Search

Synchrotron X-ray emission has been detected from nearly a dozen young supernova remnants (SNRs). X-rays of synchrotron origin exhibit linear polarization in a regular, non-randomly oriented magnetic field. The significant polarized X-ray emission from four such SNRs has already been reported on the basis of observations with the Imaging X-ray Polarimetry Explorer (IXPE). The magnetic-field structure as derived from IXPE observations is radial for Cassiopeia A, Tycho's SNR, and SN 1006, and tangential for RX J1713.7-3946. The latter together with the recent detection of a tangential magnetic field in SNR 1E 0102.2-7219 by the Australia Telescope Compact Array in the radio band shows that tangential magnetic fields can also be present in young SNRs. Thus, the dichotomy in polarization between young and middle-aged SNRs (radial magnetic fields in young SNRs, but tangential magnetic fields in middle-aged SNRs), previously noticed in the radio band, deserves additional attention. The present analysis of IXPE observations determines, for the first time, a magnetic-field structure in the northwestern rim of Vela Jr, also known as RX J0852.0-4622, and provides a new example of a young SNR with a tangential magnetic field., Comment: Accepted for publication in Astronomy and Astrophysics

X-ray polarization is a unique new probe of the particle acceleration in astrophysical jets made possible through the Imaging X-ray Polarimetry Explorer. Here we report on the first dense X-ray polarization monitoring campaign on the blazar Mrk 421. Our observations were accompanied by an even denser radio and optical polarization campaign. We find significant short-timescale variability in both X-ray polarization degree and angle, including a $\sim90^\circ$ angle rotation about the jet axis. We attribute this to random variations of the magnetic field, consistent with the presence of turbulence but also unlikely to be explained by turbulence alone. At the same time, the degree of lower-energy polarization is significantly lower and shows no more than mild variability. Our campaign provides further evidence for a scenario in which energy-stratified shock-acceleration of relativistic electrons, combined with a turbulent magnetic field, is responsible for optical to X-ray synchrotron emission in blazar jets., Comment: 23 pages, including 8 pages of appendices. 12 figures, 3 tables. Submitted to ApJ

The quest for radio signals from technologically-advanced extraterrestrial intelligence has traditionally concentrated on the vicinity of 1.4 GHz. In this paper, we extend the search to unprecedented territories, detailing our extensive observations at 6 GHz and initiating the first-ever survey at 18 GHz with the Sardinia Radio Telescope (SRT). Our strategy entailed rigorous observation sessions, totaling 36 hours, directed towards the Galactic Center and 72 selected TESS targets-making this the most comprehensive high-frequency technosignature search to date. Our narrowband signal search found no definitive evidence of drifting signals that could suggest an extraterrestrial origin from the surveyed regions. Nevertheless, our efforts have enabled us to set new constraints on the presence of radio emissions from approximately $5\times 10^{5}$ stars, establishing an isotropic radiated power limit of $1.8\times 10^{19} W$. We also provide a comparative analysis of the 'hits' recorded across both frequencies to highlight the significance of pursuing technosignature searches at higher frequencies, where the spectral landscape is less congested and more conducive to detection., Comment: Submitted to Acta Astronautica

Current data-driven methodologies for point cloud matching demand extensive training time and computational resources, presenting significant challenges for model deployment and application. In the point cloud matching task, recent advancements with an encoder-only Transformer architecture have revealed the emergence of semantically meaningful patterns in the attention heads, particularly resembling Gaussian functions centered on each point of the input shape. In this work, we further investigate this phenomenon by integrating these patterns as fixed attention weights within the attention heads of the Transformer architecture. We evaluate two variants: one utilizing predetermined variance values for the Gaussians, and another where the variance values are treated as learnable parameters. Additionally we analyze the performances on noisy data and explore a possible way to improve robustness to noise. Our findings demonstrate that fixing the attention weights not only accelerates the training process but also enhances the stability of the optimization. Furthermore, we conducted an ablation study to identify the specific layers where the infused information is most impactful and to understand the reliance of the network on this information.

We discuss the asymptotic behaviour of risk-based indifference prices of European contingent claims in discrete-time financial markets under volatility uncertainty as the number of intermediate trading periods tends to infinity. The asymptotic risk-based prices form a strongly continuous convex monotone semigroup which is uniquely determined by its infinitesimal generator and therefore only depends on the covariance of the random factors but not on the particular choice of the model. We further compare the risk-based prices with the worst-case prices given by the $G$-expectation and investigate their asymptotic behaviour as the risk aversion of the agent tends to infinity. The theoretical results are illustrated with several examples and numerical simulations showing, in particular, that the risk-based prices lead to a significant reduction of the bid-ask spread compared to the worst-case prices.

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

In \textit{JHEP} \textbf{04} (2024) 126 [arXiv:2402.06561] we recently proposed an out-of-equilibrium setup to reduce the large auto-correlations of the topological charge in two-dimensional $\mathrm{CP}^{N-1}$ models. Our proposal consists of performing open-boundaries simulations at equilibrium, and gradually switching on periodic boundary conditions out-of-equilibrium. Our setup allows to exploit the reduced auto-correlations achieved with open boundaries, avoiding at the same time unphysical boundary effects thanks to a Jarzynski-inspired reweighting-like procedure. We present preliminary results obtained applying this setup to the $4d$ $\mathrm{SU}(3)$ pure-gauge theory and we outline a computational strategy to mitigate topological freezing in this theory., Comment: 12 pages, 6 figures, poster presented at the 41st International Symposium on Lattice Field Theory (LATTICE2024), July 28th - August 3rd, 2024, The University of Liverpool

Sharing retrospectively acquired data is essential for both clinical research and training. Synthetic Data Generation (SDG), using Artificial Intelligence (AI) models, can overcome privacy barriers in sharing clinical data, enabling advancements in medical diagnostics. This study focuses on the clinical evaluation of medical SDG, with a proof-of-concept investigation on diagnosing Inflammatory Bowel Disease (IBD) using Wireless Capsule Endoscopy (WCE) images. The paper contributes by a) presenting a protocol for the systematic evaluation of synthetic images by medical experts and b) applying it to assess TIDE-II, a novel variational autoencoder-based model for high-resolution WCE image synthesis, with a comprehensive qualitative evaluation conducted by 10 international WCE specialists, focusing on image quality, diversity, realism, and clinical decision-making. The results show that TIDE-II generates clinically relevant WCE images, helping to address data scarcity and enhance diagnostic tools. The proposed protocol serves as a reference for future research on medical image-generation techniques.

