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Accreting millisecond pulsars (AMSPs) are excellent laboratories to study reflection spectra and their features from an accretion disk truncated by a rapidly rotating magnetosphere near the neutron star surface. These systems also exhibit thermonuclear (type-I) bursts that can provide insights on the accretion physics and fuel composition. We explore spectral properties of the AMSP SRGA J144459.2-0604207 observed during the outburst that recently led to its discovery in February 2024. We aim to characterize the spectral shape of the persistent emission, both its continuum and discrete features, and to analyze type-I bursts properties. We employ XMM and NuSTAR overlapping observations taken during the most recent outburst from SRGA J1444. We perform spectral analysis of the persistent (i.e., non-bursting) emission employing a semi-phenomenological continuum model composed of a dominant thermal Comptonization plus two thermal contributions, and a physical reflection model. We also perform time-resolved spectral analysis of a type-I burst employing a blackbody model. We observe a broadened iron emission line, thus suggesting relativistic effects, supported by the physical model accounting for relativistically blurred reflection. The resulting accretion disk extends down to 6 gravitational radii, inclined at ~$53^{\circ}$, and only moderately ionized (log$\xi\simeq2.3$). We observe an absorption edge at ~9.7 keV that can be interpreted as an Fe XXVI edge blueshifted by an ultrafast ($\simeq0.04$c) outflow. Our broadband observations of type-I bursts do not find evidence of photospheric radius expansion. The burst recurrence time shows a dependence on the count rate with the steepest slope ever observed in these systems. We also observe a discrepancy of ~3 between the observed and expected burst recurrence time, which we discuss in the framework of fuel composition and high NS mass scenarios., Comment: 8 pages. Submitted to A&A

We introduce a theoretical framework that accurately describes resonances beyond the reach of both multiplet-based and density-functional-theory (DFT)-based codes. When a resonance acquires strong continuum character, multiplet approaches struggle with the exponential growth of the Hilbert space driven by the increasing number of relevant orbitals, if they extend beyond a few atomic and ligand orbitals. Conversely, existing \emph{ab initio} codes, supplemented by diagrammatic techniques, remain largely confined to the Bethe--Salpeter equation, which tracks only two-particle excitations. However, many systems of interest, particularly those containing open $d$ or $f$ shells, require the propagation of an $N$-particle system, yielding a richer spectral landscape dominated by significant continuum effects. Here, we propose a theoretical framework, together with its numerical implementation, that bridges this gap and enables the rigorous exploration of such resonances.

We study the postulation of 0-dimensional schemes given by unions of 2-superfat points in general position in the plane, i.e., the union of local schemes defined by the intersection of two distinct double lines. We prove that such schemes have good postulation, i.e., they have the expected Hilbert function. We also show the good postulation of such schemes when we add a general 3-fat point. Finally, we use these results to answer a peculiar kind of interpolation problem., Comment: 12 pages

Iterative bit flipping decoders are an efficient and effective decoder choice for decoding codes which admit a sparse parity-check matrix. Among these, sparse $(v,w)$-regular codes, which include LDPC and MDPC codes are of particular interest both for efficient data correction and the design of cryptographic primitives. In attaining the decoding the choice of the bit flipping thresholds, which can be determined either statically, or during the decoder execution by using information coming from the initial syndrome value and its updates. In this work, we analyze a two-iterations parallel hard decision bit flipping decoders and propose concrete criteria for threshold determination, backed by a closed form model. In doing so, we introduce a new tightly fitting model for the distribution of the Hamming weight of the syndrome after the first decoder iteration and substantial improvements on the DFR estimation with respect to existing approaches., Comment: Extended version

We present an analysis of polarized X-ray pulses based on simulated data for accreting millisecond pulsars (AMPs). We used the open-source X-ray Pulse Simulation and Inference code (previously applied to NICER observations), that we upgraded to allow polarization analysis. We predicted neutron star parameter constraints for the Imaging X-ray Polarimetry Explorer (IXPE) and found that strong limits on the hot region geometries can be hard to obtain if the emitting hot region is large and the number of polarized photons relatively small. However, if the star is bright enough and the hot regions are small and located so that polarization degree is higher, the observer inclination and hotspot colatitude can be constrained to a precision of within a few degrees. We also found that the shape of the hot region, whether a circle or a ring, cannot be distinguished in our most optimistic scenario. Nevertheless, future X-ray polarization missions are expected to improve the constraints, and already the recent AMP polarization detections by IXPE should help to infer the neutron star mass and radius when combined with modelling of X-ray pulse data sets that do not contain polarization information., Comment: 8 pages, 3 figures, 1 table, submitted to MNRAS

The automotive industry is transitioning from federated, homogeneous, interconnected devices to integrated, heterogeneous, mixed-criticality systems (MCS). This leads to challenges in achieving timing predictability techniques due to access contention on shared resources, which can be mitigated using hardware-based spatial and temporal isolation techniques. Focusing on the interconnect as the point of access for shared resources, we propose AXI-REALM, a lightweight, modular, technology-independent, and open-source real-time extension to AXI4 interconnects. AXI-REALM uses a budget-based mechanism enforced on periodic time windows and transfer fragmentation to provide fair arbitration, coupled with execution predictability on real-time workloads. AXI-REALM features a comprehensive bandwidth and latency monitor at both the ingress and egress of the interconnect system. Latency information is also used to detect and reset malfunctioning subordinates, preventing missed deadlines. We provide a detailed cost assessment in a 12 nm node and an end-to-end case study implementing AXI-REALM into an open-source MCS, incurring an area overhead of less than 2%. When running a mixed-criticality workload, with a time-critical application sharing the interconnect with non-critical applications, we demonstrate that the critical application can achieve up to 68.2% of the isolated performance by enforcing fairness on the interconnect traffic through burst fragmentation, thus reducing the subordinate access latency by up to 24 times. Near-ideal performance, (above 95% of the isolated performance) can be achieved by distributing the available bandwidth in favor of the critical application., Comment: 14 pages, 13 figures, 3 tables, submitted to the IEEE for possible publication

Many techniques originally developed in the context of deterministic control theory have been recently applied to the quest for optimal protocols in stochastic processes. Given a system subject to environmental fluctuations, one may ask what is the best way to change in time its controllable parameters in order to maximize, on average, a certain reward function, while steering the system between two pre-assigned states. In this work we study the problem of optimal control for a wide class of stochastic systems, inspired by a model of energy harvester. The stochastic noise in this system is due to the mechanical vibrations, while the reward function is the average power extracted from them. We consider the case in which the electrical resistance of the harvester can be changed in time, and we exploit the tools of control theory to work out optimal solutions in a perturbative regime, close to the stationary state. Our results show that it is possible to design protocols that perform better than any possible solution with constant resistance.

