Search

The Zero-touch Network & Service Management (ZSM) paradigm, a direct response to the increasing complexity of communication networks, is a problem-solving approach. In this paper, taking advantage of recent advances in generative Artificial Intelligence, we introduce the Network ConFiguration Generator (LLM-NetCFG) that employs Large Language Model and architects ZSM configuration agents by Large Language Models. LLM-NetCFG can automatically generate configurations, verify them, and configure network devices based on intents expressed in natural language. We also show the automation and verification of network configurations with minimum human intervention. Moreover, we explore the opportunities and challenges of integrating LLM in functional areas of network management to fully achieve ZSM.

Subword tokenization algorithms used by Large Language Models are significantly more efficient and can independently build the necessary vocabulary of words and subwords without human intervention. However, those subwords do not always align with real morphemes, potentially impacting the models' performance, though it remains uncertain when this might occur. In previous research, we proposed a method to assess the morphological quality of vocabularies, focusing on the overlap between these vocabularies and the morphemes of a given language. Our evaluation method was built on three quality measures, relevance, cohesion, and morphological accuracy, and a procedure for their assessment. By applying this method to vocabularies created by three subword tokenization algorithms, BPE, Wordpiece, and Unigram, we concluded that these vocabularies generally exhibit very low morphological quality. In this article, we apply this evaluation to the tokenizer of BETO, a BERT language model trained on large Spanish corpora. This evaluation, along with our previous results, helped us conclude that its vocabulary has a low morphological quality, and we also found that training the tokenizer in a larger corpus does not improve the morphological quality of the generated vocabulary. Additionally, this evaluation helps clarify the algorithm used by the tokenizer, that is, Wordpiece, given the inconsistencies between the authors' claims and the model's configuration., Comment: in Spanish language

With climate change-related extreme events on the rise, high dimensional Earth observation data presents a unique opportunity for forecasting and understanding impacts on ecosystems. This is, however, impeded by the complexity of processing, visualizing, modeling, and explaining this data. To showcase how this challenge can be met, here we train a convolutional long short-term memory-based architecture on the novel DeepExtremeCubes dataset. DeepExtremeCubes includes around 40,000 long-term Sentinel-2 minicubes (January 2016-October 2022) worldwide, along with labeled extreme events, meteorological data, vegetation land cover, and topography map, sampled from locations affected by extreme climate events and surrounding areas. When predicting future reflectances and vegetation impacts through kernel normalized difference vegetation index, the model achieved an R$^2$ score of 0.9055 in the test set. Explainable artificial intelligence was used to analyze the model's predictions during the October 2020 Central South America compound heatwave and drought event. We chose the same area exactly one year before the event as counterfactual, finding that the average temperature and surface pressure are generally the best predictors under normal conditions. In contrast, minimum anomalies of evaporation and surface latent heat flux take the lead during the event. A change of regime is also observed in the attributions before the event, which might help assess how long the event was brewing before happening. The code to replicate all experiments and figures in this paper is publicly available at https://github.com/DeepExtremes/txyXAI

The second PyHEP.dev workshop, part of the "Python in HEP Developers" series organized by the HEP Software Foundation (HSF), took place in Aachen, Germany, from August 26 to 30, 2024. This gathering brought together nearly 30 Python package developers, maintainers, and power users to engage in informal discussions about current trends in Python, with a primary focus on analysis tools and techniques in High Energy Physics (HEP). The workshop agenda encompassed a range of topics, such as defining the scope of HEP data analysis, exploring the Analysis Grand Challenge project, evaluating statistical models and serialization methods, assessing workflow management systems, examining histogramming practices, and investigating distributed processing tools like RDataFrame, Coffea, and Dask. Additionally, the workshop dedicated time to brainstorming the organization of future PyHEP.dev events, upholding the tradition of alternating between Europe and the United States as host locations. This document, prepared by the session conveners in the weeks following the workshop, serves as a summary of the key discussions, salient points, and conclusions that emerged., Comment: 16 pages, 0 figures; workshop summary

Particle-producing targets in high-energy research facilities are often made from refractory metals, and they typically require dedicated cooling systems due to the challenging thermomechanical conditions they experience. However, direct contact of water with target blocks can induce erosion, corrosion, and embrittlement, especially of tungsten (W). One approach to overcoming this problem is cladding the blocks with tantalum (Ta). Unfortunately, Ta generates high decay heat when irradiated, raising safety concerns in the event of a loss-of-cooling accident. This study explored the capacity of niobium (Nb) and its alloys to form diffusion bonds with W and TZM (a molybdenum alloy with titanium and zirconium). This is because the Beam Dump Facility (BDF), a planned new fixed-target installation in CERN's North Area, uses these target materials. The bonding quality of pure Nb, Nb1Zr, and C103 (a Nb alloy with 10% hafnium and 1% titanium) with TZM and W obtained using hot isostatic pressing (HIP) was evaluated. The effects of different HIP temperatures and the introduction of a Ta interlayer were examined. Optical microscopy indicated promising bonding interfaces, which were further characterized using tensile tests and thermal-diffusivity measurements. Their performance under high-energy beam impact was validated using thermomechanical simulations. C103 exhibited higher interface strengths and safety factors than Ta2.5W, positioning it as a potential alternative cladding material for the BDF production target. The findings highlight the viability of Nb-based materials, particularly C103, for improving operational safety and efficiency in fixed-target physics experiments; however, considerations regarding the long half-life of 94Nb require further attention.

