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We present COCOLA (Coherence-Oriented Contrastive Learning for Audio), a contrastive learning method for musical audio representations that captures the harmonic and rhythmic coherence between samples. Our method operates at the level of the stems composing music tracks and can input features obtained via Harmonic-Percussive Separation (HPS). COCOLA allows the objective evaluation of generative models for music accompaniment generation, which are difficult to benchmark with established metrics. In this regard, we evaluate recent music accompaniment generation models, demonstrating the effectiveness of the proposed method. We release the model checkpoints trained on public datasets containing separate stems (MUSDB18-HQ, MoisesDB, Slakh2100, and CocoChorales)., Comment: Demo page: https://github.com/gladia-research-group/cocola, Accepted at ICASSP-25

Euclid will cover over 14000 $deg^{2}$ with two optical and near-infrared spectro-photometric instruments, and is expected to detect around ten million active galactic nuclei (AGN). This unique data set will make a considerable impact on our understanding of galaxy evolution and AGN. In this work we identify the best colour selection criteria for AGN, based only on Euclid photometry or including ancillary photometric observations, such as the data that will be available with the Rubin legacy survey of space and time (LSST) and observations already available from Spitzer/IRAC. The analysis is performed for unobscured AGN, obscured AGN, and composite (AGN and star-forming) objects. We make use of the spectro-photometric realisations of infrared-selected targets at all-z (SPRITZ) to create mock catalogues mimicking both the Euclid Wide Survey (EWS) and the Euclid Deep Survey (EDS). Using these catalogues we estimate the best colour selection, maximising the harmonic mean (F1) of completeness and purity. The selection of unobscured AGN in both Euclid surveys is possible with Euclid photometry alone with F1=0.22-0.23, which can increase to F1=0.43-0.38 if we limit at z>0.7. Such selection is improved once the Rubin/LSST filters (a combination of the u, g, r, or z filters) are considered, reaching F1=0.84 and 0.86 for the EDS and EWS, respectively. The combination of a Euclid colour with the [3.6]-[4.5] colour, which is possible only in the EDS, results in an F1-score of 0.59, improving the results using only Euclid filters, but worse than the selection combining Euclid and LSST. The selection of composite ($f_{{\rm AGN}}$=0.05-0.65 at 8-40 $\mu m$) and obscured AGN is challenging, with F1<0.3 even when including ancillary data. This is driven by the similarities between the broad-band spectral energy distribution of these AGN and star-forming galaxies in the wavelength range 0.3-5 $\mu m$., Comment: 25 pages, 28 figures, accepted for publication on A&A

PyFR is an open-source cross-platform computational fluid dynamics framework based on the high-order Flux Reconstruction approach, specifically designed for undertaking high-accuracy scale-resolving simulations in the vicinity of complex engineering geometries. Since the initial release of PyFR v0.1.0 in 2013, a range of new capabilities have been added to the framework, with a view to enabling industrial adoption of the capability. This paper provides details of those enhancements as released in PyFR v2.0.3, explains efforts to grow an engaged developer and user community, and provides latest performance and scaling results on up to 1024 AMD Instinct MI250X accelerators of Frontier at ORNL (each with two GCDs), and up to 2048 NVIDIA GH200 GPUs on Alps at CSCS.

Bogoliubov waves are fundamental excitations of Bose-Einstein Condensates (BECs). They emerge from a perturbed ground state and interact nonlinearly, triggering turbulent cascades. Here, we study turbulent BECs theoretically and numerically using the 3D Gross-Pitaevskii model and its wave-kinetic equations. We derive a new Kolmogorov-like stationary spectrum for short Bogoliubov waves and find a complete analytical expression for the spectrum in the long acoustic regime. We then use our predictions to explain the BEC equation of state reported in [Dora et al. Nature 620,521 (2023)], and to suggest new experimental settings.

Multiferroic heterostructures have gained in recent years a renewed role in spintronic applications due to their possibility in controlling the magnetic properties via interfacial coupling by exploiting the ferroelectric response to various external stimuli. Whereas the main mechanisms ruling the converse magnetoelectric coupling are considered as established, the research on how to optimize the ferroelectric properties is an active field. In particular the complex phase diagram of [Pb(Mg1/3Nb2/3)O3]-x[PbTiO3] (PMN-xPT) single crystals, that present relaxor ferroelectric and photovoltaic properties, deserves further investigation. For instance, crystalline quality and thermal stability of the ferroelectric domains in heterostructures need assessment. Here we show how, by thermal annealing over the ferroelectric Curie temperature and then cooling PMN-0.4PT/Fe heterostructures in inert atmosphere the domain population is significantly modified, evolving from a highly disordered, mostly out-of-plane domain population, to improved crystallinity and prevalent in-plane oriented domains. Upon further annealing, the domain population switches back to prevalent out-of-plane orientation suggesting that intermediate annealing steps can freeze PMN-0.4PT domain population in a metastable configuration. The structural analysis was carried out by combining micro-Raman and X-ray diffraction (XRD) measurements. In the three states the magnetic properties of interfacial Fe thin film are affected by the ferroelectric configurations as a consequence of changing interfacial strain, evolving from an isotropic behavior to an anisotropic one and back., Comment: 6 figures

