Search

We revisit the notion of nuclear parton-to-pion fragmentation functions at next-to-leading order accuracy as an effective description of hadroproduction in nuclear environments such as in semi-inclusive lepton-nucleus deep-inelastic scattering and in single inclusive proton-nucleus collisions. We assess their viability in the face of very precise data collected for the latter at the CERN-LHC over the past decade as well as recent measurements of the former carried out by the CLAS experiment at JLab., Comment: 11 pages, 6 figures

In this paper, a parallel computing method is proposed to perform the background denoising and wheezing detection from a multi-channel recording captured during the auscultation process. The proposed system is based on a non-negative matrix factorization (NMF) approach and a detection strategy. Moreover, the initialization of the proposed model is based on singular value decomposition to avoid dependence on the initial values of the NMF parameters. Additionally, novel update rules to simultaneously address the multichannel denoising while preserving an orthogonal constraint to maximize source separation have been designed. The proposed system has been evaluated for the task of wheezing detection showing a significant improvement over state-of-the-art algorithms when noisy sound sources are present. Moreover, parallel and high-performance techniques have been used to speedup the execution of the proposed system, showing that it is possible to achieve fast execution times, which enables its implementation in real-world scenarios.

The growing demand for accurate and equitable AI models in digital dermatology faces a significant challenge: the lack of diverse, high-quality labeled data. In this work, we investigate the potential of domain-specific foundation models for dermatology in addressing this challenge. We utilize self-supervised learning (SSL) techniques to pre-train models on a dataset of over 240,000 dermatological images from public and private collections. Our study considers several SSL methods and compares the resulting foundation models against domain-agnostic models like those pre-trained on ImageNet and state-of-the-art models such as MONET across 12 downstream tasks. Unlike previous research, we emphasize the development of smaller models that are more suitable for resource-limited clinical settings, facilitating easier adaptation to a broad range of use cases. Results show that models pre-trained in this work not only outperform general-purpose models but also approach the performance of models 50 times larger on clinically relevant diagnostic tasks. To promote further research in this direction, we publicly release both the training code and the foundation models, which can benefit clinicians in dermatological applications., Comment: Findings paper presented at Machine Learning for Health (ML4H) symposium 2024, December 15-16, 2024, Vancouver, Canada, 11 pages

We discuss how the two existing approximate N$^3$LO (aN$^3$LO) sets of parton distributions (PDFs) from the MSHT20 and NNPDF4.0 series can be combined for LHC phenomenology, both in the pure QCD case and for the QCD$\otimes$QED sets that include the photon PDF. Using the resulting combinations, we present predictions for the total inclusive cross-section for Higgs production in gluon fusion, vector boson fusion, and associated production at the LHC Run-3. For the gluon fusion and vector boson fusion channels, the corrections that arise when using correctly matched aN$^3$LO PDFs with N$^3$LO cross section calculations, compared to using NNLO PDFs, are significant, in many cases larger than the PDF uncertainty, and generally larger than the differences between the two aN$^3$LO PDF sets entering the combination. The combined aN$^3$LO PDF sets, MSHT20xNNPDF40_an3lo and MSHT20xNNPDF40_an3lo_qed, are made publicly available in the LHAPDF format and can be readily used for LHC phenomenology., Comment: 22 pages, 11 figures, LHAPDF grids available from https://zenodo.org/records/13843626

We construct a noncommutative (NC) AdS$_4$-charged black hole with a planar horizon topology. The NC effects of this geometry are captured by a Gaussian distribution of black hole mass codified in a dust-like energy-momentum tensor. A natural bound in radial coordinate is established, below which the scalar curvature changes its sign and defines a NC cutoff that embeds the point singularity. We study in detail the thermodynamic structure of this scenario, finding a well-defined black hole mass and an analytic criterion for its stability. Focusing on the AdS$_{2}$ structure near the horizon, we find a novel effective curvature radius with dependency on the NC cutoff. These results motivate us to explore the holographic superconducting system in terms of the nearness from the cutoff. The behavior of the magnetic field in the deep IR geometry is studied and we found semi-analytical novel expressions for the upper critical magnetic fields of a dual type-II superconductor in the canonical and grand canonical ensembles. The condensation in the form of hair is studied in terms of the bound states of the associated Schr\"odinger potential of the scalar field, interpreted as the dual to the density of Cooper pairs. The NC effects increase the hair formation due to a steeper AdS$_2$ throat comparable to the commutative case. Finally, we obtain the effective IR scalar field equation on the near horizon and near extremal NC Schwarzschild AdS$_2$ geometry and confirm that NC effects promote bound states that the commutative version forbids., Comment: 28 pages, 12 figures

Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capability of one-stage object detectors to robustly operate in the presence of out-of-distribution (OoD) data. Specifically, we propose a novel detection algorithm for detecting unknown objects in image data, which leverages the features extracted by the model from each sample. Differently from other recent approaches in the literature, our proposal does not require retraining the object detector, thereby allowing for the use of pretrained models. Our proposed OoD detector exploits the application of supervised dimensionality reduction techniques to mitigate the effects of the curse of dimensionality on the features extracted by the model. Furthermore, it utilizes high-resolution feature maps to identify potential unknown objects in an unsupervised fashion. Our experiments analyze the Pareto trade-off between the performance detecting known and unknown objects resulting from different algorithmic configurations and inference confidence thresholds. We also compare the performance of our proposed algorithm to that of logits-based post-hoc OoD methods, as well as possible fusion strategies. Finally, we discuss on the competitiveness of all tested methods against state-of-the-art OoD approaches for object detection models over the recently published Unknown Object Detection benchmark. The obtained results verify that the performance of avant-garde post-hoc OoD detectors can be further improved when combined with our proposed algorithm., Comment: 12 figures, 4 tables, under review

The search for gravitational waves generated by the inspiral phase of binaries of light compact objects holds significant promise in testing the existence of primordial black holes and/or other exotic objects. In this paper, we present a new method to detect such signals exploiting some techniques typically applied in searches for continuous quasi-monochromatic gravitational waves. We describe the signal model employed and present a new strategy to optimally construct the search grid over the parameter space investigated, significantly reducing the search computing cost. Additionally, we estimate the pipeline sensitivity corroborating the results with software injections in real data from the LIGO third observing run. The results show that the method is well suited to detect long-transient signals and standard continuous gravitational waves. According to the criteria used in the grid construction step, the method can be implemented to cover a wide parameter space with slightly reduced sensitivity and lower computational cost or to focus on a narrower parameter space with increased sensitivity at a higher computational expense. The method shows an astrophysical reach up to the Galactic Center (8kpc) for some regions of the parameter space and given search configurations.

