar-mass binary compact object mergers, wave-optics effects are important for lenses with masses $\lesssim 1000 M_{\odot}$. Therefore, minihalos below this mass range could potentially be identified by lensing diffraction. The challenge with analyzing these events is that the frequency-dependent lensing modulation, or the amplification factor, is prohibitively expensive to compute for Bayesian parameter inference. In this work, we use a novel time-domain method to construct interpolators of the amplification factor for the Navarro-Frenk-White (NFW), generalized singular isothermal sphere (gSIS) and cored isothermal sphere (CIS) lens models. Using these interpolators, we perform Bayesian inference on gravitational-wave signals lensed by minihalos injected in mock detector noise, assuming current sensitivity of ground-based detectors. We find that we could potentially identify an event when it is lensed by minihalos and extract the values of all lens parameters in addition to the parameters of the GW source. All of the methods are implemented in Glworia, the accompanying open-source Python package, and can be generalized to study lensed signals detected by current and next-generation detectors., Comment: 19 pages, 9 figures

In the aftermath of a binary black hole merger event, the gravitational wave signal emitted by the remnant black hole is modeled as a superposition of damped sinusoids known as quasinormal modes. While the dominant quasinormal modes originating from linear black hole perturbation theory have been studied extensively in this post-merger "ringdown" phase, more accurate models of ringdown radiation include the nonlinear modes arising from higher-order perturbations of the remnant black hole spacetime. We explore the detectability of quadratic quasinormal modes with both ground- and space-based next-generation detectors. We quantify how predictions of the quadratic mode detectability depend on the quasinormal mode starting times. We then calculate the signal-to-noise ratio of quadratic modes for several detectors and binary black hole populations, focusing on the ($220\times220$) mode - i.e., on the quadratic term sourced by the square of the linear $(220)$ mode. For the events with the loudest quadratic mode signal-to-noise ratios, we additionally compute statistical errors on the mode parameters in order to further ascertain the distinguishability of the quadratic mode from the linear quasinormal modes. The astrophysical models used in this paper suggest that while the quadratic mode may be detectable in at most a few events with ground-based detectors, the prospects for detection with the Laser Interferometer Space Antenna (LISA) are more optimistic., Comment: 21 pages, 16 figures, 2 tables; revised to match published version; new plots in App. D

In the realm of Business Process Management (BPM), process modeling plays a crucial role in translating complex process dynamics into comprehensible visual representations, facilitating the understanding, analysis, improvement, and automation of organizational processes. Traditional process modeling methods often require extensive expertise and can be time-consuming. This paper explores the integration of Large Language Models (LLMs) into process modeling to enhance the accessibility of process modeling, offering a more intuitive entry point for non-experts while augmenting the efficiency of experts. We propose a framework that leverages LLMs for the automated generation and iterative refinement of process models starting from textual descriptions. Our framework involves innovative prompting strategies for effective LLM utilization, along with a secure model generation protocol and an error-handling mechanism. Moreover, we instantiate a concrete system extending our framework. This system provides robust quality guarantees on the models generated and supports exporting them in standard modeling notations, such as the Business Process Modeling Notation (BPMN) and Petri nets. Preliminary results demonstrate the framework's ability to streamline process modeling tasks, underscoring the transformative potential of generative AI in the BPM field.

Using Large Language Models (LLMs) for Process Mining (PM) tasks is becoming increasingly essential, and initial approaches yield promising results. However, little attention has been given to developing strategies for evaluating and benchmarking the utility of incorporating LLMs into PM tasks. This paper reviews the current implementations of LLMs in PM and reflects on three different questions. 1) What is the minimal set of capabilities required for PM on LLMs? 2) Which benchmark strategies help choose optimal LLMs for PM? 3) How do we evaluate the output of LLMs on specific PM tasks? The answer to these questions is fundamental to the development of comprehensive process mining benchmarks on LLMs covering different tasks and implementation paradigms.

ProMoAI is a novel tool that leverages Large Language Models (LLMs) to automatically generate process models from textual descriptions, incorporating advanced prompt engineering, error handling, and code generation techniques. Beyond automating the generation of complex process models, ProMoAI also supports process model optimization. Users can interact with the tool by providing feedback on the generated model, which is then used for refining the process model. ProMoAI utilizes the capabilities LLMs to offer a novel, AI-driven approach to process modeling, significantly reducing the barrier to entry for users without deep technical knowledge in process modeling.