Search

The search for resonant mass bumps in invariant-mass distributions remains a cornerstone strategy for uncovering Beyond the Standard Model (BSM) physics at the Large Hadron Collider (LHC). Traditional methods often rely on predefined functional forms and exhaustive computational and human resources, limiting the scope of tested final states and selections. This work presents BumpNet, a machine learning-based approach leveraging advanced neural network architectures to generalize and enhance the Data-Directed Paradigm (DDP) for resonance searches. Trained on a diverse dataset of smoothly-falling analytical functions and realistic simulated data, BumpNet efficiently predicts statistical significance distributions across varying histogram configurations, including those derived from LHC-like conditions. The network's performance is validated against idealized likelihood ratio-based tests, showing minimal bias and strong sensitivity in detecting mass bumps across a range of scenarios. Additionally, BumpNet's application to realistic BSM scenarios highlights its capability to identify subtle signals while managing the look-elsewhere effect. These results underscore BumpNet's potential to expand the reach of resonance searches, paving the way for more comprehensive explorations of LHC data in future analyses.

Traditional thermoelectric materials rely on low thermal conductivity to enhance their efficiency but suffer from inherently limited power factors. Novel pathways to optimize electronic transport are thus crucial. Here, we achieve ultrahigh power factors in Ni$_3$Ge through a new materials design principle. When overlapping flat and dispersive bands are engineered to the Fermi level, charge carriers can undergo intense interband scattering, yielding an energy filtering effect similar to what has long been predicted in certain nanostructured materials. Via a multi-step DFT-based screening method developed herein, we discover a new family of L1$_2$-ordered binary compounds with ultrahigh power factors up to 11 mW m$^{-1}$ K$^{-2}$ near room temperature, which are driven by an intrinsic phonon-mediated energy filtering mechanism. Our comprehensive experimental and theoretical study of these new intriguing materials paves the way for understanding and designing high-performance scattering-tuned metallic thermoelectrics.

In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experienced machine operators may be able to diagnose defects or malfunctions, one possible way of assisting novice operators is through acoustic monitoring. As a step towards the automation of wood-processing equipment and decision support systems for machine operators, in this paper, we explore using a deep convolutional autoencoder for acoustic anomaly detection of wood planers on a new real-life dataset. Specifically, our convolutional autoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer outperform the DCASE autoencoder baseline, one-class SVM, isolation forest and a published convolutional autoencoder architecture, respectively obtaining an area under the ROC curve of 0.846 and 0.875 on a dataset of real-factory planer sounds. Moreover, we show that adding skip connections and attention mechanism under the form of a transformer encoder-decoder helps to further improve the anomaly detection capabilities.

The importance of the development of IoT and LoRaWAN in military applications has been widely established. Since security is one of its important challenges, in this paper we study two attacks scenarios: replay and sniff attacks on military LoRaWAN network. The aim is to highlight cybersecurity threats that must be taken into consideration when using such technology in critical context.

In strongly correlated materials, interacting electrons are entangled and form collective quantum states, resulting in rich low-temperature phase diagrams. Notable examples include cuprate superconductors, in which superconductivity emerges at low doping out of an unusual ``pseudogap'' metallic state above the critical temperature. The Fermi-Hubbard model, describing a wide range of phenomena associated with strong electron correlations, still offers major computational challenges despite its simple formulation. In this context, ultracold atoms quantum simulators have provided invaluable insights into the microscopic nature of correlated quantum states. Here, we use a quantum gas microscope Fermi-Hubbard simulator to explore a wide range of doping levels and temperatures in a regime where a pseudogap is known to develop. By measuring multi-point correlation functions up to fifth order, we uncover a novel universal behaviour in magnetic and higher-order spin-charge correlations. This behaviour is characterized by a doping-dependent energy scale that governs the exponential growth of the magnetic correlation length upon cooling. Accurate comparisons with determinant Quantum Monte Carlo and Minimally Entangled Typical Thermal States simulations confirm that this energy scale agrees well with the pseudogap temperature $T^{*}$. Our findings establish a qualitative and quantitative understanding of the magnetic origin and physical nature of the pseudogap and pave the way towards the exploration of pairing and collective phenomena among charge carriers expected to emerge at lower temperatures.

Direct-current resistivity is a key probe for the physical properties of materials. In metals, Fermi-liquid (FL) theory serves as the basis for understanding transport. A $T^2$ behavior of the resistivity is often taken as a signature of FL electron-electron scattering. However, the presence of impurity and phonon scattering as well as material-specific aspects such as Fermi surface geometry can complicate this interpretation. We demonstrate how density-functional theory combined with dynamical mean-field theory can be used to elucidate the FL regime. We take as examples SrVO$_{3}$ and SrMoO$_{3}$, two moderately correlated perovskite oxides, and establish a precise framework to analyze the FL behavior of the self-energy at low energy and temperature. Reviewing published low-temperature resistivity measurements, we find agreement between our calculations and experiments performed on samples with exceptionally low residual resistivity. This comparison emphasizes the need for further theoretical, synthesis, and characterization developments in these and other FL materials., Comment: 9 pages, 4 figures

The recent observation of superconductivity in the vicinity of insulating or Fermi surface reconstructed metallic states has established twisted bilayers of WSe$_2$ as an exciting platform to study the interplay of strong electron-electron interactions, broken symmetries and topology. In this work, we study the emergence of electronic ordering in twisted WSe$_2$ driven by gate-screened Coulomb interactions. Our first-principles treatment begins by constructing moir\'e Wannier orbitals that faithfully capture the bandstructure and topology of the system and project the gate-screened Coulomb interaction onto them. Using unbiased functional renormalization group calculations, we find an interplay between intervalley-coherent antiferromagnetic order and chiral, mixed-parity $d/p$-wave superconductivity for carrier concentrations near the displacement field-tunable van-Hove singularity. Our microscopic approach establishes incommensurate intervalley-coherent antiferromagnetic spin fluctuations as the dominant electronic mechanism driving the formation of superconductivity in $\theta = 5.08^{\circ}$ twisted WSe$_2$ and demonstrates that nesting properties of the Fermi surface sheets near the higher-order van-Hove point cause an asymmetric density dependence of the spin ordering as the density is varied across the van-Hove line, in good agreement with experimental observations. We show how the region of superconducting and magnetic order evolves within the two-dimensional phase space of displacement field and electronic density as twist angle is varied between $4^{\circ} \dots 5^{\circ}$., Comment: 11 pages, 6 figures

