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The Galactic bulge hosts the Milky Way's oldest stars, possibly coming from disrupted globular clusters (GCs) or the bulge's primordial building blocks, making these stars witnesses to the Galaxy's early chemical enrichment. The Galactic bar currently dominates the bulge's region, altering the orbits of objects formed before its formation and complicating the trace of the field stars' original clusters. Here, we present the discovery of a fossil record of this evolution, SOS1 -- a star trapped in the bar, exhibiting significant enhancements in nitrogen, sodium, and aluminum, typical of second-generation GC stars. SOS1 also shows an s-process Ce enhancement, suggesting an old age and early enrichment by fast-rotating massive stars in the Galaxy's earliest phases. With the purpose of finding the SOS1's parent GC, we derive its precise chemodynamical properties by combining high-precision proper motions from Gaia with APOGEE detailed chemical abundances. Our analysis suggests that SOS1 was possibly stripped from the GC Terzan 5 by the Galactic bar's gravitational influence approximately 350 Myr ago. We also found chemical similarities suggesting that SOS1 belonged to the most metal-poor, ancient, and peripheral stellar population of Terzan 5. These results not only support the hypothesis that Terzan 5 is a remnant of a primordial building block of the Galactic bulge, but also suggest this cluster continues losing stars to the bar. Our method highlights how powerful the use of chemodynamical properties in the Gaia era is for tracing the Galaxy's evolutionary history., Comment: Accepted for publication in ApJ Letters. 13 pages, 4 Figures (+ 3 in Appendix), and 1 Table (+ 1 in Appendix)

In this work we analyze the shift current conductivity in helical twisted trilayer graphene. Without loss of generality, we show that the density of states and the twist angle set an upper bound for this response, which is inversely proportional to the square of the twist angle. For the case of ABA stacking and at the magic angle, the shift photoconductivity can reach values of order $10^4~\mathrm{\mu A \cdot nm \cdot V}^{-2}$ for frequencies below 50 meV, which can be attributed to the interband transitions between the two flattened middle bands close to the Fermi level. By tuning the twist angle, we demonstrate that the photoconductivity is shifted in the frequency range and it is further influenced by two additional factors: The magnitude of the shift vector and the energy separation between the bands. Furthermore, we propose a scenario in the AAA stacked configuration, where the photoconductivity can be of order $10^5~\mathrm{\mu A \cdot nm \cdot V}^{-2}$ in the THz regime, revealing a potential influence of the stacking in the optimization of the shift current conductivity. Therefore, a large density of states, a small twist angle and the layer stacking are ingredients that hold promising functionality for photovoltaic applications in moir\'e heterostructures., Comment: 12 pages, 11 figures

Bitumen extraction for the production of synthetic crude oil in Canada's Athabasca Oil Sands industry has recently come under spotlight for being a significant source of greenhouse gas emission. A major cause of concern is methane, a greenhouse gas produced by the anaerobic biodegradation of hydrocarbons in oil sands residues, or tailings, stored in settle basins commonly known as oil sands tailing ponds. In order to determine the methane emitting potential of these tailing ponds and have future methane projections, we use real-time weather data, mechanistic models developed from laboratory controlled experiments, and industrial reports to train a physics constrained machine learning model. Our trained model can successfully identify the directions of active ponds and estimate their emission levels, which are generally hard to obtain due to data sampling restrictions. We found that each active oil sands tailing pond could emit between 950 to 1500 tonnes of methane per year, whose environmental impact is equivalent to carbon dioxide emissions from at least 6000 gasoline powered vehicles. Although abandoned ponds are often presumed to have insignificant emissions, our findings indicate that these ponds could become active over time and potentially emit up to 1000 tonnes of methane each year. Taking an average over all datasets that was used in model training, we estimate that emissions around major oil sands regions would need to be reduced by approximately 12% over a year, to reduce the average methane concentrations to 2005 levels., Comment: 19 pages, 8 figures, 2 tables

Advancements in tracking algorithms have empowered nascent applications across various domains, from steering autonomous vehicles to guiding robots to enhancing augmented reality experiences for users. However, these algorithms are application-specific and do not work across applications with different types of motion; even a tracking algorithm designed for a given application does not work in scenarios deviating from highly standard conditions. For example, a tracking algorithm designed for robot navigation inside a building will not work for tracking the same robot in an outdoor environment. To demonstrate this problem, we evaluate the performance of the state-of-the-art tracking methods across various applications and scenarios. To inform our analysis, we first categorize algorithmic, environmental, and locomotion-related challenges faced by tracking algorithms. We quantitatively evaluate the performance using multiple tracking algorithms and representative datasets for a wide range of Internet of Things (IoT) and Extended Reality (XR) applications, including autonomous vehicles, drones, and humans. Our analysis shows that no tracking algorithm works across different applications and scenarios within applications. Ultimately, using the insights generated from our analysis, we discuss multiple approaches to improving the tracking performance using input data characterization, leveraging intermediate information, and output evaluation.

Neural quantum states (NQS) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly-correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the Fermi-Hubbard and the (non-Hermitian) Hatano-Nelson-Hubbard models in one and two dimensions. In both cases, we observe that the convergence of the RNN ansatz is challenged when increasing the interaction strength. We present a physically-motivated and easy-to-implement strategy for improving the optimization, namely, by ramping of the model parameters. Furthermore, we investigate the advantages and disadvantages of the autoregressive sampling property of both network architectures. For the Hatano-Nelson-Hubbard model, we identify convergence issues that stem from the autoregressive sampling scheme in combination with the non-Hermitian nature of the model. Our findings provide insights into the challenges of the NQS approach and make the first step towards exploring strongly-correlated electrons using this ansatz.

