Search

In the RC$^\star$ collaboration, we simulate lattice QCD+QED using $C-$periodic spatial boundary conditions to ensure that locality, gauge invariance, and translational invariance are preserved throughout the calculation. We present our progress in computing isospin-breaking (IB) corrections to the leading hadronic contribution to $(g-2)_\mu$. We compare two ways of including the IB corrections: the RM123 method and dynamical QCD+QED simulations, both with $C-$periodic boundary conditions. The two calculations are performed at $\beta=3.24$ with four flavours of $\mathcal{O}(a)-$improved Wilson fermions; the QCD ensemble features $SU(3)-$symmetric sea quarks plus charm, while down and strange quarks are degenerate in QCD+QED gauge ensembles., Comment: 10 pages, RC* collaboration, Contribution to the 41st International Symposium on Lattice Field Theory, 28th July-3rd August 2024, University of Liverpool, United Kingdom

Two-sample hypothesis testing-determining whether two sets of data are drawn from the same distribution-is a fundamental problem in statistics and machine learning with broad scientific applications. In the context of nonparametric testing, maximum mean discrepancy (MMD) has gained popularity as a test statistic due to its flexibility and strong theoretical foundations. However, its use in large-scale scenarios is plagued by high computational costs. In this work, we use a Nystr\"om approximation of the MMD to design a computationally efficient and practical testing algorithm while preserving statistical guarantees. Our main result is a finite-sample bound on the power of the proposed test for distributions that are sufficiently separated with respect to the MMD. The derived separation rate matches the known minimax optimal rate in this setting. We support our findings with a series of numerical experiments, emphasizing realistic scientific data., Comment: 23 pages, 2 figures

With the 4-meter Multi-Object Spectroscopic Telescope (4MOST) expected to provide an influx of transient spectra when it begins observations in early 2026 we consider the potential for real-time classification of these spectra. We investigate three extant spectroscopic transient classifiers: the Deep Automated Supernova and Host classifier (DASH), Next Generation SuperFit (NGSF) and SuperNova IDentification (SNID), with a focus on comparing the efficiency and purity of the transient samples they produce. We discuss our method for simulating realistic, 4MOST-like, host-galaxy contaminated spectra and determining quality cuts for each classifier used to ensure pure SN Ia samples while maintaining efficient classification in other transient classes. We investigate the classifiers individually and in combinations. We find that a combination of DASH and NGSF can produce a SN Ia sample with a purity of 99.9% while successfully classifying 70% of SNe Ia. However, it struggles to classify non-SN Ia transients. We investigate photometric cuts to transient magnitude and transient flux fraction, finding that both can be used to improve transient classification efficiencies by 7--25% depending on the transient subclass. Finally, we present an example classification plan for live classification and the predicted purities and efficiencies across five transient classes: Ia, Ibc, II, superluminous and non-supernova transients., Comment: Submitted to MNRAS

Spatially discordant alternans (SDA) play a crucial role in cardiac arrhythmogenesis by creating steep repolarization gradients facilitating conduction block and reentry. While traditionally studied using electrical indicators, this work provides a novel perspective by characterizing SDA through their magnetic field signatures. Using a one-dimensional cardiac fiber model, we demonstrate that magnetic field measurements effectively detect SDA and temperature dependent changes in cardiac action potentials, offering a non-invasive alternative to conventional electrophysiological metrics. Our results reveal that the spatial organization of SDA is mirrored in the magnetic field distribution, with SDA nodes clearly identifiable via spatial mapping. Notably, magnetic restitution curves exhibit a distinct pattern from APD-based indicators, closely following the dynamics of the action potential upstroke. These findings establish the cardiac magnetic field as a powerful diagnostic tool for detecting SDA, opening new avenues for biomagnetic monitoring of arrhythmic risk.

