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Intercellular exchange networks are essential for the adaptive capabilities of populations of cells. While diffusional exchanges have traditionally been difficult to map, recent advances in nanotechnology enable precise probing of exchange fluxes with the medium at single-cell resolution. Here we introduce a tiling-based method to reconstruct the dynamic unfolding of exchange networks from flux data, subsequently applying it to an experimental mammalian co-culture system where lactate exchanges affect the acidification of the environment. We observe that the network, which initially exhibits a dense matrix of exchanges, progressively breaks up into small disconnected clusters of cells. To explain this behaviour, we develop a two-parameter Maximum-Entropy multicellular metabolic model that incorporates diffusion-driven exchanges through a set of global constraints that couple cellular behaviors. The model predicts a transition from a densely interconnected network to a sparse, motif-dominated state as glucose and oxygen consumption levels shift. We characterize such a crossover both numerically, revealing a power-law decay in the cluster-size distribution at the critical transition, and analytically, by computing the critical line through a mean-field approximation based on percolation theory. By comparing empirical data with theoretical predictions, we find that populations evolve towards the sparse phase by remaining near the crossover point between these two regimes. These findings offer new insights into the collective organization driving the adaptive dynamics of cell populations., Comment: 12 pages, 5 figures, comments are welcome

Detailed comparisons between theory and experiment for quantum electrodynamics (QED) effects in He-like ions have been performed in the literature to search for hints of new physics. Different frequentist statistical analyses of the existing atomic transition energy data have shown contradictory conclusions as to the presence of possible deviations from the theory predictions. We present here an approach using Bayesian statistics which allows to assign quantitative probabilities to the different deviation models from theory for He-like ions for $Z = 5$ to 92. Potential deviations beyond the standard model or higher order QED effects are modeled with $f(Z) \propto Z^k$ functions. Considering the currently available data, no significant difference between theory and experiment is found, and we show that recent experiments have reduced the possible deviations previously observed in the literature. Using past measurements and a weighted average on the different deviation models, we indicate the accuracy required for future measurements to investigated possible divergences., Comment: Document containing 6 pages and 3 figures. Submitted to Phys. Rev. Lett

We propose a novel approach which implements the relativistic calculations of the photon travel time into a robust timing model for pulsars orbiting supermassive black holes. We demonstrate the inability of current timing codes based on the post-Newtonian expansion of General Relativity to correctly estimate the relativistic time of arrival of the emitted pulses at an Earth-based observatory. We also show how a misestimation of the pulsar parameters can lead to the appearance of phase-dependent residual, which hints at a tremendous constraining power of the binary and intrinsic parameters for timing observations of potential pulsars at the Galactic Center., Comment: 16 pages, 6 figures, 2 tables. Comments are welcome

In this paper we address the problem of performing Bayesian inference for the parameters of a nonlinear multi-output model and the covariance matrix of the different output signals. We propose an adaptive importance sampling (AIS) scheme for multivariate Bayesian inversion problems, which is based in two main ideas: the variables of interest are split in two blocks and the inference takes advantage of known analytical optimization formulas. We estimate both the unknown parameters of the multivariate non-linear model and the covariance matrix of the noise. In the first part of the proposed inference scheme, a novel AIS technique called adaptive target adaptive importance sampling (ATAIS) is designed, which alternates iteratively between an IS technique over the parameters of the non-linear model and a frequentist approach for the covariance matrix of the noise. In the second part of the proposed inference scheme, a prior density over the covariance matrix is considered and the cloud of samples obtained by ATAIS are recycled and re-weighted to obtain a complete Bayesian study over the model parameters and covariance matrix. ATAIS is the main contribution of the work. Additionally, the inverted layered importance sampling (ILIS) is presented as a possible compelling algorithm (but based on a conceptually simpler idea). Different numerical examples show the benefits of the proposed approaches

We focus on the optimization of neutral atom transport and transfer between optical tweezers, both critical steps towards the implementation of quantum processors and simulators. We consider four different types of experimentally relevant pulses: piece-wise linear, piece-wise quadratic, minimum jerk, and a family of hybrid linear and minimum jerk ramps. We also develop a protocol using Shortcuts to Adiabaticity (STA) techniques that allows us to include the effects of static traps. By computing a measure of the error after transport and two measures of the heating for transient times, we provide a systematic characterization of the performance of all the considered pulses and show that our proposed STA protocol outperforms the experimentally inspired pulses. After pulse shape optimization we find a lower threshold for the total time of the protocol that is compatible with the limit below which the increase in the vibrational excitations exceeds half of the amount of states hosted by the moving tweezer. Since the obtained lower bound for the atom capturing or releasing stage is 9 times faster than the one reported in state-of-the-art experiments, we interpret our results as a wake-up call towards the importance of the inclusion and optimization of the transfer between tweezers, which may be the largest bottleneck to speed. For the two pulses having the best performance (minimum jerk and STA), we determine optimal regions in the experimentally accessible parameters to implement high fidelity transport pulses. Finally, our STA results prove that a modulation in the depth of the moving tweezer designed to counteract the effect of the static traps reduces errors and allows for shorter pulse duration. To motivate the use of our STA pulse in future experiments, we provide a simple analytical approximation for the tweezer position and depth controls., Comment: 21 pages, 8 figures

In general, the comprehension of any type of complex system depends on the resolution used to look at the phenomena occurring within it. But identifying a priori, for example, the best time frequencies/scales to study a certain system over-time, or the spatial distances at which looking for correlations, symmetries, and fluctuations, is most often non-trivial. Here we describe an unsupervised approach that, starting solely from the data of a system, allows learning the characteristic length-scales of the key events/processes dominating it and the optimal spatiotemporal resolutions to characterize them. We test this approach on time-series data obtained from simulation or experimental trajectories of various example many-body complex systems ranging from the atomic- to the macroscopic-scale and having diverse internal dynamic complexities. Our method automatically performs the analysis of the system's data, analyzing correlations at all relevant inter-particle distances and at all possible inter-frame intervals in which their time-series can be subdivided: namely, at all space-and-time resolutions. The optimal spatiotemporal resolution for studying a certain system thus steps-out as that maximizing information extraction-and-classification from the system's data, which we prove being related to the characteristic spatiotemporal length-scales of the local/collective physical events dominating it. This approach is broadly applicable and can be used to optimize the study of different types of data (static distributions, time-series, or signals). The concept of 'optimal resolution' has general character and provides a robust basis to characterize any type of system based on its data, as well as to guide data analysis in general.