The unitary bound restricts thermal relics to be lighter than $100$ TeV. This work investigates the production of ultraheavy dark matter and WIMPs in the presence of primordial black holes. Firstly, we describe how Hawking evaporation can produce ultraheavy dark matter with masses above $10^{12}$ GeV in radiation and matter-domination eras. Later, we assess how primordial black holes that induce a non-standard cosmology impact the predicted relic density of a thermal relic and explore the interplay between them, considering the restrictions arising from entropy injection due to the evaporation of primordial black holes. Considering a concrete B-L model, where the dark matter is a Dirac particle, we obtain the correct relic density for various freeze-out scenarios and show that a dark matter particle can nicely reproduce the correct relic density in agreement with current limits with masses above the $10$ TeV scale. Hence, this work strengthens the continuous search for heavy dark matter particles., Comment: 13 pages, 5 figures

Mercury telluride is an intriguing compound that is well known for the first realization of topological states. Despite being known for a long time, a quantitative understanding of its electronic properties remains challenging due to the presence of many concomitant subtle effects. By combining accurate first principles calculations and $k.p$ modelling, we investigate the topological phase diagram of mercury telluride as a function of strain. Our research demonstrates the significance of including the linearly $k$-dependent higher-order $C_4$ strain terms into the usual $k.p$ treatment. In particular, we report a unique $k$-dependence of the sub-band splitting, which arises from the interplay between strain and the standard bulk inversion asymmetry terms within our $k.p$ model. The impact of this phenomenon is explored in relation to the camel's back shape during the tensile strain phase and its implications for the Weyl semimetal state in the compressive strain case are discussed., Comment: 12 pages, 7 figures

This study presents the characterization and validation of the VIBES, a wearable vibrotactile device that provides high-frequency tactile information embedded in a prosthetic socket. A psychophysical characterization involving ten able-bodied participants is performed to compute the Just Noticeable Difference (JND) related to the discrimination of vibrotactile cues delivered on the skin in two forearm positions, with the goal of optimising vibrotactile actuator position to maximise perceptual response. Furthermore, system performance is validated and tested both with ten able-bodied participants and one prosthesis user considering three tasks. More specifically, in the Active Texture Identification, Slippage and Fragile Object Experiments, we investigate if the VIBES could enhance users' roughness discrimination and manual usability and dexterity. Finally, we test the effect of the vibrotactile system on prosthetic embodiment in a Rubber Hand Illusion (RHI) task. Results show the system's effectiveness in conveying contact and texture cues, making it a potential tool to restore sensory feedback and enhance the embodiment in prosthetic users.

This paper explores the intersection of data visualization and Large Language Models (LLMs). Driven by the need to make a broader range of data visualization types accessible for novice users, we present a guided LLM-based pipeline designed to transform data, guided by high-level user questions (referred to as macro-queries), into a diverse set of useful visualizations. This approach leverages various prompting techniques, fine-tuning inspired by Abela's Chart Taxonomy, and integrated SQL tool usage.

Global convolutions have shown increasing promise as powerful general-purpose sequence models. However, training long convolutions is challenging, and kernel parameterizations must be able to learn long-range dependencies without overfitting. This work introduces reparameterized multi-resolution convolutions ($\texttt{MRConv}$), a novel approach to parameterizing global convolutional kernels for long-sequence modelling. By leveraging multi-resolution convolutions, incorporating structural reparameterization and introducing learnable kernel decay, $\texttt{MRConv}$ learns expressive long-range kernels that perform well across various data modalities. Our experiments demonstrate state-of-the-art performance on the Long Range Arena, Sequential CIFAR, and Speech Commands tasks among convolution models and linear-time transformers. Moreover, we report improved performance on ImageNet classification by replacing 2D convolutions with 1D $\texttt{MRConv}$ layers., Comment: 22 pages, 7 figures

This paper explores continuous-time and state-space optimal stopping problems from a reinforcement learning perspective. We begin by formulating the stopping problem using randomized stopping times, where the decision maker's control is represented by the probability of stopping within a given time--specifically, a bounded, non-decreasing, c\`adl\`ag control process. To encourage exploration and facilitate learning, we introduce a regularized version of the problem by penalizing it with the cumulative residual entropy of the randomized stopping time. The regularized problem takes the form of an (n+1)-dimensional degenerate singular stochastic control with finite-fuel. We address this through the dynamic programming principle, which enables us to identify the unique optimal exploratory strategy. For the specific case of a real option problem, we derive a semi-explicit solution to the regularized problem, allowing us to assess the impact of entropy regularization and analyze the vanishing entropy limit. Finally, we propose a reinforcement learning algorithm based on policy iteration. We show both policy improvement and policy convergence results for our proposed algorithm., Comment: 49 pages, 3 figures