Quantum simulators have the potential to solve quantum many-body problems that are beyond the reach of classical computers, especially when they feature long-range entanglement. To fulfill their prospects, quantum simulators must be fully controllable, allowing for precise tuning of the microscopic physical parameters that define their implementation. We consider Rydberg-atom arrays, a promising platform for quantum simulations. Experimental control of such arrays is limited by the imprecision on the optical tweezers positions when assembling the array, hence introducing uncertainties in the simulated Hamiltonian. In this work, we introduce a scalable approach to Hamiltonian learning using graph neural networks (GNNs). We employ the Density Matrix Renormalization Group (DMRG) to generate ground-state snapshots of the transverse field Ising model realized by the array, for many realizations of the Hamiltonian parameters. Correlation functions reconstructed from these snapshots serve as input data to carry out the training. We demonstrate that our GNN model has a remarkable capacity to extrapolate beyond its training domain, both regarding the size and the shape of the system, yielding an accurate determination of the Hamiltonian parameters with a minimal set of measurements. We prove a theorem establishing a bijective correspondence between the correlation functions and the interaction parameters in the Hamiltonian, which provides a theoretical foundation to our learning algorithm. Our work could open the road to feedback control of the positions of the optical tweezers, hence providing a decisive improvement of analog quantum simulators., Comment: 19 pages, 11 figures

In this study, we investigate the effects of pre-germinative and post-germinative plasma treatments, applied separately or in combination, to improve maize germination and early seedling development. Pre-germinative treatment consists of priming the seeds with a dry atmospheric plasma (DAP) generated by a dielectric barrier device (DBD), characterized by minimal radiative emission, low electrical power (4 W) and high emissions of O, OH and NO radicals. Post-germinative treatment, known as plasma-activated water (PAW), uses a single-pin electrode device (SPED) to generate a DC discharge that features a power of 126 W and produces large amounts of OH radicals. The resulting PAW, after 5 minutes of SPED treatment, induces a slight acidification and increased concentrations of nitrate ions (from 24 to 250 mg/L), nitrite ions (from less than 0.1 to 56.1 mg/L) and hydrogen peroxide (from 0.3 to 18.5 mg/L). Results indicate that DAP applied on maize seeds for 20 min boosts their germination rate up to 90% (versus only 65% for untreated seeds) while reducing the median germination time by 37.5%. Then, seedling growth monitoring is achieved on control, DAP, PAW and DAP+PAW groups to assess stem length, hypocotyl length, leaf count, collar diameter and fresh/dry mass. The DAP+PAW group shows the most robust growth, demonstrating a synergistic effect of the combined treatments, particularly with significantly longer stem lengths. Additionally, physiological analyses of seedling leaves indicate a decrease in chlorophyll content despite enhanced growth, while fluorescence microscopy reveals a reduction in stomatal density in leaves treated with DAP and PAW, especially in the combined treatment group, potentially impacting photosynthetic efficiency and water regulation.

In this work we investigate an inverse problem of recovering point sources and their time-dependent strengths from {a posteriori} partial internal measurements in a subdiffusion model which involves a Caputo fractional derivative in time and a general second-order elliptic operator in space. We establish the well-posedness of the direct problem in the sense of transposition and improved local regularity. Using classical unique continuation of the subdiffusion model and improved local solution regularity, we prove the uniqueness of simultaneously recovering the locations of point sources, time-dependent strengths and initial condition for both one- and multi-dimensional cases. Moreover, in the one-dimensional case, the elliptic operator can have time-dependent coefficients. These results extend existing studies on point source identification for parabolic type problems. Additionally we present several numerical experiments to show the feasibility of numerical reconstruction., Comment: 20 pages, 7 figures

Let $k=\mathbb{Q}(\sqrt{-m})$ be an imaginary quadratic field. We consider the properties of capitulation of the $p$-class group of $k$ in the anti-cyclotomic $\mathbb{Z}_p$-extension $k^{\rm ac}$ of $k$; for this, using a new algorithm, we determine for $p=3$ the first layer $k_1^{\rm ac}$ of $k^{\rm ac}$ over $k$ (with absolutely general pari/gpprograms), and we examine if, at least, some partial capitulation may exist in $k_1^{\rm ac}$. The answer seems obviously yes ,even when $k^{\rm ac}/k$ is totally ramified. We have conjectured that this phenomenon of capitulation is specific of all the $\mathbb{Z}_p$-extensions of $k$, distinct from the cyclotomic one, whatever $p$. We characterize a family of fields $k=\mathbb{Q}(\sqrt{-m})$ (called Normal Split cases) for which $k^{\rm ac}$ is not linearly disjoint from the Hilbert class field (Theorem 2.7). Many numerical illustrations are given with computation of the $3$-class group of $k_1^{\rm ac}$. No assumptions are made on the structure of the 3-class group of $k$, nor on the splitting of 3 in $k$ and in its mirror field $k^*=\mathbb{Q}(\sqrt{3m})$., Comment: Some inaccuracies and imprecisions have been fixed and a crucial modulus, needed for the determination of the first layer of the anti-cyclotomic Z3-extension, has been modified, ensuring a simplified proof. The programs have been checked again and run well

Training large language models requires vast amounts of data, posing a challenge for less widely spoken languages like Norwegian and even more so for truly low-resource languages like S\'ami. To address this issue, we present a novel three-stage continual training approach. We also experiment with combining causal and masked language modeling to get more flexible models. Based on our findings, we train, evaluate, and openly release a new large generative language model for Norwegian Bokm\r{a}l, Nynorsk, and Northern S\'ami with 11.4 billion parameters: NorMistral-11B., Comment: pre-print, under review

We extend the buckling and clamped-plate problems to the context of differential forms on compact Riemannian manifolds with smooth boundary. We characterize their smallest eigenvalues and prove that, in the case of Euclidean domains, their spectra on forms coincide with the spectra of the corresponding problems. We obtain various estimates involving the first eigenvalues of the mentioned problems and the ones of the Hodge Laplacian with respect to Dirichlet and absolute boundary conditions on forms. These estimates generalize previous ones in the case of functions.

