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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., Comment: Accepted on ApJL

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.

Noninterference theory supports the analysis of secure computations in multi-level security systems. Classical equivalence-based approaches to noninterference mainly rely on bisimilarity. In a nondeterministic setting, assessing noninterference through weak bisimilarity is adequate for irreversible systems, whereas for reversible ones branching bisimilarity has been recently proven to be more appropriate. In this paper we address the same two families of systems, with the difference that probabilities come into play in addition to nondeterminism. For irreversible systems we extend the results of Aldini, Bravetti, and Gorrieri developed in a generative-reactive probabilistic setting, while for reversible systems we extend the results of Esposito, Aldini, Bernardo, and Rossi developed in a purely nondeterministic setting. We recast noninterference properties by adopting probabilistic variants of weak and branching bisimilarities for irreversible and reversible systems respectively. Then we investigate a taxonomy of those properties as well as their preservation and compositionality aspects, along with a comparison with the nondeterministic taxonomy. The adequacy of the extended noninterference theory is illustrated via a probabilistic smart contract example., Comment: arXiv admin note: text overlap with arXiv:2311.15670

We present a pilot study on extracting the form factors of the semileptonic decay of a $ B_s $ meson to the $P$-wave $ D_s^{**} $ states from $B_s$ four-point correlators. With their inclusive nature, four-point correlators include contributions from all possible final states. From the extracted $ P $-wave form factors, we obtain numerical results for the corresponding Isgur-Wise form factors. The results suggest significant contributions from radial excitations to the Uraltsev sum rule at zero-recoil. In this pilot study, a coarse lattice of $ 24^3\times 64 $ with lattice spacing of $0.11\,\mathrm{fm}$ is used for the analysis., Comment: 10 pages, 3 figures, Contribution to the 41st International Symposium on Lattice Field Theory (LATTICE2024), 28 July - 3 August 2024, Liverpool, UK

The advent of Large Language Models (LLMs) has significantly advanced the field of automated code generation. LLMs rely on large and diverse datasets to learn syntax, semantics, and usage patterns of programming languages. For low-resource languages (i.e., niche programming languages characterized by the scarcity of training data), the limited availability of such data hampers the models' ability to generalize effectively, resulting in poorer code generation performance as compared to high-resource languages. For this reason, there is a quest for techniques able to close this performance gap. We present an empirical study investigating the effectiveness of several approaches for boosting LLMs' performance on low-resource languages, namely: (i) a classic fine-tuning, which is however capped in size by the scarcity of training data; (ii) three variants of in-context learning, with prompts crafted to provide the LLM with additional information about the low-resource language (e.g., few-shot examples showcasing features of the targeted language); and (iii) a pre-training objective teaching the model how to translate between high- and low-resource languages. The context of our study are two low-resource languages (R and Racket) and six LLMs having different architectures and sizes. Our findings reveal that a fine-tuning is usually the best choice for smaller LLMs, possibly due to the fact that even a small dataset is sufficient to train their limited number of parameters. With the increase in size of the models, in-context learning becomes more and more effective, representing a safe and cheap bet (i.e., it always helps, but with different magnitudes). Differently, very large LLMs may deteriorate their performance on low-resource languages when fine-tuning is performed, possibly due to the lack of enough data needed to effectively update their weights., Comment: Accepted at ICPC'25

A longstanding open question in sub-Riemannian geometry is the following: are sub-Riemannian length minimizers smooth? We give a negative answer to this question, exhibiting an example of a $C^2$ but not $C^3$ length-minimizer of a real-analytic (even polynomial) sub-Riemannian structure.

This study focuses on the application of deep geometric models to solve the 3x3x3 Rubik's Cube. We begin by discussing the cube's graph representation and defining distance as the model's optimization objective. The distance approximation task is reformulated as a node classification problem, effectively addressed using Graph Neural Networks (GNNs). After training the model on a random subgraph, the predicted classes are used to construct a heuristic for $A^*$ search. We conclude with experiments comparing our heuristic to that of the DeepCubeA model.

We consider a reproducing kernel Hilbert space of discrete entire functions on the square lattice $\mathbb Z^2$ inspired by the classical Paley-Wiener space of entire functions of exponential growth in the complex plane. For such space we provide a Paley-Wiener type characterization and a sampling result., Comment: 24 pages

The destruction of metallicity due to the mutual Coulomb interaction of quasiparticles gives rise to fascinating phenomena of solid state physics such as the Mott metal-insulator transition and the pseudogap. A key observable characterizing their occurrences is the single-particle spectral function, determined by the fermionic self-energy. In this paper we investigate in detail how real space fluctuations are responsible for a self-energy that drives the Mott-Hubbard metal-insulator transition. To this aim we first introduce a real space fluctuation diagnostics approach to the Hedin equation, which connects the fermion-boson coupling vertex $\lambda$ to the self-energy $\Sigma$. Second, by using cellular dynamical mean-field theory calculations for $\lambda$ we identify the leading physical processes responsible for the destruction of metallicity across the transition., Comment: 21 pages, 17 figures