The ability of a cancer cell population to achieve heterogeneity in their phenotype distributions offers advantages in tumor invasiveness and drug resistance. Studying the mechanisms behind such observed heterogeneity in mammalian cells presents challenges due for instance to the prolonged proliferation times compared to widely studied unicellular organisms like bacteria and yeast. Here, we studied the response of leukemia cell populations to serum starvation via a protocol, we recently developed, that makes use of live cell fluorescence and flow cytometry in combination with a quantitative analytical model to follow the population proliferation while monitoring the dynamics of its phenotype distributions. We found that upon switching between a serum-rich to a serum-poor media, leukemia cells (i) maintain a memory of the previous environment up to one generation even in the presence of severe medium-depletion, before (ii) adapting their growth and division rates to the novel environment while preserving a sizer-like division strategy. Finally, looking at the mitochondria content of the proliferating vs non-proliferating cells, we found that the latter is characterized by a higher number of older mitochondria, suggesting a possible functional role of the observed asymmetric partitioning of (aged) mitochondria in leukemia cells., Comment: 10 pages, 4 figures, 1 table. arXiv admin note: text overlap with arXiv:2306.10905

Geometric trees are characterized by their tree-structured layout and spatially constrained nodes and edges, which significantly impacts their topological attributes. This inherent hierarchical structure plays a crucial role in domains such as neuron morphology and river geomorphology, but traditional graph representation methods often overlook these specific characteristics of tree structures. To address this, we introduce a new representation learning framework tailored for geometric trees. It first features a unique message passing neural network, which is both provably geometrical structure-recoverable and rotation-translation invariant. To address the data label scarcity issue, our approach also includes two innovative training targets that reflect the hierarchical ordering and geometric structure of these geometric trees. This enables fully self-supervised learning without explicit labels. We validate our method's effectiveness on eight real-world datasets, demonstrating its capability to represent geometric trees.

The wall cycle in wall-bounded turbulent flows is a complex turbulence regeneration mechanism that remains not fully understood. This study explores the potential of deep reinforcement learning (DRL) for managing the wall regeneration cycle to achieve desired flow dynamics. We integrate the StableBaselines3 DRL libraries with the open-source DNS solver CaNS to create a robust platform for dynamic flow control. The DRL agent interacts with the DNS environment, learning policies that modify wall boundary conditions to optimize objectives such as the reduction of the skin-friction coefficient or the enhancement of certain coherent structures features. Initial experiments demonstrate the capability of DRL to achieve drag-reduction rates comparable with those achieved via traditional methods, though limited to short time periods. We also propose a strategy to enhance the coherence of velocity streaks, assuming that maintaining straight streaks can inhibit instability and further reduce skin friction. The implementation makes use of the message-passing-interface (MPI) wrappers for efficient communication between the Python-based DRL agent and the DNS solver, ensuring scalability on high-performance computing architectures. Our results highlight the promise of DRL in flow control applications and underscore the need for more advanced control laws and objective functions. Future work will focus on optimizing actuation periods and exploring new computational architectures to extend the applicability and the efficiency of DRL in turbulent flow management.

The ultra-high-energy cosmic ray (UHECR) puzzle is reviewed under the hints of a few basic results: clustering, anisotropy, asymmetry, bending, and composition changes with energies. We show how the lightest UHECR nuclei from the nearest AGN or Star-Burst sources, located inside a few Mpc Local Sheets, may explain, at best, the observed clustering of Hot Spots at tens EeV energy. Among the possible local extragalactic candidate sources, we derived the main contribution of very few galactic sources. These are located in the Local Sheet plane within a distance of a few Mpc, ejecting UHECR at a few tens of EeV energy. UHECR also shine at lower energies of several EeV, partially feeding the Auger dipole by LMC and possibly a few nearer galactic sources. For the very recent highest energy UHECR event, if a nucleon, it may be explained by a model based on the scattering of UHE ZeV neutrinos on low-mass relic neutrinos. Such scatterings are capable of correlating, via Z boson resonance, the most distant cosmic sources above the GZK bound with such an enigmatic UHECR event. Otherwise, these extreme events, if made by the heaviest composition, could originate from the largest bending trajectory of heaviest nuclei or from nearby sources, even galactic ones. In summary, the present lightest to heavy nuclei model UHECR from the Local Sheet could successfully correlate UHECR clustering with the nearest galaxies and AGN. Heavy UHECR may shine by being widely deflected from the Local Sheet or from past galactic, GRB, or SGR explosive ejection., Comment: 26 pages. 10 figures, Universe Journal, https://doi.org/10.3390/universe10080323

KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on an initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photo-sensors. A high-purity neutrino sample was extracted, corresponding to an exposure of 433 kton-years. The sample of 5828 neutrino candidates is analysed following a binned log-likelihood method in the reconstructed energy and cosine of the zenith angle. The atmospheric oscillation parameters are measured to be $\sin^2\theta_{23}= 0.51^{+0.04}_{-0.05}$, and $ \Delta m^2_{31} = 2.18^{+0.25}_{-0.35}\times 10^{-3}~\mathrm{eV^2} \cup \{-2.25,-1.76\}\times 10^{-3}~\mathrm{eV^2}$ at 68\% CL. The inverted neutrino mass ordering hypothesis is disfavoured with a p-value of 0.25., Comment: 29 pages, 12 figures

This work further investigates an aspect of the phase behavior of hard circular arcs, whose phase diagram has been recently calculated by Monte Carlo numerical simulations: the non-nematicity of the filamentary phase that hard minor circular arcs form. Both second-virial density-functional theory and further Monte Carlo numerical simulations find that the positional one-particle density function is undulate in the direction transverse to the axes of the filaments while further Monte Carlo numerical simulations find that the mobility of the hard minor circular arcs across the filaments occurs via a mechanism reminiscent of the mechanism of diffusion in a smectic phase: the filamentary phase is not a {``modulated'' [``splay(-bend)'']} nematic phase.

The Cosmic Dawn Survey (DAWN survey) provides multiwavelength (UV/optical to mid-IR) data across the combined 59 deg$^{2}$ of the Euclid Deep and Auxiliary fields (EDFs and EAFs). Here, the first public data release (DR1) from the DAWN survey is presented. DR1 catalogues are made available for a subset of the full DAWN survey that consists of two Euclid Deep fields: Euclid Deep Field North (EDF-N) and Euclid Deep Field Fornax (EDF-F). The DAWN survey DR1 catalogues do not include $Euclid$ data as they are not yet public for these fields. Nonetheless, each field has been covered by the ongoing Hawaii Twenty Square Degree Survey (H20), which includes imaging from CFHT MegaCam in the new $u$ filter and from Subaru Hyper Suprime-Cam (HSC) in the $griz$ filters. Each field is further covered by $Spitzer$/IRAC 3.6-4.5$\mu$m imaging spanning 10 deg$^{2}$ and reaching $\sim$25 mag AB (5$\sigma$). All present H20 imaging and all publicly available imaging from the aforementioned facilities are combined with the deep $Spitzer$/IRAC data to create source catalogues spanning a total area of 16.87 deg$^{2}$ in EDF-N and 2.85 deg$^{2}$ in EDF-F for this first release. Photometry is measured using The Farmer, a well-validated model-based photometry code. Photometric redshifts and stellar masses are computed using two independent codes for modeling spectral energy distributions: EAZY and LePhare. Photometric redshifts show good agreement with spectroscopic redshifts ($\sigma_{\rm NMAD} \sim 0.5, \eta < 8\%$ at $i < 25$). Number counts, photometric redshifts, and stellar masses are further validated in comparison to the COSMOS2020 catalogue. The DAWN survey DR1 catalogues are designed to be of immediate use in these two EDFs and will be continuously updated. Future data releases will provide catalogues of all EDFs and EAFs and include $Euclid$ data.

Euclid will provide deep NIR imaging to $\sim$26.5 AB magnitude over $\sim$59 deg$^2$ in its deep and auxiliary fields. The Cosmic DAWN survey complements the deep Euclid data with matched depth multiwavelength imaging and spectroscopy in the UV--IR to provide consistently processed Euclid selected photometric catalogs, accurate photometric redshifts, and measurements of galaxy properties to a redshift of $z\sim 10$. In this paper, we present an overview of the survey, including the footprints of the survey fields, the existing and planned observations, and the primary science goals for the combined data set., Comment: 16 pages, 10 figures, submitted to A&A; Updated references; Updated author list

The explainability of machine learning algorithms is crucial, and numerous methods have emerged recently. Local, post-hoc methods assign an attribution score to each feature, indicating its importance for the prediction. However, these methods require recalculating explanations for each example. On the other side, while there exist global approaches they often produce explanations that are either overly simplistic and unreliable or excessively complex. To bridge this gap, we propose GLEAMS, a novel method that partitions the input space and learns an interpretable model within each sub-region, thereby providing both faithful local and global surrogates. We demonstrate GLEAMS' effectiveness on both synthetic and real-world data, highlighting its desirable properties and human-understandable insights.