Geospatial Foundation Models (GFMs) have emerged as powerful tools for extracting representations from Earth observation data, but their evaluation remains inconsistent and narrow. Existing works often evaluate on suboptimal downstream datasets and tasks, that are often too easy or too narrow, limiting the usefulness of the evaluations to assess the real-world applicability of GFMs. Additionally, there is a distinct lack of diversity in current evaluation protocols, which fail to account for the multiplicity of image resolutions, sensor types, and temporalities, which further complicates the assessment of GFM performance. In particular, most existing benchmarks are geographically biased towards North America and Europe, questioning the global applicability of GFMs. To overcome these challenges, we introduce PANGAEA, a standardized evaluation protocol that covers a diverse set of datasets, tasks, resolutions, sensor modalities, and temporalities. It establishes a robust and widely applicable benchmark for GFMs. We evaluate the most popular GFMs openly available on this benchmark and analyze their performance across several domains. In particular, we compare these models to supervised baselines (e.g. UNet and vanilla ViT), and assess their effectiveness when faced with limited labeled data. Our findings highlight the limitations of GFMs, under different scenarios, showing that they do not consistently outperform supervised models. PANGAEA is designed to be highly extensible, allowing for the seamless inclusion of new datasets, models, and tasks in future research. By releasing the evaluation code and benchmark, we aim to enable other researchers to replicate our experiments and build upon our work, fostering a more principled evaluation protocol for large pre-trained geospatial models. The code is available at https://github.com/VMarsocci/pangaea-bench.

As a foundational model, SAM has significantly influenced multiple fields within computer vision, and its upgraded version, SAM 2, enhances capabilities in video segmentation, poised to make a substantial impact once again. While SAMs (SAM and SAM 2) have demonstrated excellent performance in segmenting context-independent concepts like people, cars, and roads, they overlook more challenging context-dependent (CD) concepts, such as visual saliency, camouflage, product defects, and medical lesions. CD concepts rely heavily on global and local contextual information, making them susceptible to shifts in different contexts, which requires strong discriminative capabilities from the model. The lack of comprehensive evaluation of SAMs limits understanding of their performance boundaries, which may hinder the design of future models. In this paper, we conduct a thorough quantitative evaluation of SAMs on 11 CD concepts across 2D and 3D images and videos in various visual modalities within natural, medical, and industrial scenes. We develop a unified evaluation framework for SAM and SAM 2 that supports manual, automatic, and intermediate self-prompting, aided by our specific prompt generation and interaction strategies. We further explore the potential of SAM 2 for in-context learning and introduce prompt robustness testing to simulate real-world imperfect prompts. Finally, we analyze the benefits and limitations of SAMs in understanding CD concepts and discuss their future development in segmentation tasks. This work aims to provide valuable insights to guide future research in both context-independent and context-dependent concepts segmentation, potentially informing the development of the next version - SAM 3.

The James Webb Space Telescope (JWST) has revealed an unexpectedly high abundance of UV luminous galaxies at redshifts $z\gtrsim 10$, challenging `standard' galaxy formation models. This study investigates the role of rapidly rotating (massive) stars undergoing chemically homogeneous evolution (CHE) in reconciling this potential tension. These stars are more compact, hotter, and exhibit enhanced UV emission. We find that the rest-frame UV luminosity of star-forming galaxies can be significantly enhanced by a factor of $\sim 3-6$ when CHE stars above a minimum initial mass of $m_{\star,\min}^{\rm CHE}\sim 2-10\ \rm M_\odot$ account for more than half of the total stellar mass following a Salpeter initial mass function. As a result, the UV luminosity functions observed at $z\sim 12-16$ can be reproduced with less extreme values of star formation efficiency and UV luminosity stochastic variability. Our results highlight the potential of CHE in explaining the UV-bright galaxy populations detected by JWST and call for future work to explore the broader astrophysical implications of CHE and its associated phenomena in the early universe, such as gamma-ray bursts, compact object binaries, and metal enrichment., Comment: 7+3 pages, 6 figures, to be submitted to ApJL, comments are welcome

We study the electronic structure of bulk 1T-TaSe$_2$ in the charge density wave phase at low temperature. Our spatially and angle resolved photoemission (ARPES) data show insulating areas coexisting with metallic regions characterized by a chiral Fermi surface and moderately correlated quasiparticle bands. Additionally, high-resolution laser ARPES reveals variations in the metallic regions, with series of low-energy states, whose energy, number and dispersion can be explained by the formation of quantum well states of different thicknesses. Dynamical mean field theory calculations show that the observed rich behaviour can be rationalized by assuming occasional stacking faults of the charge density wave. Our results indicate that the diverse electronic phenomena reported previously in 1T-TaSe$_2$ are dictated by the stacking arrangement and the resulting quantum size effects while correlation effects play a secondary role., Comment: Main text including Supplemental Material

Atomic physics has a profound impact on the physical properties of correlated electron materials. This article describes a prime example of this phenomenon. We provide a non-technical introduction to the physics of Hund metals, a broad class of materials which include in particular iron pnictides and chalcogenides, as well as oxides of the 4d transition-metal series such as ruthenates. We highlight experiments which reveal distinctive signatures of Hund physics in selected materials. A key property of Hund metals is a clear separation between the energy and temperature scales associated with spin and orbital degrees of freedom. We emphasize the conceptual and practical importance of the non-perturbative renormalization group flow which identifies the relevant degrees of freedom at each energy scale. The flow begins with local atomic degrees of freedom at high energy, displays a universal behavior in the intermediate regime of spin-orbital separation and ends into a broad diversity of possible ordered phases. Ordering can take place either as an instability of the Fermi liquid regime with coherent quasiparticles or directly from the intermediate regime before Fermi liquid quasiparticles had a chance to emerge. Dynamical mean-field theory provides a natural conceptual framework as well as a powerful computational method to explain, calculate and predict many physical properties of correlated materials such as Hund metals. A copy-edited version of this article has been published (April, 2024) in Physics Today, 77(4), 46-53 (2024) \url{https://doi.org/10.1063/pt.wqrz.qpjx}, Comment: 18 pages, 5 figures. A copy-edited version of this article has been published (April, 2024) in Physics Today, 77(4), 46-53 (2024) https://doi.org/10.1063/pt.wqrz.qpjx

We argue for the existence of bounds on the tensions of $p$-branes in de Sitter space in terms of the Hubble rate and the strength of a class of Chern-Simons-like couplings. The world-volume couplings involve Abelian 1-form gauge fields in the bulk and possibly field strengths intrinsic to the brane. In many cases these couplings are the D-brane Chern-Simons terms present in string theory, while in other cases they are the interactions of axion domain walls with $U(1)$ fields. Our arguments use the same logic and assumptions as the recent Festina Lente proposal (thus utilizing the properties of Nariai de Sitter black holes) and generalize it to extended objects, thereby providing a bottom-up set of constraints independent of any particular UV completion. We compare these bounds to the properties of (wrapped) D-branes in Type II string theory in the weak coupling limit, under the assumption that these properties are not modified significantly in de Sitter constructions. We find that all constraints are satisfied by D-branes, providing further evidence for the Festina Lente conjecture. For the particular case of 2-branes with Chern-Simons interactions we obtain a bound, which however can be evaded if the theory contains a light axion. Similarly, we find the bounds do not apply to axion domain walls due to the presence of the axion., Comment: 63 pages