In social sciences, researchers often face challenges when conducting large-scale experiments, particularly due to the simulations' complexity and the lack of technical expertise required to develop such frameworks. Agent-Based Modeling (ABM) is a computational approach that simulates agents' actions and interactions to evaluate how their behaviors influence the outcomes. However, the traditional implementation of ABM can be demanding and complex. Generative Agent-Based Modeling (GABM) offers a solution by enabling scholars to create simulations where AI-driven agents can generate complex behaviors based on underlying rules and interactions. This paper introduces a framework for designing reliable experiments using GABM, making sophisticated simulation techniques more accessible to researchers across various fields. We provide a step-by-step guide for selecting appropriate tools, designing the model, establishing experimentation protocols, and validating results.

The nuclear shell model describes very accurately the structure and dynamics of atomic nuclei. However, the exponential scaling of the basis size with respect to the number of degrees of freedom hampers a direct numerical solution for heavy nuclei. In this work, we investigate the implementation of a quantum annealing protocol for nuclear ground states. We propose a driver Hamiltonian with sufficiently large gaps, and validate our approach in nuclei with up to 28 nucleons employing classical simulations of the annealing evolution using a digitalized Trotter decomposition. While the nuclear Hamiltonian is non-local and thus challenging to implement in current annealing setups, the estimated computational cost of our annealing protocol on quantum circuits is polynomial in the number of elements of the many-body basis., Comment: 16 pages, 5 figures

A new approach suitable for distributed quantum machine learning and exhibiting memory is proposed for a photonic platform. This measurement-based quantum reservoir computing takes advantage of continuous variable cluster states as the main quantum resource. Cluster states are key to several photonic quantum technologies, enabling universal quantum computing as well as quantum communication protocols. The proposed measurement-based quantum reservoir computing is based on a neural network of cluster states and local operations, where input data are encoded through measurement, thanks to quantum teleportation. In this design, measurements enable input injections, information processing and continuous monitoring for time series processing. The architecture's power and versatility are tested by performing a set of benchmark tasks showing that the protocol displays internal memory and is suitable for both static and temporal information processing without hardware modifications. This design opens the way to distributed machine learning.

In this note we give a very short introduction to Formal Concept Analysis, accompanied by an example in order to build concept lattices from a context. We build the lattice using the Java-based software Concept Explorer (ConExp) in a recent version of Linux. Installing an appropriate Java version is necessary, because ConExp was developed some time ago using a Sun Java version, which is not open-source. As a result, it has been observed that ConExp will not build a lattice when started with an open-source Java version. Therefore, we also sketch the procedure we followed to install an appropriate Java version which makes ConExp work again, i.e., to "build lattices again". We also show how to start ConExp with a 32 bit Java version, which requires a few additional libraries., Comment: 10 pages, 1 context, 9 figures

The importance of social media in our daily lives has unfortunately led to an increase in the spread of misinformation, political messages and malicious links. One of the most popular ways of carrying out those activities is using automated accounts, also known as bots, which makes the detection of such accounts a necessity. This paper addresses that problem by investigating features based on the user account profile and its content, aiming to understand the relevance of each feature as a basis for improving future bot detectors. Through an exhaustive process of research, inference and feature selection, we are able to surpass the state of the art on several metrics using classical machine learning algorithms and identify the types of features that are most important in detecting automated accounts.

Breast compression simulation is essential for accurate image registration from 3D modalities to X-ray procedures like mammography. It accounts for tissue shape and position changes due to compression, ensuring precise alignment and improved analysis. Although Finite Element Analysis (FEA) is reliable for approximating soft tissue deformation, it struggles with balancing accuracy and computational efficiency. Recent studies have used data-driven models trained on FEA results to speed up tissue deformation predictions. We propose to explore Physics-based Graph Neural Networks (PhysGNN) for breast compression simulation. PhysGNN has been used for data-driven modelling in other domains, and this work presents the first investigation of their potential in predicting breast deformation during mammographic compression. Unlike conventional data-driven models, PhysGNN, which incorporates mesh structural information and enables inductive learning on unstructured grids, is well-suited for capturing complex breast tissue geometries. Trained on deformations from incremental FEA simulations, PhysGNN's performance is evaluated by comparing predicted nodal displacements with those from finite element (FE) simulations. This deep learning (DL) framework shows promise for accurate, rapid breast deformation approximations, offering enhanced computational efficiency for real-world scenarios., Comment: Deep Breath @ MICCAI 2024 | The code is available at this URL: https://github.com/hadiiiil/GNNs-BreastCompression

This study investigates a novel method for estimating two-dimensional velocities using coarse-grained imaging data, which is particularly relevant for applications in plasma diagnostics. The method utilizes measurements from three non-collinear points and is derived from a stochastic model that describes the propagation of uncorrelated pulses through two-dimensional space. This model builds upon a well-studied one-dimensional model used to analyze turbulence in the scrape-off layer of magnetically confined plasmas. We demonstrate that the method provides exact time delay estimates when applied to a superposition of Gaussian structures and remains accurate for various other pulse functions. Through extensive numerical simulations, we evaluate the method's performance under different conditions, including variations in signal duration, pulse overlap, spatial and temporal resolution, and the presence of additive noise. Additionally, we investigate the impact of temporal pulse evolution due to linear damping and explore the so-called barberpole effect, which occurs with elongated and tilted structures. Our analysis reveals that the three-point method effectively addresses the limitations encountered with two-point techniques, particularly at coarse spatial resolutions. Although the method is susceptible to the barberpole effect, we analytically demonstrate that this effect does not occur when the elongated structures propagate parallel to one of their axes, and we establish bounds for the associated errors. Overall, our findings provide a comprehensive and robust framework for accurate two-dimensional velocity estimation, enhancing the capabilities of fusion plasma diagnostics and potentially benefiting other fields requiring precise motion analysis.