We present the result from the April 2024 observation of the low-mass X-ray binary GX 13+1 with the Imaging X-ray Polarimetry Explorer (IXPE), together with NICER and Swift-XRT coordinated observations. Two light curve dips were observed; during them, the harder Comptonized spectral component was dominant and the polarization degree higher than in the softer, off-dip intervals. Through a joint analysis of the three IXPE observations, which also included the dip from the first observation, we demonstrate that the polarization properties varied in response to the intensity and spectral hardness changes associated with the dips. The polarization degree attained values up to ~4%. The polarization angle showed a swing of ~70{\deg} across the dip and off-dip states, comparable to the continuous rotation seen during the first IXPE observation. We discuss these results in the context of models for polarized emission from the accretion disk and the boundary/spreading layer on the neutron star surface. We also draw attention to the role that an extended accretion disk corona or disk wind can play in generating high polarization degrees and, possibly, swings of the polarization angle.

We consider quantum cellular automata for one-dimensional chains of Fermionic modes and study their implementability as finite depth quantum circuits. Fermionic automata have been classified in terms of an index modulo circuits and the addition of ancillary systems. We strengthen this result removing the ancilla degrees of freedom in defining the equivalence classes. A complete characterization of nearest-neighbours automata is given. A class of Fermionic automata is found which cannot be expressed in terms of single mode and controlled-phase gates composed with shifts, as is the case for qubit cellular automata., Comment: 13 + 4 pages, 6 figures

Early JWST observations are providing growing evidence for a ubiquitous population of accreting supermassive black holes (BHs) at high redshift, many of which appear overmassive compared to the empirically-derived local scaling relation between black hole mass and host galaxy stellar mass. In this study, we leverage predictions from the semi-analytical Cosmic Archaeology Tool (CAT) to reconstruct the evolutionary pathways for this overmassive BH population, investigating how they assemble over cosmic time and interact with their host galaxies. We find that the large $M_{\rm BH}-M_{\rm star}$ ratios can be explained if light and heavy BH seeds grow by short, repeated episodes of super-Eddington accretion, triggered by major galaxy mergers. On average, we find that BH-galaxy co-evolution starts in earnest only at $z < 8$, when $\simeq 30\%$ of the final galaxy stellar mass has formed outside the massive black hole host. Our model suggests that super-Eddington bursts of accretion last between $0.5-3$ Myr, resulting in a duty cycle of $1-4 \%$ for the target BH sample. The boost in luminosity of BHs undergoing super-Eddington accretion helps explaining the luminosity function of Active Galactic Nuclei observed by JWST. At the same time, a large population of these overmassive BHs are predicted to be inactive, with Eddington ratio $\lambda_{\rm Edd} < 0.05$, in agreement with recent observations., Comment: 15 pages, 6 figures. Submitted to A&A

We present an experimental and numerical study of a piezoelectric energy harvester driven by broadband vibrations. This device can extract power from random fluctuations and can be described by a stochastic model, based on an underdamped Langevin equation with white noise, which mimics the dynamics of the piezoelectric material. A crucial point in the modelisation is represented by the appropriate description of the coupled load circuit that is necessary to harvest electrical energy. We consider a linear load (resistance) and a nonlinear load (diode bridge rectifier connected to the parallel of a capacitance and a load resistance), and focus on the characteristic curve of the extracted power as a function of the load resistance, in order to estimate the optimal values of the parameters that maximise the collected energy. In both cases, we find good agreement between the numerical simulations of the theoretical model and the results obtained in experiments. In particular, we observe a non-monotonic behaviour of the characteristic curve which signals the presence of an optimal value for the load resistance at which the extracted power is maximised. We also address a more theoretical issue, related to the inference of the non-equilibrium features of the system from data: we show that the analysis of high-order correlation functions of the relevant variables, when in the presence of nonlinearities, can represent a simple and effective tool to check the irreversible dynamics., Comment: 11 pages, 13 figures

In this article we report the fabrication of a diode-pumped Dy,Tb:LiLuF4 waveguide laser operating in the yellow region of the visible spectrum. The circular depressed-cladding waveguides have been fabricated by direct femtosecond laser writing, and showed propagation losses as low as 0.07 dB/cm. By employing these structures, we obtain a maximum output power of 86 mW at 574 nm from a 60 {\mu}m diameter waveguide, and a highest slope efficiency of 19% from a 80 {\mu}m diameter depressed cladding waveguide. In addition, we demonstrate lasing at 574 nm from a half-ring surface waveguide, with a maximum output power of 12 mW. Moreover, we also obtained dual wavelength operation at 568-574 nm, with a maximum output power of 15 mW, and stable lasing at 578 nm, with an output power of 100 mW. The latter wavelength corresponds to the main transition of the atomic clock based on the neutral ytterbium atom. To the best of the authors' knowledge, this is the first demonstration of a yellow waveguide laser based on Dy-doped materials.