The Beam Dump Facility (BDF) is a planned fixed-target installation in CERN's North Area, set to start operating in 2031. A proton beam of 400 GeV/c will be delivered in 1 s pulses of 4e13 protons every 7.2 s, amounting to 4e19 protons on target (PoT) per year, with an average thermal power deposition of 305 kW. The experiment requires high-Z and high-density materials and involves challenging thermomechanical conditions; hence, a water-cooled refractory metal core is proposed. To prevent corrosion and erosion, the target materials - tungsten and a molybdenum-based alloy, TZM-, are clad with a tantalum alloy using hot isostatic pressing. To verify the reliability of the target design and manufacturing process, a reduced-scale prototype target was manufactured and irradiated with 2.4e16 PoT in 2018. This paper presents the findings of the post-irradiation examination (PIE) conducted 2.5 years later. The PIE consisted of non-destructive and destructive activities, including film imaging, microscopy, metrology, ultrasonic testing, microstructural analysis, and mechanical and thermal characterization. No irradiation effects resulting from thermal stresses, such as geometrical changes or cracks in the cladding, were observed, and it was determined that the bonding between the core and cladding materials was robust. Moreover, no changes were detected in the microstructures or mechanical properties of the bulk materials. In conclusion, the robustness of the target baseline design and manufacturing process was confirmed, validating its suitability for operating under the desired conditions. Nonetheless, the observed brittleness of sintered tungsten indicates a potential area for improvement to enhance the lifetime of the BDF target. Overall, the examinations provided valuable insights into the performance of the prototype and indicated potential refinements for the future BDF target complex.

Using individual-level data from Gallup World Poll and the Levada Center, we provide an in-depth analysis of how Russia's invasion of Ukraine has affected sentiments in the Russian population. Our results show that after the invasion, a larger share of Russians expressed support for President Putin, optimism about the future, anti-West attitudes, and lower migration aspirations. The 2022 mobilization and the 2023 Wagner rebellion had only short-lived and no effects on sentiments, respectively. Regions with low pre-war support for Putin displayed stronger rally effects, higher casualty rates, and increased incomes, suggesting a recruitment strategy that maximizes political support. Taken together, our results suggest strong public support for the war, except among Russians abroad who became more critical of Putin in line with global views.

Human-Robot Collaboration (HRC) has evolved into a highly promising issue owing to the latest breakthroughs in Artificial Intelligence (AI) and Human-Robot Interaction (HRI), among other reasons. This emerging growth increases the need to design multi-agent algorithms that can manage also human preferences. This paper presents an extension of the Ant Colony Optimization (ACO) meta-heuristic to solve the Minimum Time Search (MTS) task, in the case where humans and robots perform an object searching task together. The proposed model consists of two main blocks. The first one is a convolutional neural network (CNN) that provides the prior probabilities about where an object may be from a segmented image. The second one is the Sub-prior MTS-ACO algorithm (SP-MTS-ACO), which takes as inputs the prior probabilities and the particular search preferences of the agents in different sub-priors to generate search plans for all agents. The model has been tested in real experiments for the joint search of an object through a Vizanti web-based visualization in a tablet computer. The designed interface allows the communication between a human and our humanoid robot named IVO. The obtained results show an improvement in the search perception of the users without loss of efficiency.

Even though a few initial works have shown on small sets of data some level of bias in the performance of fingerprint recognition technology with respect to certain demographic groups, there is still not sufficient evidence to understand the impact that certain factors such as gender, age or finger-type may have on fingerprint quality and, in turn, also on fingerprint matching accuracy. The present work addresses this still under researched topic, on a large-scale database of operational data containing 10-print impressions of almost 16,000 subjects. The results reached provide further insight into the dependency of fingerprint quality and demographics, and show that there in fact exists a certain degree of performance variability in fingerprint-based recognition systems for different segments of the population. Based on the experimental evaluation, the work points out new observations based on data-driven evidence, provides plausible hypotheses to explain such observations, and concludes with potential follow-up actions that can help to reduce the observed fingerprint quality differences. This way, the current paper can be considered as a contribution to further increase the algorithmic fairness and equality of biometric technology., Comment: Extended journal version submitted to IET Biometrics. 10 pages, 5 figures Reference conference paper: J. Galbally, A. Cepilovs, R. Blanco-Gonzalo, G. Ormiston, O. Miguel-Hurtado, and I. S. Racz, 'Fingerprint quality per individual finger type: A large-scale study on real operational data' in Proc. IEEE Intl. Workshop on Biometrics and Forensics 2023 (IWBF 2023)

We numerically investigate the noise scaling in high-Tc commensurate 1D and 2D SQUID arrays. We show that the voltage noise spectral density in 1D arrays violates the scaling rule of ~1/Np for the number Np of Josephson junctions in parallel. In contrast, in 2D arrays with Ns 1D arrays in series, the voltage noise spectral density follows more closely the expected scaling behaviour of ~Ns/Np. Additionally, we reveal how the flux and magnetic field rms noise spectral densities deviate from their expected ~(NsNp)^(-1/2) scaling and discuss their implications for designing low noise magnetometers.

Moldable development supports decision-making by making software systems explainable. This is done by making it cheap to add numerous custom tools to your software, turning it into a live, explorable domain model. Based on several years of experience of applying moldable development to both open-source and industrial systems, we have identified several mutually supporting patterns to explain how moldable development works in practice. This paper targets (i) readers curious to learn about moldable development, (ii) current users of the Glamorous Toolkit moldable IDE wanting to learn best practices, and (iii) developers interested in applying moldable development using other platforms and technology.