Artificial intelligence has significantly advanced skin cancer diagnosis by enabling rapid and accurate detection of malignant lesions. In this domain, most publicly available image datasets consist of single, isolated skin lesions positioned at the center of the image. While these lesion-centric datasets have been fundamental for developing diagnostic algorithms, they lack the context of the surrounding skin, which is critical for improving lesion detection. The iToBoS dataset was created to address this challenge. It includes 16,954 images of skin regions from 100 participants, captured using 3D total body photography. Each image roughly corresponds to a $7 \times 9$ cm section of skin with all suspicious lesions annotated using bounding boxes. Additionally, the dataset provides metadata such as anatomical location, age group, and sun damage score for each image. This dataset aims to facilitate training and benchmarking of algorithms, with the goal of enabling early detection of skin cancer and deployment of this technology in non-clinical environments., Comment: Article Submitted to Scientific Data

We present an iterative approach for planning and controlling motions of underactuated robots with uncertain dynamics. At its core, there is a learning process which estimates the perturbations induced by the model uncertainty on the active and passive degrees of freedom. The generic iteration of the algorithm makes use of the learned data in both the planning phase, which is based on optimization, and the control phase, where partial feedback linearization of the active dofs is performed on the model updated on-line. The performance of the proposed approach is shown by comparative simulations and experiments on a Pendubot executing various types of swing-up maneuvers. Very few iterations are typically needed to generate dynamically feasible trajectories and the tracking control that guarantees their accurate execution, even in the presence of large model uncertainties.

Federated testbeds enable collaborative research by providing access to diverse resources, including computing power, storage, and specialized hardware like GPUs, programmable switches and smart Network Interface Cards (NICs). Efficiently sharing these resources across federated institutions is challenging, particularly when resources are scarce and costly. GPUs are crucial for AI and machine learning research, but their high demand and expense make efficient management essential. Similarly, advanced experimentation on programmable data plane requires very expensive programmable switches (e.g., based on P4) and smart NICs. This paper introduces SHARY (SHaring Any Resource made easY), a dynamic reservation system that simplifies resource booking and management in federated environments. We show that SHARY can be adopted for heterogenous resources, thanks to an adaptation layer tailored for the specific resource considered. Indeed, it can be integrated with FIGO (Federated Infrastructure for GPU Orchestration), which enhances GPU availability through a demand-driven sharing model. By enabling real-time resource sharing and a flexible booking system, FIGO improves access to GPUs, reduces costs, and accelerates research progress. SHARY can be also integrated with SUP4RNET platform to reserve the access of P4 switches., Comment: Extended version of a paper accepted to NOMS 2025 conference as a short paper

Supermassive black holes (SMBH) are thought to grow through accretion of matter and mergers. Models of SMBH mergers have long suffered the final parsec problem, where SMBH binaries may stall before energy loss from gravitational waves (GW) becomes significant, leaving the pair unmerged. Direct evidence of coalesced SMBH remains elusive. Theory predicts that GW recoiling black holes can occur following a black hole merger. Here we present decisive spectroscopic evidence that the gas bound to the SMBH in the spatially offset quasar 3C 186 is blue-shifted relative to the host galaxy. This is exclusively explained by the GW recoil super-kick scenario. This confirmation of the ejection process implies that the final parsec problem is resolved in nature, providing evidence that even the most massive black holes can merge., Comment: 31 pages, 10 figures, submitted

In this work we report Nernst effect measurements in single crystal bismuth samples, with special emphasis on the characterization of the Nernst coefficient when the magnetic field, heat current and generated voltage are aligned along specific directions relative to the crystal axes. We found significant differences between the different orientations, reflecting the highly anisotropic electronic structure of bismuth and compatible with the Nernst characteristics obtained from polycrystalline samples. These results not only complement the experimental works published in the past but also underline the role of crystalline orientation in the context of transverse thermoelectric effects, towards an efficient design of thermomagnetic devices like the ordinary-Nernst-effect-based energy harvesters., Comment: 11 pages, 5 figures