In arXiv:2306.17553 a new supersymmetric integrable QFT was constructed from the relativistic limit of the worlsdheet theory of AdS$_3 \times$ S$^3\times $T$^4$ superstrings with mixed Ramond-Ramond and Neveu-Schwarz-Neveu-Schwarz flux. The model is closely reminiscent of one previously considered by Fendley and Intriligator, though it enjoys twice as many supersymmetries. In this paper we study its finite-volume and finite-temperature properties. We formulate the string hypothesis for the Bethe--Yang equations, write down the thermodynamic Bethe ansatz equations, and simplify them in the form of a Y-system. We obtain three decoupled sectors associated with massive, massless-chiral and massless-antichiral particles, for which we compute the UV central charge. We illustrate the similarities and differences between this model and the one of Fendley and Intriligator., Comment: 41 pages

Lattice calculations of the hadronic contributions to the muon anomalous magnetic moment are numerically highly demanding due to the necessity of reaching total errors at the sub-percent level. Noise-reduction techniques such as low-mode averaging have been applied successfully to determine the vector-vector correlator with high statistical precision in the long-distance regime, but display an unfavourable scaling in terms of numerical cost. This is particularly true for the mixed contribution in which one of the two quark propagators is described in terms of low modes. Here we report on an ongoing project aimed at investigating the potential of machine learning as a cost-effective tool to produce approximate estimates of the mixed contribution, which are then bias-corrected to produce an exact result. A second example concerns the determination of electromagnetic isospin-breaking corrections by combining the predictions from a trained model with a bias correction., Comment: 9 pages, Proceedings of the 41st International Symposium on Lattice Field Theory (LATTICE2024), University of Liverpool, 28 July - 3 August 2024

We report on our update to [1] on the hadronic running of electroweak couplings from $O(a)$-improved Wilson fermions with $N_f=2+1$ flavours. The inclusion of additional ensembles at very fine lattice spacings together with a number of techniques to split the different contributions for a better control of cutoff effects allows us to substantially improve the precision. We employ two different discretizations of the vector current to compute the subtracted Hadronic Vacuum Polarization (HVP) functions $\bar{\mathit{\Pi}}^{\gamma\gamma}$ and $\bar{\mathit{\Pi}}^{Z\gamma}$ for Euclidean time momenta up to $Q^2\leq 9 \ \mathrm{GeV}^2$ . To reduce cutoff effects in the short distance region we apply a suitable subtraction to the TMR kernel function, which cancels the leading $x_0^4$ behaviour. The subtracted term is then computed in perturbative QCD using the Adler function and added back to compensate for the subtraction. Chiral-continuum extrapolations are performed with five values of the lattice spacing and several pion masses, including its physical value, and several fit ans\"atze are explored to estimate the systematics arising from model selection. Our results show excellent prospects for high-precision estimates of $\Delta\alpha_{\mathrm{had}}^{(5)}(M_Z^2)$ at the Z-pole.

We revisit the thermal leptogenesis scenario in the type-I seesaw framework featuring three heavy Majorana neutrinos with a hierarchical mass spectrum. We focus on low energy observables, specifically the lightest neutrino mass $m_{\nu}^{\rm lightest}$ and the neutrinoless double-beta decay effective mass parameter $m^{\rm eff}_{\beta\beta}$. In particular, we numerically calculate the minimum mass of the lightest heavy Majorana neutrino, $M_1^{\rm min}$, required for successful leptogenesis as a function of $m_{\nu}^{\rm lightest}$ and $m_{\beta\beta}^{\rm eff}$, considering both normal and inverted light neutrino mass orderings. Flavour effects are taken into account within the flavoured density matrix formalism. We also examine the interplay between fine-tuned cancellations in the seesaw relation and $M_1^{\rm min}$. Recent and forthcoming searches for neutrinoless double-beta decay, along with cosmological probes of the sum of neutrino masses, motivate this analysis, as they can provide key insights into the minimal scale of thermal leptogenesis and its broader implications., Comment: 21 pages, 12 figures

We show that under mild set theoretic hypotheses we have rigidity for algebras of continuous functions over Higson coronas, topological spaces arising in coarse geometry. In particular, we show that under $\mathsf{OCA}$ and $\mathsf {MA}_{\aleph_1}$, if two uniformly locally finite metric spaces $X$ and $Y$ have homeomorphic Higson coronas $\nu X$ and $\nu Y$, then $X$ and $Y$ are coarsely equivalent, a statement which provably does not follow from $\mathsf{ZFC}$ alone., Comment: 25 pages

Explainable AI (XAI) has become a crucial component of Clinical Decision Support Systems (CDSS) to enhance transparency, trust, and clinical adoption. However, while many XAI methods have been proposed, their effectiveness in real-world medical settings remains underexplored. This paper provides a survey of human-centered evaluations of Explainable AI methods in Clinical Decision Support Systems. By categorizing existing works based on XAI methodologies, evaluation frameworks, and clinical adoption challenges, we offer a structured understanding of the landscape. Our findings reveal key challenges in the integration of XAI into healthcare workflows and propose a structured framework to align the evaluation methods of XAI with the clinical needs of stakeholders., Comment: 10 pages, 1 table

We introduce a methodology to estimate non-stabilizerness or "magic", a key resource for quantum complexity, with Neural Quantum States (NQS). Our framework relies on two schemes based on Monte Carlo sampling to quantify non-stabilizerness via Stabilizer R\'enyi Entropy (SRE) in arbitrary variational wave functions. When combined with NQS, this approach is effective for systems with strong correlations and in dimensions larger than one, unlike Tensor Network methods. Firstly, we study the magic content in an ensemble of random NQS, demonstrating that neural network parametrizations of the wave function capture finite non-stabilizerness besides large entanglement. Secondly, we investigate the non-stabilizerness in the ground state of the $J_1$-$J_2$ Heisenberg model. In 1D, we find that the SRE vanishes at the Majumdar-Ghosh point $J_2 = J_1/2$, consistent with a stabilizer ground state. In 2D, a dip in the SRE is observed near maximum frustration around $J_2/J_1 \approx 0.6$, suggesting a Valence Bond Solid between the two antiferromagnetic phases., Comment: 6 + 5 pages, 6 figures

Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like classification and generation. Inspired by this success, we propose Foundation Neural-Network Quantum States (FNQS) as an integrated paradigm for studying quantum many-body systems. FNQS leverage key principles of foundation models to define variational wave functions based on a single, versatile architecture that processes multimodal inputs, including spin configurations and Hamiltonian physical couplings. Unlike specialized architectures tailored for individual Hamiltonians, FNQS can generalize to physical Hamiltonians beyond those encountered during training, offering a unified framework adaptable to various quantum systems and tasks. FNQS enable the efficient estimation of quantities that are traditionally challenging or computationally intensive to calculate using conventional methods, particularly disorder-averaged observables. Furthermore, the fidelity susceptibility can be easily obtained to uncover quantum phase transitions without prior knowledge of order parameters. These pretrained models can be efficiently fine-tuned for specific quantum systems. The architectures trained in this paper are publicly available at https://huggingface.co/nqs-models, along with examples for implementing these neural networks in NetKet., Comment: 10 pages, 6 figures, 1 table