We provide a classification of Fueter-regular quaternionic functions $f$ in terms of the degree of complex linearity of their real differentials $df$. Quaternionic imaginary units define orthogonal almost-complex structures on the tangent bundle of the quaternionic space by left or right multiplication. Every map of two complex variables that is holomorphic with respect to one of these structures defines a Fueter-regular function. We classify the differential $df$ of a Fueter-regular function $f$, roughly speaking, in terms of how many choices of complex structures make $df$ complex linear. It turns out that, generically, $f$ is not holomorphic with respect to any choice of almost-complex structures. In the special case when it is indeed holomorphic, generically there is a unique choice of almost-complex structures making it holomorphic. The case of holomorphy with respect to several choices of almost-complex structures is limited to conformal real affine transformations or constants., Comment: 35 pages, to appear in the Journal of Geometric Analysis

We study the existence of a global periodic attractor for the reduced dynamics of a discrete toy model for rectilinear crawling locomotion, corresponding to a limit cycle in the shape and velocity variables. The body of the crawler consists of a chain of point masses, joined by active elastic links and subject to smooth friction forces, so that the dynamics is described by a system of second order differential equations. Our main result is of Massera-type, namely we show that the existence of a bounded solution implies the existence of the global periodic attractor for the reduced dynamics. In establishing this result, a contractive property of the dynamics of our model plays a central role. We then prove sufficient conditions on the friction forces for the existence of a bounded solution, and therefore of the attractor. We also provide an example showing that, if we consider more general friction forces, such as smooth approximations of dry friction, bounded solutions may not exist.

This paper develops a data-driven stabilization method for continuous-time linear time-invariant systems with theoretical guarantees and no need for signal derivatives. The framework, based on linear matrix inequalities (LMIs), is illustrated in the state-feedback and single-input single-output output-feedback scenarios. Similar to discrete-time approaches, we rely solely on input and state/output measurements. To avoid differentiation, we employ low-pass filters of the available signals that, rather than approximating the derivatives, reconstruct a non-minimal realization of the plant. With access to the filter states and their derivatives, we can solve LMIs derived from sample batches of the available signals to compute a dynamic controller that stabilizes the plant. The effectiveness of the framework is showcased through numerical examples.

In myoelectric control, simultaneous control of multiple degrees of freedom can be challenging due to the dexterity of the human hand. Numerous studies have focused on hand functionality, however, they only focused on a few degrees of freedom. In this paper, a 3DCNN-MLP model is proposed that uses high-density sEMG signals to estimate 20 hand joint positions and grip force simultaneously. The deep learning model maps the muscle activity to the hand kinematics and kinetics. The proposed models' performance is also evaluated in estimating grip forces with real-time resolution. This paper investigated three individual dynamic hand movements (2pinch, 3pinch, and fist closing and opening) while applying forces in 10% and 30% of the maximum voluntary contraction (MVC). The results demonstrated significant accuracy in estimating kinetics and kinematics. The average Euclidean distance across all joints and subjects was 11.01 $\pm$ 2.22 mm and the mean absolute error for offline and real-time force estimation were found to be 0.8 $\pm$ 0.33 N and 2.09 $\pm$ 0.9 N respectively. The results demonstrate that by leveraging high-density sEMG and deep learning, it is possible to estimate human hand dynamics (kinematics and kinetics), which is a step forward to practical prosthetic hands.

The deployment of ML models on edge devices is challenged by limited computational resources and energy availability. While split computing enables the decomposition of large neural networks (NNs) and allows partial computation on both edge and cloud devices, identifying the most suitable split layer and hardware configurations is a non-trivial task. This process is in fact hindered by the large configuration space, the non-linear dependencies between software and hardware parameters, the heterogeneous hardware and energy characteristics, and the dynamic workload conditions. To overcome this challenge, we propose DynaSplit, a two-phase framework that dynamically configures parameters across both software (i.e., split layer) and hardware (e.g., accelerator usage, CPU frequency). During the Offline Phase, we solve a multi-objective optimization problem with a meta-heuristic approach to discover optimal settings. During the Online Phase, a scheduling algorithm identifies the most suitable settings for an incoming inference request and configures the system accordingly. We evaluate DynaSplit using popular pre-trained NNs on a real-world testbed. Experimental results show a reduction in energy consumption up to 72% compared to cloud-only computation, while meeting ~90% of user request's latency threshold compared to baselines.

State conversion is a fundamental task in quantum information processing. Quantum resource theories allow to analyze and bound conversions that use restricted sets of operations. In the context of continuous-variable systems, state conversions restricted to Gaussian operations are crucial for both fundamental and practical reasons -- particularly in state preparation and quantum computing with bosonic codes. However, previous analysis did not consider the relevant case of approximate state conversion. In this work, we introduce a framework for assessing approximate Gaussian state conversion by extending the stellar rank to the approximate stellar rank, which serves as an operational measure of non-Gaussianity. We derive bounds for Gaussian state conversion under both approximate and probabilistic conditions, yielding new no-go results for non-Gaussian state preparation and enabling a reliable assessment of the performance of generic Gaussian conversion protocols.