The unchecked spread of digital information, combined with increasing political polarization and the tendency of individuals to isolate themselves from opposing political viewpoints, has driven researchers to develop systems for automatically detecting political bias in media. This trend has been further fueled by discussions on social media. We explore methods for categorizing bias in US news articles, comparing rule-based and deep learning approaches. The study highlights the sensitivity of modern self-learning systems to unconstrained data ingestion, while reconsidering the strengths of traditional rule-based systems. Applying both models to left-leaning (CNN) and right-leaning (FOX) news articles, we assess their effectiveness on data beyond the original training and test sets.This analysis highlights each model's accuracy, offers a framework for exploring deep-learning explainability, and sheds light on political bias in US news media. We contrast the opaque architecture of a deep learning model with the transparency of a linguistically informed rule-based model, showing that the rule-based model performs consistently across different data conditions and offers greater transparency, whereas the deep learning model is dependent on the training set and struggles with unseen data.

Linked micromaps were originally developed to display geographically indexed statistics in an intuitive way by connecting them to a sequence of small maps. The approach integrates several visualization design principles, such as small multiples, discrete color indexing, and ordering. Linked micromaps allow for other types of data displays that are connected to and conditional on geographic areas. Initial applications of micromaps used data from the National Cancer Institute and the Environmental Protection Agency. In this paper, we will show how linked micromaps can be used to better understand and explore relationships and distributions of statistics linked to US states and Washington, DC. We will compare linked micromaps with other popular data displays of geographic data, such as bubble maps, choropleth maps, and bar charts. We will illustrate how linked micromaps can be used for evidence-based decision-making using data from the Bureau of Labor Statistics, the Census Bureau, and the Economic Research Service. The presentations, R scripts, and the data sets used in this article are available here: https://github.com/wlmcensus/Joint-Statistical-Meetings-Presentation-2024. The work discussed in this article was presented at the Joint Statistical Meetings (JSM) 2024 and the American Association for Public Opinion Research (AAPOR) 2024 Annual Conference., Comment: 18 pages, 13 figures, JSM 2024 Conference Proceedings

Two-body decays like $K \to \pi a$ rank among the most constraining collider probes for new, low-mass, feebly interacting pseudoscalar particles $a$. We explore an alternative class of kaon decay modes, specifically three-body decays to $\pi \pi a$ or $\mu \mu a$. The former occur at tree level, while the latter is loop-suppressed yet accidentally finite. These modes specifically leverage the accurate tracking capabilities at LHCb. We present an estimation approach for the sensitivity achievable in future searches within these channels. Our argument uses the current uncertainty in leading irreducible backgrounds identified for each channel. Our findings suggest that dedicated searches could probe $f_a$ scales between $10^4$ and $10^6$ TeV, highlighting their strong potential. A direct comparison with actual searches, only available in the $K^+ \to \pi^+ \pi^0 a$ channel, supports this conclusion. Finally, we show that, in these searches, reconstruction efficiency maps are such that large efficiencies are naturally aligned with regions of higher yields in Dalitz plots., Comment: 17 pages, 10 figures

This study explores a Bayesian algorithmic approach to personalized fragrance recommendation by integrating hierarchical Relevance Vector Machines (RVM) and Jungian personality archetypes. The paper proposes a structured model that links individual scent preferences for top, middle, and base notes to personality traits derived from Jungian archetypes, such as the Hero, Caregiver, and Explorer, among others. The algorithm utilizes Bayesian updating to dynamically refine predictions as users interact with each fragrance note. This iterative process allows for the personalization of fragrance experiences based on prior data and personality assessments, leading to adaptive and interpretable recommendations. By combining psychological theory with Bayesian machine learning, this approach addresses the complexity of modeling individual preferences while capturing user-specific and population-level trends. The study highlights the potential of hierarchical Bayesian frameworks in creating customized olfactory experiences, informed by psychological and demographic factors, contributing to advancements in personalized product design and machine learning applications in sensory-based industries., Comment: 15 pages, 0 figures

Data point selection (DPS) is becoming a critical topic in deep learning due to the ease of acquiring uncurated training data compared to the difficulty of obtaining curated or processed data. Existing approaches to DPS are predominantly based on a bi-level optimisation (BLO) formulation, which is demanding in terms of memory and computation, and exhibits some theoretical defects regarding minibatches. Thus, we propose a novel Bayesian approach to DPS. We view the DPS problem as posterior inference in a novel Bayesian model where the posterior distributions of the instance-wise weights and the main neural network parameters are inferred under a reasonable prior and likelihood model. We employ stochastic gradient Langevin MCMC sampling to learn the main network and instance-wise weights jointly, ensuring convergence even with minibatches. Our update equation is comparable to the widely used SGD and much more efficient than existing BLO-based methods. Through controlled experiments in both the vision and language domains, we present the proof-of-concept. Additionally, we demonstrate that our method scales effectively to large language models and facilitates automated per-task optimization for instruction fine-tuning datasets.

We give a proof of the Kodaira vanishing theorem on smooth complex surfaces using geometric stability conditions. Likewise, we give a new proof of a result of Xie characterizing the counterexamples of the Kodaira vanishing theorem in positive characteristic., Comment: 11 pages, comments welcome!