The increasing complexity of natural disaster incidents demands innovative technological solutions to support first responders in their efforts. This paper introduces the TRIFFID system, a comprehensive technical framework that integrates unmanned ground and aerial vehicles with advanced artificial intelligence functionalities to enhance disaster response capabilities across wildfires, urban floods, and post-earthquake search and rescue missions. By leveraging state-of-the-art autonomous navigation, semantic perception, and human-robot interaction technologies, TRIFFID provides a sophisticated system com- posed of the following key components: hybrid robotic platform, centralized ground station, custom communication infrastructure, and smartphone application. The defined research and development activities demonstrate how deep neural networks, knowledge graphs, and multimodal information fusion can enable robots to autonomously navigate and analyze disaster environ- ments, reducing personnel risks and accelerating response times. The proposed system enhances emergency response teams by providing advanced mission planning, safety monitoring, and adaptive task execution capabilities. Moreover, it ensures real- time situational awareness and operational support in complex and risky situations, facilitating rapid and precise information collection and coordinated actions.

Wireless communications are typically subject to complex channel dynamics, requiring the transmission of pilot sequences to estimate and equalize such effects and correctly receive information bits. This is especially true in 6G non-terrestrial networks (NTNs) in low Earth orbit, where one end of the communication link orbits around the Earth at several kilometers per second, and a multi-carrier waveform, such as orthogonal frequency division multiplexing (OFDM), is employed. To minimize the pilot overhead, we remove pilot symbols every other OFDM slot and propose a channel predictor to obtain the channel frequency response (CFR) matrix in absence of pilots. The algorithm employs an encoder-decoder convolutional neural network and a long short-term memory layer, along with skip connections, to predict the CFR matrix on the upcoming slot based on the current one. We demonstrate the effectiveness of the proposed predictor through numerical simulations in tapped delay line channel models, highlighting the effective throughput improvement. We further assess the generalization capabilities of the model, showing minimal throughput degradation when testing under different Doppler spreads and in both line of sight (LoS) and non-LoS propagation conditions. Finally, we discuss computational-complexity-related aspects of the lightweight hybrid CNN-LSTM architecture., Comment: Submitted for publication

This paper presents a complete explainable system that interprets a set of data, abstracts the underlying features and describes them in a natural language of choice. The system relies on two crucial stages: (i) identifying emerging properties from data and transforming them into abstract concepts, and (ii) converting these concepts into natural language. Despite the impressive natural language generation capabilities demonstrated by Large Language Models, their statistical nature and the intricacy of their internal mechanism still force us to employ these techniques as black boxes, forgoing trustworthiness. Developing an explainable pipeline for data interpretation would allow facilitating its use in safety-critical environments like processing medical information and allowing non-experts and visually impaired people to access narrated information. To this end, we believe that the fields of knowledge representation and automated reasoning research could present a valid alternative. Expanding on prior research that tackled the first stage (i), we focus on the second stage, named Concept2Text. Being explainable, data translation is easily modeled through logic-based rules, once again emphasizing the role of declarative programming in achieving AI explainability. This paper explores a Prolog/CLP-based rewriting system to interpret concepts-articulated in terms of classes and relations, plus common knowledge-derived from a generic ontology, generating natural language text. Its main features include hierarchical tree rewritings, modular multilingual generation, support for equivalent variants across semantic, grammar, and lexical levels, and a transparent rule-based system. We outline the architecture and demonstrate its flexibility through some examples capable of generating numerous diverse and equivalent rewritings based on the input concept., Comment: In Proceedings ICLP 2024, arXiv:2502.08453

Language models are aligned to the collective voice of many, resulting in generic outputs that do not align with specific users' styles. In this work, we present Trial-Error-Explain In-Context Learning (TICL), a tuning-free method that personalizes language models for text generation tasks with fewer than 10 examples per user. TICL iteratively expands an in-context learning prompt via a trial-error-explain process, adding model-generated negative samples and explanations that provide fine-grained guidance towards a specific user's style. TICL achieves favorable win rates on pairwise comparisons with LLM-as-a-judge up to 91.5% against the previous state-of-the-art and outperforms competitive tuning-free baselines for personalized alignment tasks of writing emails, essays and news articles. Both lexical and qualitative analyses show that the negative samples and explanations enable language models to learn stylistic context more effectively and overcome the bias towards structural and formal phrases observed in their zero-shot outputs. By front-loading inference compute to create a user-specific in-context learning prompt that does not require extra generation steps at test time, TICL presents a novel yet simple approach for personalized alignment., Comment: NAACL 2025 Findings

In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensing, which necessitates quantization to be deployed and are subject to noise and perturbations due to experimental conditions. Our method allows assessing the robustness of ML models to such effects as a function of quantization precision and under different regularization techniques -- two crucial concerns that remained underexplored so far. By investigating the interplay between performance, efficiency, and robustness by means of loss landscape analysis, we both established a strong correlation between gently-shaped landscapes and robustness to input and weight perturbations and observed other intriguing and non-obvious phenomena. Our method allows a systematic exploration of such trade-offs a priori, i.e., without training and testing multiple models, leading to more efficient development workflows. This work also highlights the importance of incorporating robustness into the Pareto optimization of ML models, enabling more reliable and adaptive scientific sensing systems., Comment: Under review

A global scale-invariant Dark Energy model based on Induced Gravity with the addition of a small $R^2$ contribution is examined. The scalar field (quintessence), playing the role of Dark Energy, has a quartic potential and generates Newton's constant with its non-minimal coupling (after introducing a suitable symmetry breaking). Even when small, the $R^2$ contribution significantly modifies the cosmological evolution of the matter-gravity system. The solutions to this model are obtained analytically through a perturbative expansion and oscillate with transplanckian frequency. They are then compared with similar solutions found for $\Lambda$CDM cosmology plus $R^2$. Finally scalar field production is perturbatively taken into account in a simple model and the resulting effects illustrated., Comment: 24 pages, 1 figure, invited contribution to the Starobinsky Memorial Volume, Springer 2025