In safety-critical SoC applications such as automotive and aerospace, reliable transaction monitoring is crucial for maintaining system integrity. This paper introduces a drop-in Transaction Monitoring Unit (TMU) for AXI4 subordinate endpoints that detects transaction failures including protocol violations or timeouts and triggers recovery by resetting the affected subordinates. Two TMU variants address different constraints: a Tiny-Counter solution for tightly area-constrained systems and a Full-Counter solution for critical subordinates in mixed-criticality SoCs. The Tiny-Counter employs a single counter per outstanding transaction, while the Full-Counter uses multiple counters to track distinct transaction stages, offering finer-grained monitoring and reducing detection latencies by up to hundreds of cycles at roughly 2.5x the area cost. The Full-Counter also provides detailed error logs for performance and bottleneck analysis. Evaluations at both IP and system levels confirm the TMU's effectiveness and low overhead. In GF12 technology, monitoring 16-32 outstanding transactions occupies 1330-2616 um2 for the Tiny-Counter and 3452-6787 um2 for the Full-Counter; moderate prescaler steps reduce these figures by 18-39% and 19-32%, respectively, with no loss of functionality. Results from a full-system integration demonstrate the TMU's robust and precise monitoring capabilities in safety-critical SoC environments., Comment: 7 pages, 11 figures, accepted as a regular paper at DATE25

Quantum computing with superconducting circuits relies on high-fidelity driven nonlinear processes. An ideal quantum nonlinearity would selectively activate desired coherent processes at high strength, without activating parasitic mixing products or introducing additional decoherence. The wide bandwidth of the Josephson nonlinearity makes this difficult, with undesired drive-induced transitions and decoherence limiting qubit readout, gates, couplers and amplifiers. Significant strides have been recently made into building better `quantum mixers', with promise being shown by Kerr-free three-wave mixers that suppress driven frequency shifts, and balanced quantum mixers that explicitly forbid a significant fraction of parasitic processes. We propose a novel mixer that combines both these strengths, with engineered selection rules that make it essentially linear (not just Kerr-free) when idle, and activate clean parametric processes even when driven at high strength. Further, its ideal Hamiltonian is simple to analyze analytically, and we show that this ideal behavior is first-order insensitive to dominant experimental imperfections. We expect this mixer to allow significant advances in high-Q control, readout, and amplification., Comment: 23 pages (8 Main + 15 Appdx), 9 figures (3 Main + 6 Appdx)

Surgical guidance can be delivered in various ways. In neurosurgery, spatial guidance and orientation are predominantly achieved through neuronavigation systems that reference pre-operative MRI scans. Recently, there has been growing interest in providing live guidance by analyzing video feeds from tools such as endoscopes. Existing approaches, including anatomy detection, orientation feedback, phase recognition, and visual question-answering, primarily focus on aiding surgeons in assessing the current surgical scene. This work aims to provide guidance on a finer scale, aiming to provide guidance by forecasting the trajectory of the surgical instrument, essentially addressing the question of what to do next. To address this task, we propose a model that not only leverages the historical locations of surgical instruments but also integrates anatomical features. Importantly, our work does not rely on explicit ground truth labels for instrument trajectories. Instead, the ground truth is generated by a detection model trained to detect both anatomical structures and instruments within surgical videos of a comprehensive dataset containing pituitary surgery videos. By analyzing the interaction between anatomy and instrument movements in these videos and forecasting future instrument movements, we show that anatomical features are a valuable asset in addressing this challenging task. To the best of our knowledge, this work is the first attempt to address this task for manually operated surgeries.

Comets, asteroids, and other small bodies are thought to be remnants of the original planetesimal population of the Solar System. As such, their physical, chemical, and isotopic properties hold crucial details on how and where they formed and how they evolved. Yet, placing precise constraints on the formation region of these bodies has been challenging. Data from spacecraft missions have a particularly high potential of addressing the question of the origin of the visited bodies. ESA's Rosetta mission to comet 67P/Churyumov-Gerasimenko returned data from the comet for two years on its journey around the Sun. This extensive data set has revolutionised our view on comets and still holds unsolved problems. We use the Rosetta/ROSINA (Rosetta Orbiter Spectrometer for Ion and Neutral Analysis) measurement of the volatile/ice composition and the Rosetta/COSIMA (COmetary Secondary Ion Mass Analyzer) measurements of the refractory composition of comet 67P. These measurements are combined using a Monte Carlo method. The refractory-to-ice ratio is a free parameter that is constrained a posteriori. Using only the composition, we constrain the refractory-to-ice ratio to $0.5<\chi<1.7$, and derive the bulk elemental abundances for 67P of H, C, N, O, Na, Mg, Al, S, K, Ar, Ca, Cr, Mn, Fe, Kr, and Xe. We find the noble gas xenon in near solar elemental abundance in comet 67P. Krypton is slightly depleted, while argon is heavily depleted. Comet 67P is enriched in all three noble gases by up to 2.5 orders of magnitude compared to CI chondrites. We show this is consistent with a formation region between 25 and 35 au in a protoplanetary disk region with temperatures between 30 and 40 K and with the trapping of dust for a long time in rings of the protoplanetary disk., Comment: 11 pages, accepted for publication in Planetary and Space Science