This study introduces a novel approach that integrates agricultural census data with remotely sensed time series to develop precise predictive models for paddy rice yield across various regions of Peru. By utilizing sparse regression and Elastic-Net regularization techniques, the study identifies causal relationships between key remotely sensed variables-such as NDVI, precipitation, and temperature-and agricultural yield. To further enhance prediction accuracy, the first- and second-order dynamic transformations (velocity and acceleration) of these variables are applied, capturing non-linear patterns and delayed effects on yield. The findings highlight the improved predictive performance when combining regularization techniques with climatic and geospatial variables, enabling more precise forecasts of yield variability. The results confirm the existence of causal relationships in the Granger sense, emphasizing the value of this methodology for strategic agricultural management. This contributes to more efficient and sustainable production in paddy rice cultivation.

Today's most advanced multimodal models remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good performance, effectively distilling these closed models into open ones. As a result, the community is still missing foundational knowledge about how to build performant VLMs from scratch. We present Molmo, a new family of VLMs that are state-of-the-art in their class of openness. Our key innovation is a novel, highly detailed image caption dataset collected entirely from human annotators using speech-based descriptions. To enable a wide array of user interactions, we also introduce a diverse dataset mixture for fine-tuning that includes in-the-wild Q&A and innovative 2D pointing data. The success of our approach relies on careful choices for the model architecture details, a well-tuned training pipeline, and, most critically, the quality of our newly collected datasets, all of which will be released. The best-in-class 72B model within the Molmo family not only outperforms others in the class of open weight and data models but also compares favorably against proprietary systems like GPT-4o, Claude 3.5, and Gemini 1.5 on both academic benchmarks and human evaluation. We will be releasing all of our model weights, captioning and fine-tuning data, and source code in the near future. Select model weights, inference code, and demo are available at https://molmo.allenai.org.

Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.

Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do not incorporate outcome information; prominent examples include prospective cohort studies, survey weighting, and the weighting portion of augmented weighting estimators. In such applications, we explore the central role of representation learning in finding desirable weights in practice. Unlike the common approach of assuming a well-specified representation, we highlight the error due to the choice of a representation and outline a general framework for finding suitable representations that minimize this error. Building on recent work that combines balancing weights and neural networks, we propose an end-to-end estimation procedure that learns a flexible representation, while retaining promising theoretical properties. We show that this approach is competitive in a range of common causal inference tasks., Comment: UAI 2024, typos in UAI version fixed

Cross-View Geo-Localisation within urban regions is challenging in part due to the lack of geo-spatial structuring within current datasets and techniques. We propose utilising graph representations to model sequences of local observations and the connectivity of the target location. Modelling as a graph enables generating previously unseen sequences by sampling with new parameter configurations. To leverage this newly available information, we propose a GNN-based architecture, producing spatially strong embeddings and improving discriminability over isolated image embeddings. We outline SpaGBOL, introducing three novel contributions. 1) The first graph-structured dataset for Cross-View Geo-Localisation, containing multiple streetview images per node to improve generalisation. 2) Introducing GNNs to the problem, we develop the first system that exploits the correlation between node proximity and feature similarity. 3) Leveraging the unique properties of the graph representation - we demonstrate a novel retrieval filtering approach based on neighbourhood bearings. SpaGBOL achieves state-of-the-art accuracies on the unseen test graph - with relative Top-1 retrieval improvements on previous techniques of 11%, and 50% when filtering with Bearing Vector Matching on the SpaGBOL dataset.

The increasing volume and complexity of clinical documentation in Electronic Medical Records systems pose significant challenges for clinical coders, who must mentally process and summarise vast amounts of clinical text to extract essential information needed for coding tasks. While large language models have been successfully applied to shorter summarisation tasks in recent years, the challenge of summarising a hospital course remains an open area for further research and development. In this study, we adapted three pre trained LLMs, Llama 3, BioMistral, Mistral Instruct v0.1 for the hospital course summarisation task, using Quantized Low Rank Adaptation fine tuning. We created a free text clinical dataset from MIMIC III data by concatenating various clinical notes as the input clinical text, paired with ground truth Brief Hospital Course sections extracted from the discharge summaries for model training. The fine tuned models were evaluated using BERTScore and ROUGE metrics to assess the effectiveness of clinical domain fine tuning. Additionally, we validated their practical utility using a novel hospital course summary assessment metric specifically tailored for clinical coding. Our findings indicate that fine tuning pre trained LLMs for the clinical domain can significantly enhance their performance in hospital course summarisation and suggest their potential as assistive tools for clinical coding. Future work should focus on refining data curation methods to create higher quality clinical datasets tailored for hospital course summary tasks and adapting more advanced open source LLMs comparable to proprietary models to further advance this research., Comment: 20 pages, 4 figures

The Observatorio del Roque de los Muchachos will host the northern site of the Cherenkov Telescope Array Observatory (CTAO), in an area about 200 m below the mountain rim, where the optical telescopes are located. The site currently hosts the MAGIC Telescopes, which have gathered a unique series of 20 years of weather data. We use advanced profile likelihood methods to determine seasonal cycles, the occurrence of weather extremes, weather downtime, and long-term trends correctly taking into account data gaps. The fractality of the weather data is investigated by means of multifractal detrended fluctuation analysis. The data are published according to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. We find that the behaviour of wind and relative humidity show significant differences compared to the mountain rim. We observe an increase in temperature of $0.55\pm0.07\mathrm{(stat.)}\pm0.07\mathrm{(syst.)}^\circ C$/decade, the diurnal temperature range of $0.13\pm0.04\mathrm{(stat.)}\pm0.02\mathrm{(syst.)}^\circ C$/decade (accompanied by an increase of seasonal oscillation amplitude of $\Delta C_m=0.29\pm0.10\mathrm{(stat.)}\pm0.04\mathrm{(syst.)}^\circ C$/decade) and relative humidity of $4.0\pm0.4\mathrm{(stat.)}\pm1.1\mathrm{(syst.)}$%/decade, and a decrease in trade wind speeds of $0.85\pm0.12\mathrm{(stat.)}\pm0.07\mathrm{(syst.)}$(km/h)/decade. The occurrence of extreme weather, such as tropical storms and long rains, remains constant over time. We find a significant correlation of temperature with the North Atlantic Oscillation Index and multifractal behaviour of the data. The site shows a weather-related downtime of 18.5%-20.5%, depending on the wind gust limits employed. No hints are found of a degradation of weather downtime under the assumption of a linear evolution of environmental parameters over time., Comment: accepted for publication in MNRAS. For associated data, see https://dx.doi.org/10.5281/zenodo.11279074 , for associated analysis code, see https://github.com/mgaug/WS-Analysis