Early identification of respiratory irregularities is critical for improving lung health and reducing global mortality rates. The analysis of respiratory sounds plays a significant role in characterizing the respiratory system's condition and identifying abnormalities. The main contribution of this study is to investigate the performance when the input data, represented by cochleogram, is used to feed the Vision Transformer architecture, since this input classifier combination is the first time it has been applied to adventitious sound classification to our knowledge. Although ViT has shown promising results in audio classification tasks by applying self attention to spectrogram patches, we extend this approach by applying the cochleogram, which captures specific spectro-temporal features of adventitious sounds. The proposed methodology is evaluated on the ICBHI dataset. We compare the classification performance of ViT with other state of the art CNN approaches using spectrogram, Mel frequency cepstral coefficients, constant Q transform, and cochleogram as input data. Our results confirm the superior classification performance combining cochleogram and ViT, highlighting the potential of ViT for reliable respiratory sound classification. This study contributes to the ongoing efforts in developing automatic intelligent techniques with the aim to significantly augment the speed and effectiveness of respiratory disease detection, thereby addressing a critical need in the medical field.

In this paper, we use the equivariant degree theory to establish a global bifurcation result for the existence of non-stationary branches of solutions to a nonlinear, two-parameter family of hyperbolic wave equations with local delay and non-trivial damping. As a motivating example, we consider an application of our result to a system of $N$ identical vibrating strings with dihedral coupling relations.

Spin-orbit torque (SOT) can be used to efficiently manipulate the magnetic state of magnetic materials, which is an essential element for memory and logic applications. Due to symmetry constraints, only in-plane spins can be injected into the ferromagnet from the underlying SOT layer for conventional SOT materials such as heavy metals and topological materials. Through the use of materials with low symmetries, or other symmetry breaking approaches, unconventional spin currents with out-of-plane polarization has been demonstrated and enabled field-free deterministic switching of perpendicular magnetization. Despite this progress, the SOT efficiency of these materials has typically remained low. Here, we report a giant SOT efficiency of 0.85 in sputtered Ni4W/CoFeB heterostructure at room temperature, as evaluated by second harmonic Hall measurements. In addition, due to the low crystal symmetry of Ni4W, unconventional out-of-plane and Dresselhaus-like spin components were observed. Macro-spin simulation suggests our spin Hall tensor to provide about an order of magnitude improvement in the magnetization switching efficiency, thus broadening the path towards energy efficient spintronic devices using low-symmetry materials.

The rising number of bridge collapses worldwide has compelled governments to introduce predictive maintenance strategies to extend structural lifespan. In this context, vibration-based Structural Health Monitoring (SHM) techniques utilizing Operational Modal Analysis (OMA) are favored for their non-destructive and global assessment capabilities. However, long multi-span bridges instrumented with dense arrays of accelerometers present a particular challenge, as the computational demands of classical OMA techniques in such cases are incompatible with long-term SHM. To address this issue, this paper introduces Randomized Singular Value Decomposition (RSVD) as an efficient alternative to traditional SVD within Covariance-driven Stochastic Subspace Identification (CoV-SSI). The efficacy of RSVD is also leveraged to enhance modal identification results and reduce the need for expert intervention by means of 3D stabilization diagrams, which facilitate the investigation of the modal estimates over different model orders and time lags. The approach's effectiveness is demonstrated on the San Faustino Bridge in Italy, equipped with over 60 multiaxial accelerometers.

Experiments on chains of Rydberg atoms appear as a new playground to study quantum phase transitions in 1D. As a natural extension, we report a quantitative ground-state phase diagram of Rydberg atoms arranged in a two-leg ladder that interact via van der Waals potential. We address this problem numerically, using the Density Matrix Renormalization Group (DMRG) algorithm. Our results suggest that, surprisingly enough, $\mathbb{Z}_k$ crystalline phases, with the exception of the checkerboard phase, appear in pairs characterized by the same pattern of occupied rungs but distinguishable by a spontaneously broken $\tilde{\mathbb{Z}}_2$ symmetry between the two legs of the ladder. Within each pair, the two phases are separated by a continuous transition in the Ising universality class, which eventually fuses with the $\mathbb{Z}_k$ transition, whose nature depends on $k$. According to our results, the transition into the $\mathbb{Z}_2\otimes \tilde{\mathbb{Z}}_2$ phase changes its nature multiple of times and, over extended intervals, falls first into the Ashkin-Teller, latter into the $\mathbb{Z}_4$-chiral universality class and finally in a two step-process mediated by a floating phase. The transition into the $\mathbb{Z}_3$ phase with resonant states on the rungs belongs to the three-state Potts universality class at the commensurate point, to the $\mathbb{Z}_3$-chiral Huse-Fisher universality class away from it, and eventually it is through an intermediate floating phase. The Ising transition between $\mathbb{Z}_3$ and $\mathbb{Z}_3\otimes \tilde{\mathbb{Z}}_2$ phases, coming across the floating phase, opens the possibility to realize lattice supersymmetry in Rydberg quantum simulators.