Transitional millisecond pulsars (tMSPs) bridge the evolutionary gap between accreting neutron stars in low-mass X-ray binaries and millisecond radio pulsars. These systems exhibit a unique subluminous X-ray state characterized by the presence of an accretion disk and rapid switches between high and low X-ray emission modes. The high mode features coherent millisecond pulsations spanning from the X-ray to the optical band. We present multiwavelength polarimetric observations of the tMSP PSR J1023+0038 aimed at conclusively identifying the physical mechanism powering its emission in the subluminous X-ray state. During the high mode, we detect polarized emission in the 2-6 keV energy range, with a polarization degree of 12% +/- 3% and a polarization angle of -2deg +/- 9deg (1sigma) measured counterclockwise from the North celestial pole towards East. At optical wavelengths, we find a polarization degree of 1.41% +/- 0.04% and a polarization angle aligned with that in the soft X-rays, suggesting a common physical mechanism operating across these bands. Remarkably, the polarized flux spectrum matches the pulsed emission spectrum from optical to X-rays. The polarization properties differ markedly from those observed in other accreting neutron stars and isolated rotation-powered pulsars and are also inconsistent with an origin in a compact jet. Our results provide direct evidence that the polarized and pulsed emissions both originate from synchrotron radiation at the shock formed where the pulsar wind interacts with the inner regions of the accretion disk., Comment: Submitted

Radiation damage tests in hydrogenated amorphous silicon (a-Si:H) flexible flux and dose-measuring devices have been performed with a 3 MeV proton beam, to evaluate combined displacement and total ionizing dose damage. The tested devices had two different configurations and thicknesses. The first device was a 2 um thick n-i-p diode having a 5 mm x 5 mm area. The second device was a 5 um thick charge selective contact detector having the same area. Both the devices were deposited on a flexible polyimide substrate and were irradiated up to the fluence of 1016 neq/cm2. The response to different proton fluxes has been measured before irradiation and after irradiation at 1016 neq/cm2 for charge-selective contacts and n-i-p devices. The effect of annealing for partial performance recovery at 100{\deg}C for 12 hours was also studied and a final characterization on annealed devices was performed. This test is the first combined displacement and total ionizing dose test on flexible a-Si:H devices., Comment: This paper was presented at the IEEE-RTSD2024 in Tampa (USA) with presentation number: R08-04

Extracting from trajectory data meaningful information to understand complex systems might be non-trivial. High-dimensional analyses are typically assumed to be desirable, if not required, to prevent losing important information. However, to what extent such high-dimensionality is really needed/beneficial often remains not clear. Here we challenge such a fundamental general problem. As first representative cases of a system with internal dynamical complexity, we study atomistic molecular dynamics trajectories of liquid water and ice coexisting in dynamical equilibrium in correspondence of the solid/liquid transition temperature. To attain an intrinsically high-dimensional analysis, we use the Smooth Overlap of Atomic Positions (SOAP) descriptor, obtaining a large dataset containing 2.56e6 576-dimensional SOAP vectors that we analyze in various ways. Surprisingly, our results demonstrate how the time-series data contained in one single SOAP dimension accounting only for <0.001% of the total variance (thus considered negligible and discarded in typical variance-based dimensionality reduction approaches) is as much, and even more descriptive than the entire high-dimensional dataset. Adding more dimensions to the analysis is found detrimental due to "frustrated information" phenomena that are proven general, and are observed also in the analysis of, e.g., experimental trajectories of collective convolutions of active microparticles (used as a second case study). This work explicitly demonstrates how high-dimensional analyses are not necessarily better than low-dimensional ones. These results also demonstrate the importance of considering the quality (relevance) over the quantity of information, especially when dealing with noisy high-dimensional datasets, where the noise of one dimension may have higher statistical weight than the relevant information contained into another.

We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on simulating spectra. We systematically investigated the impact of various factors on the accuracy and fidelity of the results, including the use of simulated data, the effect of varying amounts of real training data, network architectures, and learning paradigms. Our approach integrates supervised and unsupervised learning techniques within autoencoder frameworks. Our methodology leverages an existing simulation model that utilizes a library of existing stellar spectra in which the emerging flux is computed from first principles rooted in physics and a HARPS instrument model to generate simulated spectra comparable to observational data. We trained standard and variational autoencoders on HARPS data to predict spectral parameters and generate spectra. Our models excel at predicting spectral parameters and compressing real spectra, and they achieved a mean prediction error of approximately 50 K for effective temperatures, making them relevant for most astrophysical applications. Furthermore, the models predict metallicity ([M/H]) and surface gravity (log g) with an accuracy of approximately 0.03 dex and 0.04 dex, respectively, underscoring their broad applicability in astrophysical research. The models' computational efficiency, with processing times of 779.6 ms on CPU and 3.97 ms on GPU, makes them valuable for high-throughput applications like massive spectroscopic surveys and large archival studies. By achieving accuracy comparable to classical methods with significantly reduced computation time, our methodology enhances the scope and efficiency of spectroscopic analysis., Comment: Accepted for publication in Astronomy & Astrophysics

Cryogenic phonon detectors are adopted in experiments searching for dark matter interactions or coherent elastic neutrino-nucleus scattering, thanks to the low energy threshold they can achieve. The phonon-mediated sensing of particle interactions in passive silicon absorbers has been demonstrated with Kinetic Inductance Detectors (KIDs). Targets with neutron number larger than silicon, however, feature higher cross section to neutrinos while multi-target absorbers in dark matter experiments would provide a stronger evidence of a possible signal. In this work we present the design, fabrication and operation of KIDs coupled to a germanium absorber, achieving phonon-sensing performance comparable to silicon absorbers. The device introduced in this work is a proof of concept for a scalable neutrino detector and for a multi-target dark matter experiment., Comment: 3 Figures, 2 Tables, 5 pages