Attention guides our gaze to fixate the proper location of the scene and holds it in that location for the deserved amount of time given current processing demands, before shifting to the next one. As such, gaze deployment crucially is a temporal process. Existing computational models have made significant strides in predicting spatial aspects of observer's visual scanpaths (where to look), while often putting on the background the temporal facet of attention dynamics (when). In this paper we present TPP-Gaze, a novel and principled approach to model scanpath dynamics based on Neural Temporal Point Process (TPP), that jointly learns the temporal dynamics of fixations position and duration, integrating deep learning methodologies with point process theory. We conduct extensive experiments across five publicly available datasets. Our results show the overall superior performance of the proposed model compared to state-of-the-art approaches. Source code and trained models are publicly available at: https://github.com/phuselab/tppgaze., Comment: Accepted at WACV 2025

We address the fundamental task of converting $n$ uses of an unknown unitary transformation into a quantum state (i.e., storage) and later retrieval of the transformation. Specifically, we consider the case where the unknown unitary is selected with equal prior probability from two options. First, we prove that the optimal storage strategy involves the sequential application of the $n$ uses of the unknown unitary, and it produces the optimal state for discrimination between the two possible unitaries. Next, we show that incoherent "measure-and-prepare" retrieval achieves the maximum fidelity between the retrieved operation and the original (qubit) unitary. We then identify the retrieval strategy that maximizes the probability of successfully and perfectly retrieving the unknown transformation. In the regime in which the fidelity between the two possible unitaries is large the probability of success scales as $ P_{succ} = 1 - \mathcal{O}(n^{-2} ) $, which is a quadratic improvement with respect to the case in which the unitaries are drawn from the entire unitary group $U(d)$ with uniform prior probability. Finally, we present an optical experiment for this approach and assess the storage and retrieval quality using quantum tomography of states and processes. The results are discussed in relation to non-optimal measure-and-prepare strategy, highlighting the advantages of our protocol., Comment: 18 pages, 10 figures

We present some addition theorems for spin-weighted spherical harmonics, generalizing previous results for scalar (spin-zero) spherical harmonics. These addition theorems involve sums over the azimuthal quantum number of products of two spin-weighted spherical harmonics at different points on the two-sphere, either (or both) of which are differentiated with respect to one of their arguments., Comment: 9 pages

Astrometry is one of the oldest branches of astronomy which measures the position, the proper motion and parallax of celestial objects. Following the Hipparcos and Gaia missions that have measured several billions of them using global astrometry, we propose to increase astrometry precision on pointed objects using differential astrometry in a large field in order to unravel rocky planets in habitable zones of stars in the Sun vicinity and investigate the nature of dark matter in galactic environments as recommended by the ESA Senior Committee in the Voyager 2050 prospective. Substantial technology developments in a number of critical areas is needed in order to reach the highest required precision of sub-micro-arcsecond. One of them is CMOS image sensors using the stitching technique to merge the multiple design structures on the wafer and produce array with very large number of pixels. Another one is to calibrate the pixel positions using projecting modulating interferometric laser fringes on the array. Finally, the distortion of the optical system can be monitored and compensated using reference stars as metrology sources. The final precision depends on the diameter and the field of view of the telescope that is used as well as the time spent on each target. We present here the science goals that can be achieved with such missions either within the framework of an ESA Medium-class mission or even in the NASA most challenging Habitable Worlds Observatory, a large space telescope recommended by the American Astronomy and Astrophysics prospective for the 2020s and designed specifically to search for signs of life on planets orbiting other stars.

Integrated sensing and communication is regarded as a key enabler for next-generation wireless networks. To optimize the transmitted waveform for both sensing and communication, various performance metrics must be considered. This work focuses on sensing, and specifically on the mean square error (MSE) of channel estimation. Given the complexity of deriving the MSE, the Bayesian Cramer-Rao Bound (BCRB) is commonly recognized as a lower bound on the minimum MSE. However, the BCRB is not applicable to channels with discrete or mixed distributions. To address this limitation, a new lower bound based on a Poincar\'e inequality is proposed and applied to fading MIMO AWGN channels with blockage probability, and the behavior of the lower bound at high SNR is precisely characterized., Comment: Submitted to IEEE WCNC 2025

Among many-body instabilities in correlated quantum systems, electronic nematicity, defined by the spontaneous breaking of rotational symmetry, has emerged as a critical phenomenon, particularly within high-temperature superconductors. Recently, this behavior has been identified in CsTi$_3$Bi$_5$, a member of the AV$_3$Sb$_5$ (A = K, Rb, Cs) kagome family, recognized for its intricate and unconventional quantum phases. Despite accumulating indirect evidence, the fundamental mechanisms driving nematicity in CsTi$_3$Bi$_5$ remain inadequately understood, sparking ongoing debates. In this study, we employ polarization-dependent angle-resolved photoemission spectroscopy to reveal definitive signatures of an orbital-selective nematic deformation in the electronic structure of CsTi$_3$Bi$_5$. This direct experimental evidence underscores the pivotal role of orbital degrees of freedom in symmetry breaking, providing new insights into the complex electronic environment. By applying the functional renormalization group technique to a fully interacting ab initio model, we demonstrate the emergence of a finite angular momentum ($d$-wave) Pomeranchuk instability in CsTi$_3$Bi$_5$, driven by the concomitant action of electronic correlations within specific orbital channels and chemical potential detuning away from Van Hove singularities. By elucidating the connection between orbital correlations and symmetry-breaking instabilities, this work lays a crucial foundation for future investigations into the broader role of orbital selectivity in quantum materials, with far-reaching implications for the design and manipulation of novel electronic phases.