Coding camps bring together individuals from diverse backgrounds to tackle given challenges within a limited timeframe. Such camps create a rich learning environment for various skills, some of which are directly associated with the camp, and some of which are a result of working as a team during the camp. Unfortunately, coding camps often remain isolated from the broader educational curriculum or other bigger context, which downplays the opportunities they can offer to students. In this paper, we present the vision of the European initiative Oscar, which aims at connecting coding camps to the educational and professional context faced by the learners. In addition, we sketch a supporting platform and its features for connected coding camps., Comment: Accepted for publication at SIGCSE TS 2025, Proceedings of the 56th ACM Technical Symposium on Computer Science Education

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

We show that every Born Lie algebra can be obtained by the bicross product construction starting from two pseudo-Riemannian Lie algebras. We then obtain a classification of all Lie algebras up to dimension four and all six-dimensional nilpotent Lie algebras admitting an integrable Born structure. Finally, we study the curvature properties of the pseudo-Riemannian metrics of the integrable Born structures obtained in our classification results., Comment: 22 pages. Comments are welcome!

Magnetic cycles of solar-like stars influence their internal physics. Thus, the frequency, amplitude, excitation rate, and damping of the acoustic oscillation modes (p-modes) vary with the cycle over time. We need to understand the impact of magnetic activity on p-modes in order to characterise precisely stars that will be observed by the ESA PLATO mission, to be launched late 2026 with the objective to find Earth-like planets around solar-type stars. In this work, we investigate the variation of mode excitation in the Sun during Cycles 23, 24 and the beginning of Cycle 25. To do so, we analyse data obtained since 1996 by two instruments onboard the SoHO satellite: the GOLF spectrometer and the VIRGO sunphotometer. We use a method enabling us to reach a better temporal resolution than classical methods. Combining the variations of energy for several modes l=[0-2] in three frequency bands (i.e. [1800, 2450], [2450, 3110], [3110, 3790] {\mu}Hz), our preliminary results show that more energy is associated to several modes during cycle minima, suggesting that there could be a second source of excitation other than turbulent convection that would excite several modes at a time during solar minima., Comment: Proceedings of the French Society for Astronomy and Astrophysics (SF2A) conference 2024, 3 pages, 2 figures

I consider the fluid-mediated approach of a deformable elastic object (``indenter'') to a rigid surface at relatively low velocity. As a fluid is squeezed between the tip and the rigid substrate, lubrication pressures develop, which in turn deform the indenter leading edge. I study the influence of the tip geometry over the lubrication pressure distribution. ''Low velocity'' means that the approach happens slowly enough for the body to adapt quasi-statically to the transient viscous pressures triggered in the mediating fluid when squeezed. The salient geometrical simplification is that the indenter shape is axisymmetric, and its height profile goes like $\sim r^n$, $r$ being the radial coordinate measured from the tip and $n$ the exponent that controls the leading edge shape. I inquire if the distribution of pressures induced by the thin lubrication film forming before touchdown corresponds to the pressure distribution predicted by an equivalent ''dry'' contact mechanics problem. Results show striking resemblance for $n \le 2$ while also partial ability to predict the pressure distribution for $n>2$. Still, the analogy is deemed exceedingly insightful., Comment: 14 pages, 4 figures

We explore a family of paraxial beams constructed by the linear superposition of Laguerre-Gaussian beams, representing an optical analogue to generalized $SU(2)$ Lie group coherent states. A single complex parameter controls a smooth transition between Laguerre-Gaussian and Hermite-Gaussian beams, with intermediate beams that merge characteristics of both families. Our beams exhibit propagation-invariant properties, up to a scaling factor, a highly desirable feature for optical applications, validated via holographic experimental results., Comment: 12 pages, 6 figures

A kernel density estimator for data on the polysphere $\mathbb{S}^{d_1}\times\cdots\times\mathbb{S}^{d_r}$, with $r,d_1,\ldots,d_r\geq 1$, is presented in this paper. We derive the main asymptotic properties of the estimator, including mean square error, normality, and optimal bandwidths. We address the kernel theory of the estimator beyond the von Mises-Fisher kernel, introducing new kernels that are more efficient and investigating normalizing constants, moments, and sampling methods thereof. Plug-in and cross-validated bandwidth selectors are also obtained. As a spin-off of the kernel density estimator, we propose a nonparametric $k$-sample test based on the Jensen-Shannon divergence. Numerical experiments illuminate the asymptotic theory of the kernel density estimator and demonstrate the superior performance of the $k$-sample test with respect to parametric alternatives in certain scenarios. Our smoothing methodology is applied to the analysis of the morphology of a sample of hippocampi of infants embedded on the high-dimensional polysphere $(\mathbb{S}^2)^{168}$ via skeletal representations ($s$-reps)., Comment: 20 pages, 4 figures. Supplementary material: 23 pages, 8 figures, 1 table

The coincidence rank, introduced by Barge and Kwapisz, measures the regularity of the maximal equicontinuous factor of minimal dynamical systems. We provide a characterization of the finiteness of coincidence rank using a multivariate notion of equicontinuity.