The transition to 100% renewable energy requires new techniques for managing energy networks, such as dividing them into sensible subsets of prosumers called micro-grids. Doing so in an optimal manner is a difficult optimization problem, as it can be abstracted to the Coalition Structure Generation problem in Induced Subgraph Games, a NP-complete problem which requires dividing an undirected, complete, weighted graph into subgraphs in a way that maximizes the sum of their internal weights. Recently, Venkatesh et al. (arXiv:2212.11372) published a Quantum Annealing (QA)-based iterative algorithm called GCS-Q, which they claim to be the best currently existing solver for the problem in terms of runtime complexity. As this algorithm makes the application of QA to the problem seem promising, but is a greedy one, this work proposes several less greedy QA-based approaches and investigates whether any of them can outperform GCS-Q in terms of solution quality. While we find that this is not the case yet on D-Wave hardware, most of them do when using the classical QBSolv software as a solver. Especially an algorithm we call 4-split iterative R-QUBO shows potential here, finding all optima in our dataset while scaling favorably with the problem size in terms of runtime. Thus, it appears to be interesting for future research on quantum approaches to the problem, assuming QA hardware will become more noise-resilient over time., Comment: 7 pages, 4 figures (8 if counting subfigures). To be published in the proceedings of the 2024 IEEE International Conference on Quantum Computing and Engineering (QCE)

Scientists generate petabytes of data daily to help uncover environmental trends or behaviors that are hard to predict. For example, understanding climate simulations based on the long-term average of temperature, precipitation, and other environmental variables is essential to predicting and establishing root causes of future undesirable scenarios and assessing possible mitigation strategies. While supercomputer centers provide a powerful infrastructure for generating petabytes of simulation output, accessing and analyzing these datasets interactively remains challenging on multiple fronts. This paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We also optimize network utilization while streaming from petascale repositories through state-of-the-art progressive compression algorithms. Based on this abstraction, we provide customizable dashboards that can be accessed from any device with any internet connection, enabling interactive visual analysis of vast amounts of data to a wide range of users - from top scientists with access to leadership-class computing environments to undergraduate students of disadvantaged backgrounds from minority-serving institutions. We focus on NASA's use of petascale climate datasets as an example of particular societal impact and, therefore, a case where achieving equity in science participation is critical. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution., Comment: 11 pages, 12 figures, conference paper

We prove a quantitative version of a Gidas-Ni-Nirenberg-type symmetry result involving the $p$-Laplacian. Quantitative stability is achieved here via integral identities based on the proof of rigidity established by J. Serra in 2013, which extended to general dimension and the $p$-Laplacian operator an argument proposed by P. L. Lions in dimension $2$ for the classical Laplacian. Stability results for the classical Gidas-Ni-Nirenberg symmetry theorem (involving the classical Laplacian) via the method of moving planes were established by Rosset in 1994 and by Ciraolo, Cozzi, Perugini, Pollastro in 2024. To the authors' knowledge, the present paper provides the first quantitative Gidas-Ni-Nirenberg-type result involving the $p$-Laplacian for $p \neq 2$. Even for the classical Laplacian (i.e., for $p=2$), this is the first time that integral identities are used to achieve stability for a Gidas-Ni-Nirenberg-type result. In passing, we obtain a quantitative estimate for the measure of the singular set and an explicit uniform gradient bound.

We present GRAFX, an open-source library designed for handling audio processing graphs in PyTorch. Along with various library functionalities, we describe technical details on the efficient parallel computation of input graphs, signals, and processor parameters in GPU. Then, we show its example use under a music mixing scenario, where parameters of every differentiable processor in a large graph are optimized via gradient descent. The code is available at https://github.com/sh-lee97/grafx., Comment: Accepted to DAFx 2024 demo

Starting from the notion of multivariate fractional Brownian Motion introduced in [F. Lavancier, A. Philippe, and D. Surgailis. Covariance function of vector self-similar processes. Statistics & Probability Letters, 2009] we define a multivariate version of the fractional Ornstein-Uhlenbeck process. This multivariate Gaussian process is stationary, ergodic and allows for different Hurst exponents on each component. We characterize its correlation matrix and its short and long time asymptotics. Besides the marginal parameters, the cross correlation between one-dimensional marginal components is ruled by two parameters. We consider the problem of their inference, proposing two types of estimator, constructed from discrete observations of the process. We establish their asymptotic theory, in one case in the long time asymptotic setting, in the other case in the infill and long time asymptotic setting. The limit behavior can be asymptotically Gaussian or non-Gaussian, depending on the values of the Hurst exponents of the marginal components. The technical core of the paper relies on the analysis of asymptotic properties of functionals of Gaussian processes, that we establish using Malliavin calculus and Stein's method. We provide numerical experiments that support our theoretical analysis and also suggest a conjecture on the application of one of these estimators to the multivariate fractional Brownian Motion., Comment: 38 pages, 6 figures