In recent years, the serverless paradigm has been widely adopted to develop cloud applications, as it enables building scalable solutions while delegating operational concerns such as infrastructure management and resource provisioning to the serverless provider. Despite bringing undisputed advantages, the serverless model requires a change in programming paradigm that may add complexity in software development. In particular, in the Function-as-a-Service (FaaS) paradigm, functions are inherently stateless. As a consequence, developers carry the burden of directly interacting with external storage services and handling concurrency and state consistency across function invocations. This results in less time spent on solving the actual business problems they face. Moving from these premises, this paper proposes Histrio, a programming model and execution environment that simplifies the development of complex stateful applications in the FaaS paradigm. Histrio grounds on the actor programming model, and lifts concerns such as state management, database interaction, and concurrency handling from developers. It enriches the actor model with features that simplify and optimize the interaction with external storage. It guarantees exactly-once-processing consistency, meaning that the application always behaves as if any interaction with external clients was processed once and only once, masking failures. Histrio has been compared with a classical FaaS implementation to evaluate both the development time saved due to the guarantees the system offers and the applicability of Histrio in typical applications. In the evaluated scenarios, Histrio simplified the implementation by significantly removing the amount of code needed to handle operational concerns. It proves to be scalable and it provides configuration mechanisms to trade performance and execution costs.

The era of large-scale astronomical surveys demands innovative approaches for rapid and accurate analysis of extensive spectral data, and a promising direction to address this challenge is offered by artificial intelligence (AI). Here we introduce a new pipeline, M-TOPnet (Multi-Task network Outputting Probabilities), which employs a convolutional neural network (CNN) with residual learning to simultaneously derive redshift and other key physical properties of galaxies from their spectra. Our tool efficiently encodes spectral information into a latent space, employing distinct downstream branches for each physical quantity, thereby benefiting from multi-task learning. Notably, our method handles the redshift output as a probability distribution, allowing for a more refined and robust estimation of this critical parameter. We demonstrate preliminary results using simulated data from the MOONS instrument, which will be soon operating at the ESO/VLT. We highlight the effectiveness of our tool in accurately predicting redshift, stellar mass, and star-formation rate for galaxies at z>~1-3, even for faint sources (m_H >~ 24) where traditional methods often struggle. Through analysis of the output probability distributions, we demonstrate that our pipeline enables robust quality screening of the results, achieving accuracy rates of up to 99% in redshift determination (defined as predictions within |Delta_z| < 0.01 relative to the true redshift) with 8 h exposure spectra, while automatically identifying potentially problematic cases. Our AI pipeline thus emerges as a powerful solution for the upcoming challenges in observational astronomy, combining precision, interpretability, and efficiency, all aspects which are crucial for analysing the massive datasets expected from next-generation instruments., Comment: submitted for publication, comments welcome

Large Language Models (LLMs) are capable of reproducing human-like inferences, including inferences about emotions and mental states, from text. Whether this capability extends beyond text to other modalities remains unclear. Humans possess a sophisticated ability to read the mind in the eyes of other people. Here we tested whether this ability is also present in GPT-4o, a multimodal LLM. Using two versions of a widely used theory of mind test, the Reading the Mind in Eyes Test and the Multiracial Reading the Mind in the Eyes Test, we found that GPT-4o outperformed humans in interpreting mental states from upright faces but underperformed humans when faces were inverted. While humans in our sample showed no difference between White and Non-white faces, GPT-4o's accuracy was higher for White than for Non-white faces. GPT-4o's errors were not random but revealed a highly consistent, yet incorrect, processing of mental-state information across trials, with an orientation-dependent error structure that qualitatively differed from that of humans for inverted faces but not for upright faces. These findings highlight how advanced mental state inference abilities and human-like face processing signatures, such as inversion effects, coexist in GPT-4o alongside substantial differences in information processing compared to humans.

Personalization in Information Retrieval (IR) is a topic studied by the research community since a long time. However, there is still a lack of datasets to conduct large-scale evaluations of personalized IR; this is mainly due to the fact that collecting and curating high-quality user-related information requires significant costs and time investment. Furthermore, the creation of datasets for Personalized IR (PIR) tasks is affected by both privacy concerns and the need for accurate user-related data, which are often not publicly available. Recently, researchers have started to explore the use of Large Language Models (LLMs) to generate synthetic datasets, which is a possible solution to generate data for low-resource tasks. In this paper, we investigate the potential of Large Language Models (LLMs) for generating synthetic documents to train an IR system for a Personalized Community Question Answering task. To study the effectiveness of IR models fine-tuned on LLM-generated data, we introduce a new dataset, named Sy-SE-PQA. We build Sy-SE-PQA based on an existing dataset, SE-PQA, which consists of questions and answers posted on the popular StackExchange communities. Starting from questions in SE-PQA, we generate synthetic answers using different prompt techniques and LLMs. Our findings suggest that LLMs have high potential in generating data tailored to users' needs. The synthetic data can replace human-written training data, even if the generated data may contain incorrect information., Comment: Accepted in WI-IAT '24

To gain deeper insight into the complex, stable, and robust configurations of magnetic textures, topological characterisation has proven essential. In particular, while the skyrmion number is a well-established topological invariant for 2D magnetic textures, the Hopf index serves as a key topological descriptor for 3D magnetic structures. In this work, we present and compare various methods for numerically calculating the Hopf index, provide implementations, and offer a detailed analysis of their accuracy and computational efficiency. Additionally, we identify and address common pitfalls and challenges associated with the numerical computation of the Hopf index, offering insights for improving the robustness of these techniques.