The time until black hole formation in a binary neutron-star (NS) merger contains invaluable information about the nuclear equation of state (EoS) but has thus far been difficult to measure. We propose a new way to constrain the merger remnant's NS lifetime, which is based on the tendency of the NS remnant neutrino-driven winds to enrich the ejected material with helium. Based on the He I $\lambda 1083.3$ nm line, we show that the feature around 800-1200 nm in AT2017gfo at 4.4 days seems inconsistent with a helium mass fraction of $X_{\mathrm{He}} \gtrsim 0.05$ in the polar ejecta. Recent neutrino-hydrodynamic simulations of merger remnants are only compatible with this limit if the NS remnant collapses within 20-30 ms. Such a short lifetime implies that the total binary mass of GW170817, $M_\mathrm{\rm tot}$, lay close to the threshold binary mass for direct gravitational collapse, $M_\mathrm{thres}$, for which we estimate $M_{\mathrm{thres}}\lesssim 2.93 M_\odot$. This upper bound on $M_\mathrm{thres}$ yields upper limits on the radii and maximum mass of cold, non-rotating NSs, which rule out simultaneously large values for both quantities. In combination with causality arguments, this result implies a maximum NS mass of $M_\mathrm{max}\lesssim2.3 M_\odot$. The combination of all limits constrains the radii of 1.6 M$_\odot$ NSs to about 12$\pm$1 km for $M_\mathrm{max}$ = 2.0 M$_\odot$ and 11.5$\pm$1 km for $M_\mathrm{max}$ = 2.15 M$_\odot$. This $\sim2$ km allowable range then tightens significantly for $M_\mathrm{max}$ above $\approx2.15$ M$_\odot$. This rules out a significant number of current EoS models. The short NS lifetime also implies that a black-hole torus, not a highly magnetized NS, was the central engine powering the relativistic jet of GRB170817A. Our work motivates future developments... [abridged], Comment: 30 pages, 16 figures, 2 tables, submitted to PRX

The proposed system consists of a two-stage cascade. The first stage performs a rough heartbeat detection while the second stage refines the previous one, improving the temporal localization and also classifying the heartbeats into types S1 and S2. The first contribution is a novel approach that combines the dissimilarity matrix with the frame-level spectral divergence to locate heartbeats using the repetitiveness shown by the heart sounds and the temporal relationships between the intervals defined by the events S1/S2 and non-S1/S2 (systole and diastole). The second contribution is a verification-correction-classification process based on a sliding window that allows the preservation of the temporal structure of the cardiac cycle in order to be applied in the heart sound classification. The proposed method has been assessed using the open access databases PASCAL, CirCor DigiScope Phonocardiogram and an additional sound mixing procedure considering both Additive White Gaussian Noise (AWGN) and different kinds of clinical ambient noises from a commercial database. The proposed method provides the best detection/classification performance in realistic scenarios where the presence of cardiac anomalies as well as different types of clinical environmental noises are active in the PCG signal. Of note, the promising modelling of the temporal structures of the heart provided by the dissimilarity matrix together with the frame-level spectral divergence, as well as the removal of a significant number of spurious heart events and recovery of missing heart events, both corrected by the proposed verification-correction-classification algorithm, suggest that our proposal is a successful tool to be applied in heart segmentation.

Highly forbidden $\beta$ decays provide a sensitive test to nuclear models in a regime in which the decay goes through high spin-multipole states, similar to the neutrinoless double-$\beta$ decay process. There are only 3 nuclei ($^{50}$V, $^{113}$Cd, $^{115}$In) which undergo a $4^{\rm th}$ forbidden non-unique $\beta$ decay. In this work, we compare the experimental $^{113}$Cd spectrum to theoretical spectral shapes in the framework of the spectrum-shape method. We measured with high precision, with the lowest energy threshold and the best energy resolution ever, the $\beta$ spectrum of $^{113}$Cd embedded in a 0.43 kg CdWO$_4$ crystal, operated over 26 days as a bolometer at low temperature in the Canfranc underground laboratory (Spain). We performed a Bayesian fit of the experimental data to three nuclear models (IBFM-2, MQPM and NSM) allowing the reconstruction of the spectral shape as well as the half-life. The fit has two free parameters, one of which is the effective weak axial-vector coupling constant, $g_A^{\text{eff}}$, which resulted in $g_A^{\text{eff}}$ between 1.0 and 1.2, compatible with a possible quenching. Based on the fit, we measured the half-life of the $^{113}$Cd $\beta$ decay including systematic uncertainties as $7.73^{+0.60}_{-0.57} \times 10^{15}$ yr, in agreement with the previous experiments. These results represent a significant step towards a better understanding of low-energy nuclear processes., Comment: Accepted for publication by EPJC

Tropospheric ozone is an atmospheric pollutant that negatively impacts human health and the environment. Precise estimation of ozone levels is essential for preventive measures and mitigating its effects. This work compares the accuracy of multiple regression models in forecasting ozone levels in Mexico City, first without adding temporal features and interactions, and then with these features included. Our findings show that incorporating temporal features and interactions improves the accuracy of the models., Comment: 11 pages, 5 figures

Differential dynamic microscopy (DDM) typically relies on movies containing hundreds or thousands of frames to accurately quantify motion in soft matter systems. Using movies much shorter in duration produces noisier and less accurate results. This limits the applicability of DDM to situations where the dynamics are stationary over extended times. Here, we investigate a method to denoise the DDM process, particularly suited to when a limited number of imaging frames are available or when dynamics are quickly evolving in time. We use a convolutional neural network encoder-decoder (CNN-ED) model to reduce the noise in the intermediate scattering function that is computed via DDM. We demonstrate this approach of combining machine learning and DDM on samples containing diffusing micron-sized colloidal particles. We quantify how the particles' diffusivities change over time as the fluid they are suspended in gels. We also quantify how the diffusivity of particles varies with position in a sample containing a viscosity gradient. These test cases demonstrate how studies of non-equilibrium dynamics and high-throughput screens could benefit from a method to denoise the outputs of DDM.