Ferromagnetic superconductors are exceptionally rare because the strong ferromagnetic exchange field usually destroys singlet superconductivity. EuFe$_2$(As$_{1-x}$P$_x$)$_2$, an iron-based superconductor with a maximum critical temperature of $\sim$25 K, is a unique material that exhibits full coexistence with ferromagnetic order below $T_\mathrm{FM} \approx 19$ K. The interplay between the two leads to a narrowing of ferromagnetic domains at higher temperatures and the spontaneous nucleation of vortices/antivortices at lower temperatures. Here we demonstrate how the underlying magnetic structure directly controls the superconducting vortex dynamics in applied magnetic fields. Just below $T_\mathrm{FM}$ we observe a pronounced temperature-dependent peak in both the coercivity and the creep activation energy, the latter becoming rapidly suppressed in large applied magnetic fields. We attribute this behaviour to the formation of vortex polarons arising from the unique interaction between free vortices and magnetic stripe domains. We present a theoretical description of the properties of vortex polarons that explains our main observations, showing how they lead to vortex trapping and an attractive vortex-vortex interaction at short distances. In stark contrast, strong magnetic irreversibility at low temperatures is linked to a critical current governed by giant flux creep over an activation barrier for vortex-antivortex annihilation near domain walls. Our work reveals unexplored new routes for the magnetic enhancement of vortex pinning with particularly important applications in high-current conductors for operation at high magnetic fields., Comment: 15 pages, 5 figures

The adoption of FIDO2 authentication by major tech companies in web applications has grown significantly in recent years. However, we argue FIDO2 has broader potential applications. In this paper, we introduce EAP-FIDO, a novel Extensible Authentication Protocol (EAP) method for use in IEEE 802.1X-protected networks. This allows organisations with WPA2/3-Enterprise wireless networks or MACSec-enabled wired networks to leverage FIDO2's passwordless authentication in compliance with existing standards. Additionally, we provide a comprehensive security and performance analysis to support the feasibility of this approach., Comment: This is an author version. It has not been peer reviewed

The increasing availability and diversity of multimodal data in recommender systems offer new avenues for enhancing recommendation accuracy and user satisfaction. However, these systems must contend with high-dimensional, sparse user-item rating matrices, where reconstructing the matrix with only small subsets of preferred items for each user poses a significant challenge. To address this, we propose CADMR, a novel autoencoder-based multimodal recommender system framework. CADMR leverages multi-head cross-attention mechanisms and Disentangled Learning to effectively integrate and utilize heterogeneous multimodal data in reconstructing the rating matrix. Our approach first disentangles modality-specific features while preserving their interdependence, thereby learning a joint latent representation. The multi-head cross-attention mechanism is then applied to enhance user-item interaction representations with respect to the learned multimodal item latent representations. We evaluate CADMR on three benchmark datasets, demonstrating significant performance improvements over state-of-the-art methods.

Recently, deep matrix factorization has been established as a powerful model for unsupervised tasks, achieving promising results, especially for multi-view clustering. However, existing methods often lack effective feature selection mechanisms and rely on empirical hyperparameter selection. To address these issues, we introduce a novel Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering (DMFAW). Our method simultaneously incorporates feature selection and generates local partitions, enhancing clustering results. Notably, the features weights are controlled and adjusted by a parameter that is dynamically updated using Control Theory inspired mechanism, which not only improves the model's stability and adaptability to diverse datasets but also accelerates convergence. A late fusion approach is then proposed to align the weighted local partitions with the consensus partition. Finally, the optimization problem is solved via an alternating optimization algorithm with theoretically guaranteed convergence. Extensive experiments on benchmark datasets highlight that DMFAW outperforms state-of-the-art methods in terms of clustering performance.

We study an extension of the natural inflation model comprising a non-Abelian gauge sector coupled to the axion-inflaton kinetic term. We show how such non-minimal coupling serves as a source of friction for the rolling inflaton granting sixty or more $e$-folds of accelerated expansion for sub-Planckian values of the axion decay constant. The analysis of perturbations reveals a negative sound speed, thus signaling an instability. Implementing a Chern-Simons-type coupling between the inflaton and gauge sectors cures the instability by delivering a positive speed. We perform a numerical study of scalar and tensor perturbations for a fiducial set of parameters finding that the corresponding observables are compatible with current CMB bounds., Comment: 24 pages, 8 figures

KM3NeT/ORCA is an underwater neutrino telescope under construction in the Mediterranean Sea. Its primary scientific goal is to measure the atmospheric neutrino oscillation parameters and to determine the neutrino mass ordering. ORCA can constrain the oscillation parameters $\Delta m^{2}_{31}$ and $\theta_{23}$ by reconstructing the arrival direction and energy of multi-GeV neutrinos crossing the Earth. Searches for deviations from the Standard Model of particle physics in the forward scattering of neutrinos inside Earth matter, produced by Non-Standard Interactions, can be conducted by investigating distortions of the standard oscillation pattern of neutrinos of all flavours. This work reports on the results of the search for non-standard neutrino interactions using the first six detection units of ORCA and 433 kton-years of exposure. No significant deviation from standard interactions was found in a sample of 5828 events reconstructed in the 1 GeV$-$1 TeV energy range. The flavour structure of the non-standard coupling was constrained at 90\% confidence level to be $|\varepsilon_{\mu\tau} | \leq 5.4 \times 10^{-3}$, $|\varepsilon_{e\tau} | \leq 7.4 \times 10^{-2}$, $|\varepsilon_{e\mu} | \leq 5.6 \times 10^{-2}$ and $-0.015 \leq \varepsilon_{\tau\tau} - \varepsilon_{\mu\mu} \leq 0.017$. The results are comparable to the current most stringent limits placed on the parameters by other experiments.