We present a novel approach for the reconstruction of time-resolved free-running first-pass myocardial perfusion MRI, named CMR-MOTUS. This method builds upon the MR-MOTUS framework and addresses the challenges of a contrast varying reference image. By integrating a low-rank plus sparse (L+S) decomposition, CMR-MOTUS efficiently captures both motion fields and contrast variations. This innovative technique eliminates the need for acquiring a motion-static reference image prior to the examination, thereby reducing examination time and complexity for cardiac MRI examinations. Our results demonstrate that CMR-MOTUS can successfully disentangle different dynamic components, offering high-quality motion fields and motion correct a myocardial first-pass perfusion image series.

We investigate the cosmological consequences of axion interactions with standard model fermions accurately and precisely. Our analysis is entirely based on a phase space framework that allows us to keep track of the axion distribution in momentum space across the entire expansion history. First, we consider flavor-diagonal couplings to charged leptons and quantify the expected amount of dark radiation as a function of the coupling strength. Leptophilic axions are immune from complications due to strong interactions and our predictions do not suffer from theoretical uncertainties. We then focus on flavor-diagonal interactions with the three heavier quarks whose masses are all above the scale where strong interactions become non-perturbative. The top quark case is rather safe because its mass is orders of magnitude above the confinement scale, and the consequent predictions are solid. The bottom and charm masses are in more dangerous territory because they are very close to the QCD crossover. We present a comprehensive discussion of theoretical uncertainties due to both the choice of the scale where we stop the Boltzmann evolution and the running of QCD parameters. Finally, we compute the predicted amount of dark radiation expressed as an effective number of additional neutrino species. We compare our predictions with the ones obtained via standard approximate procedures, and we find that adopting a rigorous phase space framework alters the prediction by an amount larger than the sensitivity of future CMB observatories., Comment: 16 pages, 9 figures

Strong coupling between matter and vacuum electromagnetic fields in a cavity can induce novel quantum phases in thermal equilibrium via symmetry breaking. Particularly, coupling with circularly polarized fields can break time-reversal symmetry, leading to topological modifications in the band structure. Therefore, chiral optical cavities that host chiral vacuum fields are being sought, especially in the terahertz (THz) frequency range, where various large-oscillator-strength resonances exist. Here, we present a novel approach to achieving THz chiral photonic-crystal cavities (PCCs) with high-quality factors (>400) using a magnetoplasma in a lightly doped semiconductor. Numerical simulations of an optimized structure based on InSb in a small perpendicular magnetic field (~0.2 T) show chiral cavity resonances with near-perfect ellipticity at the surfaces of the central dielectric layer, where one can place atomically thin materials like monolayer graphene. We theoretically estimate an energy gap on the order of 1 meV in graphene when coupled to our proposed chiral cavity, which is potentially measurable in experiments. These THz chiral PCCs offer a promising platform for exploring new phases in cavity-dressed condensed matter with broken time-reversal symmetry., Comment: 30 pages, 11 figures

We provide a new version of the Poincar\'e-Birkhoff theorem for possibly multivalued successor maps associated with planar non-autonomous Hamiltonian systems. As an application, we prove the existence of periodic and subharmonic solutions of the scalar second order equation $\ddot x + \lambda g(t,x) = 0$, for $\lambda>0$ sufficiently small, with $g(t,x)$ having a superlinear growth at infinity, without requiring the existence of an equilibrium point., Comment: 21 pages, 4 figures

We present a linear response theory for stationary density accumulations in anomalous transport phenomena, such as the orbital Hall effect, where the transported density is odd under time reversal and the underlying charge is not conserved. Our framework applies to both metals and insulators, topologically trivial or nontrivial, and distinguishes between contributions from bulk and edge states, as well as undergap and dissipative currents. In time-reversal invariant systems, we prove a microscopic reciprocity theorem showing that only dissipative currents at the Fermi level contribute to density accumulation, while undergap currents do not. In contrast, in non-time-reversal invariant systems, non-dissipative density accumulations, such as magnetoelectric polarization, can appear in both the bulk and edges. Importantly, we find that the net density accumulation does not always vanish, pointing to a global non-conservation that implies the existence of a non-vanishing integrated ``net torque'' in addition to a ``distributed torque'', which has zero spatial average. We show that the distributed torque can be absorbed in the divergence of a redefined current that satisfies Onsager reciprocity, while the net torque must be explicitly accounted for. Finally, we apply our theory to two-dimensional models with edge terminations., Comment: 18 pages, 3 figures

We performed a series of 739 full numerical simulations of high energy collision of black holes to search for the maximum gravitational energy emitted $E_{rad}$, during their merger. We consider equal mass binaries with spins pointing along their orbital angular momentum $\vec{L}$ and perform a search over impact parameters $b$ and initial linear momenta $p/m=\gamma v$ to find the maximum $E_{rad}$ for a given spin $\vec{S}$. The total radiated energy proves to have a weak dependence on the intrinsic spin $s$ of the holes, for the sequence $s=+0.8, 0.0, -0.8$ studied here. We thus estimate the maximum $E_{rad}^{max}/M_{ADM}\approx25\%\pm2\%$ for these direct merger encounters. We also explore the radiated angular momentum and the maximum spin of the merger remnant (within these configurations), finding $\alpha_f^{max}=0.987$. We then use the zero frequency limit expansion to analytically model the radiated energy in the small impact parameter and large initial linear momentum regime., Comment: 11 pages, 7 figures, 4 tables