The development of color centers in silicon enables scalable quantum technologies by combining telecom-wavelength emission and compatibility with mature silicon fabrication. However, large-scale integration requires precise control of each emitter's optical transition to generate indistinguishable photons for quantum networking. Here, we demonstrate a foundry-fabricated photonic integrated circuit (PIC) combining suspended silicon waveguides with a microelectromechanical (MEMS) cantilever to apply local strain and spectrally tune individual G-centers. Applying up to 35 V between the cantilever and the substrate induces a reversible wavelength shift of the zero-phonon line exceeding 100 pm, with no loss in brightness. Moreover, by modeling the strain-induced shifts with a `digital twin' physical model, we achieve vertical localization of color centers with sub-3 nm vertical resolution, directly correlating their spatial position, dipole orientation, and spectral behavior. This method enables on-demand, low-power control of emission spectrum and nanoscale localization of color centers, advancing quantum networks on a foundry-compatible platform., Comment: 30 pages, 28 figures

Color centers are among the most promising candidates for quantum information processing. Central requirements for their practical applications include controlled and efficient local activation in nanophotonic devices and identical spectral features. However, producing color centers in a controlled and reliable way is inherently challenging due to the lack of comprehensive theoretical insights into their formation and the difficulty of streamlining the generation process for rapid in-situ optimization. We address these challenges by developing an end-to-end first-principles model that captures the underlying formation process of color centers. Emitters are activated through laser annealing, which allows for in-situ creation and the possibility of model-based control. Notably, our model enables the estimation of the emitters' inhomogeneous broadening down to 16 GHz in bare silicon, which translates into the creation of emitters with highly similar spectral properties. Finally, we address the challenge of in-situ deterministic activation of color centers in nanophotonic devices by going beyond bare silicon and demonstrating successful laser writing in photonic crystal optical cavities. These results lay the foundation for deterministic and large-scale integration of color centers within quantum photonic platforms., Comment: 17 pages, 10 figures, 1 table

Combining multiple events into population analyses is a cornerstone of gravitational-wave astronomy. A critical component of such studies is the assumed population model, which can range from astrophysically motivated functional forms to non-parametric treatments that are flexible but difficult to interpret. In practice, the current approach is to fit the data multiple times with different population models to identify robust features. We propose an alternative strategy: assuming the data have already been fit with a flexible model, we present a practical recipe to reconstruct the population distribution of a different model. As our procedure postprocesses existing results, it avoids the need to access the underlying gravitational-wave data again and handle selection effects. Additionally, our reconstruction metric provides a goodness-of-fit measure to compare multiple models. We apply this method to the mass distribution of black-hole binaries detected by LIGO/Virgo/KAGRA. Our work paves the way for streamlined gravitational-wave population analyses by fitting the data once and for all with advanced non-parametric methods and careful handling of selection effects, while the astrophysical interpretation is then made accessible using our reconstruction procedure on targeted models. The key principle is that of conceptually separating data description from data interpretation., Comment: 9 pages, 4 figures

We present the third phase of the largest high-frequency, high-resolution imaging survey of 231 nearby, hard X-ray selected AGN, with a very high $98 \pm 1\%$ detection fraction. This survey presents VLA 22 GHz radio observations with 1" spatial resolution covering over $6$ orders of magnitude in radio luminosity in nearby AGN that span $\sim4$ orders of magnitude in black hole mass and X-ray luminosity. We identify three different radio morphologies: $44 \pm 3\%$ (102/231) are compact or unresolved, $46 \pm 3\%$ (106/231) show an extended structure (star formation, possible one-sided jets, etc.), and $8 \pm 2\%$ (19/231) have a biconical or two-sided jet-like morphology. The remaining $2 \pm 1\%$ (4/231) sources are non-detections. The radio-to-X-ray luminosity ratios of the Swift-BAT AGN ($\text{L}_R/\text{L}_{14-195 \text{keV}} \sim 10^{-5.5}$ and $\text{L}_R/\text{L}_{2-10 \text{keV}} \sim 10^{-5}$) with a scatter of $\sim0.5$ dex are similar to that of coronally active stars ($\text{L}_R/\text{L}_X \sim 10^{-5}$). For most targets, extended emission in radio-quiet objects is broadly consistent with the expectation for star formation from previous FIR observations, once the contribution from the radio core has been subtracted. Our sample represents nearby analogs of distant AGN at the peak of black hole growth, and thus the high detection fraction in our work has important implications for future high frequency AGN radio surveys with the next generation VLA (ngVLA) or Square Kilometre Array (SKA), both of which should detect large fractions of more distant AGN., Comment: 26 pages, 8 figures, 4tables. Accepted for publication in ApJ