Robot navigation methods allow mobile robots to operate in applications such as warehouses or hospitals. While the environment in which the robot operates imposes requirements on its navigation behavior, most existing methods do not allow the end-user to configure the robot's behavior and priorities, possibly leading to undesirable behavior (e.g., fast driving in a hospital). We propose a novel approach to adapt robot motion behavior based on natural language instructions provided by the end-user. Our zero-shot method uses an existing Visual Language Model to interpret a user text query or an image of the environment. This information is used to generate the cost function and reconfigure the parameters of a Model Predictive Controller, translating the user's instruction to the robot's motion behavior. This allows our method to safely and effectively navigate in dynamic and challenging environments. We extensively evaluate our method's individual components and demonstrate the effectiveness of our method on a ground robot in simulation and real-world experiments, and across a variety of environments and user specifications.

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

Over the last few decades, machine learning (ML) applications have grown exponentially, yielding several benefits to society. However, these benefits are tempered with concerns of discriminatory behaviours exhibited by ML models. In this regard, fairness in machine learning has emerged as a priority research area. Consequently, several fairness metrics and algorithms have been developed to mitigate against discriminatory behaviours that ML models may possess. Yet still, very little attention has been paid to the problem of naturally occurring changes in data patterns (\textit{aka} data distributional drift), and its impact on fairness algorithms and metrics. In this work, we study this problem comprehensively by analyzing 4 fairness-unaware baseline algorithms and 7 fairness-aware algorithms, carefully curated to cover the breadth of its typology, across 5 datasets including public and proprietary data, and evaluated them using 3 predictive performance and 10 fairness metrics. In doing so, we show that (1) data distributional drift is not a trivial occurrence, and in several cases can lead to serious deterioration of fairness in so-called fair models; (2) contrary to some existing literature, the size and direction of data distributional drift is not correlated to the resulting size and direction of unfairness; and (3) choice of, and training of fairness algorithms is impacted by the effect of data distributional drift which is largely ignored in the literature. Emanating from our findings, we synthesize several policy implications of data distributional drift on fairness algorithms that can be very relevant to stakeholders and practitioners.

The advent of ultra-high dose rate irradiation, known as FLASH radiation therapy, has shown promising potential in reducing toxicity while maintaining tumor control. However, the clinical translation of these benefits necessitates efficient treatment planning strategies. This study introduces a novel approach to optimize proton therapy for FLASH effects using traveling salesperson problem (TSP) heuristics. We applied these heuristics to optimize the arrangement of proton spots in treatment plans for 26 prostate cancer patients, comparing the performance against conventional sorting methods and global optimization techniques. Our results demonstrate that TSP-based heuristics significantly enhance FLASH coverage to the same extent as the global optimization technique, but with computation times reduced from hours to a few seconds. This approach offers a practical and scalable solution for enhancing the effectiveness of FLASH therapy, paving the way for more effective and personalized cancer treatments. Future work will focus on further optimizing run times and validating these methods in clinical settings.

Current images of the supermassive black hole (SMBH) candidates at the center of our Galaxy and M87 have opened an unprecedented era for studying strong gravity and the nature of relativistic sources. Very-long-baseline interferometry (VLBI) data show images consistent with a central SMBH within General Relativity (GR). However, it is essential to consider whether other well-motivated dark compact objects within GR could produce similar images. Recent studies have shown that dark matter (DM) halos modeled as self-gravitating systems of neutral fermions can harbor very dense fermionic cores at their centers, which can mimic the spacetime features of a black hole (BH). Such dense, horizonless DM cores can satisfy the observational constraints: they can be supermassive and compact and lack a hard surface. We investigate whether such cores can produce similar observational signatures to those of BHs when illuminated by an accretion disk. We compute images and spectra of the fermion cores with a general-relativistic ray tracing technique, assuming the radiation originates from standard $\alpha$ disks, which are self-consistently solved within the current DM framework. Our simulated images possess a central brightness depression surrounded by a ring-like feature, resembling what is expected in the BH scenario. For Milky Way-like halos, the central brightness depressions have diameters down to $\sim 35\, \mu$as as measured from a distance of approximately $8\,$kpc. Finally, we show that the DM cores do not possess photon rings, a key difference from the BH paradigm, which could help discriminate between the models., Comment: 11 pages, 7 figures. Accepted for publication in MNRAS

The generalization of photon spheres by considering the trajectories of massive particles leads to the definition of Massive Particle Surfaces (MPS). These surfaces are built with the trajectories of massive particles, and have a partial umbilicity property. Using the geodesic and Gaussian curvature of the Jacobi metric (a Riemannian metric) we derive a general condition for the existence of a Massive Particle Surface defined for an asymptotically flat spacetime metric. Our results can be applied to the worldlines of charged massive particle surfaces. We provide a simple characterization for timelike and null trajectories using a Riemannian geometric approach. We are able to recover the results for the existence of Light Rings (LR's) and timelike circular orbits (TCO's). We show how an event horizon gets characterized using the curvatures of a Riemannian metric. We discuss several examples, where we derive conditions for the existence of photon sphere and a massive particle surface. We calculate the radius of the photon sphere and the radius of the Innermost Stable Circular Orbits (ISCO)., Comment: 19 pages