In this study, we provide a full experimental characterization of the parametric longitudinal coupling between a CMOS charge qubit and an off-chip RF resonator. Following Corrigan et al, Phys. Rev. Applied 20, 064005 (2023), we activate parametric longitudinal coupling by driving the charge qubit at the resonator frequency. Managing the crosstalk between the drive applied to the qubit and the resonator allows for the systematic study of the dependence of the longitudinal and dispersive charge-photon couplings on the qubit-resonator detuning and the applied drive. Our experimental estimations of the charge-photon couplings are perfectly reproduced by theoretical simple formulas, without relying on any fitting parameter. We go further by showing a parametric displacement of the resonator's steady state, conditional on the qubit state, and the insensitivity of the longitudinal coupling constant on the photon population of the resonator. Our results open to the exploration of the photon-mediated longitudinal readout and coupling of multiple and distant spins, with long coherent times, in hybrid CMOS cQED architectures., Comment: 11 pages, 12 figures

In daily interactions, emotions are frequently conveyed and triggered through verbal exchanges. Sometimes, we must modulate our emotional reactions to align with societal norms. Among the emotional words, taboo words represent a specific category that has been poorly studied. One intriguing question is whether these word categories can be predicted from EEG responses with the use of machine learning methods. To address this question, Support Vector Machine (SVM) was applied to decode the word categories from Event Related Potential (ERP) in 40 native Italian speakers. 240 neutral, negative and taboo words were used to this aim. Results indicate that the SVM classifier successfully distinguished between the three-word categories, with significant differences in neural activity ascribed to the late positive potential mainly detected in the central-parietal-occipital and anterior right scalp areas in the time windows of 450-649 ms and 650-850 ms. These findings were in line with the established distribution pattern of the late positive potential. Intriguingly, the study also revealed that word categories were still detectable in the regulate condition. This study extends previous results on the domain of the cortical responses of taboo words, and how machine learning methods can be used to predict word categories from EEG responses.

Although planets have been found orbiting binary systems, whether they can survive binary interactions is debated. While the tightest-orbit binaries should host the most dynamically stable and long-lived circumbinary planetary systems, they are also the systems that are expected to experience mass transfer, common envelope evolution, or stellar mergers. In this study, we explore the effect of stable non-conservative mass transfer on the dynamical evolution of circumbinary planets. We present a new script that seamlessly integrates binary evolution data from the 1D binary stellar evolution code MESA into the N-body simulation code REBOUND. This integration framework enables a comprehensive examination of the dynamical evolution of circumbinary planets orbiting mass-transferring binaries, while simultaneously accounting for the detailed stellar structure evolution. In addition, we introduce a recalibration method to mitigate numerical errors from updates of binary properties during the system's dynamical evolution. We construct a reference binary model in which a $2.21\, M_\odot$ star loses its hydrogen-rich envelope through non-conservative mass transfer to the $1.76\, M_\odot$ companion star, creating a $0.38\,M_\odot$ subdwarf. We find the tightest stable orbital separation for circumbinary planets to be $\simeq 2.5$ times the binary separation after mass transfer. Accounting for tides by using the interior stellar structure, we find that tidal effects become apparent after the rapid mass transfer phase and start to fade away during the latter stage of the slow mass transfer phase. Our research provides a new framework for exploring circumbinary planet dynamics in interacting binary systems., Comment: Submitted to MNRAS, comments are welcome

The remarkable capability of over-parameterised neural networks to generalise effectively has been explained by invoking a ``simplicity bias'': neural networks prevent overfitting by initially learning simple classifiers before progressing to more complex, non-linear functions. While simplicity biases have been described theoretically and experimentally in feed-forward networks for supervised learning, the extent to which they also explain the remarkable success of transformers trained with self-supervised techniques remains unclear. In our study, we demonstrate that transformers, trained on natural language data, also display a simplicity bias. Specifically, they sequentially learn many-body interactions among input tokens, reaching a saturation point in the prediction error for low-degree interactions while continuing to learn high-degree interactions. To conduct this analysis, we develop a procedure to generate \textit{clones} of a given natural language data set, which rigorously capture the interactions between tokens up to a specified order. This approach opens up the possibilities of studying how interactions of different orders in the data affect learning, in natural language processing and beyond., Comment: 10 pages, 5 figures, Accepted at NeurIPS 2024

Using Hernandez-Leclerc's isomorphism between the derived Hall algebra of a representation-finite quiver $Q$ and the quantum Grothendieck ring of the quantum loop algebra of the Dynkin type of $Q$, we lift the (quantum) cluster algebra structure of the quantum Grothendieck ring to the semi-derived Hall algebra, introduced by Gorsky, of the category of bounded complexes of projective modules over the path algebra of $Q$. We also construct a braid group action on the semi-derived Hall algebra, lifting Kashiwara-Kim-Oh-Park's braid group action on the quantum Grothendieck ring.

This paper introduces a nonconforming virtual element method for general second-order elliptic problems with variable coefficients on domains with curved boundaries and curved internal interfaces. We prove arbitrary order optimal convergence in the energy and $L^2$ norms, confirmed by numerical experiments on a set of polygonal meshes. The accuracy of the numerical approximation provided by the method is shown to be comparable with the theoretical analysis.

Coordinated reply attacks are a tactic observed in online influence operations and other coordinated campaigns to support or harass targeted individuals, or influence them or their followers. Despite its potential to influence the public, past studies have yet to analyze or provide a methodology to detect this tactic. In this study, we characterize coordinated reply attacks in the context of influence operations on Twitter. Our analysis reveals that the primary targets of these attacks are influential people such as journalists, news media, state officials, and politicians. We propose two supervised machine-learning models, one to classify tweets to determine whether they are targeted by a reply attack, and one to classify accounts that reply to a targeted tweet to determine whether they are part of a coordinated attack. The classifiers achieve AUC scores of 0.88 and 0.97, respectively. These results indicate that accounts involved in reply attacks can be detected, and the targeted accounts themselves can serve as sensors for influence operation detection.