The increased availability of data and computing resources has enabled researchers to successfully adopt machine learning (ML) techniques and make significant contributions in several engineering areas. ML and in particular deep learning (DL) algorithms have shown to perform better in tasks where a physical bottom-up description of the phenomenon is lacking and/or is mathematically intractable. Indeed, they take advantage of the observations of natural phenomena to automatically acquire knowledge and learn internal relations. Despite the historical model-based mindset, communications engineering recently started shifting the focus towards top-down data-driven learning models, especially in domains such as channel modeling and physical layer design, where in most of the cases no general optimal strategies are known. In this thesis, we aim at solving some fundamental open challenges in physical layer communications exploiting new DL paradigms. In particular, we mathematically formulate, under ML terms, classic problems such as channel capacity and optimal coding-decoding schemes, for any arbitrary communication medium. We design and develop the architecture, algorithm and code necessary to train the equivalent DL model, and finally, we propose novel solutions to long-standing problems in the field., Comment: PhD Thesis

Full QCD+QED simulations allow to evaluate isospin breaking corrections to hadron masses. With the openQxD code, we are able to perform these simulations employing C-periodic boundary conditions, implemented through a doubling of the physical lattice along one spatial direction. The use of these boundary conditions introduces non-zero Wick contractions between two quark or two antiquark fields, that, in the case of the computation of baryon masses, lead to partially connected additional contributions that we expect to vanish in the infinite volume limit. These contributions are challenging because they involve an all-to-all propagator connecting one point in the physical lattice and one in the mirror lattice. We present a way to compute these corrections to the $\Omega^-$ baryon mass using a combination of point and stochastic source inversions. This work is part of the program of the RC* collaboration.

Sea-quark isospin-breaking effects (IBE) are difficult to compute since they require the evaluation of all-to-all propagators. However, the quest for high-precision calculations motivates a detailed study of these contributions. There are strong arguments that the stochastic error associated with these quantities should diverge in the continuum and infinite-volume limit, resulting in a possible bottleneck for the method. In this work, we present the study of the error scaling for these quantities using $N_f=3$ $O(a)$-improved Wilson fermions QCD with C-periodic boundary conditions in space, a pion mass $M_{\pi}=400$ MeV, a range of lattice spacings $a=0.05, 0.075, 0.1$ fm, and volumes $L=1.6, 2.4, 3.2$ fm. The analysis of the error as a function of the number of stochastic sources shows that we reach the gauge error for the dominant contributions. The errors do not show the leading order divergence $1/a$ for strong-IBE and $1/a^2$ for electromagnetic IBE, in the considered range of lattice spacings. On the other hand, our data are consistent with the predicted leading divergence $\sqrt{V}$., Comment: 9 pages, 8 figures, RC* collaboration, Contribution to the 41st International Symposium on Lattice Field Theory, 28th July-3rd August 2024, University of Liverpool, United Kingdom. Corrected the bibliography, the abstract and equation 2

Markov Chain Monte Carlo simulations of lattice Quantum Chromodynamics (QCD) are the only known tool to investigate non-perturbatively the theory of the strong interaction and are required to perform precision tests of the Standard Model of Particle Physics. As the Markov Chain is a serial process, the sole option for improving the sampling rate is accelerating each individual update step. Heterogeneous clusters of GPU-accelerated nodes offer large total memory bandwidth which can be used to speed-up our application, openQxD-1.1, which is dominated by inversions of the Dirac operator, a large sparse matrix. In this work we investigate offloading the inversion to GPU using the lattice-QCD library QUDA, and our early results demonstrate a significant potential speed-up in the time-to-solution for state-of-the-art problem sizes. Minimal extensions to the existing QUDA library are required for our specific physics programme while greatly enhancing the performance portability of our code and retaining the reliability and robustness of existing applications in openQxD-1.1. Our new interface will enable us to utilize pre-exascale infrastructure and reduce the systematic uncertainty in our physics predictions by incorporating the effects of quantum electromagnetism (QED) in our simulations.

The KOTO II experiment is proposed to measure the branching ratio of the decay $K_L\to\pi^0\nu\bar{\nu}$ at J-PARC. With a beamline to extract long-lived neutral kaons at 5 degrees from a production target, the single event sensitivity of the decay is $8.5\times 10^{-13}$, which is much smaller than the Standard Model prediction $3\times 10^{-11}$. This allows searches for new physics beyond the Standard Model and the first discovery of the decay with a significance exceeding $5\sigma$. As the only experiment proposed in the world dedicated to rare kaon decays, KOTO II will be indispensable in the quest for a complete understanding of flavor dynamics in the quark sector. Moreover, by combining efforts from the kaon community worldwide, we plan to develop the KOTO II detector further and expand the physics reach of the experiment to include measurements of the branching ratio of the $K_L\to\pi^0\ell^+\ell^-$ decays, studies of other $K_L$ decays, and searches for dark photons, axions, and axion-like particles. KOTO II will therefore obtain a comprehensive understanding of $K_L$ decays, providing further constraints on new physics scenarios with existing $K^+$ results., Comment: Submitted to the J-PARC PAC. arXiv admin note: substantial text overlap with arXiv:2110.04462