The seesaw mechanism with three heavy Majorana right-handed neutrinos provides an elegant explanation for neutrino masses and, combined with leptogenesis, can generate the baryon asymmetry of the universe (BAU). Naturally embedded in a Grand Unified Theory, this framework stands as one of the best-motivated extensions beyond the Standard Model. However, it does not account for dark matter (DM). In this paper, we propose a minimal extension that introduces a dark sector with a singlet Majorana fermion (as the DM candidate) and a complex scalar singlet. The heavy right-handed neutrinos serve multiple roles: i) explaining the small masses of active neutrinos via the seesaw mechanism, ii) generating the BAU through leptogenesis, and iii) producing the cold DM density through their decays. Interestingly, the model also predicts a subdominant DM component from late scalar decays, which in some cases may be hot or warm at the onset of structure formation, as well as an equal number of non-thermal neutrinos. These components leave distinct signatures in various cosmological observables. Furthermore, electromagnetic energy injection from scalar decays alter predictions from Big Bang Nucleosynthesis and induce spectral distortions in the Cosmic Microwave Background black-body spectrum. Upcoming experiments, such as the Primordial Inflation Explorer (PIXIE), could probe a significant portion of the parameter space of this extended high-scale seesaw scenario., Comment: 9 pages, 2 figures, 1 table

Interoperability is the key to implementing a wide range of energy systems applications. It involves the seamless cooperation of different methods and components. With smart energy systems, interoperability faces challenges due to integrating differ-ent approaches and technologies. This includes dealing with heterogeneous approaches with various communication proto-cols and data formats. However, it is essential for smart energy systems to carry out thorough interoperability tests. They are usually diverse, and challenging, thus requiring careful consideration of compatibility issues and complex integration scenari-os. Overcoming these challenges requires a systematic approach that includes thorough test planning, rigorous testing, and continuous test monitoring. Although numerous testing approaches exist, most are more developed at the component/device level than at the system level. Consequently, there are few approaches and related facilities to test the interoperability of smart energy approaches and solutions at the system level. This work analyses existing interoperability test concepts, identi-fies enablers and the potential for harmonisation of procedures, and proposes further developments of these approaches., Comment: 14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion (MED POWER 2024)

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mis-modeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.

Gas-phase molecules capable of repeatable, narrow-band spontaneous photon scattering are prized for direct laser cooling and quantum state detection. Recently, large molecules incorporating phenyl rings have been shown to exhibit similar vibrational closure to the small molecules demonstrated so far, and it is not yet known if the high vibrational-mode density of even larger species will eventually compromise optical cycling. Here, we systematically increase the size of hydrocarbon ligands attached to single alkaline-earth-phenoxides from (-H) to -C$_{14}$H$_{19}$ while measuring the vibrational branching fractions of the optical transition. We find that varying the ligand size from 1 to more than 30 atoms does not systematically reduce the cycle closure, which remains around 90%. Theoretical extensions to larger diamondoids and bulk diamond surface suggest that alkaline earth phenoxides may maintain the desirable scattering behavior as the system size grows further, with no indication of an upper limit., Comment: 9 pages, 5 figures, SI(34 pages)

Evolved cool stars have three distinct evolutionary status: shell Hydrogen-burning (RGB), core Helium and shell Hydrogen burning (RC), and double shell burning (AGB). Asteroseismology can distinguish between the RC and the other status, but distinguishing RGB and AGB has been difficult seismically and spectroscopically. The precise boundaries of different status in the HR diagram have also been difficult to establish. In this article, we present a comprehensive catalog of asteroseismic evolutionary status, RGB and RC, for evolved red giants in the Kepler field. We carefully examine boundary cases to define the lower edge of the RC phase in radius and surface gravity. We also test different published asteroseisemic methods claiming to distinguish AGB and RGB stars against a sample where AGB candidates were selected using a spectrocopic identification method. We used six different seismic techniques to distinguish RC and RGB stars, and tested two proposed methods for distinguishing AGB and RGB stars. These status were compared with those inferred from spectroscopy. We present consensus evolutionary status for 18,784 stars out of the 30,337 red giants present in the Kepler data, including 11,516 stars with APOGEE spectra available. The agreement between seismic and spectroscopic classification is excellent for distinguishing RC stars, agreeing at the 94% level. Most disagreements can be traced to uncertainties in spectroscopic parameters, but some are caused by blends with background stars. We find a sharp lower boundary in surface gravity at log(g) = 2.99+/-0.01 for the RC and discuss the implications. We demonstrate that asteroseismic tools for distinguishing AGB and RGB stars are consistent with spectroscopic evolutionary status at near the RC but that the agreement between the different methods decreases rapidly as the star evolves., Comment: Article submitted to Astronomy&Astrophysics, 16 pages, 11 figures

Idiomatic expressions are an integral part of human languages, often used to express complex ideas in compressed or conventional ways (e.g. eager beaver as a keen and enthusiastic person). However, their interpretations may not be straightforwardly linked to the meanings of their individual components in isolation and this may have an impact for compositional approaches. In this paper, we investigate to what extent word representation models are able to go beyond compositional word combinations and capture multiword expression idiomaticity and some of the expected properties related to idiomatic meanings. We focus on noun compounds of varying levels of idiomaticity in two languages (English and Portuguese), presenting a dataset of minimal pairs containing human idiomaticity judgments for each noun compound at both type and token levels, their paraphrases and their occurrences in naturalistic and sense-neutral contexts, totalling 32,200 sentences. We propose this set of minimal pairs for evaluating how well a model captures idiomatic meanings, and define a set of fine-grained metrics of Affinity and Scaled Similarity, to determine how sensitive the models are to perturbations that may lead to changes in idiomaticity. The results obtained with a variety of representative and widely used models indicate that, despite superficial indications to the contrary in the form of high similarities, idiomaticity is not yet accurately represented in current models. Moreover, the performance of models with different levels of contextualisation suggests that their ability to capture context is not yet able to go beyond more superficial lexical clues provided by the words and to actually incorporate the relevant semantic clues needed for idiomaticity.