In the differentially private partition selection problem (a.k.a. private set union, private key discovery), users hold subsets of items from an unbounded universe. The goal is to output as many items as possible from the union of the users' sets while maintaining user-level differential privacy. Solutions to this problem are a core building block for many privacy-preserving ML applications including vocabulary extraction in a private corpus, computing statistics over categorical data, and learning embeddings over user-provided items. We propose an algorithm for this problem, MaximumAdaptiveDegree (MAD), which adaptively reroutes weight from items with weight far above the threshold needed for privacy to items with smaller weight, thereby increasing the probability that less frequent items are output. Our algorithm can be efficiently implemented in massively parallel computation systems allowing scalability to very large datasets. We prove that our algorithm stochastically dominates the standard parallel algorithm for this problem. We also develop a two-round version of our algorithm where results of the computation in the first round are used to bias the weighting in the second round to maximize the number of items output. In experiments, our algorithms provide the best results across the board among parallel algorithms and scale to datasets with hundreds of billions of items, up to three orders of magnitude larger than those analyzed by prior sequential algorithms.

Photoacoustic tomography is an emerging medical imaging technology whose primary aim is to map the high-contrast optical properties of biological tissues by leveraging high-resolution ultrasound measurements. Mathematically, this can be framed as an inverse source problem for the wave equation over a specific domain. In this work, for the first time, it is shown how, by assuming signal sparsity, it is possible to establish rigorous stable recovery guarantees when the data collection is given by spatial averages restricted to a limited portion of the boundary. Our framework encompasses many approaches that have been considered in the literature. The result is a consequence of a general framework for subsampled inverse problems developed in previous works and refined stability estimates for an inverse problem for the wave equation with surface measurements., Comment: 32 pages, 2 figures

Partial Differential Equations (PDEs) are central to science and engineering. Since solving them is computationally expensive, a lot of effort has been put into approximating their solution operator via both traditional and recently increasingly Deep Learning (DL) techniques. A conclusive methodology capable of accounting both for (continuous) time and parameter dependency in such DL models however is still lacking. In this paper, we propose an autoregressive and data-driven method using the analogy with classical numerical solvers for time-dependent, parametric and (typically) nonlinear PDEs. We present how Dimensionality Reduction (DR) can be coupled with Neural Ordinary Differential Equations (NODEs) in order to learn the solution operator of arbitrary PDEs. The idea of our work is that it is possible to map the high-fidelity (i.e., high-dimensional) PDE solution space into a reduced (low-dimensional) space, which subsequently exhibits dynamics governed by a (latent) Ordinary Differential Equation (ODE). Solving this (easier) ODE in the reduced space allows avoiding solving the PDE in the high-dimensional solution space, thus decreasing the computational burden for repeated calculations for e.g., uncertainty quantification or design optimization purposes. The main outcome of this work is the importance of exploiting DR as opposed to the recent trend of building large and complex architectures: we show that by leveraging DR we can deliver not only more accurate predictions, but also a considerably lighter and faster DL model compared to existing methodologies.

Spatially discordant alternans (SDA) play a crucial role in cardiac arrhythmogenesis by creating steep repolarization gradients facilitating conduction block and reentry. While traditionally studied using electrical indicators, this work provides a novel perspective by characterizing SDA through their magnetic field signatures. Using a one-dimensional cardiac fiber model, we demonstrate that magnetic field measurements effectively detect SDA and temperature dependent changes in cardiac action potentials, offering a non-invasive alternative to conventional electrophysiological metrics. Our results reveal that the spatial organization of SDA is mirrored in the magnetic field distribution, with SDA nodes clearly identifiable via spatial mapping. Notably, magnetic restitution curves exhibit a distinct pattern from APD-based indicators, closely following the dynamics of the action potential upstroke. These findings establish the cardiac magnetic field as a powerful diagnostic tool for detecting SDA, opening new avenues for biomagnetic monitoring of arrhythmic risk.

Supercurrent field-effect transistors made from thin metallic films are a promising option for next-generation high-performance computation platforms. Despite extensive research, there is still no complete quantitative microscopic explanation for how an external DC electric field suppresses superconductivity in thin films. This study aims to provide a quantitative description of superconductivity as a function of film thickness based on Eliashberg's theory. The calculation considers the electrostatics of the electric field, its realistic penetration depth in the film, and its effect on the Cooper pair, which is described as a standard s-wave bound state according to BCS theory. The estimation suggests that an external electric field of approximately $10^8$ V/m is required to suppress superconductivity in 10-30-nm-thick films, which aligns with experimental observations. Ultimately, the study offers "materials by design" guidelines for suppressing supercurrent when an external electric field is applied to the film surface. Furthermore, the proposed framework can be easily extended to investigate the same effects for ultrathin films.

We investigate the asymptotic behavior of the $\mathrm{SU}(2)$-Yang-Mills-Higgs energy $E(\Phi,A)=\int_M|d_A\Phi|^2+|F_A|^2$ in the large mass limit, proving convergence to the codimension-three area functional in the sense of De Giorgi's $\Gamma$-convergence. More precisely, for a compact manifold with boundary $M$ and any family of pairs $\Phi_m\in\Omega^0(M;\mathfrak{su}(2))$ and $A_m\in \Omega^1(M;\mathfrak{su}(2))$ indexed by a mass parameter $m\to\infty$, satisfying $$E(\Phi_m,A_m)\leq Cm\quad\text{and}\quad\lim_{m\to\infty}\frac{1}{m}\int_M(m-|\Phi_m|)^2=0,$$ we prove that the $(n-3)$-currents dual to $\frac{1}{2\pi m}\mathrm{tr}(d_{A_m}\Phi_m\wedge F_{A_m})$ converge subsequentially to a relative integral $(n-3)$-cycle $T$ of mass \begin{equation} \mathbb{M}(T)\leq \liminf_{m\to\infty}\frac{1}{4\pi m}E(\Phi_m,A_m), \end{equation} and show conversely that any integral $(n-3)$-current $T$ with $[T]=0\in H_{n-3}(M,\partial M;\mathbb{Z})$ admits such an approximation, with equality in the above inequality. In the special case of pairs $(\Phi_m,A_m)$ satisfying the generalized monopole equation $*d_{A_m}\Phi_m=F_{A_m}\wedge \Theta$ for a calibration form $\Theta\in \Omega^{n-3}(M)$, we deduce that the limit $\nu=\lim_{m\to\infty}\frac{1}{2\pi m}|d_{A_m}\Phi_m|^2$ of the Dirichlet energy measures satisfies $\nu\leq |T|$, with equality if and only if $T$ is calibrated by $\Theta$, giving evidence for predictions of Donaldson-Segal in the settings of $G_2$-manifolds and Calabi-Yau $3$-folds., Comment: 37 pages