Alkali metal intercalation of graphene layers has been of particular interest due to potential applications in electronics, energy storage, and catalysis. Rubidium (Rb) is one of the largest alkali metals and the one less investigated as intercalant. Here, we report a systematic investigation, with a multi-technique approach, of the phase formation of Rb under epitaxial monolayer graphene on SiC(0001). We explore a wide phase space with two control parameters: the Rb density (i.e., deposition time) and sample temperature (i.e., room- and low-temperature). We reveal the emergence of $(2 \times 2)$ and $(\sqrt{3} \times \sqrt{3})$R30{\deg} structures formed by a single alkali metal layer intercalated between monolayer graphene and the interfacial C-rich reconstructed surface, also known as buffer layer. Rb intercalation also results in a strong n-type doping of the graphene layer. Progressively annealing to high temperatures, we first reveal diffusion of Rb atoms which results in the enlargement of intercalated areas. As desorption sets in, intercalated regions progressively shrink and fragment. Eventually, at approximately 600{\deg}C the initial surface is retrieved, indicating the reversibility of the intercalation process.

Classification of spectra (1) and anomaly detection (2) are fundamental steps to guarantee the highest accuracy in redshift measurements (3) in modern all-sky spectroscopic surveys. We introduce a new Galaxy Spectra Neural Network (GaSNet-III) model that takes advantage of generative neural networks to perform these three tasks at once with very high efficiency. We use two different generative networks, an autoencoder-like network and U-Net, to reconstruct the rest-frame spectrum (after redshifting). The autoencoder-like network operates similarly to the classical PCA, learning templates (eigenspectra) from the training set and returning modeling parameters. The U-Net, in contrast, functions as an end-to-end model and shows an advantage in noise reduction. By reconstructing spectra, we can achieve classification, redshift estimation, and anomaly detection in the same framework. Each rest-frame reconstructed spectrum is extended to the UV and a small part of the infrared (covering the blueshift of stars). Owing to the high computational efficiency of deep learning, we scan the chi-squared value for the entire type and redshift space and find the best-fitting point. Our results show that generative networks can achieve accuracy comparable to the classical PCA methods in spectral modeling with higher efficiency, especially achieving an average of $>98\%$ classification across all classes ($>99.9\%$ for star), and $>99\%$ (stars), $>98\%$ (galaxies) and $>93\%$ (quasars) redshift accuracy under cosmology research requirements. By comparing different peaks of chi-squared curves, we define the ``robustness'' in the scanned space, offering a method to identify potential ``anomalous'' spectra. Our approach provides an accurate and high-efficiency spectrum modeling tool for handling the vast data volumes from future spectroscopic sky surveys., Comment: 17 pages and 16 figures. Submitted to MNRAS

CHANCES, the CHileAN Cluster galaxy Evolution Survey, will study the evolution of galaxies in and around ${\sim}$150 massive galaxy clusters, from the local universe out to z=0.45. CHANCES will use the new 4MOST Spectroscopic Survey Facility on the VISTA 4m telescope to obtain spectra for ${\sim}$500,000 galaxies with magnitudes $r_\mathrm{AB} < 20.5$, providing comprehensive spectroscopic coverage of each cluster out to $5r_{200}$. Its wide and deep scope will trace massive and dwarf galaxies from the surrounding filaments and groups to the cores of galaxy clusters, enabling the study of galaxy pre-processing and the role of the evolving environment on galaxy evolution. In this paper we present and characterize the sample of clusters and superclusters to be targeted by CHANCES. We used literature catalogues based on X-ray emission and Sunyaev-Zel'dovich effect to define the cluster sample in a homogeneous way, with attention to cluster mass and redshift, as well as the availability of ancillary data. We calibrated literature mass estimates from various surveys against each other and provide an initial mass estimate for each cluster, which we used to define the radial extent of the 4MOST coverage. We also present an initial assessment of the structure surrounding these clusters based on the redMaPPer red-sequence algorithm as a preview of some of the science CHANCES will enable., Comment: 11 pages, 9 figures, plus references and appendix containing catalog tables, submitted to A&A