In this note, we give a definitive basis for the dimension-eight operators leading to quartic -- but no cubic -- interactions among electroweak gauge bosons. These are often called anomalous quartic gauge couplings, or aQGCs. We distinguish in particular the CP-even ones from their CP-odd counterparts., Comment: LHC Effective Field Theory Working Group Note, 8 pages, 3 tables

Whether considering rare astrophysical events on cosmological scales or unresolved stellar populations, accurate models must account for the integrated contribution from the entire history of star formation upon which that population is built. Here, we describe the second version of POSYDON, an open-source binary population synthesis code based on extensive grids of detailed binary evolution models computed using the MESA code, which follows both stars' structures as a binary system evolves through its complete evolution from the zero-age main sequence, through multiple phases of mass transfer and supernovae, to their death as compact objects. To generate synthetic binary populations, POSYDON uses advanced methods to interpolate between our large, densely spaced grids of simulated binaries. In our updated version of POSYDON, we account for the evolution of stellar binaries across a cosmological range of metallicities, extending from $10^{-4}\,Z_{\odot}$ to $2\,Z_{\odot}$, including grids specifically focused on the Small and Large Magellanic Clouds ($0.2\,Z_{\odot}$ and $0.45\,Z_{\odot}$). In addition to describing our model grids and detailing our methodology, we outline several improvements to POSYDON. These include the incorporation of single stars in stellar populations, a treatment for stellar mergers, and a careful modeling of "reverse-mass transferring" binaries, in which an once-accreting star later becomes a donor star. Our simulations are focused on binaries with at least one high-mass component, such as those that host neutron stars and black holes, and we provide post-processing methods to account for the cosmological evolution of metallicity and star formation as well as rate calculations for gravitational wave events, gamma-ray bursts, and other transients., Comment: 48 pages, 26 figures, submitted to AAS Journals

We present the detection of directional muon beams produced using a PW laser at the Lawrence Berkeley National Laboratory. The muon source is a multi-GeV electron beam generated in a 30 cm laser plasma accelerator interacting with a high-Z converter target. The GeV photons resulting from the interaction are converted into a high-flux, directional muon beam via pair production. By employing scintillators to capture delayed events, we were able to identify the produced muons and characterize the source. Using theoretical knowledge of the muon production process combined with simulations that show outstanding agreement with the experiments, we demonstrate that the multi-GeV electron beams produce muon beams with GeV energies and fluxes, at a few meters from the source, up to 4 orders of magnitude higher than cosmic ray muons. Laser-plasma-accelerator-based muon sources can therefore enhance muon imaging applications thanks to their compactness, directionality, and high fluxes which reduce the exposure time by orders of magnitude compared to cosmic ray muons. Using the Geant4-based simulation code we developed to gain insight into the experimental results, we can design future experiments and applications based on LPA-generated muons.

Fast radio bursts (FRBs) represent one of the most intriguing phenomena in modern astrophysics. However, their classification into repeaters and non-repeaters is challenging. Here, we present the application of the graph theory Minimum Spanning Tree (MST) methodology as an unsupervised classifier of repeaters and non-repeaters FRBs. By constructing MSTs based on various combinations of variables, we identify those that lead to MSTs that exhibit a localized high density of repeaters at each side of the node with the largest betweenness centrality. Comparing the separation power of this methodology against known machine learning methods, and with the random expectation results, we assess the efficiency of the MST-based approach to unravel the physical implications behind the graph pattern. We finally propose a list of potential repeater candidates derived from the analysis using the MST.

Models of inelastic (or pseudo-Dirac) dark matter commonly introduce a gauge symmetry spontaneously broken by the introduction of a dark sector version of the Higgs mechanism. We find that this ubiquitous introduction of two extra fields, a vector and complex scalar boson, is indeed unnecessary, with only a mass generating real scalar field being actually required. We consider a simple UV-complete model realizing this minimal setup and study the decays of the excited dark matter state as well as constraints from perturbative unitarity, (in)direct detection and colliders. We find that, in the visible freeze-out scenario ($ \text{DM} \, \text{DM} \leftrightarrow \text{SM} \, \text{SM} $), we still have unconstrained regions of parameter space for dark matter masses $\gtrsim 100$ GeV. Moreover, most of the available regions either present long-lived excited states, which are expected to interfere with the standard cosmological history, or will be probed by future direct detection experiments, such as DARWIN, due to the unavoidable residual elastic interactions. The only regions remaining out of experimental reach present highly fine-tuned parameters., Comment: 7 pages, 3 figures

Cognitive and neurological impairments are very common, but only a small proportion of affected individuals are diagnosed and treated, partly because of the high costs associated with frequent screening. Detecting pre-illness stages and analyzing the progression of neurological disorders through effective and efficient intelligent systems can be beneficial for timely diagnosis and early intervention. We propose using Large Language Models to extract features from free dialogues to detect cognitive decline. These features comprise high-level reasoning content-independent features (such as comprehension, decreased awareness, increased distraction, and memory problems). Our solution comprises (i) preprocessing, (ii) feature engineering via Natural Language Processing techniques and prompt engineering, (iii) feature analysis and selection to optimize performance, and (iv) classification, supported by automatic explainability. We also explore how to improve Chatgpt's direct cognitive impairment prediction capabilities using the best features in our models. Evaluation metrics obtained endorse the effectiveness of a mixed approach combining feature extraction with Chatgpt and a specialized Machine Learning model to detect cognitive decline within free-form conversational dialogues with older adults. Ultimately, our work may facilitate the development of an inexpensive, non-invasive, and rapid means of detecting and explaining cognitive decline.