Extensive research has been dedicated to optimizing the cyclist's position on the bike to enhance aerodynamic performance. This study aims to further investigate the aerobars position effect on cycling speed. Drawing from previous work (Fintelman et al., 2015), a relationship is established between position variations and hip angle, a critical determinant of power output. Based on a 3D scan of an elite athlete on his Time Trial (TT) bike, a digital twin with upper body mobility is created, utilizing inverse kinematics with aerobars as a root. Adjustments to the aerobars position translate into alterations in the cyclist's upper body posture. These changes influence both aerodynamic drag -- quantified by Computational Fluid Dynamics method (CFD) -- and hip angle -- computed by 3D software, directly affecting the athlete's capacity for power generation. The interplay between aerodynamic efficiency and power output is analyzed, with varying parameters such as speed and slope angle considered to ascertain the optimal aerobar position for individual athletes in a specific cycling context. Results show impactful variations in cycling speed as a function of the aerobars position, the latter having a strong influence on aerodynamic drag and theoretical power production.

The emergence of movable antenna (MA) technology has marked a significant advancement in the field of wireless communication research, paving the way for enhanced connectivity, improved signal quality, and adaptability across diverse environments. By allowing antennas to adjust positions dynamically within a finite area at transceivers, this technology enables more favourable channel conditions, optimizing performance across applications like mobile telecommunications and remote sensing. However, throughout history, the introduction of every new technology has presented opportunities for misuse by malicious individuals. Just as MAs can enhance connectivity, they may also be exploited for disruptive purposes such as jamming. In this paper, we examine the impact of an MA-enhanced jamming system equipped with $M$ antennas in a downlink multi-user communication scenario, where a base station (BS) with $N$ antennas transmits data to $K$ single-antenna users. Simulation results show that an adversary equipped with MAs reduce the system sum rate by $30\%$ more effectively than fixed-position antennas (FPAs). Additionally, MAs increase the outage probability by $25\%$ over FPAs, leading to a $20\%$ increase in the number of users experiencing outages. The highlighted risks posed by unauthorized use of this technology, underscore the urgent need for effective regulations and countermeasures to ensure its secure application., Comment: 6 pages, 4 figures, submitted to IEEE International Conference on Communications

Context. Carbon, nitrogen, and oxygen are the most abundant elements throughout the universe, after hydrogen and helium. Studying these elements in low-metallicity stars can provide crucial information on the chemical composition in the early Galaxy and possible internal mixing processes that can alter the surface composition of the stars. Aims. This work aims to investigate the chemical abundance patterns for CNO elements and Li in a homogeneously analyzed sample of 52 metal-poor halo giant stars. Methods. We used high-resolution spectra with a high signal-to-noise ratio (S/N) to carry out a spectral synthesis to derive detailed C, N, O, and Li abundances for a sample of stars with metallicities in the range of -3.58 <= [Fe/H] <= -1.79 dex. Our study was based on the assumption of one-dimensional (1D) local thermodynamic equilibrium (LTE) atmospheres. Results. Based on carbon and nitrogen abundances, we investigated the deep mixing taking place within stars along the red giant branch (RGB). The individual abundances of carbon decrease towards the upper RGB while nitrogen shows an increasing trend, indicating that carbon has been converted into nitrogen. No signatures of ON-cycle processed material were found for the stars in our sample. We computed a set of galactic chemical evolution (GCE) models, implementing different sets of massive star yields, both with and without including the effects of stellar rotation on nucleosynthesis. We confirm that stellar rotation is necessary to explain the highest [N/Fe] and [N/O] ratios observed in unmixed halo stars. The predicted level of N enhancement varies sensibly in dependence of the specific set of yields that are adopted. For stars with stellar parameters similar to those of our sample, heavy elements such as Sr, Y, and Zr appear to have unchanged abundances despite the stellar evolution mixing processes., Comment: 19 pages, 17 figures. Accepted for publication in A&A on Semptember 04, 2024. Table with derived abundances will be available at the CDS after the paper will be published

Recently, neural networks (NNs) have become a powerful tool for detecting quantum phases of matter. Unfortunately, NNs are black boxes and only identify phases without elucidating their properties. Novel physics benefits most from insights about phases, traditionally extracted in spin systems using spin correlators. Here, we combine two approaches and design TetrisCNN, a convolutional NN with parallel branches using different kernels that detects the phases of spin systems and expresses their essential descriptors, called order parameters, in a symbolic form based on spin correlators. We demonstrate this on the example of snapshots of the one-dimensional transverse-field Ising model taken in various bases. We show also that TetrisCNN can detect more complex order parameters using the example of two-dimensional Ising gauge theory. This work can lead to the integration of NNs with quantum simulators to study new exotic phases of matter., Comment: 13 pages, 7 figures. Accepted as a poster at the NeurIPS ML4PS 2024 workshop. Example code is available at https://github.com/kcybinski/TetrisCNN_for_spin_systems

We propose to review the main stages in the computer history of virtual actors, with a view to the exploration of virtual reality and discussion on different approaches to human simulation. The notion of autonomy emerges as a key issue for the virtual entities. We then explore one way of building elements of autonomy and conclude with an example of avatar stage direction leading to a simulacrum of autonomy in a live performance.

We present a simple way to merge masked language modeling with causal language modeling. This hybrid training objective results in a model that combines the strengths of both modeling paradigms within a single transformer stack: GPT-BERT can be transparently used like any standard causal or masked language model. We test the pretraining process that enables this flexible behavior on the BabyLM Challenge 2024. The results show that the hybrid pretraining outperforms masked-only or causal-only models. We openly release the models, training corpora and code., Comment: 22 pages; submission to the BabyLM Challenge 2024