This work uses twin 22-year free-running simulations of the Gulf of Mexico hydrodynamics performed with the HYCOM, one considering only ocean dynamics and the other incorporating atmospheric forcing, to study the behavior of the Yucatan Channel transport (YCT), the Loop Current (LC), the Loop Current Eddies (LCEs), their relationships, and the atmospheric forcing effect on them in short (daily) and long (monthly) time scales. A more comprehensive description of the LC intrusion and LCE separations was obtained by considering the upper eastern or western YCT (whose magnitudes are determined by the longitudinal displacements of the Yucatan Current's core), a perspective not evident when considering the upper total YCT; specifically, the eastern YCT provides the most meaningful description of the studied processes. Atmospheric forcing mainly affects the extended stage of the LC by creating a higher dispersion in the YCT and LC circulation values in comparison when considering only ocean dynamics. For the long-term analysis, standardized indexes that integrate the daily values of the eastern YCT and LC circulation in time were used; their temporal propagation and persistence (the changes of their characteristics from short to long time scales) were studied. Intrinsic ocean dynamics produces a persistent YCT and LC intrusion behavior and consistent LCE separation patterns from daily to 5-month scales. The atmospheric forcing effects are more emphasized on the LC intrusion and LCE separations than on the YCT: the YCT persistence is maintained but not that of the LC intrusion. An increased occurrence of LCE separations with low or moderate LC intrusion is expected due to climate change. Using the standardized indexes of the LC metrics to construct a predictive model of the LC intrusion and LCE separations using only current and past LC information is proposed for future research., Comment: 14 pages, 5 figures

Future cosmic microwave background (CMB) experiments are primarily targeting a detection of the primordial $B$-mode polarisation. The faintness of this signal requires exquisite control of systematic effects which may bias the measurements. In this work, we derive requirements on the relative calibration accuracy of the overall polarisation gain ($\Delta g_\nu$) for LiteBIRD experiment, through the application of the blind Needlet Internal Linear Combination (NILC) foreground-cleaning method. We find that minimum variance techniques, as NILC, are less affected by gain calibration uncertainties than a parametric approach, which requires a proper modelling of these instrumental effects. The tightest constraints are obtained for frequency channels where the CMB signal is relatively brighter (166 GHz channel, $\Delta {g}_\nu \approx 0.16 \%$), while, with a parametric approach, the strictest requirements were on foreground-dominated channels. We then propagate gain calibration uncertainties, corresponding to the derived requirements, into all frequency channels simultaneously. We find that the overall impact on the estimated $r$ is lower than the required budget for LiteBIRD by almost a factor $5$. The adopted procedure to derive requirements assumes a simple Galactic model. We therefore assess the robustness of obtained results against more realistic scenarios by injecting the gain calibration uncertainties, according to the requirements, into LiteBIRD simulated maps and assuming intermediate- and high-complexity sky models. In this case, we employ the so-called Multi-Clustering NILC (MC-NILC) foreground-cleaning pipeline and obtain that the impact of gain calibration uncertainties on $r$ is lower than the LiteBIRD gain systematics budget for the intermediate-complexity sky model. For the high-complexity case, instead, it would be necessary to tighten the requirements by a factor $1.8$., Comment: 29 pages, 11 figures

Methods of Machine and Deep Learning are gradually being integrated into industrial operations, albeit at different speeds for different types of industries. The aerospace and aeronautical industries have recently developed a roadmap for concepts of design assurance and integration of neural network-related technologies in the aeronautical sector. This paper aims to contribute to this paradigm of AI-based certification in the context of supervised learning, by outlining a complete validation pipeline that integrates deep learning, optimization and statistical methods. This pipeline is composed by a directed graphical model of ten steps. Each of these steps is addressed by a merging key concepts from different contributing disciplines (from machine learning or optimization to statistics) and adapting them to an industrial scenario, as well as by developing computationally efficient algorithmic solutions. We illustrate the application of this pipeline in a realistic supervised problem arising in aerostructural design: predicting the likelikood of different stress-related failure modes during different airflight maneuvers based on a (large) set of features characterising the aircraft internal loads and geometric parameters., Comment: 38 pages, 17 figures

We present a search for long-lived particles (LLPs) produced from kaon decay that decay to two muons inside the ICARUS neutrino detector. This channel would be a signal of hidden sector models that can address outstanding issues in particle physics such as the strong CP problem and the microphysical origin of dark matter. The search is performed with data collected in the Neutrinos at the Main Injector (NuMI) beam at Fermilab corresponding to $2.41\times 10^{20}$ protons-on-target. No new physics signal is observed, and we set world-leading limits on heavy QCD axions, as well as for the Higgs portal scalar among dedicated searches. Limits are also presented in a model-independent way applicable to any new physics model predicting the process $K\to \pi+S(\to\mu\mu)$, for a long-lived particle S. This result is the first search for new physics performed with the ICARUS detector at Fermilab. It paves the way for the future program of long-lived particle searches at ICARUS.

We study a class of semi-linear differential Volterra equations with polynomial-type potentials that incorporates the effects of memory while being subjected to random perturbations via an additive Gaussian noise. We show that for a broad class of non-linear potentials, the system always admits invariant probability measures. However, the presence of memory effects precludes access to compactness in a typical fashion. In this paper, this obstacle is overcome by introducing functional spaces adapted to the memory kernels, thereby allowing one to recover compactness. Under the assumption of sufficiently smooth noise, it is then shown that the statistically stationary states possess higher-order regularity properties dictated by the structure of the nonlinearity. This is established through a control argument that asymptotically transfers regularity onto the solution by exploiting the underlying Lyapunov structure of the system in a novel way., Comment: arXiv admin note: substantial text overlap with arXiv:2212.05646