We study two- and three-point correlation functions of chiral primary half-BPS operators in four-dimensional $\mathcal{N}=2$ superconformal circular, cyclic symmetric quiver theories. Using supersymmetric localization, these functions can be expressed as matrix integrals which, in the planar limit, reduce to Fredholm determinants of certain semi-infinite matrices. This powerful representation allows us to investigate the correlation functions across the parameter space of the quiver theory, including both weak and strong coupling regimes and various limits of the number of nodes and the operator scaling dimensions. At strong coupling, the standard semiclassical AdS/CFT expansion diverges in the long quiver limit. However, by incorporating both perturbative corrections (in negative powers of the 't Hooft coupling) and an infinite tower of nonperturbative, exponentially suppressed contributions, we derive a remarkably simple expression for the correlation functions in this limit. These functions exhibit exponential decay with increasing node separation and admit an interpretation within a five-dimensional effective theory. We determine the mass spectrum of excitations propagating along the emergent fifth dimension within this theory, finding it to be given by the zeros of Bessel functions., Comment: 39 pages, 4 figures

A group of newly observed extreme trans-Neptunian objects exhibit unexpected orbital confinement, characterized by the alignment of orbital angular momentum vectors and apsidal lines. It is proposed that an undiscovered giant planet, named Planet-9, exists in the solar system's outer regions and causes this clustering. Initial studies suggested Planet-9 could have a mass of 15 Earth masses. However, such a massive planet strongly interacts with scattered disk objects (SDOs; 50 < a < 1000 au) and influences the orbits of short-period comets, resulting in orbital inclinations inconsistent with observations. This study models the formation and long-term evolution of trans-Neptunian object populations and the Oort cloud during the solar system's dynamical instability, using revised parameters for Planet-9. Simulations assume Planet-9 has a mass of 7.5 Earth masses, an inclination of ~20 degrees, a semi-major axis of ~600 au, and an eccentricity of ~0.3. Results suggest a less massive Planet-9 aligns with observed trans-Neptunian object inclinations and the number of ecliptic comets (D > 10 km). Distant Kuiper belt objects with 40 < q < 100 au and 200 < a < 500 au, particularly with significant inclinations, are more likely to align apsidally with Planet-9, with an anti-aligned-to-aligned ratio of 0.5-0.7. Lower inclination objects (<20 degrees) exhibit significant apsidal anti-alignment, with an anti-aligned-to-aligned ratio of 2-4. These findings offer a new observational direction to refine the search for Planet-9., Comment: Accepted in Icarus

Control loops closed over wireless links greatly benefit from accurate estimates of the communication channel condition. To this end, the finite-state Markov channel model allows for reliable channel state estimation. This paper develops a Markov jump linear system representation for wireless networked control with persistent channel state observation, stochastic message losses, and generalized packet dropout compensation. With this model, we solve the finite- and infinite-horizon linear quadratic regulation problems and introduce an easy-to-test stability condition for any given infinite-horizon control law. We also thoroughly analyze the impact of a scalar general dropout compensation factor on the stability and closed-loop performance of a rotary inverted pendulum controlled remotely through a wireless link. Finally, we validate the results numerically via extensive Monte Carlo simulations, showing the benefits of the proposed control strategy., Comment: An extended version of the article accepted for publication in Automatica

This study evaluates the use of Quantum Convolutional Neural Networks (QCNNs) for identifying signals resembling Gamma-Ray Bursts (GRBs) within simulated astrophysical datasets in the form of light curves. The task addressed here focuses on distinguishing GRB-like signals from background noise in simulated Cherenkov Telescope Array Observatory (CTAO) data, the next-generation astrophysical observatory for very high-energy gamma-ray science. QCNNs, a quantum counterpart of classical Convolutional Neural Networks (CNNs), leverage quantum principles to process and analyze high-dimensional data efficiently. We implemented a hybrid quantum-classical machine learning technique using the Qiskit framework, with the QCNNs trained on a quantum simulator. Several QCNN architectures were tested, employing different encoding methods such as Data Reuploading and Amplitude encoding. Key findings include that QCNNs achieved accuracy comparable to classical CNNs, often surpassing 90\%, while using fewer parameters, potentially leading to more efficient models in terms of computational resources. A benchmark study further examined how hyperparameters like the number of qubits and encoding methods affected performance, with more qubits and advanced encoding methods generally enhancing accuracy but increasing complexity. QCNNs showed robust performance on time-series datasets, successfully detecting GRB signals with high precision. The research is a pioneering effort in applying QCNNs to astrophysics, offering insights into their potential and limitations. This work sets the stage for future investigations to fully realize the advantages of QCNNs in astrophysical data analysis., Comment: 9 pages, Accepted for publication in 33rd Euromicro/IEEE International Conference on Parallel, Distributed and Network-Based Processing (PDP 2025)