Memetic algorithms are techniques that orchestrate the interplay between population-based and trajectory-based algorithmic components. In particular, some memetic models can be regarded under this broad interpretation as a group of autonomous basic optimization algorithms that interact among them in a cooperative way in order to deal with a specific optimization problem, aiming to obtain better results than the algorithms that constitute it separately. Going one step beyond this traditional view of cooperative optimization algorithms, this work tackles deep meta-cooperation, namely the use of cooperative optimization algorithms in which some components can in turn be cooperative methods themselves, thus exhibiting a deep algorithmic architecture. The objective of this paper is to demonstrate that such models can be considered as an efficient alternative to other traditional forms of cooperative algorithms. To validate this claim, different structural parameters, such as the communication topology between the agents, or the parameter that influences the depth of the cooperative effort (the depth of meta-cooperation), have been analyzed. To do this, a comparison with the state-of-the-art cooperative methods to solve a specific combinatorial problem, the Tool Switching Problem, has been performed. Results show that deep models are effective to solve this problem, outperforming metaheuristics proposed in the literature., Comment: 32 pages, 5 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.

Recent advancements have enabled Large Language Models (LLMs) to function as agents that can perform actions using external tools. This requires registering, i.e., integrating tool information into the LLM context prior to taking actions. Current methods indiscriminately incorporate all candidate tools into the agent's context and retain them across multiple reasoning steps. This process remains opaque to LLM agents and is not integrated into their reasoning procedures, leading to inefficiencies due to increased context length from irrelevant tools. To address this, we introduce EcoAct, a tool using algorithm that allows LLMs to selectively register tools as needed, optimizing context use. By integrating the tool registration process into the reasoning procedure, EcoAct reduces computational costs by over 50% in multiple steps reasoning tasks while maintaining performance, as demonstrated through extensive experiments. Moreover, it can be plugged into any reasoning pipeline with only minor modifications to the prompt, making it applicable to LLM agents now and future., Comment: 16 pages, 10 figures

This work introduces a quantum algorithm for computing the arcsine function to an arbitrary accuracy. We leverage a technique from embedded computing and field-programmable gate array (FPGA), called COordinate Rotation DIgital Computer (CORDIC). CORDIC is a family of iterative algorithms that, in a classical context, can approximate various trigonometric, hyperbolic, and elementary functions using only bit shifts and additions. Adapting CORDIC to the quantum context is non-trivial, as the algorithm traditionally uses several non-reversible operations. We detail a method for CORDIC which avoids such non-reversible operations. We propose multiple approaches to calculate the arcsine function reversibly with CORDIC. For n bits of precision, our method has space complexity of order n qubits, a layer count in the order of n times log n, and a CNOT count in the order of n squared. This primitive function is a required step for the Harrow-Hassidim-Lloyd (HHL) algorithm, is necessary for quantum digital-to-analog conversion, can simplify a quantum speed-up for Monte-Carlo methods, and has direct applications in the quantum estimation of Shapley values., Comment: 6 pages, 3 figures, 3 algorithms, pending acceptance at peer-reviewed conference

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.

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.

Quantum circuits with local unitaries have emerged as a rich playground for the exploration of many-body quantum dynamics of discrete-time systems. While the intrinsic locality makes them particularly suited to run on current quantum processors, the task of verification at non-trivial scales is complicated for non-integrable systems. Here, we study a special class of maximally chaotic circuits known as dual unitary circuits -- exhibiting unitarity in both space and time -- that are known to have exact analytical solutions for certain correlation functions. With advances in noise learning and the implementation of novel error mitigation methods, we show that a superconducting quantum processor with 91 qubits is able to accurately simulate these correlators. We then probe dynamics beyond exact verification, by perturbing the circuits away from the dual unitary point, and compare our results to classical approximations with tensor networks. These results cement error-mitigated digital quantum simulation on pre-fault-tolerant quantum processors as a trustworthy platform for the exploration and discovery of novel emergent quantum many-body phases., Comment: 7 + 23 pages, 3 + 17 figures, 0 + 2 tables

Background: Brain maturation and aging involve significant microstructural changes, resulting in functional and cognitive alterations. Quantitative MRI (qMRI) can measure this evolution, distinguishing the physiological effects of normal aging from pathological deviations. Methods: We conducted a multicentre study using qMRI metrics (R1, R2*, and Quantitative Susceptibility Mapping) to model age trajectories across brain structures, including tractography-based white matter bundles (TWMB), superficial white matter (SWM), and cortical grey matter (CGM). MRI data from 537 healthy subjects, aged 8 to 79 years, were harmonized using two independent methods. We modeled age trajectories and performed regional analyses to capture maturation patterns and aging effects across the lifespan. Findings: Our findings revealed a distinct brain maturation gradient, with early qMRI peak values in TWMB, followed by SWM, and culminating in CGM regions. This gradient was observed as a posterior-to-anterior maturation pattern in the cortex and an inferior-to-superior maturation pattern in white matter tracts. R1 demonstrated the most robust age trajectories, while R2* and susceptibility exhibited greater variability and different patterns. The normative modeling framework confirmed the reliability of our age-modelled trajectories across datasets. Interpretation: Our study highlights the potential of multiparametric qMRI to capture complex, region-specific brain development patterns, addressing the need for comprehensive, age-spanning studies across multiple brain structures. Various harmonization strategies can merge qMRI cohorts, improving the robustness of qMRI-based age models and facilitating the understanding of normal patterns and disease-associated deviations.