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including Variational AutoEncoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, Diffusion models, and models based on Conditional Flow Matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in 1-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space., Comment: 204 pages, 100+ figures, 30+ tables

To validate the second-by-second dynamics of turbines in field experiments, it is necessary to accurately reconstruct the winds going into the turbine. Current time-resolved inflow reconstruction techniques estimate wind behavior in unobserved regions using relatively simple spectral-based models of the atmosphere. Here, we develop a technique for time-resolved inflow reconstruction that is rooted in a large-eddy simulation model of the atmosphere. Our "large-eddy reconstruction" technique blends observations and atmospheric model information through a diffusion model machine learning algorithm, allowing us to generate probabilistic ensembles of reconstructions for a single 10-min observational period. Our generated inflows can be used directly by aeroelastic codes or as inflow boundary conditions in a large-eddy simulation. We verify the second-by-second reconstruction capability of our technique in three synthetic field campaigns, finding positive Pearson correlation coefficient values (0.20>r>0.85) between ground-truth and reconstructed streamwise velocity, as well as smaller positive correlation coefficient values for unobserved fields (spanwise velocity, vertical velocity, and temperature). We validate our technique in three real-world case studies by driving large-eddy simulations with reconstructed inflows and comparing to independent inflow measurements. The reconstructions are visually similar to measurements, follow desired power spectra properties, and track second-by-second behavior (0.25 > r > 0.75)., Comment: 30 pages, 15 figures

The Charting Cluster Construction with VUDS and ORELSE (C3VO) survey is an ongoing imaging and spectroscopic campaign aiming to map out the growth of structure up to $z\sim5$ and was born from the combination of the Visible Multi-Object Spectrograph Ultra Deep Survey and the Observations of Redshift Evolution in Large-Scale Environments (ORELSE) survey. As we previously accomplished with the ORELSE survey, we apply our technique known as Voronoi tessellation Monte Carlo (VMC) mapping to search for serendipitous galaxy overdensities at $2 14$ and $2 14.5$. In the C3VO fields, we were able to recover many of the previously known structures in the literature as well as find hundreds of new overdensity candidates, once again demonstrating the powerful capabilities of VMC mapping when applied to wide-field optical and infrared galaxy evolution surveys at ever higher redshifts., Comment: 39 pages, 16 figures, 7 tables, published by ApJ

We propose a robust methodology to evaluate the performance and computational efficiency of non-parametric two-sample tests, specifically designed for high-dimensional generative models in scientific applications such as in particle physics. The study focuses on tests built from univariate integral probability measures: the sliced Wasserstein distance and the mean of the Kolmogorov-Smirnov statistics, already discussed in the literature, and the novel sliced Kolmogorov-Smirnov statistic. These metrics can be evaluated in parallel, allowing for fast and reliable estimates of their distribution under the null hypothesis. We also compare these metrics with the recently proposed unbiased Fr\'echet Gaussian Distance and the unbiased quadratic Maximum Mean Discrepancy, computed with a quartic polynomial kernel. We evaluate the proposed tests on various distributions, focusing on their sensitivity to deformations parameterized by a single parameter $\epsilon$. Our experiments include correlated Gaussians and mixtures of Gaussians in 5, 20, and 100 dimensions, and a particle physics dataset of gluon jets from the JetNet dataset, considering both jet- and particle-level features. Our results demonstrate that one-dimensional-based tests provide a level of sensitivity comparable to other multivariate metrics, but with significantly lower computational cost, making them ideal for evaluating generative models in high-dimensional settings. This methodology offers an efficient, standardized tool for model comparison and can serve as a benchmark for more advanced tests, including machine-learning-based approaches., Comment: v1: 42 pages, 5 figures, 7 tables, additional plots and tables available on GitHub and linked in the article