We analyze the evolution of the radial profiles and the azimuthal variations of the stellar metallicities from the Vintergatan simulation of a Milky Way-like galaxy. We find that negative gradients exist as soon as the disk settles at high redshift, and are maintained throughout the long term evolution of the galaxy, including during major merger events. The inside-out growth of the disk and an overall outward radial migration tend to flatten these gradients in time. Major merger events only have a moderate and short-lived imprint on the [Fe/H] distributions with almost no radial dependence. The reason lies in the timescale for enrichment in Fe being significantly longer than the duration of the starbursts episodes, themselves slower than dynamical mixing during typical interactions. It results that signatures of major mergers become undetectable in [Fe/H] only a few Myr after pericenter passages. We note that considering other tracers like the warm interstellar medium, or monitoring the evolution of the metallicity gradient as a single value instead of a radial full profile could lead to different interpretations, and warn against an oversimplification of this complex problem., Comment: submitted to A&A

Interpretation of data from faint dwarf galaxies is made challenging by observations limited to only the brightest stars. We present a major improvement to tackle this challenge by undertaking zoomed cosmological simulations that resolve the evolution of all individual stars more massive than $0.5\,{\rm M}_{\odot}$, thereby explicitly tracking all observable stars for the Hubble time. For the first time, we predict observable color-magnitude diagrams and the spatial distribution of $\approx 100,000$ stars within four faint ($M_{\star} \approx 10^5 \, \,{\rm M}_{\odot}$) dwarf galaxies directly from their cosmological initial conditions. In all cases, simulations predict complex light profiles with multiple components, implying that typical observational measures of structural parameters can make total V-band magnitudes appear up to 0.5 mag dimmer compared to estimates from simulations. Furthermore, when only small ($\lessapprox100$) numbers of stars are observable, shot noise from realizations of the color-magnitude diagram introduces uncertainties comparable to the population scatter in, e.g., total magnitude, half-light radius, and mean iron abundance measurements. Estimating these uncertainties with fully self-consistent mass growth, star formation and chemical enrichment histories paves the way for more robust interpretation of dwarf galaxy data., Comment: Submitted to ApJL, comments welcome

As AI systems become more advanced, concerns about large-scale risks from misuse or accidents have grown. This report analyzes the technical research into safe AI development being conducted by three leading AI companies: Anthropic, Google DeepMind, and OpenAI. We define safe AI development as developing AI systems that are unlikely to pose large-scale misuse or accident risks. This encompasses a range of technical approaches aimed at ensuring AI systems behave as intended and do not cause unintended harm, even as they are made more capable and autonomous. We analyzed all papers published by the three companies from January 2022 to July 2024 that were relevant to safe AI development, and categorized the 80 included papers into nine safety approaches. Additionally, we noted two categories representing nascent approaches explored by academia and civil society, but not currently represented in any research papers by these leading AI companies. Our analysis reveals where corporate attention is concentrated and where potential gaps lie. Some AI research may stay unpublished for good reasons, such as to not inform adversaries about the details of security techniques they would need to overcome to misuse AI systems. Therefore, we also considered the incentives that AI companies have to research each approach, regardless of how much work they have published on the topic. We identified three categories where there are currently no or few papers and where we do not expect AI companies to become much more incentivized to pursue this research in the future. These are model organisms of misalignment, multi-agent safety, and safety by design. Our findings provide an indication that these approaches may be slow to progress without funding or efforts from government, civil society, philanthropists, or academia., Comment: Sep-25 update adds papers that we missed in the original dataset, as well as more acknowledgements

Bell's states are among the most useful in quantum computing. These state are an orthonormal base of entagled states with two qubits. We propose alternative bases of entangled states. Some of these states depend on a continuous parameter. We present the quantum circuit and code of these alternative bases. In addition, we study quantum teleportation with these entangled states and present their quantum circuits and codes associated., Comment: 29 pages, 7 figures. Comments welcome

We compute baryon and electric charge deposition in high-energy heavy-ion collisions using the Color Glass Condensate (CGC) Effective Field Theory, where at leading order charge is deposited through multiple scatterings of valence quarks with a saturated gluon target. A simplified phenomenological formula is derived to describe charge deposition, from which the parametrical dependence with collisional energy and geometry can be extracted. We present an approximate analytical prediction of the so-called baryon stopping parameter $\alpha_B$, which shows excellent agreement with the state-of-the art extractions of $\alpha_B$ from experimental data. These results are further validated using the McDIPPER framework, by computing charge deposition at midrapidity across a range of collision energies ($\sqrt{s_{\rm NN}}= 62.4 - 5020$ GeV).

We present a sample of 50 H-alpha detected broad-line active galactic nuclei (BLAGN) at redshifts 3.50), independent of the contributions of emission lines to the broadband photometry. We construct the black hole (BH) mass function at 3.5

Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising methodologies, due to their more accurate estimations and better perceptual quality. This work tackles this flow sensing problem in the vicinity of the wall, addressing for the first time the reconstruction of the entire three-dimensional (3-D) field with a single network, i.e. a 3-D GAN. With this methodology, a single training and prediction process overcomes the limitation presented by the former approaches based on the independent estimation of wall-parallel planes. The network is capable of estimating the 3-D flow field with a level of error at each wall-normal distance comparable to that reported from wall-parallel plane estimations and at a lower training cost in terms of computational resources. The direct full 3-D reconstruction also unveils a direct interpretation in terms of coherent structures. It is shown that the accuracy of the network depends directly on the wall footprint of each individual turbulent structure. It is observed that wall-attached structures are predicted more accurately than wall-detached ones, especially at larger distances from the wall. Among wall-attached structures, smaller sweeps are reconstructed better than small ejections, while large ejections are reconstructed better than large sweeps as a consequence of their more intense footprint.