Rank-metric codes have been a central topic in coding theory due to their theoretical and practical significance, with applications in network coding, distributed storage, crisscross error correction, and post-quantum cryptography. Recent research has focused on constructing new families of rank-metric codes with distinct algebraic structures, emphasizing the importance of invariants for distinguishing these codes from known families and from random ones. In this paper, we introduce a novel geometric invariant for linear rank-metric codes, inspired by the Schur product used in the Hamming metric. By examining the sequence of dimensions of Schur powers of the extended Hamming code associated with a linear code, we demonstrate its ability to differentiate Gabidulin codes from random ones. From a geometric perspective, this approach investigates the vanishing ideal of the linear set corresponding to the rank-metric code., Comment: 17 pages, 2 figures

Recent advancements in artificial intelligence have sparked interest in scientific assistants that could support researchers across the full spectrum of scientific workflows, from literature review to experimental design and data analysis. A key capability for such systems is the ability to process and reason about scientific information in both visual and textual forms - from interpreting spectroscopic data to understanding laboratory setups. Here, we introduce MaCBench, a comprehensive benchmark for evaluating how vision-language models handle real-world chemistry and materials science tasks across three core aspects: data extraction, experimental understanding, and results interpretation. Through a systematic evaluation of leading models, we find that while these systems show promising capabilities in basic perception tasks - achieving near-perfect performance in equipment identification and standardized data extraction - they exhibit fundamental limitations in spatial reasoning, cross-modal information synthesis, and multi-step logical inference. Our insights have important implications beyond chemistry and materials science, suggesting that developing reliable multimodal AI scientific assistants may require advances in curating suitable training data and approaches to training those models.

The entertainment-driven dynamics of social media platforms encourage users to engage with like-minded individuals and consume content aligned with their beliefs. These dynamics may amplify polarization by reinforcing shared perspectives and reducing exposure to diverse viewpoints. Simultaneously, users migrate from one platform to another, either forced by moderation policies, such as de-platforming, or spontaneously seeking environments more aligned with their preferences. These migrations foster the specialization and differentiation of the social media ecosystem, with platforms increasingly organized around specific user communities and shared content preferences. This shift marks an evolution from echo chambers enclosed within platforms to "echo platforms", i.e., entire platforms functioning as ideologically homogeneous niches. This study introduces an operational framework to systematically analyze these dynamics, by examining three key dimensions: platform centrality (central vs. peripheral), news consumption (reliable vs questionable), and user base composition (uniform vs diverse). To this aim, we leverage a dataset of 126M URLs posted by nearly 6M users on nine social media platforms, namely Facebook, Reddit, Twitter (now X), YouTube, BitChute, Gab, Parler, Scored, and Voat. We find a clear separation between mainstream and alt-tech platforms, with the second category being characterized by a peripheral role in the social media ecosystem, a greater prevalence of unreliable content, and a heightened ideological uniformity. These findings outline the main dimensions defining the fragmentation and polarization of the social media ecosystem.

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research., Comment: Updating author information, the submission remains largely unchanged. 98 pages total

Recent experiments with quantum simulators using ultracold atoms and superconducting qubits have demonstrated the potential of controlled dissipation as a versatile tool for realizing correlated many-body states. However, determining the dynamics of dissipative quantum many-body systems remains a significant analytical and numerical challenge. In this work, we focus on a dissipative impurity problem as a testbed for new methodological developments. We introduce an efficient non-perturbative framework that combines the superposition of Gaussian states (SGS) variational ansatz with the quantum trajectory approach to simulate open systems featuring a dissipative impurity. Applying this method to a spinful impurity subject to two-body losses and embedded in a bath of noninteracting fermions, we explore the full crossover from weak to strong dissipation regimes. The non-perturbative nature of the SGS ansatz allows us to thoroughly examine this crossover, providing comprehensive insights into the system's behavior. In the strong dissipation regime, our approach reproduces the finding that localized two-body losses can induce the Kondo effect [arXiv:2406.03527], characterized by a slowdown of spin relaxation and an enhancement of charge conductance. Furthermore, we reveal an exotic "reverse conductance" phenomenon at zero potential bias -- a counter-intuitive single-body effect resulting from intermediate dissipation and finite bandwidth. Finally, we investigate the formation of ferromagnetic domains and propose an extension to realize a higher-spin Kondo model using localized dissipation., Comment: 18 pages, 7 figures

Reconstructing the physical complexity of many-body dynamical systems can be challenging. Starting from the trajectories of their constitutive units (raw data), typical approaches require selecting appropriate descriptors to convert them into time-series, which are then analyzed to extract interpretable information. However, identifying the most effective descriptor is often non-trivial. Here, we report a data-driven approach to compare the efficiency of various descriptors in extracting information from noisy trajectories and translating it into physically relevant insights. As a prototypical system with non-trivial internal complexity, we analyze molecular dynamics trajectories of an atomistic system where ice and water coexist in equilibrium near the solid/liquid transition temperature. We compare general and specific descriptors often used in aqueous systems: number of neighbors, molecular velocities, Smooth Overlap of Atomic Positions (SOAP), Local Environments and Neighbors Shuffling (LENS), Orientational Tetrahedral Order, and distance from the fifth neighbor ($d_5$). Using Onion Clustering -- an efficient unsupervised method for single-point time-series analysis -- we assess the maximum extractable information for each descriptor and rank them via a high-dimensional metric. Our results show that advanced descriptors like SOAP and LENS outperform classical ones due to higher signal-to-noise ratios. Nonetheless, even simple descriptors can rival or exceed advanced ones after local signal denoising. For example, $d_5$, initially among the weakest, becomes the most effective at resolving the system's non-local dynamical complexity after denoising. This work highlights the critical role of noise in information extraction from molecular trajectories and offers a data-driven approach to identify optimal descriptors for systems with characteristic internal complexity., Comment: 19 pages, 5 figures + 3 in supporting information (at the bottom of the manuscript)