Sonar-based indoor mapping systems have been widely employed in robotics for several decades. While such systems are still the mainstream in underwater and pipe inspection settings, the vulnerability to noise reduced, over time, their general widespread usage in favour of other modalities(\textit{e.g.}, cameras, lidars), whose technologies were encountering, instead, extraordinary advancements. Nevertheless, mapping physical environments using acoustic signals and echolocation can bring significant benefits to robot navigation in adverse scenarios, thanks to their complementary characteristics compared to other sensors. Cameras and lidars, indeed, struggle in harsh weather conditions, when dealing with lack of illumination, or with non-reflective walls. Yet, for acoustic sensors to be able to generate accurate maps, noise has to be properly and effectively handled. Traditional signal processing techniques are not always a solution in those cases. In this paper, we propose a framework where machine learning is exploited to aid more traditional signal processing methods to cope with background noise, by removing outliers and artefacts from the generated maps using acoustic sensors. Our goal is to demonstrate that the performance of traditional echolocation mapping techniques can be greatly enhanced, even in particularly noisy conditions, facilitating the employment of acoustic sensors in state-of-the-art multi-modal robot navigation systems. Our simulated evaluation demonstrates that the system can reliably operate at an SNR of $-10$dB. Moreover, we also show that the proposed method is capable of operating in different reverberate environments. In this paper, we also use the proposed method to map the outline of a simulated room using a robotic platform.

We conduct a study on the relationship between galaxy environments and their active galactic nuclei (AGN) activity at high redshifts ($2.010^{12.8}M_\odot$) overdense ($\sigma_\delta>5.0$) peak, normalized with respect to the size of the peak. These findings indicate that the prevalence of AGN activity is highly dependent on the environment in which a host galaxy resides, even at early times in the formation history of the Universe., Comment: 15 pages, 11 figures, submitted to A&A, comments welcome

Gellan gum has gained significant attention due to its versatility in multiple applications in the form of hydrogels and microgels. A thorough understanding of the rheological behaviour of these systems is crucial both for fundamental research and to optimize the manufacturing needs. To this aim, here we extensively characterize the rheological behaviour of gellan based hydrogels and microgels recently used for efficient paper cleaning for restoration interventions. In particular, we study their viscoelastic properties, also during hydrogel and microgel formation, assessing the role of temperature, gellan concentration, and, importantly, the presence of different cations, which plays a crucial role in the gelation process. We find the interesting result that, in the conditions where they are efficient for cleaning, gellan hydrogels exhibit a double yielding behavior. In addition, we provide a detailed description of gellan microgels preparation, ensuring high control and reproducibility of the samples. Altogether our study sheds light on the mechanical stability, network structure, and overall functionality of the gellan-based gels, providing valuable insights into optimizing conditions for desired applications in paper cleaning.

The combination of a strong pump and a weak probe has been widely applied to investigate both optical and nanomechanical devices. Such pump-probe measurements allows for the exploration of nonlinear dynamics, driven by the large pump tone, by measuring the system response to a probe tone. In contrast, here we report on the dynamics of a mechanical Duffing resonator driven with a combination of two large tones at different frequencies. Our results indicate the presence of various distinct regimes with very different dynamics. We systematically investigate the impact of the relative strength and detuning between the two drives on the dynamical response. This provides an illustrative example of dynamical phase transitions in out-of-equilibrium systems., Comment: 9 pages, 6 figures

In this work, we address the question of how to enhance signal-agnostic searches by leveraging multiple testing strategies. Specifically, we consider hypothesis tests relying on machine learning, where model selection can introduce a bias towards specific families of new physics signals. We show that it is beneficial to combine different tests, characterised by distinct choices of hyperparameters, and that performances comparable to the best available test are generally achieved while providing a more uniform response to various types of anomalies. Focusing on the New Physics Learning Machine, a methodology to perform a signal-agnostic likelihood-ratio test, we explore a number of approaches to multiple testing, such as combining p-values and aggregating test statistics., Comment: 17 pages, 5 tables, 6 figures

The related concepts of partisan belief systems, issue alignment, and partisan sorting are central to our understanding of politics. These phenomena have been studied using measures of alignment between pairs of topics, or how much individuals' attitudes toward a topic reveal about their attitudes toward another topic. We introduce a higher-order measure that extends the assessment of alignment beyond pairs of topics by quantifying the amount of information individuals' opinions on one topic reveal about a set of topics simultaneously. Applying this approach to legislative voting behavior shows that parliamentary systems typically exhibit similar multiway alignment characteristics, but can change in response to shifting intergroup dynamics. In American National Election Studies surveys, our approach reveals a growing significance of party identification together with a consistent rise in multiway alignment over time.