We provide an update on the work of di Serego Alighieri (2015), focusing on recent developments regarding constraints on Cosmic Polarization Rotation (CPR), also known as Cosmic Birefringence (CB), derived from Cosmic Microwave Background (CMB) polarization data., Comment: 8 pages, 1 table. To appear in International Journal of Modern Physics D. This paper will also be published in the book "One Hundred and Ten Years of General Relativity: From Genesis and Empirical Foundations to Gravitational Waves, Cosmology, and Quantum Gravity", edited by Wei-Tou Ni (World Scientific, Singapore, 2025)

We develop an economic framework to analyze the optimal pricing and product design of Large Language Models (LLM). Our framework captures several key features of LLMs: variable operational costs of processing input and output tokens; the ability to customize models through fine-tuning; and high-dimensional user heterogeneity in terms of task requirements and error sensitivity. In our model, a monopolistic seller offers multiple versions of LLMs through a menu of products. The optimal pricing structure depends on whether token allocation across tasks is contractible and whether users face scale constraints. Users with similar aggregate value-scale characteristics choose similar levels of fine-tuning and token consumption. The optimal mechanism can be implemented through menus of two-part tariffs, with higher markups for more intensive users. Our results rationalize observed industry practices such as tiered pricing based on model customization and usage levels.

We construct families of integrable deformations that interpolate between the $AdS_3\times S^3\times S^3\times S^1$ and either $AdS_3\times S^3\times S^2\times T^2$ or $AdS_3\times S^2\times S^2\times T^3$. They preserve half of the supersymmetry of the original background, namely one copy of the $\mathfrak{d}(2,1;\alpha)$ algebra. From this it follows a similar integrable interpolation between $AdS_3\times S^3\times T^4$ and $AdS_3\times S^2\times T^5$, which also preserves half of the supersymmetry, namely a copy of the $\mathfrak{psu}(1,1|2)$ algebra. In all cases, the interpolating backgrounds are constructed by using TsT transformations, which makes it easy to implement them in the integrability formalism in the full quantum theory. To illustrate this point, we discuss the lightcone gauge fixing of the models and compute their pp-wave Hamiltonian., Comment: 27 pages

We present our progress on the computation of the axial form factor of the nucleon with flavour structure $u+d-2s$ from lattice QCD. We employ a set of $N_f=2+1$ CLS ensembles with $O(a)$-improved Wilson fermions and the L\"uscher-Weisz gauge action, with lattice spacings ranging from $0.05\,\text{fm}$ to $0.086\,\text{fm}$ and pion masses spanning between $130\,\text{MeV}$ and $350\,\text{MeV}$. We employ multiple source-sink separations and use the summation method to suppress the contamination from excited states. We use a $z$-expansion on each ensemble to parametrize the $Q^2$-dependence of the form factor and simultaneously fit the available source-sink separations for all $Q^2\leq 0.7 \,{\rm GeV}^2$. We outline our analysis of the stability of the fits varying the ans\"atze and different estimations of the covariance matrix and report on our strategy for a comprehensive determination of the physical form factor., Comment: 10 pages, 5 figures. Proceedings of the 41st International Symposium on Lattice Field Theory (Lattice 2024), University of Liverpool, UK, 2024

Quantum computers have the potential to provide an advantage over classical computers in a number of areas. Numerous metrics to benchmark the performance of quantum computers, ranging from their individual hardware components to entire applications, have been proposed over the years. Navigating the resulting extensive literature can be overwhelming. Objective comparisons are further hampered in practice as different variations of the same metric are used, and the data disclosed together with a reported metric value is often not sufficient to reproduce the measurements. This article addresses these challenges by providing a review of metrics and benchmarks for quantum computers and 1) a comprehensive collection of benchmarks allowing holistic comparisons of quantum computers, 2) a consistent format of the definitions across all metrics including a transparent description of the methodology and of the main assumptions and limitations, and 3) a reproducible approach by linking the metrics to open-source software used to evaluate them. We identify five areas where international standardization working groups could be established, namely: i) the identification and agreement on the categories of metrics that comprehensively benchmark device performance; ii) the identification and agreement on a set of well-established metrics that together comprehensively benchmark performance; iii) the identification of metrics specific to hardware platforms, including non-gate-based quantum computers; iv) inter-laboratory comparison studies to develop best practice guides for measurement methodology; and v) agreement on what data and software should be reported together with a metric value to ensure trust, transparency and reproducibility. We provide potential routes to advancing these areas. We expect this compendium to accelerate the progress of quantum computing hardware towards quantum advantage.

We study the application of the Grouped Fixed Effects (GFE) estimator (Bonhomme et al., ECMTA 90(2):625-643, 2022) to binary choice models for network and panel data. This approach discretizes unobserved heterogeneity via k-means clustering and performs maximum likelihood estimation, reducing the number of fixed effects in finite samples. This regularization helps analyze small/sparse networks and rare events by mitigating complete separation, which can lead to data loss. We focus on dynamic models with few state transitions and network formation models for sparse networks. The effectiveness of this method is demonstrated through simulations and real data applications.