Quantization-aware training (QAT) schemes have been shown to achieve near-full precision accuracy. They accomplish this by training a quantized model for multiple epochs. This is computationally expensive, mainly because of the full precision backward pass. On the other hand, post-training quantization (PTQ) schemes do not involve training and are therefore computationally cheap, but they usually result in a significant accuracy drop. We address these challenges by proposing EfQAT, which generalizes both schemes by optimizing only a subset of the parameters of a quantized model. EfQAT starts by applying a PTQ scheme to a pre-trained model and only updates the most critical network parameters while freezing the rest, accelerating the backward pass. We demonstrate the effectiveness of EfQAT on various CNNs and Transformer-based models using different GPUs. Specifically, we show that EfQAT is significantly more accurate than PTQ with little extra compute. Furthermore, EfQAT can accelerate the QAT backward pass between 1.44-1.64x while retaining most accuracy., Comment: 12 pages, 5 figures

Indirect dark matter detection methods are used to observe the products of dark matter annihilations or decays originating from astrophysical objects where large amounts of dark matter are thought to accumulate. With neutrino telescopes, an excess of neutrinos is searched for in nearby dark matter reservoirs, such as the Sun and the Galactic Centre, which could potentially produce a sizeable flux of Standard Model particles. The KM3NeT infrastructure, currently under construction, comprises the ARCA and ORCA undersea \v{C}erenkov neutrino detectors located at two different sites in the Mediterranean Sea, offshore of Italy and France, respectively. The two detector configurations are optimised for the detection of neutrinos of different energies, enabling the search for dark matter particles with masses ranging from a few GeV/c$^2$ to hundreds of TeV/c$^2$. In this work, searches for dark matter annihilations in the Galactic Centre and the Sun with data samples taken with the first configurations of both detectors are presented. No significant excess over the expected background was found in either of the two analyses. Limits on the velocity-averaged self-annihilation cross section of dark matter particles are computed for five different primary annihilation channels in the Galactic Centre. For the Sun, limits on the spin-dependent and spin-independent scattering cross sections of dark matter with nucleons are given for three annihilation channels.

Context: The emergence of Large Language Models (LLMs) has significantly transformed Software Engineering (SE) by providing innovative methods for analyzing software repositories. Objectives: Our objective is to establish a practical framework for future SE researchers needing to enhance the data collection and dataset while conducting software repository mining studies using LLMs. Method: This experience report shares insights from two previous repository mining studies, focusing on the methodologies used for creating, refining, and validating prompts that enhance the output of LLMs, particularly in the context of data collection in empirical studies. Results: Our research packages a framework, coined Prompt Refinement and Insights for Mining Empirical Software repositories (PRIMES), consisting of a checklist that can improve LLM usage performance, enhance output quality, and minimize errors through iterative processes and comparisons among different LLMs. We also emphasize the significance of reproducibility by implementing mechanisms for tracking model results. Conclusion: Our findings indicate that standardizing prompt engineering and using PRIMES can enhance the reliability and reproducibility of studies utilizing LLMs. Ultimately, this work calls for further research to address challenges like hallucinations, model biases, and cost-effectiveness in integrating LLMs into workflows.

We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy. Edify Image utilizes cascaded pixel-space diffusion models trained using a novel Laplacian diffusion process, in which image signals at different frequency bands are attenuated at varying rates. Edify Image supports a wide range of applications, including text-to-image synthesis, 4K upsampling, ControlNets, 360 HDR panorama generation, and finetuning for image customization.

Generating high output powers while achieving narrow line single mode lasing are often mutual exclusive properties of commercial laser diodes. For this reason, efficient and scalable amplification of narrow line laser light is still a major driving point in modern laser system designs. Commonly, injection locking of high-power semiconductor laser diodes are used for this purpose. However, for many laser diodes it is very challenging to achieve stable operation of the injection locked state due to a complex interplay of non-linearities and thermal effects. Different approaches of active or passive stabilization usually require a large overhead of optical and electrical equipment and are not generally applicable. In our work we present a passive stabilization scheme, that is generally applicable, technically easy to implement and extremely cost-effective. It is based on the externally synchronized automatic acquisition of the optimal injection state. Central to our simple but powerful scheme is the management of thermalization effects during lock acquisition. By periodical relocking, spectrally pure amplified light is maintained in a quasi-CW manner over long timescales. We characterize the performance of our method for laser diodes amplifying 671 nm light and demonstrate the general applicability by confirming the method to work also for laser diodes at 401 nm, 461 nm and 689 nm. Our scheme enables the scaled operation of injection locks, even in cascaded setups, for the distributed amplification of single frequency laser light.

We investigate the role of constraints in the computational complexity of min-max optimization. The work of Daskalakis, Skoulakis, and Zampetakis [2021] was the first to study min-max optimization through the lens of computational complexity, showing that min-max problems with nonconvex-nonconcave objectives are PPAD-hard. However, their proof hinges on the presence of joint constraints between the maximizing and minimizing players. The main goal of this paper is to understand the role of these constraints in min-max optimization. The first contribution of this paper is a fundamentally new proof of their main result, which improves it in multiple directions: it holds for degree 2 polynomials, it is essentially tight in the parameters, and it is much simpler than previous approaches, clearly highlighting the role of constraints in the hardness of the problem. Second, we show that with general constraints (i.e., the min player and max player have different constraints), even convex-concave min-max optimization becomes PPAD-hard. Along the way, we also provide PPAD-membership of a general problem related to quasi-variational inequalities, which has applications beyond our problem.

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

In response to the growing demand for more efficient and compact refrigeration and energy conversion devices, electrocaloric poly(vinylidene fluoride-trifluoroethylene-chlorofluoroethylene) is among the most promising active substances. However, despite its high electrocaloric response, the maximum efficiency achievable over a cooling cycle is hampered by losses. To overcome this major limitation, losses have been reduced by using an electrothermal poling treatment as well as by controlling the surface roughness. The upper bound of the efficiency computed over a thermodynamic cycle mimicking the working conditions of an actual cooling device is increased from 1% to 10% of the Carnot efficiency. This represents a major improvement in enhancing ferroelectric materials for advanced energy applications.