Innovative solutions and developments are being inspected to tackle rising electrical power demand to be supplied by clean forms of energy. The integration of renewable energy generations, varying nature loads, importance of active role of distribution system and consumer participation in grid operation has changed the landscape of classical power grids. Implementation of smarter applications to plan, monitor, operate the grid safely are deemed paramount for efficient, secure and reliable functioning of grid. Although sophisticated computations to process gigantic volume of data to produce useful information in a time critical manner is the paradigm of future grid operations, it brings along the burden of computational complexity. Advancements in quantum technologies holds promising solution for dealing with demanding computational complexity of power system related applications. In this article, we lay out clear motivations for seeking quantum solutions for solving computational burden challenges associated with power system applications. Next we present an overview of quantum solutions for various power system related applications available in current literature and suggest future topics for research. We further highlight challenges with existing quantum solutions for exploiting full quantum capabilities. Additionally, this paper serves as a bridge for power engineers to the quantum world by outlining essential quantum computation fundamentals for enabling smoother transition to future of power system computations.

We present the spectro-polarimetric results obtained from simultaneous X-ray observations with IXPE, NuSTAR and NICER of the dipping neutron star X-ray binary 4U 1624-49. This source is the most polarized Atoll source so far observed with IXPE, with a polarization degree of 2.7% $\pm$ 0.9% in the 2-8 keV band during the non-dip phase and marginal evidence of an increasing trend with energy. The higher polarization degree compared to other Atolls can be explained by the high inclination of the system ($i \approx 60${\deg}). The spectra are well described by the combination of a soft thermal emission, a Comptonized component, plus reflection of soft photons off the accretion disk. During the dips, the hydrogen column density of the highly-ionized absorber increases while the ionization state decreases. The Comptonized radiation seems to be the dominant contribution to the polarized signal, with additional reflected photons which significantly contribute even if their fraction in the total flux is not high., Comment: 12 pages, 7 figures, 5 tables. Accepted for publication in Astronomy & Astrophysics

We argue that an ensemble of backgrounds best understands hydrodynamic dispersion relations in a medium with few degrees of freedom and is therefore subject to strong thermal fluctuations. In the linearized regime, dispersion relations become describeable by polynomials with random coefficients. We give a short review of this theory and perform a numerical study of the distribution of the roots of polynomials whose coefficients are of the order of a Knudsen series but fluctuate in accordance with canonical fluctuations of temperature. We find that, remarkably, the analytic structure of the poles of fluctuating dispersion relations is very different from deterministic ones, particularly regarding the distribution of imaginary parts with respect to real components. We argue that this provides evidence that hydrodynamic behavior persists, and is enhanced, by non-perturbative background fluctuations., Comment: Version accepted for publication, Phys.Rev.D

We construct a $d$-dimensional Eddy Damped Quasi-Normal Markovian (EDQNM) Closure Model to study dynamo action in arbitrary dimensions. In particular, we find lower $d_L$ and upper $d_U$ critical dimensions for sustained dynamo action in this incompressible problem. Our model is adaptable for future studies incorporating helicity, compressible effects and a wide range of magnetic Reynolds and Prandtl numbers., Comment: 15 pages, 5 figures

The use of soft robotics for real-world underwater applications is limited, even more than in terrestrial applications, by the ability to accurately measure and control the deformation of the soft materials in real time without the need for feedback from an external sensor. Real-time underwater shape estimation would allow for accurate closed-loop control of soft propulsors, enabling high-performance swimming and manoeuvring. We propose and demonstrate a method for closed-loop underwater soft robotic foil control based on a flexible capacitive e-skin and machine learning which does not necessitate feedback from an external sensor. The underwater e-skin is applied to a highly flexible foil undergoing deformations from 2% to 9% of its camber by means of soft hydraulic actuators. Accurate set point regulation of the camber is successfully tracked during sinusoidal and triangle actuation routines with an amplitude of 5% peak-to-peak and 10-second period with a normalised RMS error of 0.11, and 2% peak-to-peak amplitude with a period of 5 seconds with a normalised RMS error of 0.03. The tail tip deflection can be measured across a 30 mm (0.15 chords) range. These results pave the way for using e-skin technology for underwater soft robotic closed-loop control applications., Comment: 6 pages, 10 figures

We study the statistics of longitudinal and transverse structure functions, as well as velocity circulation in the inverse energy cascade of two-dimensional turbulence. By means of direct numerical simulations of the incompressible Navier-Stokes equations, we show that transverse structure functions exhibit an anomalous scaling, in contrast to the self-similar behavior of longitudinal ones. We derive an analytical relation that shows that the scaling exponents of transverse structure functions and velocity circulation are related in two-dimensional turbulence., Comment: 8 pages, 6 figures