We study the dynamics and interactions of the solitonic domain walls that occur in realistic axion electrodynamics models including the Chern-Simons interaction, $a\epsilon_{\mu\nu\lambda\sigma}F^{\mu\nu} F^{\lambda\sigma}$, between an axion $a(x)$ of mass $m_a$, and a massless U(1) gauge field, e.g. EM, interacting with strength $\alpha=e^2/4\pi$ with charged matter, e.g. electron-positron pairs. In particular, in the presence of a U(1) gauge-and-matter relativistic thermal plasma we study the friction experienced by the walls due to the Chern-Simons term. Utilizing the linear response method we include the collective effects of the plasma, as opposed to purely particle scattering across the wall (as is done in previous treatments) which is valid only in the thin wall regime that is rarely applicable in realistic cases. We show that the friction depends on the Lorentz-$\gamma$-factor-dependent inverse thickness of the wall in the plasma frame, $\ell^{-1} \sim \gamma m_a$, compared to the three different plasma scales, the temperature $T$, the Debye mass $m_D\sim\sqrt{\alpha} T$, and the damping rate $\Gamma \sim \alpha^2 T$, and elucidate the underlying physical intuition for this behavior. (For friction in the thin-wall-limit we correct previous expressions in the literature.) We further consider the effects of long-range coherent magnetic fields that are possibly present in the early universe and compare their effect with that of thermal magnetic fields. Finally, we briefly discuss the possible early universe consequences of our results for domain wall motion and network decay, stochastic gravitational wave production from domain wall networks, and possible primordial black hole production from domain wall collapse, though a more complete discussion of these topics is reserved for a companion paper., Comment: 56 Pages, 8 Figures. Added references

Artificial Intelligence is rapidly advancing and radically impacting everyday life, driven by the increasing availability of computing power. Despite this trend, the adoption of AI in real-world healthcare is still limited. One of the main reasons is the trustworthiness of AI models and the potential hesitation of domain experts with model predictions. Explainable Artificial Intelligence (XAI) techniques aim to address these issues. However, explainability can mean different things to people with different backgrounds, expertise, and goals. To address the target audience with diverse needs, we develop storytelling XAI. In this research, we have developed an approach that combines multi-task distillation with interpretability techniques to enable audience-centric explainability. Using multi-task distillation allows the model to exploit the relationships between tasks, potentially improving interpretability as each task supports the other leading to an enhanced interpretability from the perspective of a domain expert. The distillation process allows us to extend this research to large deep models that are highly complex. We focus on both model-agnostic and model-specific methods of interpretability, supported by textual justification of the results in healthcare through our use case. Our methods increase the trust of both the domain experts and the machine learning experts to enable a responsible AI., Comment: Pre-print of the accepted manuscript in EXPLIMED - First Workshop on Explainable Artificial Intelligence for the Medical Domain, European Conference on Artificial Intelligence (ECAI) - 2024, Santiago de Compostela, Spain

Randomized arcade processes are a class of continuous stochastic processes that interpolate in a strong sense, i.e., omega by omega, between any given ordered set of random variables, at fixed pre-specified times. Utilizing these processes as generators of partial information, a class of continuous-time martingale -- the filtered arcade martingales (FAMs) -- is constructed. FAMs interpolate through a sequence of target random variables, which form a discrete-time martingale. The research presented in this paper treats the problem of selecting the worst martingale coupling for given, convexly ordered, probability measures contingent on the paths of FAMs that are constructed using the martingale coupling. This optimization problem, that we term the information-based martingale optimal transport problem (IB-MOT), can be viewed from different perspectives. It can be understood as a model-free construction of FAMs, in the case where the coupling is not determined, a priori. It can also be considered from the vantage point of optimal transport (OT), where the problem is concerned with introducing a noise factor in martingale optimal transport, similarly to how the entropic regularization of optimal transport introduces noise in OT. The IB-MOT problem is static in its nature, since it is concerned with finding a coupling, but a corresponding dynamical solution can be found by considering the FAM constructed with the identified optimal coupling. Existence and uniqueness of its solution are shown, and an algorithm for empirical measures is proposed., Comment: 19 pages. arXiv admin note: substantial text overlap with arXiv:2301.05936

Receptor occupancy (RO) studies using PET neuroimaging play a critical role in the development of drugs targeting the central nervous system (CNS). The conventional approach to estimate drug receptor occupancy consists in estimation of binding potential changes between two PET scans (baseline and post-drug injection). This estimation is typically performed separately for each scan by first reconstructing dynamic PET scan data before fitting a kinetic model to time activity curves. This approach fails to properly model the noise in PET measurements, resulting in poor RO estimates, especially in low receptor density regions. Objective: In this work, we evaluate a novel joint direct parametric reconstruction framework to directly estimate distributions of RO and other kinetic parameters in the brain from a pair of baseline and post-drug injection dynamic PET scans. Methods: The proposed method combines the use of regularization on RO maps with alternating optimization to enable estimation of occupancy even in low binding regions. Results: Simulation results demonstrate the quantitative improvement of this method over conventional approaches in terms of accuracy and precision of occupancy. The proposed method is also evaluated in preclinical in-vivo experiments using 11C-MK-6884 and a muscarinic acetylcholine receptor 4 positive allosteric modulator drug, showing improved estimation of receptor occupancy as compared to traditional estimators. Conclusion: The proposed joint direct estimation framework improves RO estimation compared to conventional methods, especially in intermediate to low-binding regions. Significance: This work could potentially facilitate the evaluation of new drug candidates targeting the CNS.

Calibrating verbalized probabilities presents a novel approach for reliably assessing and leveraging outputs from black-box Large Language Models (LLMs). Recent methods have demonstrated improved calibration by applying techniques like Platt scaling or temperature scaling to the confidence scores generated by LLMs. In this paper, we explore the calibration of verbalized probability distributions for discriminative tasks. First, we investigate the capability of LLMs to generate probability distributions over categorical labels. We theoretically and empirically identify the issue of re-softmax arising from the scaling of verbalized probabilities, and propose using the invert softmax trick to approximate the "logit" by inverting verbalized probabilities. Through extensive evaluation on three public datasets, we demonstrate: (1) the robust capability of LLMs in generating class distributions, and (2) the effectiveness of the invert softmax trick in estimating logits, which, in turn, facilitates post-calibration adjustments., Comment: 21 pages

We consider the magnetic Steklov eigenvalue problem on compact Riemannian manifolds with boundary for generic magnetic potentials and establish various results concerning the spectrum. We provide equivalent characterizations of magnetic Steklov operators which are unitarily equivalent to the classical Steklov operator and study bounds for the smallest eigenvalue. We prove a Cheeger-Jammes type lower bound for the first eigenvalue by introducing magnetic Cheeger constants. We also obtain an analogue of an upper bound for the first magnetic Neumann eigenvalue due to Colbois, El Soufi, Ilias and Savo. In addition, we compute the full spectrum in the case of the Euclidean $2$-ball and $4$-ball for a particular choice of magnetic potential given by Killing vector fields, and discuss the behavior. Finally, we establish a comparison result for the magnetic Steklov operator associated with the manifold and the square root of the magnetic Laplacian on the boundary, which generalizes the uniform geometric upper bounds for the difference of the corresponding eigenvalues in the non-magnetic case due to Colbois, Girouard and Hassannezhad.