The Parameterized Post-Newtonian (PPN) formalism offers an agnostic framework for evaluating theories of gravity that extend beyond General Relativity. Departures from General Relativity are represented by a set of dimensionless parameters that, at the first order in the expansion, reduce to $\beta$ and $\gamma$, which describe deviations in spatial curvature and non-linear superposition effects of gravity, respectively. We exploit future observations of stars at the Galactic Center, orbiting the supermassive black hole Sagittarius A*, to forecast the ability to constrain the first-order PPN parameters $\gamma$ and $\beta$. We have generated a mock catalog of astrometric and spectroscopic data for S2, based on the Schwarzschild metric, simulating observations over multiple orbital periods with the GRAVITY and SINFONI instruments. Our analysis includes the effects of relativistic orbital precession and line-of-sight (LOS) velocity gravitational redshift. Since future data for S2 can provide constraints only on a linear combination of the PPN parameters $\beta$ and $\gamma$ we also analyzed the impact of future observations of the gravitational lensing for stars that pass closer in the sky to Sgr A*, like the known star S62, which can potentially provide tight constraints on the parameter $\gamma$, that alone regulates the amplitude of the astrometric deviations due to lensing. When combining lensing observations for S62, and the precise orbital tracking of S2, one obtains independent constraints on both $\gamma$ (with a potential precision as good as $\sim 1\%$) and $\beta$ (with a corresponding precision of $\sim 2\%$), providing a precision test of General Relativity and its extensions., Comment: 9 pages, 7 figures, 1 table. Comments are welcome

This exploratory pilot study investigated the potential of combining a domain-specific model, BERN2, with large language models (LLMs) to enhance automated disease phenotyping from research survey data. Motivated by the need for efficient and accurate methods to harmonize the growing volume of survey data with standardized disease ontologies, we employed BERN2, a biomedical named entity recognition and normalization model, to extract disease information from the ORIGINS birth cohort survey data. After rigorously evaluating BERN2's performance against a manually curated ground truth dataset, we integrated various LLMs using prompt engineering, Retrieval-Augmented Generation (RAG), and Instructional Fine-Tuning (IFT) to refine the model's outputs. BERN2 demonstrated high performance in extracting and normalizing disease mentions, and the integration of LLMs, particularly with Few Shot Inference and RAG orchestration, further improved accuracy. This approach, especially when incorporating structured examples, logical reasoning prompts, and detailed context, offers a promising avenue for developing tools to enable efficient cohort profiling and data harmonization across large, heterogeneous research datasets.

Fuzzy Dark Matter (FDM) is among the most suitable candidates to replace WIMPs and to resolve the puzzling mystery of dark matter. A galactic dark matter halo made of these ultralight bosonic particles leads to the formation of a solitonic core surrounded by quantum interference patterns that, on average, reproduce a Navarro-Frenk-White-like mass density profile in the outskirts of the halo. The structure of such a core is determined once the boson mass and the total mass of the halo are set. We investigated the capability of future astrometric Theia-like missions to detect the properties of such a halo within the FDM model, namely the boson mass and the core radius. To this aim, we built mock catalogs containing three-dimensional positions and velocities of stars within a target dwarf galaxy. We exploited these catalogs using a Markov Chain Monte Carlo algorithm and found that measuring the proper motion of at least 2000 stars within the target galaxy, with uncertainty $\sigma_v \leq 3$ km/s on the velocity components, will constrain the boson mass and the core radius with 3\% accuracy. Furthermore, the transition between the solitonic core and the outermost NFW-like density profile could be detected with an uncertainty of 7\%. Such results would not only help to confirm the existence of FDM, but they would also be useful for alleviating the current tension between galactic and cosmological estimations of the boson mass, or demonstrating the need for multiple particles with a broad mass spectrum as naturally arise String Axiverse., Comment: 11 pages, 3 Figures, 4 Tables

Stacking van der Waals magnets holds promise for creating new hybrid materials with properties that do not exist in bulk materials. Here we investigate orthogonally twisted stacks of the van der Waals antiferromagnet CrSBr, aiming to exploit an extreme misalignment of magnetic anisotropy across the twisted interface.Using nitrogen-vacancy centre microscopy, we construct vector maps of the magnetisation, and track their evolution under an external field, in a range of twisted compensated and uncompensated configurations differing by the number of layers. We show that twisted stacking consistently modifies the local magnetic switching behaviour of constituent flakes, and that these modifications are spatially non-uniform. In the case of compensated component flakes (even number of layers), we demonstrate that the combination of dipolar coupling and stacking-induced strain can reduce the switching field by over an order of magnitude. Conversely, in uncompensated component flakes (odd number of layers), we observe indications of a non-zero interlayer exchange interaction between twisted flakes during magnetization reversal, which can persistently modify magnetic order. This work highlights the importance of spatial imaging in investigating stacking-induced magnetic effects, particularly in the case of twistronics where spatial variation is expected and can be conflated with structural imperfections., Comment: 6 main text pages, 4 figures plus 8 supplementary information pages, 9 figures

We compute and apply the minimum spanning tree (MST) of the binary millisecond pulsar population, and discuss aspects of the known phenomenology of these systems in this context. We find that the MST effectively separates different classes of spider pulsars, eclipsing radio pulsars in tight binary systems either with a companion with a mass in the range of approximately 0.1-0.8 solar masses (redbacks) or with a companion of less than or approximately 0.06 solar masses (black widows), into distinct branches. The MST also separates black widows located in globular clusters from those found in the field and groups other pulsar classes of interest, including transitional millisecond pulsars. Using the MST and a defined ranking for similarity, we identify possible candidates likely to belong to these pulsar classes. In particular, based on this approach, we propose the black widows' classification of J1300+1240, J1630+3550, J1317-0157, J1221-0633, J1627+3219, J1737-0314A, and J1701-3006F, discuss that of J1908+2105, and analyze J1723-2837, J1431-4715, and J1902-5105 as possible transitional systems. We introduce an algorithm that quickly locates where new pulsars fall within the MST and use this to examine the positions of the transitional millisecond pulsar IGR J18245-2452 (PSR J1824-2452I), the transitional millisecond pulsar candidate 3FGL J1544.6-1125, and the accreting millisecond X-ray pulsar SAX J1808.4-3658. Assessing the positions of these sources in the MST assuming a range for their unknown variables (e.g., the spin period derivative of PSR J1824-2452I) we can effectively narrow down the parameter space necessary for searching and determining key pulsar parameters through targeted observations., Comment: For the millisecond pulsar tree website, see http://www.pulsartree.ice.csic.es/millisecondpulsartree