In this review, we present a self-contained introduction to axion-like particles (ALPs) with a particular focus on their effects on photon polarization: both theoretical and phenomenological aspects are discussed. We derive the photon survival probability in the presence of photon--ALP interaction, the corresponding final photon degree of linear polarization, and the polarization angle in a wide energy interval. The presented results can be tested by current and planned missions such as IXPE (already operative), eXTP, XL-Calibur, NGXP, XPP in the X-ray band and like COSI (approved to launch), e-ASTROGAM, and AMEGO in the high-energy range. Specifically, we describe ALP-induced polarization effects on several astrophysical sources, such as galaxy clusters, blazars, and gamma-ray bursts, and we discuss their real detectability. In particular, galaxy clusters appear as very good observational targets in this respect. Moreover, in the very-high-energy (VHE) band, we discuss a peculiar ALP signature in photon polarization, in principle capable of proving the ALP existence. Unfortunately, present technologies cannot detect photon polarization up to such high energies, but the observational capability of the latter ALP signature in the VHE band could represent an interesting challenge for the future. As a matter of fact, the aim of this review is to show new ways to make progress in the physics of ALPs, thanks to their effects on photon polarization, a topic that has aroused less interest in the past, but which is now timely with the advent of many new polarimetric missions., Comment: 33 pages, 11 figures, Review paper published in Universe, 10, 312 (2024)

In this Thesis we investigate properties of stability, rigidity and unitarity of the string landscape in ten and lower dimensions. The dissertation explores these aspects by intertwining a detailed analysis of string vacua, with and without supersymmetry, with a bottom-up study driven by unitarity. In particular, in Chapter 1 the possibility of formulating a necessary and sufficient condition for the classical stability of non-supersymmetric string vacua is discussed, emphasising the examples in ten and nine dimensions. In Chapter 2, new solutions are presented in six dimensions both for BSB and supersymmetric $T^4/\mathbb{Z}_6$ orientifold vacua, arising from a non-trivial cancellation of the R-R tadpoles. The consitency of these theories is shown by checking the cancellation of local anomalies and verifying the unitarity constraints arising from the introduction of string defects. Finally, Chapter 3 discusses the role played by a new kind of global anomaly, arising from the inconsistency of effective field theories under topology change. A systematic analysis of six-dimensional supergravity theories with $\text{SU}(2)$ gauge group and one tensor multiplet and $\text{U}(1)$ gauge group with no tensor multiplets is discussed. Novel constraints are found and their cancellation in string vacua is verified. In addition, we have investigated the $\text{SO}(16)\times \text{SO}(16)$ heterotic theory compactified on the $T^4/\mathbb{Z}_6$ orbifold and the Gepner orientifold with no tensor multiples, showing how such anomalies are cancelled. Even though expected, this result provides a non-trivial consistency check that is not guaranteed by any theorem known in the literature., Comment: PhD Thesis, 152 pages, 2 figures

In the past, high-z AGNs were given a minor role as possible drivers of reionization, despite initial evidences in favor of their large space densities at low luminosities by Chandra and HST. Recent observations from JWST are finding relatively large numbers of faint AGNs at z>4, convincingly confirming these early results. We present a sample of z~5 AGNs (both from wide, shallow ground-based surveys and from deep, pencil-beam observations from JWST), allowing to estimate their space densities with unprecedented accuracy. The bright end (M1450<-26) of the z~5 AGN luminosity function is well constrained, with a rather steep slope. The faint end (M1450>-22) indicates a high space density, the scatter is significant and the knee (M1450~-24) is mostly undetermined. Comparisons with state-of-the-art models find reasonable agreement with the observed AGN luminosity function at z=5, while the predicted space density evolution at higher redshifts appears to be too fast with respect to observational constraints. Given the large variance at the faint end, we consider different options in fitting the luminosity functions and deriving the ionizing emissivity. Even in the most conservative scenario, the photo-ionization rate produced by z~5 AGNs is consistent with the UV background measurements. A slow evolution of the space density of faint AGNs is observed, indicating that active SMBHs are probably producing large amounts of ionizing photons at z>6, well into the epoch of reionization. This is an important indication that high-z AGNs could be the major contributors to the reionization of the Universe., Comment: Accepted for publication on ApJ, 20 pages, 9 figures, 3 tables

Electric Vehicles (EVs) are emerging as battery energy storage systems (BESSs) of increasing importance for different power grid services. However, the unique characteristics of EVs makes them more difficult to operate than dedicated BESSs. In this work, we apply a data-driven learning approach to leverage EVs as a BESS to provide capacity-related services to the grid. The approach uses machine learning to predict how to charge and discharge EVs while satisfying their operational constraints. As a paradigm application, we use flexible energy commercialization in the wholesale markets, but the approach can be applied to a broader range of capacity-related grid services. We evaluate the proposed approach numerically and show that when the number of EVs is large, we can obtain comparable objective values to CPLEX and approximate dynamic programming, but with shorter run times. These reduced run times are important because they allow us to (re)optimize frequently to adapt to the time-varying system conditions.