In the Python ecosystem, the adoption of idiomatic constructs has been fostered because of their expressiveness, increasing productivity and even efficiency, despite controversial arguments concerning familiarity or understandability issues. Recent research contributions have proposed approaches -- based on static code analysis and transformation -- to automatically identify and enact refactoring opportunities of non-idiomatic code into idiomatic ones. Given the potential recently offered by Large Language Models (LLMs) for code-related tasks, in this paper, we present the results of a replication study in which we investigate GPT-4 effectiveness in recommending and suggesting idiomatic refactoring actions. Our results reveal that GPT-4 not only identifies idiomatic constructs effectively but frequently exceeds the benchmark in proposing refactoring actions where the existing baseline failed. A manual analysis of a random sample shows the correctness of the obtained recommendations. Our findings underscore the potential of LLMs to achieve tasks where, in the past, implementing recommenders based on complex code analyses was required.

A crucial aspect in phase-field modeling, based on the variational formulation of brittle fracture, is the accurate representation of how the fracture surface energy is dissipated during the fracture process in the energy competition within a minimization problem. In general, the family of AT1 functionals showcases a well-defined elastic limit and narrow transition regions before crack onset, as opposed to AT2 models. On the other hand, high-order functionals provide similar accuracy as low-order ones but allow for larger mesh sizes in their discretization, remarkably reducing the computational cost. In this work, we aim to combine both these advantages and propose a novel AT1 fourth-order phase-field model for brittle fracture within an isogeometric framework, which provides a straightforward discretization of the high-order term in the crack surface density functional. For the introduced AT1 functional, we first prove a {\Gamma}-convergence result (in both the continuum and discretized isogeometric setting) based on a careful study of the optimal transition profile, which ultimately provides the explicit correction factor for the toughness and the exact size of the transition region. Fracture irreversibility is modeled by monotonicity of the damage variable and is conveniently enforced using the Projected Successive Over-Relaxation algorithm. Our numerical results indicate that the proposed fourth-order AT1 model is more accurate than the considered lower-order AT1 and AT2 models; this allows to employ larger mesh sizes, entailing a lower computational cost., Comment: 35 pages, 15 images

Albeit the ubiquitous use of Graph Neural Networks (GNNs) in machine learning (ML) prediction tasks involving graph-structured data, their interpretability remains challenging. In explainable artificial intelligence (XAI), the Shapley Value (SV) is the predominant method to quantify contributions of individual features to a ML model's output. Addressing the limitations of SVs in complex prediction models, Shapley Interactions (SIs) extend the SV to groups of features. In this work, we explain single graph predictions of GNNs with SIs that quantify node contributions and interactions among multiple nodes. By exploiting the GNN architecture, we show that the structure of interactions in node embeddings are preserved for graph prediction. As a result, the exponential complexity of SIs depends only on the receptive fields, i.e. the message-passing ranges determined by the connectivity of the graph and the number of convolutional layers. Based on our theoretical results, we introduce GraphSHAP-IQ, an efficient approach to compute any-order SIs exactly. GraphSHAP-IQ is applicable to popular message passing techniques in conjunction with a linear global pooling and output layer. We showcase that GraphSHAP-IQ substantially reduces the exponential complexity of computing exact SIs on multiple benchmark datasets. Beyond exact computation, we evaluate GraphSHAP-IQ's approximation of SIs on popular GNN architectures and compare with existing baselines. Lastly, we visualize SIs of real-world water distribution networks and molecule structures using a SI-Graph., Comment: Preprint Version. Accepted at ICLR 2025

Existing extremum-seeking control (ESC) approaches typically rely on applying repeated perturbations to input parameters and performing measurements of the corresponding performance output. Performing these measurements can be costly in practical applications, e.g., due to the use of resources, making it desirable to reduce the number of performed measurements. Moreover, the required separation between the different timescales in the ESC loop typically results in slow convergence. With these challenges in mind, this work presents an approach aimed at both increasing the convergence rate and reducing the number of measurements that need to be performed. In the proposed approach, input-output data obtained during operation is used to construct online an approximation of the system's underlying cost function. By using this approximation to perform parameter updates when a decrease in the cost can be guaranteed, instead of performing additional measurements to perform this update, more efficient use is made of the collected data. As a result, reductions in both the required number of measurements and update steps are indeed obtained. In addition, a stability analysis of the novel ESC approach is provided. The benefits of the synergy between kernel-based function approximation and standard ESC is demonstrated in simulation on a multi-input dynamical system., Comment: 16 pages, 5 figures, submitted to Automatica