Incorporating novelties into deep learning systems remains a challenging problem. Introducing new information to a machine learning system can interfere with previously stored data and potentially alter the global model paradigm, especially when dealing with non-stationary sources. In such cases, traditional approaches based on validation error minimization offer limited advantages. To address this, we propose a training algorithm inspired by Stuart Kauffman's notion of the Adjacent Possible. This novel training methodology explores new data spaces during the learning phase. It predisposes the neural network to smoothly accept and integrate data sequences with different statistical characteristics than expected. The maximum distance compatible with such inclusion depends on a specific parameter: the sampling temperature used in the explorative phase of the present method. This algorithm, called Dreaming Learning, anticipates potential regime shifts over time, enhancing the neural network's responsiveness to non-stationary events that alter statistical properties. To assess the advantages of this approach, we apply this methodology to unexpected statistical changes in Markov chains and non-stationary dynamics in textual sequences. We demonstrated its ability to improve the auto-correlation of generated textual sequences by $\sim 29\%$ and enhance the velocity of loss convergence by $\sim 100\%$ in the case of a paradigm shift in Markov chains., Comment: Accepted at the NeurIPS 2024 workshop on Intrinsically Motivated Open-ended Learning

We compute the distribution of triplets of stationary points in the energy landscape of the spherical p-spin model, by evaluating the quenched three-point complexity by means of the Kac-Rice formalism. We show the occurrence of transitions in the organization of stationary points in the landscape, identifying regions where local minima and saddles accumulate and cluster around other stationary points, thus displaying the presence of correlations in the landscape. We discuss the implications of these findings for the dynamical exploration of the energy landscape in the activated regime, specifying conditions under which transitions between local minima are expected to exhibit correlated rates and when, conversely, activated jumps are likely to be memoryless.

The SIDDHARTA-2 collaboration aims to measure for the first time the shift and width induced on the $1s$ level of kaonic deuterium by the strong interaction. In the preliminary phase to the experiment, a test run using a Helium-4 target was performed to optimize the performance of the full experimental apparatus. This preliminary study highlighted the possibility to measure transition lines coming from intermediate mass kaonic atoms, such as kaonic carbon and kaonic aluminum. In order to measure transitions where strong interaction is manifesting at higher energies, out of the energy range of the SIDDHARTA-2 apparatus, the collaboration is testing a new detector system which exploits a novel compound semiconductor, the Cadmium Zinc Telluride. Tests are now running at DA$\Phi$NE to study the performance of this detector, exploring the possibility to build a dedicated setup., Comment: 10 pages, 3 figures, eQCD conference

Controlling the symmetry of optical and mechanical waves is pivotal to their full exploitation in technological applications and topology-linked fundamental physics experiments. Leveraging on the control of orbital angular momentum, we introduce here a device forming super-high-frequency-modulated optical vortex beams via acousto-optic interaction of light with acoustic vortices. Originated by shape-engineering of a single-contact bulk acoustic wave resonator, we generate vortices in a wide band of frequencies around 4 GHz and with frequency/geometrically tunable topological charge from 1 to 13 and beyond. With all electrical control and on-chip integration our device offers new solutions for angular-momentum-based light communication, three-dimensional particle manipulation, as well as novel interaction schemes for optomechanical devices., Comment: 6 pages, 4 figures

We study BPS surface observables of $\mathcal{N}=2$ four dimensional $SU(2)$ gauge theory in gravitational $\Omega$-background at perturbative and at Argyres-Douglas superconformal fixed points. This is done by formulating the equivariant gauge theory on the blow-up of $\mathbb{C}^2$ and considering the decoupling Nekrasov-Shatashvili limit. We show that in this limit the blow-up equations are solved by corresponding Painlev\'e $\mathcal{T}$-functions and exploit operator/state correspondence to compute their expansion in an integer basis, given in terms of the moduli of the quantum Seiberg-Witten curve. We study the modular properties of these solutions and show that they do directly lead to BCOV holomorphic anomaly equations for the corresponding topological string partition function. The resulting $\mathcal{T}$-functions are holomorphic and modular and as such they provide a natural non-perturbative completion of topological strings partition functions., Comment: 67 pages, 2 figures

The heterogeneous distribution of water-repellent materials at the soil surface causes a phenomenon known as fractional wettability. This condition frequently triggers destabilization of the wetting front during water infiltration, resulting in the formation of fingered bypass flow. However, few analytical tools exist to understand and model this behavior. Moreover, existing infiltration models fail to fit certain infiltration curves that exist in experimental data. For these reasons, we introduce a novel infiltration model to simulate water infiltration under fractional wettable conditions. We conceptualize the soil surface as a composite of two distinct portions: a water-repellent fraction, where hydrophobic effects impede water infiltration, and a wettable fraction, where capillarity and gravity are the dominant forces controlling the process. The new model was validated using a dataset comprising infiltration data from 60 field measurements. Additionally, validation was performed using 660 analytically generated infiltration curves from six synthetic soils with varying textures. This innovative approach enabled us to account for the combined influence of these two fractions and to enhance the interpretation of infiltration curves with mixed shapes, which other common methods are unable to reproduce.