This study presents a novel methodology for stratifying post-myocardial infarction patients at risk of ventricular arrhythmias using patient-specific 3D cardiac models derived from late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) images. The method integrates imaging and computational simulation with a simplified cellular automaton model, Arrhythmic3D, enabling rapid and accurate VA risk assessment in clinical timeframes. Applied to 51 patients, the model generated thousands of personalized simulations to evaluate arrhythmia inducibility and predict VA risk. Key findings include the identification of slow conduction channels (SCCs) within scar tissue as critical to reentrant arrhythmias and the localization of high-risk zones for potential intervention. The Arrhythmic Risk Score (ARRISK), developed from simulation results, demonstrated strong concordance with clinical outcomes and outperformed traditional imaging-based risk stratification. The methodology is fully automated, requiring minimal user intervention, and offers a promising tool for improving precision medicine in cardiac care by enhancing patient-specific arrhythmia risk assessment and guiding treatment strategies.

We investigate the implications of memory burden on the gravitational wave (GW) spectrum arising from the Hawking evaporation of light primordial black holes (PBHs). By considering both rotating (Kerr) and non-rotating (Schwarzschild) PBHs, we demonstrate that the overproduction of primordial GWs from burdened PBHs could impose stringent constraints on the parameters governing backreaction effects. These constraints, derived from $\Delta N_{\rm eff}$ measurements by Planck and prospective experiments such as CMB-S4 and CMB-HD, offer novel insights into the impact of memory burden on PBH dynamics., Comment: 20 pages, 6 figures, version accepted for JCAP

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

Uranus and Neptune have atmospheres dominated by molecular hydrogen and helium. In the upper troposphere, methane is the third main molecule and condenses, yielding a vertical gradient in CH4. This condensable species being heavier than H2 and He, the resulting change in mean molecular weight due to condensation comes as a factor countering dry and moist convection. As observations also show latitudinal variations in methane abundance, one can expect different vertical gradients from one latitude to another. In this paper, we investigate the impact of this methane vertical gradient on the atmospheric regimes, especially on the formation and inhibition of moist convective storms in the troposphere of ice giants. We develop a 3D cloud-resolving model to simulate convective processes. Using our simulations, we conclude that typical velocities of dry convection in the deep atmosphere are rather low (of the order of 1 m/s) but sufficient to sustain upward methane transport, and that moist convection at methane condensation level is strongly inhibited. Previous studies derived an analytical criterion on the methane vapor amount above which moist convection should be inhibited. We first validate this analytical criterion numerically. We then show that the critical methane abundance governs the inhibition and formation of moist convective storms, and we conclude that the intensity and intermittency of these storms should depend on the methane abundance and saturation. In ice giants, dry convection is weak, and moist convection is strongly inhibited. However, when enough methane is transported upwards, through dry convection and turbulent diffusion, sporadic moist convective storms can form. These storms should be more frequent on Neptune than on Uranus, because of Neptune's internal heat flow. Our results can explain the observed sporadicity of clouds in ice giants.

This paper addresses the perturbation of higher-dimensional non-smooth autonomous differential systems characterized by two zones separated by a codimension-one manifold, with an integral manifold foliated by crossing periodic solutions. Our primary focus is on developing the Melnikov method to analyze the emergence of limit cycles originating from the periodic integral manifold. While previous studies have explored the Melnikov method for autonomous perturbations of non-smooth differential systems with a linear switching manifold and with a periodic integral manifold, either open or of codimension 1, our work extends to non-smooth differential systems with a non-linear switching manifold and more general periodic integral manifolds, where the persistence of periodic orbits is of interest. We illustrate our findings through several examples, highlighting the applicability and significance of our main result.

GitHub Actions (GA) is an orchestration platform that streamlines the automatic execution of software engineering tasks such as building, testing, and deployment. Although GA workflows are the primary means for automation, according to our experience and observations, human intervention is necessary to correct defects, update dependencies, or refactor existing workflow files. In fact, previous research has shown that software artifacts similar to workflows, such as build files and bots, can introduce additional maintenance tasks in software projects. This suggests that workflow files, which are also used to automate repetitive tasks in professional software production, may generate extra workload for developers. However, the nature of such effort has not been well studied. This paper presents a large-scale empirical investigation towards characterizing the maintenance of GA workflows by studying the evolution of workflow files in almost 200 mature GitHub projects across ten programming languages. Our findings largely confirm the results of previous studies on the maintenance of similar artifacts, while also revealing GA-specific insights such as bug fixing and CI/CD improvement being among the major drivers of GA maintenance. A direct implication is that practitioners should be aware of proper resource planning and allocation for maintaining GA workflows, thus exposing the ``hidden costs of automation.'' Our findings also call for identifying and documenting best practices for such maintenance, and for enhanced tool features supporting dependency tracking and better error reporting of workflow specifications.