We present TDS Simulator, a new software tool aimed at modelling thermal desorption spectroscopy (TDS) experiments. TDS is a widely used technique for quantifying key characteristics of hydrogen-material interactions, such as diffusivity and trapping. However, interpreting the output of TDS experiments is non-trivial and requires appropriate post-processing tools. This work introduces the first software tool capable of simulating TDS curves for arbitrary choices of material parameters and hydrogen trap characteristics, using the primary hydrogen diffusion and trapping models (Oriani, McNabb-Foster). Moreover, TDS Simulator contains a specific functionality for loading experimental TDS data and conducting the inverse calibration of a selected transport model, providing automatic estimates of the density and binding energy of each hydrogen trap type in the material. In its first version, TDS Simulator is provided as a MATLAB App, which is made freely available to the community and provides a simple graphical user interface (GUI) to make use of TDS Simulator straightforward. As reported in the present manuscript, the outputs of TDS Simulator have been extensively validated against literature data. Demonstrations of automatic determination of trap characteristics from experimental data through the optimisation tool are also provided. The present work enables an efficient and straightforward characterisation of hydrogen-material characteristics relevant to multiple applications, from nuclear fusion to the development of hydrogen-compatible materials for the hydrogen economy. TDS Simulator can be downloaded from https://mechmat.web.ox.ac.uk/codes.

We demonstrate the efficacy of using intermediate representations from a single foundation model to enhance various music downstream tasks. We introduce SoniDo, a music foundation model (MFM) designed to extract hierarchical features from target music samples. By leveraging hierarchical intermediate features, SoniDo constrains the information granularity, leading to improved performance across various downstream tasks including both understanding and generative tasks. We specifically evaluated this approach on representative tasks such as music tagging, music transcription, music source separation, and music mixing. Our results reveal that the features extracted from foundation models provide valuable enhancements in training downstream task models. This highlights the capability of using features extracted from music foundation models as a booster for downstream tasks. Our approach not only benefits existing task-specific models but also supports music downstream tasks constrained by data scarcity. This paves the way for more effective and accessible music processing solutions., Comment: 41 pages with 14 figures

This article investigates and compares three approaches to link prediction in colaboration networks, namely, an ERGM (Exponential Random Graph Model; Robins et al. 2007), a GCN (Graph Convolutional Network; Kipf and Welling 2017), and a Word2Vec+MLP model (Word2Vec model combined with a multilayer neural network; Mikolov et al. 2013a and Goodfellow et al. 2016). The ERGM, grounded in statistical methods, is employed to capture general structural patterns within the network, while the GCN and Word2Vec+MLP models leverage deep learning techniques to learn adaptive structural representations of nodes and their relationships. The predictive performance of the models is assessed through extensive simulation exercises using cross-validation, with metrics based on the receiver operating characteristic curve. The results clearly show the superiority of machine learning approaches in link prediction, particularly in large networks, where traditional models such as ERGM exhibit limitations in scalability and the ability to capture inherent complexities. These findings highlight the potential benefits of integrating statistical modeling techniques with deep learning methods to analyze complex networks, providing a more robust and effective framework for future research in this field., Comment: 17 pages, 5 figures, 1 table

The aim of this study is to implement a method to remove ambient noise in biomedical sounds captured in auscultation. We propose an incremental approach based on multichannel non-negative matrix partial co-factorization (NMPCF) for ambient denoising focusing on high noisy environment with a Signal-to-Noise Ratio (SNR) <= -5 dB. The first contribution applies NMPCF assuming that ambient noise can be modelled as repetitive sound events simultaneously found in two single-channel inputs captured by means of different recording devices. The second contribution proposes an incremental algorithm, based on the previous multichannel NMPCF, that refines the estimated biomedical spectrogram throughout a set of incremental stages by eliminating most of the ambient noise that was not removed in the previous stage at the expense of preserving most of the biomedical spectral content. The ambient denoising performance of the proposed method, compared to some of the most relevant state-of-the-art methods, has been evaluated using a set of recordings composed of biomedical sounds mixed with ambient noise that typically surrounds a medical consultation room to simulate high noisy environments with a SNR from -20 dB to -5 dB. Experimental results report that: (i) the performance drop suffered by the proposed method is lower compared to MSS and NLMS; (ii) unlike what happens with MSS and NLMS, the proposed method shows a stable trend of the average SDR and SIR results regardless of the type of ambient noise and the SNR level evaluated; and (iii) a remarkable advantage is the high robustness of the estimated biomedical sounds when the two single-channel inputs suffer from a delay between them.

The sulfur content in dense molecular regions of the interstellar medium is highly depleted in comparison to diffuse clouds. The reason of this phenomenon is unclear, thus it is necessary to carry out observational studies of sulfur-bearing species towards dense regions, mainly at early evolutive stages to uncover the early sulfur chemistry. Using data from the Atacama Large Millimeter Array (ALMA) data archive, we investigated a sample of 37 dense cores embedded in the most massive infrared-quiet molecular clumps from the ATLASGAL survey. Lines of 34SO, SO2, NS, SO, SO+, and H2CS were analyzed and column densities of each molecular species were obtained. From the continuum emission, and two CH3OH lines, the 37 cores were characterized in density and temperature, and the corresponding H2 column densities were derived. The abundances of such sulfur-bearing species were derived and studied. We find that the abundances of the analyzed species increase with the growth of the gas temperature, suggesting that the chemistry involved in the formation of each of the analyzed molecule may have a similar dependence with Tk in the range 20 to 100 K. We find that the comparisons among abundances are, in general, highly correlated. Given that such correlation decreases in more evolved sources, we suggest that the sulfur-bearing species here analyzed should have a similar chemical origin. From the measured line widths we point out that molecules with oxygen content (34SO, SO2, SO, and SO+) may be associated with warmer and more turbulent gas than the other ones. H2CS and NS are associated with more quiescent gas, probably in the external envelopes of the cores. This work gives quantitative information about abundances that could be useful in chemical models pointing to explain the sulfur chemistry in the interstellar medium., Comment: Accepted for publication in A&A (October 31, 2024)

The objective of the coronagraphic IFU of RISTRETTO is to enable High Dispersion Coronagraphy of planets at a distance of 2$\lambda$/D from their star, without compromising on transmission. The new idea of a PIAA Nuller (PIAAN) allows contrast down to 10$^{-5}$ over large bandwidth $\ge$ 25%, with high transmission $\ge$ 70% at the distance of 2$\lambda$/D. While RISTRETTO will be installed on a VLT, this development is of tremendous importance for fully exploiting future ELTs XAO. We will discuss our PIAAN prototyping activities. This covers 1) the characterisation of our 2nd set of IFU bundles, with 3D-printed MLAs; 2) the characterisation of our first PIAA optics; 3) the integration of a high contrast bench, planned for prototyping of Front-End control strategies; 4) the characterisation of the PIAAN system on the bench.