Context. As software systems become more integrated into society's infrastructure, the responsibility of software professionals to ensure compliance with various non-functional requirements increases. These requirements include security, safety, privacy, and, increasingly, non-discrimination. Motivation. Fairness in pricing algorithms grants equitable access to basic services without discriminating on the basis of protected attributes. Method. We replicate a previous empirical study that used black box testing to audit pricing algorithms used by Italian car insurance companies, accessible through a popular online system. With respect to the previous study, we enlarged the number of tests and the number of demographic variables under analysis. Results. Our work confirms and extends previous findings, highlighting the problematic permanence of discrimination across time: demographic variables significantly impact pricing to this day, with birthplace remaining the main discriminatory factor against individuals not born in Italian cities. We also found that driver profiles can determine the number of quotes available to the user, denying equal opportunities to all. Conclusion. The study underscores the importance of testing for non-discrimination in software systems that affect people's everyday lives. Performing algorithmic audits over time makes it possible to evaluate the evolution of such algorithms. It also demonstrates the role that empirical software engineering can play in making software systems more accountable., Comment: 14 pages, 1 figure

The off-switch problem is a critical challenge in AI control: if an AI system resists being switched off, it poses a significant risk. In this paper, we model the off-switch problem as a signalling game, where a human decision-maker communicates its preferences about some underlying decision problem to an AI agent, which then selects actions to maximise the human's utility. We assume that the human is a bounded rational agent and explore various bounded rationality mechanisms. Using real machine learning models, we reprove prior results and demonstrate that a necessary condition for an AI system to refrain from disabling its off-switch is its uncertainty about the human's utility. We also analyse how message costs influence optimal strategies and extend the analysis to scenarios involving incomparability.

Diffusion models have emerged as powerful tools for solving inverse problems, yet prior work has primarily focused on observations with Gaussian measurement noise, restricting their use in real-world scenarios. This limitation persists due to the intractability of the likelihood score, which until now has only been approximated in the simpler case of Gaussian likelihoods. In this work, we extend diffusion models to handle inverse problems where the observations follow a distribution from the exponential family, such as a Poisson or a Binomial distribution. By leveraging the conjugacy properties of exponential family distributions, we introduce the evidence trick, a method that provides a tractable approximation to the likelihood score. In our experiments, we demonstrate that our methodology effectively performs Bayesian inference on spatially inhomogeneous Poisson processes with intensities as intricate as ImageNet images. Furthermore, we demonstrate the real-world impact of our methodology by showing that it performs competitively with the current state-of-the-art in predicting malaria prevalence estimates in Sub-Saharan Africa.

We investigate the problem of verifying different properties of discrete time dynamical systems, namely, reachability, safety and reach-while-avoid. To achieve this, we adopt a data driven perspective and using past systems' trajectories as data, we aim at learning a specific function termed \emph{certificate} for each property we wish to verify. The certificate construction problem is treated as a safety informed neural network training process, where we use a neural network to learn the parameterization of each certificate, while the loss function we seek to minimize is designed to encompass conditions on the certificate to be learned that encode the satisfaction of the associated property. Besides learning a certificate, we quantify probabilistically its generalization properties, namely, how likely it is for a certificate to be valid (and hence for the associated property to be satisfied) when it comes to a new system trajectory not included in the training data set. We view this problem under the realm of probably approximately correct (PAC) learning under the notion of compression, and use recent advancements of the so-called scenario approach to obtain scalable generalization bounds on the learned certificates. To achieve this, we design a novel algorithm that minimizes the loss function and hence constructs a certificate, and at the same time determines a quantity termed compression, which is instrumental in obtaining meaningful probabilistic guarantees. This process is novel per se and provides a constructive mechanism for compression set calculation, thus opening the road for its use to more general non-convex optimization problems. We verify the efficacy of our methodology on several numerical case studies, and compare it (both theoretically and numerically) with closely related results on data-driven property verification., Comment: 17 pages, submitted to Automatica

Growth models with internal habit formation have been studied in various settings under the assumption of deterministic dynamics. The purpose of this paper is to explore a stochastic version of the model in Carroll et al. [1997, 2000], one the most influential on the subject. The goal is twofold: on one hand, to determine how far we can advance in the technical study of the model; on the other, to assess whether at least some of the deterministic outcomes remain valid in the stochastic setting. The resulting optimal control problem proves to be challenging, primarily due to the lack of concavity in the objective function. This feature is present in the model even in the deterministic case (see, e.g., Bambi and Gozzi [2020]). We develop an approach based on Dynamic Programming to establish several useful results, including the regularity of the solution to the corresponding HJB equation and a verification theorem. There results lay the groundwork for studying the model optimal paths and comparing them with the deterministic case.

In April 2023, HEPScore23, the new benchmark based on HEP specific applications, was adopted by WLCG, replacing HEP-SPEC06. As part of the transition to the new benchmark, the CPU corepower published by the sites needed to be compared with the effective power observed while running ATLAS workloads. One aim was to verify the conversion rate between the scores of the old and the new benchmark. The other objective was to understand how the HEPScore performs when run on multi-core job slots, so exactly like the computing sites are being used in the production environment. Our study leverages the HammerCloud infrastructure and the PanDA Workload Management System to collect a large benchmark statistic across 136 computing sites using an enhanced HEP Benchmark Suite. It allows us to collect not only performance metrics, but, thanks to plugins, it also collects information such as machine load, memory usage and other user-defined metrics during the execution and stores it in an OpenSearch database. These extensive tests allow for an in-depth analysis of the actual, versus declared computing capabilities of these sites. The results provide valuable insights into the real-world performance of computing resources pledged to ATLAS, identifying areas for improvement while spotlighting sites that underperform or exceed expectations. Moreover, this helps to ensure efficient operational practices across sites. The collected metrics allowed us to detect and fix configuration issues and therefore improve the experienced performance., Comment: 8 pages, 7 figures, Paper submitted to the proceedings of the CHEP 2024, 27th Conference on Computing in High Energy and Nuclear Physics and will be published in EPJ Web of Conferences (EPJ WoC)

Data-driven closures correct the standard reduced order models (ROMs) to increase their accuracy in under-resolved, convection-dominated flows. There are two types of data-driven ROM closures in current use: (i) structural, with simple ansatzes (e.g., linear or quadratic); and (ii) machine learning-based, with neural network ansatzes. We propose a novel symbolic regression (SR) data-driven ROM closure strategy, which combines the advantages of current approaches and eliminates their drawbacks. As a result, the new data-driven SR closures yield ROMs that are interpretable, parsimonious, accurate, generalizable, and robust. To compare the data-driven SR-ROM closures with the structural and machine learning-based ROM closures, we consider the data-driven variational multiscale ROM framework and two under-resolved, convection-dominated test problems: the flow past a cylinder and the lid-driven cavity flow at Reynolds numbers Re = 10000, 15000, and 20000. This numerical investigation shows that the new data-driven SR-ROM closures yield more accurate and robust ROMs than the structural and machine learning ROM closures.