We introduce the concept of a sum-rank saturating system and outline its correspondence to a covering properties of a sum-rank metric code. We consider the problem of determining the shortest sum-rank-$\rho$-saturating systems of a fixed dimension, which is equivalent to the covering problem in the sum-rank metric. We obtain upper and lower bounds on this quantity. We also give constructions of saturating systems arising from geometrical structures.

The inhomogeneous magnetic stray field of micromagnets has been extensively used to manipulate electron spin qubits. By means of micromagnetic simulations and scanning superconducting quantum interference device microscopy, we show that the polycrystallinity of the magnet and non-uniform magnetization significantly impact the stray field and corresponding qubit properties. We find that the random orientation of the crystal axis in polycrystalline Co magnets alters the qubit frequencies by up to 0.5 GHz, compromising single qubit addressability and single gate fidelities. We map the stray field of Fe micromagnets with an applied magnetic field of up to 500 mT (mimicking conditions when operating qubits), finding field gradients above 1 mT/nm. The measured gradients and the lower magnetocrystalline anisotropy of Fe demonstrate the advantage of using Fe instead of Co for magnets in spin qubit devices. These properties of Fe also enabled us to design a 2D arrangement of nanomagnets for driving spin qubits distributed on a 2D lattice.

As machine learning (ML) and artificial intelligence (AI) technologies become more widespread, concerns about their environmental impact are increasing due to the resource-intensive nature of training and inference processes. Green AI advocates for reducing computational demands while still maintaining accuracy. Although various strategies for creating sustainable ML systems have been identified, their real-world implementation is still underexplored. This paper addresses this gap by studying 168 open-source ML projects on GitHub. It employs a novel large language model (LLM)-based mining mechanism to identify and analyze green strategies. The findings reveal the adoption of established tactics that offer significant environmental benefits. This provides practical insights for developers and paves the way for future automation of sustainable practices in ML systems., Comment: Accepted at the 2025 IEEE/ACM 47th International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS)

For an algebraic torus defined over a local (or global) field $F$, a celebrated result of R.P. Langlands establishes a natural homomorphism from the group of continuous cohomology classes of the Weil group, valued in the dual torus, onto the space of complex characters of the rational points of the torus (or automorphic characters in the global case). We slightly extend this result by showing that, if we topologize the relevant spaces of continuous homomorphisms and continuous cochains with the compact-open topology, then Langlands's homomorphism is continuous and open. Moreover, we demonstrate that, in both the local and global settings, the subset of unramified characters corresponds to the identity component of the relevant space of characters when viewed in this topological framework. Finally, we compare the group of unramified characters and the Galois (co)invariants of the dual torus., Comment: 27 pages, in this new version, we corrected some inaccuracies in the manuscript, added a few more references and corrected some typos

We have probed the capability of third-generation Gravitational Waves (GW) interferometers, such as the Einstein Telescope and Cosmic Explorer, to constrain a cosmological model with an interacting dark sector. We focused on GW events with a detected electromagnetic counterpart being the $\gamma$ or X emission of Gamma-Ray Burst, and a Kilonova emission. We assume the first one to be detected by the THESEUS satellite, while the second one to be detected by the Vera Rubin Observatory. We probed three different interaction kernels and found that the posterior estimation of the cosmological parameters is biased due to the existing degeneracies between the dark and matter sectors. We also found that introducing an external prior on the matter density parameter breaks the degeneracy, removes the bias results, and improves the accuracy on the dark sector parameters., Comment: 16 pages, 6 Table, and 3 Figures. Accepted for publication in Physical Review D

Holomorphic functions are fundamental in operator theory and their Cauchy formula is a crucial tool for defining functions of operators. The Fueter-Sce extension theorem (often called Fueter-Sce mapping theorem) provides a two-step procedure for extending holomorphic functions to hyperholomorphic functions. In the first step, slice hyperholomorphic functions are obtained, and their associated Cauchy formula establishes the $S$-functional calculus for noncommuting operators on the $S$-spectrum. The second step produces axially monogenic functions, which lead to the development of the monogenic functional calculus. In this review paper we discuss the second operator in the Fueter-Sce mapping theorem that takes slice hyperholomorphic to axially monogenic functions. This operator admits several factorizations which generate various function spaces and their corresponding functional calculi, thereby forming the so-called fine structures of spectral theories on the $S$-spectrum., Comment: To appear in the Special Volume of ICHAA 2024. arXiv admin note: substantial text overlap with arXiv:2303.00253

Classical finance models are based on the premise that investors act rationally and utilize all available information when making portfolio decisions. However, these models often fail to capture the anomalies observed in intertemporal choices and decision-making under uncertainty, particularly when accounting for individual differences in preferences and consumption patterns. Such limitations hinder traditional finance theory's ability to address key questions like: How do personal preferences shape investment choices? What drives investor behaviour? And how do individuals select their portfolios? One prominent contribution is Pompian's model of four Behavioral Investor Types (BITs), which links behavioural finance studies with Keirsey's temperament theory, highlighting the role of personality in financial decision-making. Yet, traditional parametric models struggle to capture how these distinct temperaments influence intertemporal decisions, such as how individuals evaluate trade-offs between present and future outcomes. To address this gap, the present study employs Functional Data Analysis (FDA) to specifically investigate temporal discounting behaviours revealing nuanced patterns in how different temperaments perceive and manage uncertainty over time. Our findings show heterogeneity within each temperament, suggesting that investor profiles are far more diverse than previously thought. This refined classification provides deeper insights into the role of temperament in shaping intertemporal financial decisions, offering practical implications for financial advisors to better tailor strategies to individual risk preferences and decision-making styles.