Many scientific problems involve data that is embedded in a space with periodic boundary conditions. This can for instance be related to an inherent cyclic or rotational symmetry in the data or a spatially extended periodicity. When analyzing such data, well-tailored methods are needed to obtain efficient approaches that obey the periodic boundary conditions of the problem. In this work, we present a method for applying a clustering algorithm to data embedded in a periodic domain based on the DBSCAN algorithm, a widely used unsupervised machine learning method that identifies clusters in data. The proposed method internally leverages the conventional DBSCAN algorithm for domains with open boundaries, such that it remains compatible with all optimized implementations for neighborhood searches in open domains. In this way, it retains the same optimized runtime complexity of $O(N\log N)$. We demonstrate the workings of the proposed method using synthetic data in one, two and three dimensions and also apply it to a real-world example involving the clustering of bubbles in a turbulent flow. The proposed approach is implemented in a ready-to-use Python package that we make publicly available.

In this paper, we investigate stochastic versions of the Hopf-Lax formula which are based on compositions of the Hopf-Lax operator with the transition kernel of a L\'evy process taking values in a separable Banach space. We show that, depending on the order of the composition, one obtains upper and lower bounds for the value function of a stochastic optimal control problem associated to the drift controlled L\'evy dynamics. Dynamic consistency is restored by iterating the resulting operators. Moreover, the value function of the control problem is approximated both from above and below as the number of iterations tends to infinity, and we provide explicit convergence rates and guarantees for the approximation procedure.

Deep extragalactic X-ray surveys, such as the Chandra COSMOS-Legacy field (CCLS), are prone to be biased against active galactic nuclei (AGN) with high column densities due to their lower count rates at a given luminosity. To quantify this selection effect, we forward model nearby ($z\sim0.05$) AGN from the BAT AGN Spectroscopic Survey (BASS) with well-characterized ($\gtrsim$1000 cts) broadband X-ray spectra (0.5-195 keV) to simulate the CCLS absorption distribution. We utilize the BASS low-redshift analogs with similar luminosities to the CCLS ($L_\mathrm{2-10\ keV}^\mathrm{int}\sim10^{42-45}\ \mathrm{erg}\ \mathrm{s}^{-1}$), which are much less affected by obscuration and low-count statistics, as the seed for our simulations, and follow the spectral fitting of the CCLS. Our simulations reveal that Chandra would fail to detect the majority (53.3%; 563/1056) of obscured ($N_\mathrm{H}>10^{22}\ \mathrm{cm}^{-2}$) simulated BASS AGN given the observed redshift and luminosity distribution of the CCLS. Even for detected sources with sufficient counts ($\geq30$) for spectral modeling, the level of obscuration is significantly overestimated. This bias is most extreme for objects whose best fit indicates a high-column density AGN ($N_\mathrm{H}\geq10^{24}\ \mathrm{cm}^{-2}$), since the majority (66.7%; 18/27) of these are actually unobscured sources ($N_\mathrm{H}<10^{22}\ \mathrm{cm}^{-2}$). This implies that previous studies may have significantly overestimated the increase in the obscured fraction with redshift and the fraction of luminous obscured AGN. Our findings highlight the importance of directly considering obscuration biases and forward modeling in X-ray surveys, as well as the need for higher-sensitivity X-ray missions such as the Advanced X-ray Imaging Satellite (AXIS), and the importance of multi-wavelength indicators to estimate obscuration in distant supermassive black holes., Comment: 32 pages, 20 figures. Accepted for publication in The Astrophysical Journal

Smart grids are critical for addressing the growing energy demand due to global population growth and urbanization. They enhance efficiency, reliability, and sustainability by integrating renewable energy. Ensuring their availability and safety requires advanced operational control and safety measures. Researchers employ AI and machine learning to assess grid stability, but challenges like the lack of datasets and cybersecurity threats, including adversarial attacks, persist. In particular, data scarcity is a key issue: obtaining grid instability instances is tough due to the need for significant expertise, resources, and time. However, they are essential to test novel research advancements and security mitigations. In this paper, we introduce a novel framework to detect instability in smart grids by employing only stable data. It relies on a Generative Adversarial Network (GAN) where the generator is trained to create instability data that are used along with stable data to train the discriminator. Moreover, we include a new adversarial training layer to improve robustness against adversarial attacks. Our solution, tested on a dataset composed of real-world stable and unstable samples, achieve accuracy up to 97.5\% in predicting grid stability and up to 98.9\% in detecting adversarial attacks. Moreover, we implemented our model in a single-board computer demonstrating efficient real-time decision-making with an average response time of less than 7ms. Our solution improves prediction accuracy and resilience while addressing data scarcity in smart grid management., Comment: This work has been submitted to the IEEE Internet of Things Journal for possible publication

I briefly review solutions to the strong CP problem based on axions, parity invariance, CP-invariance, and present a new idea based on CP as part of a spontaneously broken flavour symmetry such as a U(1) or modular invariance., Comment: Talk at QCHSC24

We show that the intersection of the irreducible components of a hypersurface defined by a polynomial with square-free support has F-rational singularities in characteristic $p>0$. As a consequence, we obtain that hypersurfaces defined by irreducible polynomials with square-free support have F-rational singularities, positively answering a question of Bath, Musta\c{t}\u{a}, and Walther.