A reinforcement learning (RL) based methodology is proposed and implemented for online fine-tuning of PID controller gains, thus, improving quadrotor effective and accurate trajectory tracking. The RL agent is first trained offline on a quadrotor PID attitude controller and then validated through simulations and experimental flights. RL exploits a Deep Deterministic Policy Gradient (DDPG) algorithm, which is an off-policy actor-critic method. Training and simulation studies are performed using Matlab/Simulink and the UAV Toolbox Support Package for PX4 Autopilots. Performance evaluation and comparison studies are performed between the hand-tuned and RL-based tuned approaches. The results show that the controller parameters based on RL are adjusted during flights, achieving the smallest attitude errors, thus significantly improving attitude tracking performance compared to the hand-tuned approach.

JWST's MIRI LRS provides the first opportunity to spectroscopically characterize the surface compositions of close-in terrestrial exoplanets. Models for the bare-rock spectra of these planets often utilize a spectral library from R. Hu et al., which is based on room temperature reflectance measurements of materials that represent archetypes of rocky planet surfaces. Here we present an expanded library that includes hemispherical reflectance measurements for a greater variety of compositions, varying textures (solid slab, coarsely crushed, and fine powder), as well as high temperature (500-800 K) emissivity measurements for select samples. We incorporate this new library into version 6.3 of the retrieval package PLATON and use it to show that surfaces with similar compositions can have widely varying albedos and surface temperatures. We additionally demonstrate that changing the texture of a material can significantly alter its albedo, making albedo a poor proxy for surface composition. We identify key spectral features -- the 5.6 \textmu{m} olivine feature, the transparency feature, the Si-O stretching feature, and the Christiansen feature -- that indicate silicate abundance and surface texture. We quantify the number of JWST observations needed to detect these features in the spectrum of the most favorable super-Earth target, LHS 3844 b, and revisit the interpretation of its Spitzer photometry. Lastly, we show that temperature-dependent changes in spectral features are likely undetectable at the precision of current exoplanet observations. Our results illustrate the importance of spectroscopically-resolved thermal emission measurements, as distinct from surface albedo constraints, for characterizing the surface compositions of hot, rocky exoplanets., Comment: 28 pages, 15 figures, accepted for publication in ApJ

A photonic heat amplifier (PHA) designed for cryogenic operations is introduced and analyzed. This device comprises two Anderson insulator reservoirs connected by lossless lines, allowing them to exchange heat through photonic modes. This configuration enables negative differential thermal conductance (NDTC), which can be harnessed to amplify thermal signals. To achieve this, we maintain one reservoir at a high temperature, serving as the source terminal of a thermal transistor. Concurrently, in the other one, we establish tunnel contacts to metallic reservoirs, which function as the gate and drain terminals. With this arrangement, it is possible to control the heat flux exchange between the source and drain by adjusting the gate temperature. We present two distinct parameter choices that yield different performances: the first emphasizes modulating the source-drain heat current, while the second focuses on the temperature modulation of the colder Anderson insulator. Lastly, we present a potential design variation in which all electronic reservoirs are thermally connected through only photonic modes, allowing interactions between distant elements. The proposal of the PHA addresses the lack of thermal transistors and amplifiers in the mK range while being compatible with the rich toolbox of circuit quantum electrodynamics. It can be adapted to various applications, including sensing and developing thermal circuits and control devices at sub-Kelvin temperatures, which are relevant to quantum technologies., Comment: 13 pages, 6 figures

Microcausality -- the vanishing of commutators outside the lightcone -- is a fundamental property of relativistic quantum field theories. We derive its implications for two-point functions of scalar operators on {\it Lorentz-breaking} states. We restrict to spatially homogeneous and isotropic states, at zero and finite temperature, such as finite-density states of matter and primordial inflationary states. In a mixed $(t, \vec k \, )$ representation, we find certain analyticity and exponential boundedness conditions, which we verify in a variety of examples. Crucially, we discuss how our conditions can be tested within the regime of validity of Lorentz-breaking low-energy effective field theories, clarifying the role of the group velocity of low-energy excitations. In the cosmological case, we derive a positivity condition on an EFT coefficient in an inflationary background. Lastly, we comment on how microcausality can be used to constrain higher-point correlation functions, via suitable nested commutators., Comment: 45 pages, 3 figures

Achieving human-like dexterity in robotic grippers remains an open challenge, particularly in ensuring robust manipulation in uncertain environments. Soft robotic hands try to address this by leveraging passive compliance, a characteristic that is crucial to the adaptability of the human hand, to achieve more robust manipulation while reducing reliance on high-resolution sensing and complex control. Further improvements in terms of precision and postural stability in manipulation tasks are achieved through the integration of variable stiffness mechanisms, but these tend to lack residual compliance, be bulky and have slow response times. To address these limitations, this work introduces a Compliant Joint Jamming mechanism for anthropomorphic fingers that exhibits passive residual compliance and adjustable stiffness, while achieving a range of motion in line with that of human interphalangeal joints. The stiffness range provided by the mechanism is controllable from 0.48 Nm/rad to 1.95 Nm/rad (a 4x increase). Repeatability, hysteresis and stiffness were also characterized as a function of the jamming force. To demonstrate the importance of the passive residual compliance afforded by the proposed system, a peg-in-hole task was conducted, which showed a 60% higher success rate for a gripper integrating our joint design when compared to a rigid one., Comment: 6 pages, 9 figures, accepted by the 8th IEEE-RAS International Conference on Soft Robotics, RoboSoft 2025