We study a cylindrical plasmonic waveguide consisting of a magnetic Weyl semimetal embedded in a dielectric medium. We determine the dispersion relation of the surface plasmon polaritons and show how it depends on the plasma frequency, the radius of the semimetal and the separation between the nodes. We show that the band structure, which modifies the electrodynamics in the medium, manifests itself through a pronounced asymmetry in the dispersion curves and a giant splitting in the group velocity, with the orbital angular momentum as a control parameter for the direction of propagation.

Neutrinos described as an open quantum system may interact with the environment which introduces stochastic perturbations to their quantum phase. This mechanism leads to a loss of coherence along the propagation of the neutrino $-$ a phenomenon commonly referred to as decoherence $-$ and ultimately, to a modification of the oscillation probabilities. Fluctuations in space-time, as envisaged by various theories of quantum gravity, are a potential candidate for a decoherence-inducing environment. Consequently, the search for decoherence provides a rare opportunity to investigate quantum gravitational effects which are usually beyond the reach of current experiments. In this work, quantum decoherence effects are searched for in neutrino data collected by the KM3NeT/ORCA detector from January 2020 to November 2021. The analysis focuses on atmospheric neutrinos within the energy range of a few GeV to $100\,\mathrm{GeV}$. Adopting the open quantum system framework, decoherence is described in a phenomenological manner with the strength of the effect given by the parameters $\Gamma_{21}$ and $\Gamma_{31}$. Following previous studies, a dependence of the type $\Gamma_{ij} \propto (E/E_0)^n$ on the neutrino energy is assumed and the cases $n = -2,-1$ are explored. No significant deviation with respect to the standard oscillation hypothesis is observed. Therefore, $90\,\%$ CL upper limits are estimated as $\Gamma_{21} < 4.6\cdot 10^{-21}\,$GeV and $\Gamma_{31} < 8.4\cdot 10^{-21}\,$GeV for $n = -2$, and $\Gamma_{21} < 1.9\cdot 10^{-22}\,$GeV and $\Gamma_{31} < 2.7\cdot 10^{-22}\,$GeV for $n = -1$, respectively., Comment: 17 pages, 5 figures

Almost all accreting black hole and neutron star X-ray binary systems (XRBs) exhibit prominent brightness variations on a few characteristic time-scales and their harmonics. These quasi-periodic oscillations (QPOs) are thought to be associated with the precession of a warped accretion disc, but the physical mechanism that generates the precessing warp remains uncertain. Relativistic frame dragging (Lense-Thirring precession) is one promising candidate, but a misaligned magnetic field is an alternative, especially for neutron star XRBs. Here, we report the discovery of 5 accreting white dwarf systems (AWDs) that display strong optical QPOs with characteristic frequencies and harmonic structures that suggest they are the counterpart of the QPOs seen in XRBs. Since AWDs are firmly in the classical (non-relativistic) regime, Lense-Thirring precession cannot account for these QPOs. By contrast, a weak magnetic field associated with the white dwarf can drive disc warping and precession in these systems, similar to what has been proposed for neutron star XRBs. Our observations confirm that magnetically driven warping is a viable mechanism for generating QPOs in disc-accreting astrophysical systems, certainly in AWDs and possibly also in (neutron star) XRBs. Additionally, they establish a new way to estimate magnetic field strengths, even in relatively weak-field systems where other methods are not available., Comment: 16 pages, 9 figures, accepted for publication in MNRAS

We present our findings from the first long X-ray observation of the hard X-ray source Swift J0826.2-7033 with XMM-Newton, which has shown characteristics of magnetic accretion. The system appears to have a long orbital period (~7.8 h) accompanied by short timescale variabilities, which we tentatively interpret as the spin and beat periods of an intermediate polar. These short- and long-timescale modulations are energy-independent, suggesting that photoelectric absorption does not play any role in producing the variabilities. If our suspected spin and beat periods are true, then Swift J0826.2-7033 accretes via disc-overflow with an equal fraction of accretion taking place via disc and stream. The XMM-Newton and Swift-BAT spectral analysis reveals that the post-shock region in Swift J0826.2-7033 has a multi-temperature structure with a maximum temperature of ~43 keV, which is absorbed by a material with an average equivalent hydrogen column density of ~1.6$\times$10$^{22}$ cm$^{-2}$ that partially covers ~27% of the X-ray source. The suprasolar abundances, with hints of an evolved donor, collectively make Swift J0826.2-7033 an interesting target, which likely underwent a thermal timescale mass transfer phase., Comment: 10 pages, 7 figures. Accepted for publication in A&A

CXOU J110926.4-650224 is a candidate transitional millisecond pulsar (tMSP) with X-ray and radio emission properties reminiscent of those observed in confirmed tMSPs in their X-ray 'subluminous' disc state. We present the results of observing campaigns that, for the first time, characterise the optical and near-infrared variability of this source and establish a connection with the mode-switching phenomenon observed in X-rays. The optical emission exhibited flickering activity, frequent dipping episodes where it appeared redder, and a multi-peaked flare where it was bluer. The variability pattern was strongly correlated with that of the X-ray emission. Each dip matched an X-ray low-mode episode, indicating that a significant portion of the optical emission originates from nearly the same region as the X-ray emission. The near-infrared emission also displayed remarkable variability, including a dip of 20 min in length during which it nearly vanished. Time-resolved optical spectroscopic observations reveal significant changes in the properties of emission lines from the disc and help infer the spectral type of the companion star to be between K0 and K5. We compare the properties of CXOU J110926.4-650224 with those of other tMSPs in the X-ray subluminous disc state and discuss our findings within the context of a recently proposed scenario that explains the phenomenology exhibited by the prototypical tMSP PSR J1023+0038., Comment: 12 pages, 7 figures, 2 tables. Accepted for publication on Astronomy & Astrophysics