We show that the remarkably small Fermi-liquid coherence scale and large effective mass observed in LiV$_2$O$_4$ are due to the proximity of a Hund-assisted orbital-selective Mott state. Our work is based on an ab initio dynamical mean-field approach, combining several quantum impurity solvers to capture the physics from high to very low temperature. We find that the Hund coupling plays a crucial role in rearranging the orbital populations and in generating the heavy mass and low coherence scale. The latter is found to be approximately 1-2 Kelvin, even though the most correlated orbital is found to be significantly doped ~10% away from half-filling. A flat quasiparticle band appears near the Fermi level as a result of the strong electronic correlations. Finally, we discuss our results in comparison to experiments., Comment: 8 pages, 6 Figures

During the fourth observing run, the LIGO-Virgo-KAGRA Collaboration reported the detection of a coalescing compact binary (GW230529$_{-}$181500) with component masses estimated at $2.5-4.5\, M_\odot$ and $1.2-2.0\, M_\odot$ with 90\% credibility. Given the current constraints on the maximum neutron star (NS) mass, this event is most likely a lower-mass-gap (LMG) black hole-neutron star (BHNS) binary. The spin magnitude of the BH, especially when aligned with the orbital angular momentum, is critical in determining whether the NS is tidally disrupted. An LMG BHNS merger with a rapidly spinning BH is an ideal candidate for producing electromagnetic counterparts. However, no such signals have been detected. In this study, we employ a detailed binary evolution model, incorporating new dynamical tide implementations, to explore the origin of BH spin in an LMG BHNS binary. If the NS forms first, the BH progenitor (He-rich star) must begin in orbit shorter than 0.35 days to spin up efficiently, potentially achieving a spin magnitude of $\chi_{\rm BH} > 0.3$. Alternatively, if a non-spinning BH (e.g., $M_{\rm BH} = 3.6\, M_\odot$) forms first, it can accrete up to $\sim 0.2\, M_\odot$ via Case BA mass transfer (MT), reaching a spin magnitude of $\chi_{\rm BH} \sim 0.18$ under Eddington-limited accretion. With a higher Eddington accretion limit (i.e., 10.0 $\Dot{M}_{\rm Edd}$), the BH can attain a significantly higher spin magnitude of $\chi_{\rm BH} \sim\,0.65$ by accreting approximately $1.0\, M_\odot$ during Case BA MT phase., Comment: Accepted by A&A Letters (09/11/2024)

We analyse high-resolution ESPaDOnS/CFHT spectra of 20 very metal-poor stars ([Fe/H]~$<-2.0$) in the solar neighbourhood (within $\sim2$ kpc) selected to be on planar orbits (with a maximum height of $\lesssim4$ kpc). Targets include 11 prograde and 9 retrograde stars, spanning a wide range of eccentricities ($0.20-0.95$). Their chemical abundances are consistent with those observed in the Galactic halo but show a smaller spread, with no notable difference between progrades and retrogrades. This suggests a common chemical evolution and likely a shared formation site (except for one star). In this case, chemical evolution models indicate that the formation site would have had a baryonic mass of $\sim1.4\times10^9\msun$, similar to classical dwarf galaxies. High-energy supernovae and hypernovae are needed to reproduce the [X/Fe] up to the Fe-peak, while fast-rotating massive stars and neutron star merger events explain the [X/Fe] of the neutron-capture elements. The absence of Type Ia supernova signatures suggests a star formation duration of $\lesssim1$~Gyr. Cosmological zoom-in simulations support the scenario that an in-plane infall of a single system could disperse stars over a wide range of angular momenta during the early Galactic assembly. We propose that these stars originated in a proto-Galactic building block, which we name Loki. Less likely, if progrades and retrogrades formed in two different systems, their chemical evolution must have been very similar, with a combined baryonic mass twice that of a single system. Forthcoming surveys will provide a large and homogeneous dataset to investigate whether Loki is associated with any of the known detected structures. A comparison (primarily [$\alpha$/Fe]) with other VMPs moving in planar orbits suggests multiple systems contributed to the Galactic planar population, presenting some differences in their kinematical parameters., Comment: REference list updated; discussion on GSE updated

On April 25th, 2019, the LIGO-Virgo Collaboration discovered a Gravitational-wave (GW) signal from a binary neutron star (BNS) merger, i.e., GW190425. Due to the inferred large total mass, the origin of GW190425 remains unclear. We perform detailed stellar structure and binary evolution calculations that take into account mass-loss, internal differential rotation, and tidal interactions between a He-rich star and a NS companion. We explore the parameter space of the initial binary properties, including initial NS and He-rich masses and initial orbital period. We find that the immediate post-common-envelope progenitor system, consisting of a primary $\sim2.0\,M_\odot$ ($\sim1.7\,M_\odot$) NS and a secondary He-rich star with an initial mass of $\sim3.0-5.5\,M_\odot$ ($\sim5.5-6.0\,M_\odot$) in a close binary with an initial period of $\sim0.08-0.5\,{\rm{days}}$ ($\sim 0.08-0.4\,{\rm{days}}$), that experiences stable Case BB/BC mass transfer (MT) during binary evolution, can reproduce the formation of GW190425-like BNS events. Our studies reveal that the secondary He-rich star of the GW190425's progenitor before its core collapse can be efficiently spun up through tidal interaction, finally remaining as a NS with rotational energy even reaching $\sim10^{52}\,{\rm{erg}}$, which is always much higher than the neutrino-driven energy of the supernova (SN) explosion. If the newborn secondary NS is a magnetar, we expect that GW190425 can be the remnant of a magnetar-driven SN, e.g., a magnetar-driven ultra-stripped SN, a superluminous SN, or a broad-line Type Ic SN. Our results show that GW190425 could be formed through the isolated binary evolution, which involves a stable Case BB/BC MT just after the common envelope phase. On top of that, we show the He-rich star can be tidally spun up, potentially forming a spinning magnetized NS (magnetar) during the second SN explosion., Comment: 7 pages, 8 figures, accepted for publication in A&A