A central question which originates in the celebrated work in the 1980's of DiPerna and Majda asks what is the optimal decay $f > 0$ such that uniform rates $|\omega|(Q) \leq f(|Q|)$ of the vorticity maximal functions guarantee strong convergence without concentrations of approximate solutions to energy-conserving weak solutions of the $2$D Euler equations with vortex sheet initial data. A famous result of Majda (1993) shows $f(r) = [\log (1/r)]^{-1/2}$, $r<1/2$, as the optimal decay for \emph{distinguished} sign vortex sheets. In the general setting of \emph{mixed} sign vortex sheets, DiPerna and Majda (1987) established $f(r) = [\log (1/r)]^{-\alpha}$ with $\alpha > 1$ as a sufficient condition for the lack of concentrations, while the expected gap $\alpha \in (1/2, 1]$ remains as an open question. In this paper we resolve the DiPerna-Majda $2$D gap problem: In striking contrast to the well-known case of distinguished sign vortex sheets, we identify $f(r) = [\log (1/r)]^{-1}$ as the optimal regularity for mixed sign vortex sheets that rules out concentrations. For the proof, we propose a novel method to construct explicitly solutions with mixed sign to the $2$D Euler equations in such a way that wild behaviour creates within the relevant geometry of \emph{sparse} cubes (i.e., these cubes are not necessarily pairwise disjoint, but their possible overlappings can be controlled in a sharp fashion). Such a strategy is inspired by the recent work of the first author and Milman \cite{DM} where strong connections between energy conservation and sparseness are established., Comment: 24 pages

Software systems should be explainable, that is, they should help us to answer questions while exploring, developing or using them. Textual documentation is a very weak form of explanation, since it is not causally connected to the code, so easily gets out of date. Tests, on the other hand, are causally connected to code, but they are also a weak form of explanation. Although some tests encode interesting scenarios that answer certain questions about how the system works, most tests don't make interesting reading. Examples are tests that are also factories for interesting system entities. Instead of simply succeeding or failing, an example returns the object under test so that it can be inspected, or reused to compose further tests. An example is causally connected to the system, is always live and tested, and can be embedded into live documentation. Although technically examples constitute just a small change to the way that to test methods work, their impact is potentially ground-breaking. We show (i) how Example-Driven Development (EDD) enriches TDD with live programming, (ii) how examples can be molded with tiny tools to answer analysis questions, and (iii) how examples can be embedded within live documentation to make a system explainable.

Debugging is hard. Interactive debuggers are mostly the same. They show you a stack, a way to sample the state of the stack, and, if the debugger is live, a way to step through execution. The standard interactive debugger for a general-purpose programming language provided by a mainstream IDE mostly offers a low-level interface in terms of generic language constructs to track down and fix bugs. A custom debugger, such as those developed for specific application domains, offers alternative interfaces more suitable to the specific execution context of the program being debugged. Custom debuggers offering contextual debugging views and actions can greatly improve our ability to reason about the current problem. Implementing such custom debuggers, however, is non-trivial, and poses a barrier to improving the debugging experience. In this paper we introduce "moldable exceptions", a lightweight mechanism to adapt a debugger's interface based on contextual information provided by a raised exception. We present, through a series of examples, how moldable exceptions can enhance a live programming environment.

This study presents a machine learning-aided approach to accurately estimate the region of attraction (ROA) of a multi-rotor unmanned aerial vehicle (UAV) controlled using a linear quadratic regulator (LQR) controller. Conventional ROA estimation approaches rely on a nominal dynamic model for ROA calculation, leading to inaccurate estimation due to unknown dynamics and disturbances associated with the physical system. To address this issue, our study utilizes a neural network to predict these unknown disturbances of a planar quadrotor. The nominal model integrated with the learned disturbances is then employed to calculate the ROA of the planer quadrotor using a graphical technique. The estimated ROA is then compared with the ROA calculated using Lyapunov analysis and the graphical approach without incorporating the learned disturbances. The results illustrated that the proposed method provides a more accurate estimation of the ROA, while the conventional Lyapunov-based estimation tends to be more conservative.

Notwithstanding the evidence against them, classical variational phase-field models continue to be used and pursued in an attempt to describe fracture nucleation in elastic brittle materials. In this context, the main objective of this paper is to provide a comprehensive review of the existing evidence against such a class of models as descriptors of fracture nucleation. To that end, a review is first given of the plethora of experimental observations of fracture nucleation in nominally elastic brittle materials under quasi-static loading conditions, as well as of classical variational phase-field models, without and with energy splits. These models are then confronted with the experimental observations. The conclusion is that they cannot possibly describe fracture nucleation in general. This because classical variational phase-field models cannot account for material strength as an independent macroscopic material property. The last part of the paper includes a brief summary of a class of phase-field models that can describe fracture nucleation. It also provides a discussion of how pervasively material strength has been overlooked in the analysis of fracture at large, as well as an outlook into the modeling of fracture nucleation beyond the basic setting of elastic brittle materials.

This paper presents a generative adversarial network (GAN) based approach for radar image enhancement. Although radar sensors remain robust for operations under adverse weather conditions, their application in autonomous vehicles (AVs) is commonly limited by the low-resolution data they produce. The primary goal of this study is to enhance the radar images to better depict the details and features of the environment, thereby facilitating more accurate object identification in AVs. The proposed method utilizes high-resolution, two-dimensional (2D) projected light detection and ranging (LiDAR) point clouds as ground truth images and low-resolution radar images as inputs to train the GAN. The ground truth images were obtained through two main steps. First, a LiDAR point cloud map was generated by accumulating raw LiDAR scans. Then, a customized LiDAR point cloud cropping and projection method was employed to obtain 2D projected LiDAR point clouds. The inference process of the proposed method relies solely on radar images to generate an enhanced version of them. The effectiveness of the proposed method is demonstrated through both qualitative and quantitative results. These results show that the proposed method can generate enhanced images with clearer object representation compared to the input radar images, even under adverse weather conditions.