Online social media platforms significantly influence public debates by shaping the information users encounter. Content visibility on these platforms is regulated by recommendation algorithms designed to maximize user engagement using individual-level data, including personal preferences and interactions. These algorithms play a crucial role in information dissemination, yet their inner workings are often undisclosed, raising concerns about potential manipulation of visibility. While algorithms may be intended to limit the spread of harmful content, they can also be exploited to suppress dissenting voices without users' awareness. The suspicion that platforms deliberately reduce the visibility of certain users or content - commonly known as shadow banning - has garnered significant public attention, with numerous figures advocating for greater transparency around this practice. In this study, we perform a quantitative study geared to identify suspicious changes in content visibility on Twitter (now known as X). We build and study a dataset of over 13 million tweets from more than 5 million users discussing the Ukraine conflict, including each tweet's number of views and metadata, aiming to detect reduced or inflated visibility patterns. We investigate how visibility correlates with factors such as authors' stance, role, interaction networks, and content narratives. Our findings reveal significant variations in visibility, likely driven by algorithmic interventions. These results highlight the need for greater transparency in regulating online information ecosystems to prevent algorithmic manipulation that could undermine public discourse and the fairness of debates.

We consider the four-dimensional $\mathcal{N}=2$ quiver gauge theory arising from a $\mathbb{Z}_2$ orbifold of $\mathcal{N}=4$ super Yang-Mills with gauge group $SU(2N)$. We study the integrated correlator between a half-BPS Wilson line and two Higgs branch operators of conformal dimension 2. Using supersymmetric localization, we resum the perturbative series for this observable and obtain exact expressions for both the planar and next-to-planar terms of its large $N$ expansion, valid for arbitrary values of the 't Hooft coupling. Finally, through a combination of analytical and numerical methods, we determine the leading terms of the corresponding strong-coupling expansions., Comment: 21 pages

Extreme-Mass-Ratio Inspirals (EMRIs) are one of the key targets for future space-based gravitational wave detectors, such as LISA. The scientific potential of these sources can only be fully realized with fast and accurate waveform models. In this work, we extend the \textsc{FastEMRIWaveform} (\texttt{FEW}) framework by providing fully relativistic waveforms at adiabatic order for circular, equatorial orbits in Kerr spacetime, for mass ratios up to $10^{-3}$. We study the importance of including relativistic corrections in the waveform for both vacuum and non-vacuum environments. For EMRIs in vacuum, we find that non-relativistic waveforms can result in $\sim 35\%$ error in the predicted source's horizon redshift. By developing relativistic non-vacuum EMRI waveforms, we demonstrate significant improvements in detecting environmental effects. Our analysis shows that incorporating relativistic corrections enhances constraints on accretion disks, modeled through power-law torques, and improves the constraints on disk parameters (error $\sim6\%$), representing a significant improvement from previous estimates. We also estimated the evidence for models in a scenario where ignoring the accretion disk causes bias in parameter estimation (PE) and report a $\log_{10}$ Bayes factor of $1.1$ in favor of the accretion disk model. Additionally, in a fully relativistic setup, we estimate the parameters of superradiant scalar clouds with high accuracy, achieving errors below $5\%$ for the scalar cloud's mass and below $0.5\%$ for the ultralight field's mass. These results demonstrate that incorporating relativistic effects greatly enhances the accuracy and reliability of waveform predictions, essential for PE and model selection., Comment: 22 pages, 13 figures

We investigate the effect of dynamically coupling gas torques with gravitational wave (GW) emission during the orbital evolution of an equal-mass massive black hole binary (MBHB). We perform hydrodynamical simulations of eccentric MBHBs with total mass $M=10^6~{\rm M}_\odot$ embedded in a prograde locally isothermal circumbinary disk (CBD). We evolve the binary from $53$ to $30$ Schwarzschild radii separations using up to 2.5 post-Newtonian (PN) corrections to the binary dynamics, which allow us to follow the GW-driven in-spiral. For the first time, we report the measurement of gas torques onto a live binary a few years before the merger, with and without concurrent GW radiation. We also identify and measure a novel GW-gas coupling term in the in-spiral rate that makes gas effects an order of magnitude stronger than the gas-only contribution. We show that the evolution rate ($\dot a$) of the MBHB can be neatly expressed as the sum of the GW rate ($\dot a_{\rm GW}$), the pure gas-driven rate ($\dot a_{\rm gas}$), and their cross-term $\propto\dot a_{\rm GW}\dot a_{\rm gas}$. The source-frame gas-induced dephasing in the GW waveform is equivalent to losing $\sim0.5$ GW cycles over the expected $\sim1700$ cycles in a vacuum, which LISA should detect at redshift $z=1$. We also propose a phenomenological model that captures the essence of simulations and can be used to perform Bayesian inference. Our results show how GWs alone can be used to probe the astrophysical properties of CBDs and have important implications on multi-messenger strategies aimed at studying the environments of MBHBs., Comment: 8 pages, 4 figures. To be submitted to ApJ Letters

Large pre-trained models exhibit impressive zero-shot performance across diverse tasks, but fine-tuning often leads to catastrophic forgetting, where improvements on a target domain degrade generalization on other tasks. To address this challenge, we introduce LiNeS, Layer-increasing Network Scaling, a post-training editing technique designed to preserve pre-trained generalization while enhancing fine-tuned task performance. LiNeS scales parameter updates linearly based on their layer depth within the network, maintaining shallow layers close to their pre-trained values to preserve general features while allowing deeper layers to retain task-specific representations. We further extend this approach to multi-task model merging scenarios, where layer-wise scaling of merged parameters reduces negative task interference. LiNeS demonstrates significant improvements in both single-task and multi-task settings across various benchmarks in vision and natural language processing. It mitigates forgetting, enhances out-of-distribution generalization, integrates seamlessly with existing multi-task model merging baselines improving their performance across benchmarks and model sizes, and can boost generalization when merging LLM policies aligned with different rewards via RLHF. Importantly, our method is simple to implement and complementary to many existing techniques., Comment: The first two authors contributed equally to this work; Project website: \url{https://lines-merging.github.io/}