In this paper, we present a novel hemispherical antenna array (HAA) designed for high-altitude platform stations (HAPS). A significant limitation of traditional rectangular antenna arrays for HAPS is that their antenna elements are oriented downward, resulting in low gains for distant users. Cylindrical antenna arrays were introduced to mitigate this drawback; however, their antenna elements face the horizon leading to suboptimal gains for users located beneath the HAPS. To address these challenges, in this study, we introduce our HAA. An HAA's antenna elements are strategically distributed across the surface of a hemisphere to ensure that each user is directly aligned with specific antenna elements. To maximize users minimum signal-to-interference-plus-noise ratio (SINR), we formulate an optimization problem. After performing analog beamforming, we introduce an antenna selection algorithm and show that this method achieves optimality when a substantial number of antenna elements are selected for each user. Additionally, we employ the bisection method to determine the optimal power allocation for each user. Our simulation results convincingly demonstrate that the proposed HAA outperforms the conventional arrays, and provides uniform rates across the entire coverage area. With a $20~\mathrm{MHz}$ communication bandwidth, and a $50~\mathrm{dBm}$ total power, the proposed approach reaches sum rates of $14~\mathrm{Gbps}$., Comment: 15 pages, 16 figures

This paper explores downlink Cooperative Rate-Splitting Multiple Access (C-RSMA) in a multi-cell wireless network with the assistance of Joint-Transmission Coordinated Multipoint (JT-CoMP). In this network, each cell consists of a base station (BS) equipped with multiple antennas, one or more cell-center users (CCU), and multiple cell-edge users (CEU) located at the edge of the cells. Through JT-CoMP, all the BSs collaborate to simultaneously transmit the data to all the users including the CCUs and CEUs. To enhance the signal quality for the CEUs, CCUs relay the common stream to the CEUs by operating in either half-duplex (HD) or full-duplex (FD) decode-and-forward (DF) relaying mode. In this setup, we aim to jointly optimize the beamforming vectors at the BS, the allocation of common stream rates, the transmit power at relaying users, i.e., CCUs, and the time slot fraction, aiming to maximize the minimum achievable data rate. However, the formulated optimization problem is non-convex and is challenging to solve directly. To address this challenge, we employ change-of-variables, first-order Taylor approximations, and a low-complexity algorithm based on Successive Convex Approximation (SCA). We demonstrate through simulation results the efficacy of the proposed scheme, in terms of average achievable data rate, and we compare its performance to that of four baseline schemes, including HD/FD cooperative non-orthogonal multiple access (C-NOMA), NOMA, and RSMA without user cooperation. The results show that the proposed FD C-RSMA can achieve 25% over FD C-NOMA and the proposed HD C-RSMA can achieve 19% over HD C-NOMA respectively, when the BS transmit power is 20 dBm., Comment: 14 pages

$\textbf{Purpose:}$ To develop a new method for free-breathing 3D extracellular volume (ECV) mapping of the whole heart at 3T. $\textbf{Methods:}$ A free-breathing 3D cardiac ECV mapping method was developed at 3T. T1 mapping was performed before and after contrast agent injection using a free-breathing ECG-gated inversion-recovery sequence with spoiled gradient echo readout. A linear tangent space alignment (LTSA) model-based method was used to reconstruct high-frame-rate dynamic images from (k,t)-space data sparsely sampled along a random stack-of-stars trajectory. Joint T1 and transmit B1 estimation was performed voxel-by-voxel for pre- and post-contrast T1 mapping. To account for the time-varying T1 after contrast agent injection, a linearly time-varying T1 model was introduced for post-contrast T1 mapping. ECV maps were generated by aligning pre- and post-contrast T1 maps through affine transformation. $\textbf{Results:}$ The feasibility of the proposed method was demonstrated using in vivo studies with six healthy volunteers at 3T. We obtained 3D ECV maps at a spatial resolution of 1.9$\times$1.9$\times$4.5 $mm^{3}$ and a FOV of 308$\times$308$\times$144 $mm^{3}$, with a scan time of 10.1$\pm$1.4 and 10.6$\pm$1.6 min before and after contrast agent injection, respectively. The ECV maps and the pre- and post-contrast T1 maps obtained by the proposed method were in good agreement with the 2D MOLLI method both qualitatively and quantitatively. $\textbf{Conclusion:}$ The proposed method allows for free-breathing 3D ECV mapping of the whole heart within a practically feasible imaging time. The estimated ECV values from the proposed method were comparable to those from the existing method. $\textbf{Keywords:}$ cardiac extracellular volume (ECV) mapping, cardiac T1 mapping, linear tangent space alignment (LTSA), manifold learning, Comment: 4496 words, 10 figures, 10 supporting information figures

Train operational incidents are so far diagnosed individually and manually by train maintenance technicians. In order to assist maintenance crews in their responsiveness and task prioritization, a learning machine is developed and deployed in production to suggest diagnostics to train technicians on their phones, tablets or laptops as soon as a train incident is declared. A feedback loop allows to take into account the actual diagnose by designated train maintenance experts to refine the learning machine. By formulating the problem as a discrete set classification task, feature engineering methods are proposed to extract physically plausible sets of events from traces generated on-board railway vehicles. The latter feed an original ensemble classifier to class incidents by their potential technical cause. Finally, the resulting model is trained and validated using real operational data and deployed on a cloud platform. Future work will explore how the extracted sets of events can be used to avoid incidents by assisting human experts in the creation predictive maintenance alerts.

We propose a blind ML-based modulation detection for OFDM-based technologies. Unlike previous works that assume an ideal environment with precise knowledge of subcarrier count and cyclic prefix location, we consider blind modulation detection while accounting for realistic environmental parameters and imperfections. Our approach employs a ResNet network to simultaneously detect the modulation type and accurately locate the cyclic prefix. Specifically, after eliminating the environmental impact from the signal and accurately extracting the OFDM symbols, we convert these symbols into scatter plots. Due to their unique shapes, these scatter plots are then classified using ResNet. As a result, our proposed modulation classification method can be applied to any OFDM-based technology without prior knowledge of the transmitted signal. We evaluate its performance across various modulation schemes and subcarrier numbers. Simulation results show that our method achieves a modulation detection accuracy exceeding $80\%$ at an SNR of $10$ dB and $95\%$ at an SNR of $25$ dB.