In the framework of the theory of linear viscoelasticity, we derive an analytical expression of the relaxation modulus in the Andrade model $G_{\alpha }\left( t\right) $ for the case of rational parameter \mbox{$\alpha =m/n\in (0,1)$} in terms of Mittag--Leffler functions from its Laplace transform $\tilde{G}_{\alpha }\left( s\right) $. It turns out that the expression obtained can be rewritten in terms of Rabotnov functions. Moreover, for the original parameter $\alpha =1/3$ in the Andrade model, we obtain an expression in terms of Miller-Ross functions. The asymptotic behaviours of $G_{\alpha }\left( t\right) $ for $t\rightarrow 0^{+}$ and $t\rightarrow +\infty $ are also derived applying the Tauberian theorem. The analytical results obtained have been numerically checked by solving the Volterra integral equation satisfied by $G_{\alpha }\left( t\right) $ by using a successive approximation approach, as well as computing the inverse Laplace transform of $\tilde{G}_{\alpha }\left( s\right) $ by using Talbot's method.

Galaxy proto-clusters are receiving an increased interest since most of the processes shaping the structure of clusters of galaxies and their galaxy population are happening at early stages of their formation. The Euclid Survey will provide a unique opportunity to discover a large number of proto-clusters over a large fraction of the sky (14 500 square degrees). In this paper, we explore the expected observational properties of proto-clusters in the Euclid Wide Survey by means of theoretical models and simulations. We provide an overview of the predicted proto-cluster extent, galaxy density profiles, mass-richness relations, abundance, and sky-filling as a function of redshift. Useful analytical approximations for the functions of these properties are provided. The focus is on the redshift range z= 1.5 to 4. We discuss in particular the density contrast with which proto-clusters can be observed against the background in the galaxy distribution if photometric galaxy redshifts are used as supplied by the ESA Euclid mission together with the ground-based photometric surveys. We show that the obtainable detection significance is sufficient to find large numbers of interesting proto-cluster candidates. For quantitative studies, additional spectroscopic follow-up is required to confirm the proto-clusters and establish their richness., Comment: Submitted to Astronomy and Astrophysics, 24 pages, 28 figures

Today's large language models (LLMs) can solve challenging question-answering tasks, and prompt engineering techniques, such as chain-of-thought (CoT), have gained attention for enhancing the explanation and correctness of outputs. However, many models and techniques tend to produce excessively verbose and lengthy answers, leading to issues with both conciseness and generation time. To address this, this paper analyzes the impact of output lengths on LLM inference pipelines by introducing and proposing novel metrics to evaluate the \textit{correct conciseness} of a model and related prompting techniques. Then, we examine the impact of controlling output length through a refined prompt engineering strategy, Constrained-CoT (CCoT), which encourages the model to produce more concise outputs. To better understand the effects of such a prompt, we also introduce two additional scores for analyzing the conciseness, measured in terms of redundancy and information flow in generated answers. Experiments on pretrained LLMs and multiple datasets demonstrate the benefits of the proposed metrics and the effectiveness of CCoT across different models., Comment: Preprint version, under review

We investigate the existence and the properties of normalized ground states of a nonlinear Schr\"odinger equation on a quantum hybrid formed by two planes connected at a point. The nonlinearities are of power type and $L^2$-subcritical, while the matching condition between the two planes generates two point interactions of different strengths on each plane, together with a coupling condition between the two planes. We prove that ground states exist for every value of the mass and two different qualitative situations are possible depending on the matching condition: either ground states concentrate on one of the plane only, or ground states distribute on both the planes and are positive, radially symmetric, decreasing and present a logarithmic singularity at the origin of each plane. Moreover, we discuss how the mass distributes on the two planes and compare the strengths of the logarithmic singularities on the two planes when the parameters of the matching condition and the powers of the nonlinear terms vary., Comment: 19 pages, 2 figures

The Reggeized pion is expected to provide the main contribution to the forward cross section in light meson photoproduction reactions with charge exchange at high energies. We discuss the Reggeization of pion exchange in charged pion photoproduction with an emphasis on consistency with current conservation. We show that the gauge-invariant amplitude for the exchange of a particle with generic even spin $J\geq 2$ in the $t$-channel is analytic at $J=0$, and that it can be interpreted in terms of the nucleon electric current. This enables us to reconcile the dynamics in the $s$- and $u$-channel, which involves also nucleon exchanges, with the amplitude expressed in terms of $t$-channel partial waves, as required by Regge theory., Comment: 16 pages, 4 figures