Living systems rely on coordinated molecular interactions, especially those related to gene expression and protein activity. The Unfolded Protein Response is a crucial mechanism in eukaryotic cells, activated when unfolded proteins exceed a critical threshold. It maintains cell homeostasis by enhancing protein folding, initiating quality control, and activating degradation pathways when damage is irreversible. This response functions as a dynamic signaling network, with proteins as nodes and their interactions as edges. We analyze these protein-protein networks across different organisms to understand their intricate intra-cellular interactions and behaviors. In this work, analyzing twelve organisms, we assess how fundamental measures in network theory can individuate seed-proteins and specific pathways across organisms. We employ network robustness to evaluate and compare the strength of the investigated PPI networks, and the structural controllability of complex networks to find and compare the sets of driver nodes necessary to control the overall networks. We find that network measures are related to phylogenetics, and advanced network methods can identify main pathways of significance in the complete Unfolded Protein Response mechanism.

We undertake a comprehensive investigation into the distribution of insitu stars within Milky Way-like galaxies, leveraging TNG50 simulations and comparing their predictions with data from the H3 survey. Our analysis reveals that 28% of galaxies demonstrate reasonable agreement with H3, while only 12% exhibit excellent alignment in their profiles, regardless of the specific spatial cut employed to define insitu stars. To uncover the underlying factors contributing to deviations between TNG50 and H3 distributions, we scrutinize correlation coefficients among internal drivers(e.g., virial radius, star formation rate [SFR]) and merger-related parameters (such as the effective mass-ratio, mean distance, average redshift, total number of mergers, average spin-ratio and maximum spin alignment between merging galaxies). Notably, we identify significant correlations between deviations from observational data and key parameters such as the median slope of virial radius, mean SFR values, and the rate of SFR change across different redshift scans. Furthermore, positive correlations emerge between deviations from observational data and parameters related to galaxy mergers. We validate these correlations using the Random Forest Regression method. Our findings underscore the invaluable insights provided by the H3 survey in unraveling the cosmic history of galaxies akin to the Milky Way, thereby advancing our understanding of galactic evolution and shedding light on the formation and evolution of Milky Way-like galaxies in cosmological simulations., Comment: 20 pages, 13 figures

We explore the elemental abundances in Galactic planetary nebulae (PNe) compared with those of their stellar progenitors (Red Giant Branch and Asymptotic Giant Branch, RGB and AGB, stars), to explore and quantify the expected -- i.e., due to AGB evolution or condensation onto grains -- differences. We gleaned the current literature for the nebular abundances while we used the APOGEE DR~17 survey data for the stellar sample. We examined the elements in common between the nebular and stellar samples, namely, C, N, O, Fe, and S. We confirm that iron in PNe is mostly entrapped in grains, with an average depletion $<$D[Fe/H]$>$=1.741$\pm$0.486 dex, and we disclose a weak correlation between iron depletion and the [O/H] abundance, D[Fe/H]$=(6.6003\pm2.443)\times{\rm [O/H]} +(1.972\pm0.199)$. Sulfur may also be mildly depleted in PNe, with $<$D[S/H]$>=0.179\pm0.291$ dex. We also found an indication of nitrogen enrichment for PNe $<$E[N/H]$>$=0.393$\pm$0.421 dex, with maximum enrichment (0.980$\pm$0.243) occurring for the PNe whose progenitors have gone through the HBB. The carbon enrichment is $<$E[C/H]$>$=0.332$\pm$0.460 dex when measured for the general PN populations. Our results will be relevant for future Galactic and extragalactic studies comparing nebular and stellar samples., Comment: 20 pages, 13 Figures The Astrophysical Journal, in press

Developing new methods for predicting electromagnetic instabilities in cardiac activity is of primary importance. However, we still need a comprehensive view of the heart's magnetic activity at the tissue scale. To fill this gap, we present a model of soft active matter, including thermo-electric coupling, suitably modified to reproduce cardiac magnetic field. Our theoretical framework shows that periodic stimulations of cardiac cells create an external magnetic field evidencing restitution features of nonlinear cardiac dynamics and magnetic restitution curves better discriminate instabilities and bifurcations in cardiac activity. This new framework lays the foundation for innovative, non-invasive diagnostic tools for cardiac arrhythmias.