The rapid integration of generative artificial intelligence (GenAI) technology into education necessitates precise measurement of GenAI literacy to ensure that learners and educators possess the skills to effectively engage with and critically evaluate this transformative technology. Existing instruments often rely on self-reports, which may be biased. In this study, we present the GenAI Literacy Assessment Test (GLAT), a 20-item multiple-choice instrument developed following established procedures in psychological and educational measurement. Structural validity and reliability were confirmed with responses from 355 higher education students using classical test theory and item response theory, resulting in a reliable 2-parameter logistic (2PL) model (Cronbach's alpha = 0.80; omega total = 0.81) with a robust factor structure (RMSEA = 0.03; CFI = 0.97). Critically, GLAT scores were found to be significant predictors of learners' performance in GenAI-supported tasks, outperforming self-reported measures such as perceived ChatGPT proficiency and demonstrating incremental validity. These results suggest that GLAT offers a reliable and more objective method for assessing GenAI literacy, with the potential to inform educational practices and policy decisions that aim to enhance learners' and educators' GenAI literacy, ultimately equipping them to navigate an AI-enhanced future.

We pursue a generalization isometries and Killing equations to N=1 4D supergravity. Continuous symmetries of spacetime such as spatial homogeneity and isotropy are defined in terms of Killing vectors. In supergravity, the metric, or rather the tetrad, is not a standalone entity, but is part of a multiplet containing also the Rarita-Schwinger spinor-vector and some auxiliary fields. Starting with a superfield generalization of the isometry definition in tetrad form, we derive two extensions of the Killing equations for the whole supergravity multiplet; for spatial isotropy, the first one yields a vanishing spinor-vector, whereas the second one admits a nonzero solution by a spin-$\frac{1}{2}$ truncation of the Rarita-Schwinger field., Comment: 14 pages, 0 figures

We analyze how neutrino oscillation and coherent elastic neutrino-nucleus scattering data impact the global SMEFT fit. We first review the mapping between the SMEFT parameters and the so-called NSI framework, commonly considered in the neutrino literature. We also present a detailed discussion of how the measurements for the normalization of neutrino fluxes and cross sections, that will also be affected by the new physics, indirectly impact the measured oscillation probabilities. We then analyze two well-motivated simplified scenarios. Firstly, we study a lepton flavour conserving case, usually assumed in global SMEFT analyses, showing the complementarity of neutrino oscillation and CE$\nu$NS experiments with other low-energy observables. We find that the inclusion of neutrino data allows to constrain previously unbounded SMEFT operators involving the tau flavour and confirm the improvement of the constraint on a combination of Wilson coefficients previously identified. Moreover, we find that neutrino oscillation constraints on NSI are improved when embedded in the global SMEFT framework. Secondly, we study a lepton flavour violating scenario and find that neutrino data also improves over previously derived global constraints thanks to its sensitivity to new combinations of Wilson coefficients., Comment: 25 pages + Appendix

The KM3NeT Collaboration has tackled a common challenge faced by the astroparticle physics community, namely adapting the experiment-specific simulation software to work with the CORSIKA air shower simulation output. The proposed solution is an extension of the open-source code gSeaGen, allowing for the transport of muons generated by CORSIKA to a detector of any size at an arbitrary depth. The gSeaGen code was not only extended in terms of functionalities but also underwent a thorough redesign of the muon propagation routine, resulting in a more accurate and efficient simulation. This paper presents the capabilities of the new gSeaGen code as well as prospects for further developments., Comment: 27 pages, 13 figures

The magnetised near detector (ND280) of the T2K long-baseline neutrino oscillation experiment has been recently upgraded aiming to satisfy the requirement of reducing the systematic uncertainty from measuring the neutrinonucleus interaction cross section, which is the largest systematic uncertainty in the search for leptonic charge-parity symmetry violation. A key component of the upgrade is SuperFGD, a 3D segmented plastic scintillator detector made of approximately 2,000,000 optically-isolated 1 cm3 cubes. It will provide a 3D image of GeV neutrino interactions by combining tracking and stopping power measurements of final state particles with sub-nanosecond time resolution. The performance of SuperFGD is characterized by the precision of its response to charged particles as well as the systematic effects that might affect the physics measurements. Hence, a detailed Geant4 based optical simulation of the SuperFGD building block, i.e. a plastic scintillating cube read out by three wavelength shifting fibers, has been developed and validated with the different datasets collected in various beam tests. In this manuscript the description of the optical model as well as the comparison with data are reported., Comment: 31 pages, 15 figures

In this paper, we extend the museum pass problem to incorporate the market structure. To be more precise, we consider that museums are organized into several pass programs or consortia. Within this framework, we propose four allocation mechanisms based on the market structure and the principles of proportionality and egalitarianism. All these mechanisms satisfy different reasonable properties related to fairness and stability which serve to axiomatically characterize them.

Spot patterns, in which vegetation patches form a hexagonal lattice, are frequent in nature and could serve as an early-warning indicator of abrupt vegetation collapses. Consequently, they have been intensively studied using both individual-based models and density-based field equations. Yet, the relationship between these two approaches remains unclear, particularly in scenarios where vegetation dynamics exhibit strong long-range spatial correlations and traditional mean-field approximations fail. To solve this issue, we develop a new method that refines mean-field approximations by describing both the dynamics of the biomass density field and its spatial correlations. This new approach harnesses the strengths of both individual and density-based mdoels, treating spatial correlations explicitly and allowing for the identification of spatial instabilities resulting in periodic patterns. Our results indicate that this new approximation predicts the parameter regimes where regular periodic patterns emerge more accurately than mean-field models, suggesting that it could provide a more robust framework to perform further nonlinear analysis.