The relationship between professional skills and higher education programs is modeled as a non-directed bipartite network with binary entries representing the links between 28 skills (as captured by the occupational information network, O*NET) and 258 graduate program summaries (as captured by commercial brochures of graduate programs in marketing with accreditation standards of the Association to Advance Collegiate Schools of Business). While descriptive analysis for skills suggests a qualitative lack of alignment between the job demands captured by O*NET, inferential analyses based on exponential random graph model estimates show that skills' popularity and homophily coexist with a systematic yet weak alignment to job demands for marketing managers., Comment: 14 pages, 4 figures, 4 tables

The time series theory is set in this work under the domain of general elliptically contoured distributions. The advent of a time series approach that is in accordance with the expected reality of dependence between errors, transfers the increasingly complex and difficult to handle correlation analysis into a discipline that models volatility from a new view of a likelihood based on dependent probabilistic samples. The equally important problem of model selection is strengthened, but at the same time criticized with the introduction of degrees of evidence of significant difference in the modified BIC criterion . The demanding scale of differentiation puts a well-known database in trouble by observing insignificant relevance between the hierarchical models most used in the theory of time series under independence, such as Arch, Garch, Tgarch and Egarch. For extreme cases where the probabilistic independence of the samples is exceptionally demonstrated by an expert, the article also proposes the theory of time series under elliptical models, but with the same demanding comparison of the degrees of evidence of differences of the modified BIC. The example studied under this approach also does not denote any advantage of the hierarchical models studied. Such a new perspective for a likelihood based on dependent probabilistic samples has arisen naturally in similar context in finance under the setting of multivector variate distributions.

Modern digital applications extensively integrate Artificial Intelligence models into their core systems, offering significant advantages for automated decision-making. However, these AI-based systems encounter reliability and safety challenges when handling continuously generated data streams in complex and dynamic scenarios. This work explores the concept of resilient AI systems, which must operate in the face of unexpected events, including instances that belong to patterns that have not been seen during the training process. This is an issue that regular closed-set classifiers commonly encounter in streaming scenarios, as they are designed to compulsory classify any new observation into one of the training patterns (i.e., the so-called \textit{over-occupied space} problem). In batch learning, the Open Set Recognition research area has consistently confronted this issue by requiring models to robustly uphold their classification performance when processing query instances from unknown patterns. In this context, this work investigates the application of an Open Set Recognition framework that combines classification and clustering to address the \textit{over-occupied space} problem in streaming scenarios. Specifically, we systematically devise a benchmark comprising different classification datasets with varying ratios of known to unknown classes. Experiments are presented on this benchmark to compare the performance of the proposed hybrid framework with that of individual incremental classifiers. Discussions held over the obtained results highlight situations where the proposed framework performs best, and delineate the limitations and hurdles encountered by incremental classifiers in effectively resolving the challenges posed by open-world streaming environments., Comment: 12 pages, 3 figures, an updated version of this article is published in LNAI,volume 14857 as part of the conference proceedings HAIS 2024

We perform cosmological parameters estimation on Planck Cosmic Microwave Background (CMB) maps masking the recently discovered foreground related to nearby spiral galaxies. In addition, we also analyse the association between these foreground regions and recent claims of cosmological causal horizons in localized CMB parameter estimates. Our analysis shows consistent cosmological parameter values regardless of the masking approach, though reduced sky areas introduce larger uncertainties. By modelling the new extragalactic foreground, we identify a resemblance with local parameter variation maps with a statistical significance at the 3 sigma level, suggesting that a simplified foreground model partially accounts, (40-50)% correlation with 15% uncertainty, for the observed causal horizons. These findings add new evidence to the existence of the new foreground associated with large spiral galaxies and show that estimates of cosmological parameters on smaller patches on the sky can be largely affected by these foregrounds, but that the parameters taken over the full sky are unaltered., Comment: 13 pages, 10 figures. Submitted to Physical Review D

Solar magnetic activity follows regular cycles of about 11 years with an inversion of polarity in the poles every 22 years. This changing surface magnetism impacts the properties of the acoustic modes. The acoustic mode frequency shifts are a good proxy of the magnetic cycle. In this Letter we investigate solar magnetic activity cycles 23 and 24 through the evolution of the frequency shifts of low-degree modes (l= 0, 1, and 2) in three frequency bands. These bands probe properties between 74 and 1575 km beneath the surface. The analysis was carried out using observations from the space instrument Global Oscillations at Low Frequency and the ground-based Birmingham Solar Oscillations Network and Global Oscillation Network Group. The frequency shifts of radial modes suggest that changes in the magnetic field amplitude and configuration likely occur near the Sun's surface rather than near its core. The maximum shifts of solar cycle 24 occurred earlier at mid and high latitudes (relative to the equator) and about 1550 km beneath the photosphere. At this depth but near the equator, this maximum aligns with the surface activity but has a stronger magnitude. At around 74 km deep, the behaviour near the equator mirrors the behaviour at the surface, while at higher latitudes, it matches the strength of cycle 23., Comment: Letter accepted in Astronomy and Astrophysics, 10 pages, 5 figures