We study the equation of state of three-dimensional compact U(1) gauge theory on the lattice by means of numerical simulations, and discuss the implications of our results for the spectrum of the theory, in connection with previous results from the literature. We also compare our findings to the case of non-Abelian gauge theories and comment on the continuum limit., Comment: 1+16 pages, 4 figures

We investigate thin plates where out-of-plane deformations arise due to membrane kinematic incompatibility of rotational type, specifically Volterra wedge disclinations, which are commonly observed in metal plates and graphene. We present theorems that guarantee the existence and regularity of equilibrium solutions in the presence of a finite number of disclinations and a dead load, for clamped plates. To solve the equilibrium equations, we implement a numerical code in the FEniCS environment and apply it to a series of parametric test studies. Our Finite Element method follows the Discontinuous Galerkin approach with C0 elements., Comment: 24 pages, 18 Figures

In 1492, for the first time, an unknown ocean opened up before sailors: weeks of navigation and no idea how to pinpoint their location. Since ancient times, navigators had known how to determine latitude by using the North Star, but the "problem of longitude" was different. More than a century later, Galileo Galilei discovered in Padua Jupiter's satellites and quickly realized that a sailor who could observe their eclipses would know his own longitude. Yet his brilliant insight was 400 years ahead of the technology of his time. Impractical at sea, on land this idea became a formidable tool for cartography and ushered in the age of the image of the world. Today the technique can be realized thanks to artificial satellites, and the Tuscan genius' name has reached space with the European satellite system named Galileo. An exhibition in Paris, organized by the Permanent Representation of Italy to the International Organizations, Sorbonne University, and the Galileo Museum in Florence, and directed by Asia Ruffo di Calabria of the Musee des Arts et Metiers, by Quentin Cheval-Galland of the Sorbonne University, and by Alessandro De Angelis, allowed visitors to observe inventions of the time and some writings by Galileo on the theme of geolocation. The exhibition was held in Paris in June 2024. It was replicated in Prague in October 2024, in Amsterdam in December 2025, and at the Perimeter Institute in Waterloo, Canada, in February 2025.

Introduction: Generative Adversarial Networks (GANs) are increasingly used to generate synthetic medical images, addressing the critical shortage of annotated data for training Artificial Intelligence (AI) systems. This study introduces a novel memory-efficient GAN architecture, incorporating Conditional Random Fields (CRFs) to generate high-resolution 3D medical images and evaluates its performance against the state-of-the-art hierarchical (HA)-GAN model. Materials and Methods: The CRF-GAN was trained using the open-source lung CT LUNA16 dataset. The architecture was compared to HA-GAN through a quantitative evaluation, using Frechet Inception Distance (FID) and Maximum Mean Discrepancy (MMD) metrics, and a qualitative evaluation, through a two-alternative forced choice (2AFC) test completed by a pool of 12 resident radiologists, in order to assess the realism of the generated images. Results: CRF-GAN outperformed HA-GAN with lower FID (0.047 vs. 0.061) and MMD (0.084 vs. 0.086) scores, indicating better image fidelity. The 2AFC test showed a significant preference for images generated by CRF-Gan over those generated by HA-GAN with a p-value of 1.93e-05. Additionally, CRF-GAN demonstrated 9.34% lower memory usage at 256 resolution and achieved up to 14.6% faster training speeds, offering substantial computational savings. Discussion: CRF-GAN model successfully generates high-resolution 3D medical images with non-inferior quality to conventional models, while being more memory-efficient and faster. Computational power and time saved can be used to improve the spatial resolution and anatomical accuracy of generated images, which is still a critical factor limiting their direct clinical applicability.

In a sequence $S=\{(X,f_n)\}_{n\in\mathbb{N}}$ of dynamical systems sharing a common ambient space, the point $f^k_n(x)$ visited by a certain $x\in X$ depends on the iteration order $k$ and on the index $n$ specifying the system in $S$. If the sequence of maps $\{f_n\}_{n\in\mathbb{N}}$ is eventually constant at every point, the $f_n$-orbits show an emergent poset structure. A maximal initial segment of this poset is isomorphic to a certain countable ordinal $\ge\omega$. We study this transfinite emergent structure from the point of view of topological dynamics, investigating orbits, recurrence, limit sets, attractors and conjugacy.