The actuator line method (ALM) is an approach commonly used to represent lifting and dragging devices like wings and blades in large-eddy simulations (LES). The crux of the ALM is the projection of the actuator point forces onto the LES grid by means of a Gaussian regularisation kernel. The minimum width of the kernel is constrained by the grid size; however, for most practical applications like LES of wind turbines, this value is an order of magnitude larger than the optimal value which maximises accuracy. This discrepancy motivated the development of corrections for the actuator line, which, however, neglect the effect of unsteady spanwise shed vorticity. In this work, we develop a model for the impact of spanwise shed vorticity on the unsteady loading of an airfoil modelled as a Gaussian body force. The model solution is derived both in the time and frequency domain and features an explicit dependence on the Gaussian kernel width. We validate the model with LES within the linear regime of the lift curve for both pitch steps and periodic pitching with reduced frequencies of k=0.1, 0.2 and 0.3. The Gaussian kernel width affects, in particular, the amplitude of the unsteady lift, which can be up to 40% smaller than the quasi-steady amplitude within the considered range of reduced frequencies and kernel widths.

We prove, assuming resolution of singularities in positive characteristic, an analogue of Siegel's theorem on sum of squares in positive characteristic. The method of proof combines techniques from central simple algebras with model theory and builds on work of Anscombe, Dittmann and Fehm. As an application, we show that, for each finite field $\mathbb{F}$ of odd characteristic and any positive integer $n$ coprime with the characteristic of $\mathbb{F}$, the first-order theory of the field given by the compositum of the fields generated by adjoining the $n$--th roots of all monic irreducible polynomials in $\mathbb{F}[t]$, of degree divisible by $n$ is undecidable in the language of rings with the variable $t$ as a constant., Comment: 15 pages

We establish the existence of smooth, finite-energy solutions to the 2D incompressible porous media equation (IPM), with a compactly supported uniform smooth source, which develop singularities in finite time., Comment: 53 pages. Minor corrections added

Language models are typically trained on large corpora of text in their default orthographic form. However, this is not the only option; representing data as streams of phonemes can offer unique advantages, from deeper insights into phonological language acquisition to improved performance on sound-based tasks. The challenge lies in evaluating the impact of phoneme-based training, as most benchmarks are also orthographic. To address this, we develop a pipeline to convert text datasets into a continuous stream of phonemes. We apply this pipeline to the 100-million-word pre-training dataset from the BabyLM challenge, as well as to standard language and grammatical benchmarks, enabling us to pre-train and evaluate a model using phonemic input representations. Our results show that while phoneme-based training slightly reduces performance on traditional language understanding tasks, it offers valuable analytical and practical benefits.

Curriculum Learning has been a popular strategy to improve the cognitive plausibility of Small-Scale Language Models (SSLMs) in the BabyLM Challenge. However, it has not led to considerable improvements over non-curriculum models. We assess whether theoretical linguistic acquisition theories can be used to specify more fine-grained curriculum learning strategies, creating age-ordered corpora of Child-Directed Speech for four typologically distant language families to implement SSLMs and acquisition-inspired curricula cross-lingually. Comparing the success of three objective curricula (Growing, Inwards and MMM) that precisely replicate the predictions of acquisition theories on a standard SSLM architecture, we find fine-grained acquisition-inspired curricula can outperform non-curriculum baselines and performance benefits of curricula strategies in SSLMs can be derived by specifying fine-grained language-specific curricula that precisely replicate language acquisition theories., Comment: BabyLM Shared Task 2024 (Accepted, Poster), co-located in EMNLP 2024

Very-high-energy (0.1-100 TeV) gamma-ray emission was observed in HAWC data from the lobes of the microquasar SS 433, making them the first set of astrophysical jets that were resolved at TeV energies. In this work, we update the analysis of SS 433 using 2,565 days of data from the High Altitude Water Cherenkov (HAWC) observatory. Our analysis reports the detection of a point-like source in the east lobe at a significance of $6.6\,\sigma$ and in the west lobe at a significance of $8.2\,\sigma$. For each jet lobe, we localize the gamma-ray emission and identify a best-fit position. The locations are close to the X-ray emission sites "e1" and "w1" for the east and west lobes, respectively. We analyze the spectral energy distributions and find that the energy spectra of the lobes are consistent with a simple power-law $\text{d}N/\text{d}E\propto E^{\alpha}$ with $\alpha = -2.44^{+0.13+0.04}_{-0.12-0.04}$ and $\alpha = -2.35^{+0.12+0.03}_{-0.11-0.03}$ for the east and west lobes, respectively. The maximum energy of photons from the east and west lobes reaches 56 TeV and 123 TeV, respectively. We compare our observations to various models and conclude that the very-high-energy gamma-ray emission can be produced by a population of electrons that were efficiently accelerated.

This paper demonstrates the first on-chip frequency multiplexed readout of two co-integrated single-electron transistors without the need for bulky resonators. We characterize single electron dynamics in both single electron transistors at 4.2K before validating their simultaneous readout within 2.2{\mu}s, achieving a 99.9% fidelity with a 1MHz frequency spacing. This experimental demonstration paves the way towards resonator-free large-scale quantum-classical architectures that would be required for future universal and reprogrammable quantum computers.

Efficient hybrid DFT simulations of solid state materials would be extremely beneficial for computational chemistry and materials science, but is presently bottlenecked by difficulties in computing Hartree-Fock (HF) exchange with plane wave orbital bases. We present a GPU-accelerated, Gaussian orbital based integral algorithm for systems with periodic boundary conditions, which takes advantage of Ewald summation to efficiently compute electrostatic interactions. We have implemented this approach into the TeraChem software package within the $\Gamma$ point approximation, enabling simulation of unit cells with hundreds or thousands of atoms at the HF or hybrid DFT level on a single GPU card. Our implementation readily parallelizes over multiple GPUs and paves the road to accurate simulation of the properties and dynamics of extended materials in both the ground and excited states.