The decomposition of a square matrix into a sum of Pauli strings is a classical pre-processing step required to realize many quantum algorithms. Such a decomposition requires significant computational resources for large matrices. We present a new exact and explicit formula for the Pauli string coefficients which inspires an efficient algorithm to compute them. More specifically, we show that up to a permutation of the matrix elements, the decomposition coefficients are related to the original matrix by a multiplication of a generalised Hadamard matrix. This allows one to use the Fast Walsh-Hadamard transform and calculate all Pauli decomposition coefficients in $\mathcal{O}(N^2\log N)$ time and using $\mathcal{O}(1)$ additional memory, for an $N\times N$ matrix. A numerical implementation of our equation outperforms currently available solutions., Comment: added more references to previous works, and one new comparison, added script to produce results to zenodo

Stars with initial mass above roughly 8 solar masses will evolve to form a core made of iron group elements at which point no further exothermic nuclear reactions between charged nuclei may prevent the core collapse. Electron captures, neutrino losses, and the photo-disintegration of heavy nuclei trigger the collapse of these stars. Models at the brink of core collapse are produced using stellar evolution codes and these pre-collapse models may be used in the study of the subsequent dynamical evolution (including their explosion as supernovae and the formation of compact remnants such as neutron stars or black holes). We upgrade the physical ingredients employed by the GENeva stellar Evolution Code, GENEC, so that it may cover the regime of high temperatures and high densities required to produce progenitors of core-collapse. We have improved GENEC in three directions, equation of state, the nuclear reaction network and the radiative and conductive opacities adapted for the computation of the advanced phases of evolution. We produce a small grid of pre-supernova models of stars with zero-age main sequence masses of 15, 20 and 25 solar masses at solar and less than half solar metallicities. The results are compared with analogous models produced with the MESA code. The global properties of our new models, particularly of their inner cores, are comparable to models computed with MESA and pre-existing progenitors in the literature. Between codes the exact shell structure varies impacting explosion predictions. Using GENEC with state-of-the-art physics, we have produced massive stellar progenitors prior to collapse. These progenitors are suitable for follow-up studies, including the dynamical collapse and supernova phases. Larger grids of supernova progenitors are now feasible, with potential for further dynamical evolution., Comment: 22 pages, 17 figures

Quantum computation of the energy of molecules and materials is one of the most promising applications of fault-tolerant quantum computers. However, practical applications require algorithms with reduced resource requirements. Previous work has mainly represented the Hamiltonian of the system in second quantization. Existing methods in first quantization are limited to grid-based approaches that do not allow for active space calculations. In this work, we present a method to solve the generic ground-state chemistry problem in first quantization on a fault-tolerant quantum computer using any basis set. This allows for calculations in the active space using modern quantum chemistry basis sets. We derive a linear-combination-of-unitaries decomposition for a chemical Hamiltonian in first quantization and then construct an efficient block encoding, exploiting sparsity of the Hamiltonian. For active space calculations using a molecular orbital basis set, we achieve an asymptotic speed up in Toffoli-gate count compared to the equivalent method in second quantization [Berry, et. al. Quantum 3, 208 (2019)]. We also consider the dual plane waves for materials simulations and find that in physically interesting regimes we achieve orders of magnitude improvement in quantum resources compared to the second quantization counterpart. In some instances, our approach provides similar or even lower resources compared to the first quantization plane wave algorithm of Refs.[Babbush, et. al npj Quantum Inf 5(1) 92 (2019), Su et. al PRX Quantum 2(4), 040332 (2021)] that, unlike our approach, avoids loading the classical data from quantum memory. This work opens up possibilities to reduce quantum resources even further using factorization methods of a Hamiltonian or modern pseudopotentials. Furthermore, our approach can be adapted to other applications, such as the vibrational properties of chemical systems., Comment: 1) Fixed typo in Hadamard matrix. 2) Notations are now consistent with Pauli decomposition paper. 3) More precise statement about PAW/pseudopotentials (replaced 'pseudopotenitals' with 'norm-conserving pseudopotentials')

Delineating lesions and anatomical structure is important for image-guided interventions. Point-supervised medical image segmentation (PSS) has great potential to alleviate costly expert delineation labeling. However, due to the lack of precise size and boundary guidance, the effectiveness of PSS often falls short of expectations. Although recent vision foundational models, such as the medical segment anything model (MedSAM), have made significant advancements in bounding-box-prompted segmentation, it is not straightforward to utilize point annotation, and is prone to semantic ambiguity. In this preliminary study, we introduce an iterative framework to facilitate semantic-aware point-supervised MedSAM. Specifically, the semantic box-prompt generator (SBPG) module has the capacity to convert the point input into potential pseudo bounding box suggestions, which are explicitly refined by the prototype-based semantic similarity. This is then succeeded by a prompt-guided spatial refinement (PGSR) module that harnesses the exceptional generalizability of MedSAM to infer the segmentation mask, which also updates the box proposal seed in SBPG. Performance can be progressively improved with adequate iterations. We conducted an evaluation on BraTS2018 for the segmentation of whole brain tumors and demonstrated its superior performance compared to traditional PSS methods and on par with box-supervised methods., Comment: 2024 IEEE Nuclear Science Symposium and Medical Imaging Conference

This paper studies the statistical characterization of ground-to-air (G2A) and reconfigurable intelligent surface (RIS)-assisted air-to-ground (A2G) communications in RIS-assisted UAV networks under the impact of channel aging. A comprehensive channel model is presented, which incorporates the time-varying fading, three-dimensional (3D) mobility, Doppler shifts, and the effects of channel aging on array antenna structures. We provide analytical expressions for the G2A signal-to-noise ratio (SNR) probability density function (PDF) and cumulative distribution function (CDF), demonstrating that the G2A SNR follows a mixture of noncentral $\chi^2$ distributions. The A2G communication is characterized under RIS arbitrary phase-shift configurations, showing that the A2G SNR can be represented as the product of two correlated noncentral $\chi^2$ random variables (RVs). Additionally, we present the PDF and the CDF of the product of two independently distributed noncentral $\chi^2$ RVs, which accurately characterize the A2G SNR's distribution. Our paper confirms the effectiveness of RISs in mitigating channel aging effects within the coherence time. Finally, we propose an adaptive spectral efficiency method that ensures consistent system performance and satisfactory outage levels when the UAV and the ground user equipments are in motion.

We introduce the fluctuating Line-of-Sight (fLoS) fading model, characterized by parameters $K$, $k$, $\lambda$, and $\Omega$. The fLoS fading distribution is expressed in terms of the multivariate confluent hypergeometric functions $\Psi_2$, $\Phi_3^{(n)}$, and $\Phi_3 = \Phi_3^{(2)}$ and encompasses well-known distributions, such as the Nakagami-$m$, Hoyt, Rice, and Rician shadowed fading distributions as special cases. An efficient method to numerically compute the fLoS fading distribution is also addressed. Notably, for a positive integer $k$, the fLoS fading distribution simplifies to a finite mixture of $\kappa$-$\mu$ distributions. Additionally, we analyze the outage probability and Ergodic capacity, presenting a tailored Prony's approximation method for the latter. Numerical results are presented to show the impact of the fading parameters and verify the accuracy of the proposed approximation. Moreover, we illustrate an application of the proposed fLoS fading distribution for characterizing wireless systems affected by channel aging.