We present a numerical method for the analysis of mutual coupling effects in large, dense and irregular arrays with identical antennas. Building on the Method of Moments (MoM), our technique employs a Macro Basis Function (MBF) approach for rapid direct inversion of the MoM impedance matrix. To expedite the reduced matrix filling, we propose an extension of the Steepest-Descent Multipole expansion which remains numerically stable and efficient across a wide bandwidth. This broadband multipole-based approach is well suited to quasi-planar problems and requires only the pre-computation of each MBF's complex patterns, resulting in low antenna-dependent pre-processing costs. The method also supports arrays with arbitrarily rotated antennas at low additional cost. A simulation of all embedded element patterns of irregular arrays of 256 complex log-periodic antennas completes in just 10 minutes per frequency point on a current laptop, with an additional minute per new layout.

The acceleration of Large Language Models (LLMs) with speculative decoding provides a significant runtime improvement without any loss of accuracy. Currently, EAGLE-2 is the state-of-the-art speculative decoding method, improving on EAGLE with a dynamic draft tree. We introduce Dynamic Depth Decoding (DDD), which optimises EAGLE-2's tree drafting method using a dynamic depth. This extends the average speedup that EAGLE-2 achieves over EAGLE by $44\%$, giving DDD an average speedup of $3.16$x.

Variational regularization is a classical technique to solve statistical inference tasks and inverse problems, with modern data-driven approaches parameterizing regularizers via deep neural networks showcasing impressive empirical performance. Recent works along these lines learn task-dependent regularizers. This is done by integrating information about the measurements and ground-truth data in an unsupervised, critic-based loss function, where the regularizer attributes low values to likely data and high values to unlikely data. However, there is little theory about the structure of regularizers learned via this process and how it relates to the two data distributions. To make progress on this challenge, we initiate a study of optimizing critic-based loss functions to learn regularizers over a particular family of regularizers: gauges (or Minkowski functionals) of star-shaped bodies. This family contains regularizers that are commonly employed in practice and shares properties with regularizers parameterized by deep neural networks. We specifically investigate critic-based losses derived from variational representations of statistical distances between probability measures. By leveraging tools from star geometry and dual Brunn-Minkowski theory, we illustrate how these losses can be interpreted as dual mixed volumes that depend on the data distribution. This allows us to derive exact expressions for the optimal regularizer in certain cases. Finally, we identify which neural network architectures give rise to such star body gauges and when do such regularizers have favorable properties for optimization. More broadly, this work highlights how the tools of star geometry can aid in understanding the geometry of unsupervised regularizer learning., Comment: Accepted to NeurIPS 2024

We study the possibility of obtaining the Standard Model (SM) of particle physics as an effective theory of a more fundamental one, whose electroweak sector includes two non-universal local $U(1)$ gauge groups, with the chiral anomaly cancellation taking place through an interplay among families. As a result of the spontaneous symmetry breaking, a massive gauge boson $Z'$ arises, which couples differently to the third family of fermions (by assumption, we restrict ourselves to the scenario in which the $Z'$ couples in the same way to the first two families). Two Higgs doublets and one scalar singlet are necessary to generate the SM fermion masses and break the gauge symmetries. We show that in our model, the flavor-changing neutral currents (FCNC) of the Higgs sector are identically zero if each right-handed SM fermion is only coupled with a single Higgs doublet. This result represents a FCNC cancellation mechanism different from the usual procedure in Two-Higgs Doublet Models~(2HDM). The non-universal nature of our solutions requires the presence of three right-handed neutrino fields, one for each family. Our model generates all elements of the Dirac mass matrix for quarks and leptons, which is quite non-trivial for non-universal models. Thus, we can fit all the masses and mixing angles with two scalar doublets. Finally, we show the distribution of solutions for the scalar boson masses in our model by scanning well-motivated intervals for the model parameters. We consider two possibilities for the scalar potential and compare these results with the Higgs-like resonant signals recently reported by the ATLAS and CMS experiments at the LHC. Finally, we also report collider, electroweak, and flavor constraints on the model parameters., Comment: 27 pages, 3 figures

The Giant Radio Array for Neutrino Detection (GRAND) aims to detect highly inclined extensive air showers (EAS) with down-going and up-going trajectories. Several working groups in the GRAND collaboration are developing methods to reconstruct the incoming direction, core position, primary energy, and composition of the showers. The reconstruction pipeline -- currently under development in the France/IAP working group -- relies on a model of spherical wavefront emission for arrival times, which is possible because the radio signals are generated far away from the antenna stations. The amplitude distribution of the signals at the antenna level is described by an Angular Distribution Function that considers various asymmetries in the data, including geomagnetic effects. In this contribution, we present preliminary results from testing our EAS reconstruction procedure using realistic mock observations., Comment: V2: Fixed typo in axis labels of Fig.5 and collaboration author list updated

Text-to-image diffusion models have been widely adopted in real-world applications due to their ability to generate realistic images from textual descriptions. However, recent studies have shown that these methods are vulnerable to backdoor attacks. Despite the significant threat posed by backdoor attacks on text-to-image diffusion models, countermeasures remain under-explored. In this paper, we address this research gap by demonstrating that state-of-the-art backdoor attacks against text-to-image diffusion models can be effectively mitigated by a surprisingly simple defense strategy - textual perturbation. Experiments show that textual perturbations are effective in defending against state-of-the-art backdoor attacks with minimal sacrifice to generation quality. We analyze the efficacy of textual perturbation from two angles: text embedding space and cross-attention maps. They further explain how backdoor attacks have compromised text-to-image diffusion models, providing insights for studying future attack and defense strategies. Our code is available at https://github.com/oscarchew/t2i-backdoor-defense., Comment: ECCV 2024 Workshop The Dark Side of Generative AIs and Beyond