Classical parking functions are a generalization of permutations that appear in many combinatorial structures. Prime parking functions are indecomposable components such that any classical parking function can be uniquely described as a direct sum of prime ones. In this article, we extend the notion of primeness to three generalizations of classical parking functions: vector parking functions, $(p,q)$-parking functions, and two-dimensional vector parking functions. We study their enumeration by obtaining explicit formulas for the number of prime vector parking functions when the vector is an arithmetic progression, prime $(p,q)$-parking functions, and prime two-dimensional vector parking functions when the weight matrix is an affine transformation of the coordinates.

Many nucleosynthetic channels create the elements, but two-parameter models characterized by $\alpha$ and Fe nonetheless predict stellar abundances in the Galactic disk to accuracies of 0.02 to 0.05 dex for most measured elements, near the level of current abundance uncertainties. It is difficult to make individual measurements more precise than this to investigate lower-amplitude nucleosynthetic effects, but population studies of mean abundance patterns can reveal more subtle abundance differences. Here we look at the detailed abundances for 67315 stars from APOGEE DR17, but in the form of abundance residuals away from a best-fit two-parameter, data-driven nucleosynthetic model. We find that these residuals show complex structures with respect to age, guiding radius, and vertical action that are not random and are also not strongly correlated with sources of systematic error such as surface gravity, effective temperature, and radial velocity. The residual patterns, especially in Na, C+N, Ni, Mn, and Ce, trace kinematic structures in the Milky Way, such as the inner disk, thick disk, and flared outer disk. A principal component analysis suggests that most of the observed structure is low-dimensional and can be explained by a few eigenvectors. We find that some, but not all, of the effects in the low-$\alpha$ disk can be explained by dilution with fresh gas, so that abundance ratios resemble those of stars with higher metallicity. The patterns and maps we provide could be combined with accurate forward models of nucleosynthesis, star formation, and gas infall to provide a more detailed picture of star and element formation in different Milky Way components.

In the realm of human-machine interaction, artificial intelligence has become a powerful tool for accelerating data modeling tasks. Object detection methods have achieved outstanding results and are widely used in critical domains like autonomous driving and video surveillance. However, their adoption in high-risk applications, where errors may cause severe consequences, remains limited. Explainable Artificial Intelligence (XAI) methods aim to address this issue, but many existing techniques are model-specific and designed for classification tasks, making them less effective for object detection and difficult for non-specialists to interpret. In this work we focus on model-agnostic XAI methods for object detection models and propose D-MFPP, an extension of the Morphological Fragmental Perturbation Pyramid (MFPP), which uses segmentation-based mask generation. Additionally, we introduce D-Deletion, a novel metric combining faithfulness and localization, adapted specifically to meet the unique demands of object detectors. We evaluate these methods on real-world industrial and robotic datasets, examining the influence of parameters such as the number of masks, model size, and image resolution on the quality of explanations. Our experiments use single-stage object detection models applied to two safety-critical robotic environments: i) a shared human-robot workspace where safety is of paramount importance, and ii) an assembly area of battery kits, where safety is critical due to the potential for damage among high-risk components. Our findings evince that D-Deletion effectively gauges the performance of explanations when multiple elements of the same class appear in the same scene, while D-MFPP provides a promising alternative to D-RISE when fewer masks are used.

Discotic colloids give rise to a paradigmatic family of liquid crystals with sound applications in Materials Science. In this paper, Monte Carlo simulations are employed to characterize the low-temperature liquid crystal phase diagram and the vapour-liquid coexistence of discotic colloids interacting via a Kihara potential. Discoidal particles with thickness-diameter aspect ratios $L^*\equiv L/D$=\,0.5, 0.3, 0.2 and 0.1 are considered. For the less anisotropic particles ($L^*$$\ge$0.2), coexistence of a vapour phase with the isotropic fluid and with the columnar liquid crystal phase is observed. As the particle anisotropy increases, the vapour-liquid coexistence shifts to lower temperatures and its density range diminishes, eventually merging with coexistences involving the liquid crystal phases. The $L^*=$\,0.1 fluid displays a rich sequence of mesophases, including a nematic phase and a novel lamellar phase in which particles arrange in layers perpendicular to the nematic director.

Fluid antenna system (FAS) is gaining attention as an innovative technology for boosting diversity and multiplexing gains. As a key innovation, it presents the possibility to overcome interference by position reconfigurability on one radio frequency (RF) chain, giving rise to the concept of fluid antenna multiple access (FAMA). While FAMA is originally designed to deal with interference mainly by position change and treat interference as noise, this is not rate optimal, especially when suffering from a strong interference channel (IC) where all positions have strong interference. To tackle this, this paper considers a two-user strong IC where FAMA is used in conjunction with simultaneous nonunique decoding (SND). Specifically, we analyze the key statistics for the signal-to-noise ratio (SNR) and interference-to-noise ratio (INR) for a canonical two-user IC setup, and subsequently derive the delay outage rate (DOR), outage probability (OP) and ergodic capacity (EC) of the FAMA-IC. Our numerical results illustrate huge benefits of FAMA with SND over traditional fixed-position antenna systems (TAS) with SND in the fading IC.

Let $A$ and $B$ be sets of $k\ge5$ elements in $F=\mathbb{Z}/p\mathbb{Z}$ the field with $p>2k-2$ elements. We denote by $A\dot{+}B$ the set of different elements of $F$ that can be written in the form $a+b$, where $a\in A$, $b\in B$, $a\neq b$. The number of elements of this set is at least $2k-3$. K\'{a}rolyi showed that, except from some particular cases, The equality can only occur if $A = B$ and $A$ is an arithmetic progression with non zero difference. We prove that in the case that $|A\dot{+}B| = 2k - 2$ and $|A|=|B|$ the equality $A=B$ holds., Comment: 9 pages